Method and system for analyzing characteristics of dissolved organic matter in aquaculture tail water

Abstract

The application discloses a kind of aquaculture tail water solubility organic matter characteristic analysis method and system, specifically related to water quality monitoring and analysis technical field, for solving the problem that existing solubility organic matter characterization method is difficult to effectively associate and predict its actual degradation behavior in biological treatment process.Synchronously obtain the three-dimensional fluorescence spectrum data of the water sample to be measured and the liquid chromatography-organic carbon detection combined analysis data, obtain the hydrophilic-hydrophobic component ratio and the proportion of tyrosine-like and humus-like fluorescence components from the analysis, and then judge whether there is significant humus-like component.According to the judgment result, selectively start the deep analysis of the molecular aggregation state of humus-like component, and comprehensively consider the related proportion parameters and aggregation state information, finally realize the evaluation of the biological degradation potential trend of solubility organic matter, and provide key basis for the stability warning of biological treatment system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water quality monitoring and analysis, more particularly, the present application relates to a method and system for analyzing the characteristics of dissolved organic matter in aquaculture tail water. BACKGROUND

[0002] In the field of aquaculture tail water treatment, in order to achieve water resource reuse and standard discharge, biological treatment technology is widely used. In order to ensure the operation efficiency and stability of the biological treatment system, such as activated sludge method and biofilm method, it is necessary to effectively monitor the dissolved organic matter entering the system. At present, in addition to the conventional chemical oxygen demand, biological oxygen demand and other comprehensive indicators, the composition and source of dissolved organic matter can be analyzed by using spectral and chromatographic analysis techniques, and detailed information about the functional groups, molecular weight distribution and fluorescence components of the organic matter can be obtained, which can be used to evaluate the overall condition of the water quality.

[0003] However, the existing dissolved organic matter characteristic analysis method mainly focuses on the characterization of its static composition and structure, and when applied to the monitoring and early warning of the biological treatment process of aquaculture tail water, the composition characteristic information obtained by the existing method is difficult to effectively correlate and deduce the actual degradation behavior and potential of the organic matter in the subsequent dynamic biological treatment process, resulting in that the stability change of the biological treatment unit cannot be predicted in advance according to the current analysis results, and the process control often lags behind the substantial deterioration of the treatment effect, thereby increasing the system operation risk and control cost. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a method and system for analyzing the characteristics of dissolved organic matter in aquaculture tail water to solve the problems raised in the background art.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A method for analyzing the characteristics of dissolved organic matter in aquaculture tail water, comprising the following steps:

[0007] S1, obtaining three-dimensional fluorescence spectrum data and liquid chromatography-organic carbon detection combined analysis data of the aquaculture tail water sample to be tested;

[0008] S2, obtaining the hydrophilic-hydrophobic component ratio of the dissolved organic matter in the sample according to the liquid chromatography-organic carbon detection combined analysis data;

[0009] S3, obtaining the ratio of tyrosine-like fluorescence components and humus-like fluorescence components of the dissolved organic matter in the sample according to the three-dimensional fluorescence spectrum data;

[0010] S4, judging whether the ratio of tyrosine-like fluorescence components and humus-like fluorescence components indicates the presence of significant humus-like components;

[0011] S5, if no, determining the biodegradation potential trend of the dissolved organic matter in the sample based on the proportion of hydrophilic and hydrophobic components and the proportion of tyrosine-like fluorescent component and humus-like fluorescent component; if yes, analyzing the molecular aggregation state of the humus-like fluorescent component, and determining the biodegradation potential trend of the dissolved organic matter in the sample based on the proportion of hydrophilic and hydrophobic components, the proportion of tyrosine-like fluorescent component and humus-like fluorescent component, and the molecular aggregation state.

[0012] Further, S1 comprises:

[0013] Filtering the aquaculture tail water sample to be tested to obtain a filtered water sample;

[0014] Performing full wavelength scanning on the filtered water sample by using a fluorescence spectrophotometer to obtain three-dimensional fluorescence spectrum data;

[0015] Analyzing the filtered water sample by using a liquid chromatograph combined with an organic carbon detector to obtain liquid chromatography-organic carbon detection combined analysis data.

[0016] Further, S2 comprises:

[0017] In the chromatogram corresponding to the liquid chromatography-organic carbon detection combined analysis data, the hydrophobic component region and the hydrophilic component region are divided according to the retention time;

[0018] Integrating the chromatographic peaks of the hydrophobic component region and the hydrophilic component region respectively to obtain the hydrophobic component organic carbon response value and the hydrophilic component organic carbon response value;

[0019] According to the hydrophobic component organic carbon response value and the hydrophilic component organic carbon response value, the proportion of hydrophilic and hydrophobic components is calculated.

[0020] Further, S3 comprises:

[0021] In the contour map corresponding to the three-dimensional fluorescence spectrum data, the tyrosine-like fluorescent component region and the humus-like fluorescent component region are identified;

[0022] Volume-integrating the fluorescence intensities of the tyrosine-like fluorescent component region and the humus-like fluorescent component region respectively to obtain the tyrosine-like fluorescent component response value and the humus-like fluorescent component response value;

[0023] According to the tyrosine-like fluorescent component response value and the humus-like fluorescent component response value, the proportion of tyrosine-like fluorescent component and humus-like fluorescent component is calculated.

[0024] Further, S4 comprises:

[0025] Obtaining the proportion of tyrosine-like fluorescent component and humus-like fluorescent component;

[0026] comparing the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component with a preset determination threshold value;

[0027] if the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component is less than or equal to the determination threshold value, it is determined that there is a significant humus-like component;

[0028] if the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component is greater than the determination threshold value, it is determined that there is no significant humus-like component.

[0029] Further, when it is determined in step S4 that there is no significant humus-like component, the following steps are performed: obtaining the hydrophilic-hydrophobic component ratio obtained in step S2 and the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component obtained in step S3; and determining the biodegradability potential trend of the dissolved organic matter in the sample based on the hydrophilic-hydrophobic component ratio and the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component.

[0030] Further, when it is determined in step S4 that there is a significant humus-like component, the following steps are performed: analyzing the molecular aggregation state of the humus-like fluorescent component; obtaining the hydrophilic-hydrophobic component ratio obtained in step S2, the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component obtained in step S3, and the analyzed molecular aggregation state of the humus-like fluorescent component; and determining the biodegradability potential trend of the dissolved organic matter in the sample based on the hydrophilic-hydrophobic component ratio, the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component, and the molecular aggregation state.

[0031] Further, in step S5, the molecular aggregation state of the humus-like fluorescent component is analyzed, specifically: based on the three-dimensional fluorescence spectrum data, it is detected whether the humus-like fluorescent component has a fluorescence self-quenching effect of non-linear reduction of fluorescence intensity with increasing concentration, so as to determine the molecular aggregation state of the humus-like fluorescent component.

[0032] Further, in step S5, the biodegradability potential trend of the dissolved organic matter in the sample is determined, specifically: the hydrophilic-hydrophobic component ratio, the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component, and the molecular aggregation state are compared with preset references respectively, and the comparison results are integrated to obtain a judgment conclusion of the biodegradability potential trend.

[0033] On the other hand, the present application provides a system for analyzing characteristics of dissolved organic matter in aquaculture tail water, comprising the following modules:

[0034] a data acquisition module, configured to acquire three-dimensional fluorescence spectrum data and liquid chromatography-organic carbon detection combined analysis data of a sample of the aquaculture tail water to be tested;

[0035] The first calculation module is configured to obtain a hydrophilic-hydrophobic component ratio of the dissolved organic matter in the sample according to liquid chromatography-organic carbon detection combined analysis data.

[0036] The second calculation module is configured to obtain a ratio of tyrosine-like fluorescent component and humus-like fluorescent component of the dissolved organic matter in the sample according to three-dimensional fluorescence spectrum data.

[0037] The significant judgment module is configured to judge whether the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component indicates that there is a significant humus-like component.

[0038] The trend judgment module is configured to, if not, judge a biodegradation potential trend of the dissolved organic matter in the sample based on the hydrophilic-hydrophobic component ratio and the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component; and if yes, analyze a molecular aggregation state of the humus-like fluorescent component, and judge the biodegradation potential trend of the dissolved organic matter in the sample based on the hydrophilic-hydrophobic component ratio, the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component, and the molecular aggregation state.

[0039] Compared with the prior art, the present application has the following beneficial effects:

[0040] 1. By integrating multi-dimensional component feature information and establishing an analysis path with a discriminant branch, the static composition of dissolved organic matter can be dynamically related to its future biological treatment behavior, so as to realize effective advance prediction of the operation stability of the biological treatment unit. Firstly, three-dimensional fluorescence spectrum data reflecting functional group characteristics and liquid chromatography-organic carbon detection combined analysis data reflecting polarity distribution are synchronously obtained, and key tyrosine-like and humus-like fluorescent component ratios and hydrophilic-hydrophobic component ratios are extracted therefrom, which constitute a multi-parameter basis for evaluating degradation potential. Instead of stopping at parameter acquisition, a significant judgment step based on the tyrosine-like and humus-like fluorescent component ratios is introduced, which serves as a logical branch point to guide the subsequent differentiated deep analysis path. The analysis process has intelligent screening and focusing capabilities, and can automatically identify water quality situations that need to be paid more attention to and have higher potential hazards.

[0041] 2. When it is judged that the humus-like component is not significant, the system can perform rapid trend evaluation based on the simpler component ratio for efficient screening; when it is judged that there is a significant humus-like component, further analysis of the molecular aggregation state of the humus-like component is started to more finely judge the inhibition risk it may have on the biodegradation process from the molecular interaction level, and finally a more accurate biodegradation potential trend judgment is made by comprehensively analyzing multiple source information. The originally isolated static component parameters are converted into dynamic early warning signals that can directly serve the process control decision-making through a logically rigorous process, effectively overcoming the control lag problem caused by the disconnection between analysis and process in the prior art, and providing key monitoring technical support for stable and low-consumption operation of aquaculture effluent biological treatment systems. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A flowchart of a method for analyzing characteristics of dissolved organic matter in aquaculture effluent according to the present application;

[0043] Figure 2 A structural schematic diagram of a system for analyzing characteristics of dissolved organic matter in aquaculture effluent according to the present application. DETAILED DESCRIPTION

[0044] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] Embodiment 1: Figure 1 A method for analyzing characteristics of dissolved organic matter in aquaculture effluent according to the present application is given, which comprises the following steps:

[0046] S1. Obtain three-dimensional fluorescence spectrum data and liquid chromatography-organic carbon detection combined analysis data of the aquaculture effluent sample to be tested;

[0047] S2. Obtain the hydrophilic-hydrophobic component ratio of the dissolved organic matter in the sample according to the liquid chromatography-organic carbon detection combined analysis data;

[0048] S3. Obtain the ratio of the tyrosine-like fluorescence component and the humus-like fluorescence component of the dissolved organic matter in the sample according to the three-dimensional fluorescence spectrum data;

[0049] S4. Judge whether the ratio of the tyrosine-like fluorescence component and the humus-like fluorescence component indicates the presence of a significant humus-like component;

[0050] S5, if no, determining the biodegradation potential trend of the dissolved organic matter in the sample based on the proportion of hydrophilic / hydrophobic components and the proportion of tyrosine-like fluorescent component and humus-like fluorescent component; if yes, analyzing the molecular aggregation state of the humus-like fluorescent component, and determining the biodegradation potential trend of the dissolved organic matter in the sample based on the proportion of hydrophilic / hydrophobic components, the proportion of tyrosine-like fluorescent component and humus-like fluorescent component, and the molecular aggregation state.

[0051] S1, acquiring three-dimensional fluorescence spectrum data and liquid chromatography-organic carbon detection combined analysis data of the aquaculture tail water sample to be tested, and the specific implementation is as follows:

[0052] First, the aquaculture tail water sample to be tested is filtered to obtain a filtered water sample. Specifically, the on-site collected aquaculture tail water sample is left to stand for a period of time, for example, 30 minutes, so that the large particles of suspended matter in the sample naturally settle, and then the supernatant is filtered. The filtration process uses a mixed cellulose ester filter membrane with a pore size of 0.45 microns, which can effectively intercept particulate matter, bacteria and most colloidal substances in the sample, so that the permeate mainly contains dissolved organic matter and inorganic ions. The permeate is the filtered water sample. The filtration operation is carried out on a vacuum filtration device to provide stable filtration driving force and ensure filtration efficiency and consistency. After filtration is completed, the filtered water sample is immediately transferred to a clean sample bottle and stored at 4 degrees Celsius in the dark to inhibit biological or chemical degradation of the dissolved organic matter therein, and the subsequent spectrum and chromatogram analysis should be completed within 24 hours to ensure the representativeness of the analysis results.

[0053] After obtaining the filtered water sample, a fluorescence spectrophotometer is used to scan the filtered water sample over a full wavelength range to obtain three-dimensional fluorescence spectral data. In practice, the filtered water sample is placed in a standard quartz cuvette and is placed in the sample chamber of a preheated and stabilized fluorescence spectrophotometer. The instrument is set to scan the excitation wavelength range from 200 nm to 450 nm, the emission wavelength range from 250 nm to 550 nm, and the scanning interval is set to, for example, 5 nm. The integration time is set to, for example, 0.5 seconds. During the scanning process, the instrument sequentially irradiates the sample at each set excitation wavelength and simultaneously records the fluorescence intensity signal at a corresponding series of emission wavelengths, and finally obtains a data matrix with excitation wavelength, emission wavelength and fluorescence intensity as three dimensions. This data matrix is the three-dimensional fluorescence spectral data. To eliminate the background signal interference caused by the instrument itself and the solvent, a blank scan is performed using ultrapure water before the sample scan, and the three-dimensional fluorescence spectral data obtained is deducted from the blank background. In addition, to correct the possible slight differences in instrument response on different dates, the fluorescence intensity of the quinine sulfate standard solution is measured and recorded in each analysis sequence, and the known fluorescence characteristics of the standard solution are used to standardize and correct the three-dimensional fluorescence spectral data of the sample. The final obtained and stored three-dimensional fluorescence spectral data will be used for subsequent fluorescence component identification and quantitative analysis.

[0054] After the acquisition of the three-dimensional fluorescence spectroscopy data, the same filtered water sample is analyzed by liquid chromatography-organic carbon detection to obtain the liquid chromatography-organic carbon detection data. Before analysis, the liquid chromatography system needs to be equilibrated, and the mobile phase is a degassed phosphate buffer solution with a pH value of, for example, 6.8. The chromatographic column is selected to be a hydrophilic interaction chromatographic column, and the column oven temperature is maintained at, for example, 30 degrees Celsius. The sample injection volume is set to, for example, 1000 microliters. After starting the analysis program, the dissolved organic matter components in the filtered water sample are separated in the chromatographic column according to their polarity and molecular size, and then flow out of the chromatographic column and into the organic carbon detector. The organic carbon detector first oxidizes the flowing organic matter into carbon dioxide by ultraviolet light irradiation, and then uses a thin film conductivity detector to measure the conductivity change caused by the generated carbon dioxide, which is proportional to the organic carbon content, so as to record the response signal of the organic carbon at different retention times in real time, forming a chromatogram with retention time as the horizontal coordinate and organic carbon response value as the vertical coordinate. The chromatogram and the complete data set contained therein are the liquid chromatography-organic carbon detection data. To ensure the accuracy and comparability of the analysis data, a known concentration of organic carbon standard solution, such as potassium hydrogen phthalate solution, is used for system calibration before each batch of sample analysis. By measuring the response values of different concentrations of standard solutions, a quantitative relationship curve between the organic carbon response value and the actual concentration is established. The entire analysis process needs to be carried out in a constant temperature laboratory to minimize the impact of environmental temperature fluctuations on chromatographic separation and detector stability. The liquid chromatography-organic carbon detection data obtained will be used as the input basis for subsequent calculation of the hydrophilic and hydrophobic component proportions.

[0055] S2, according to the liquid chromatography-organic carbon detection data, obtaining the hydrophilic and hydrophobic component proportions of the dissolved organic matter in the sample, which is specifically implemented as:

[0056] First, the liquid chromatography-organic carbon detection analysis data obtained in step S1 is acquired, which is expressed as a chromatogram with retention time as the abscissa and organic carbon response value as the ordinate. In the chromatogram, the hydrophobic component region and the hydrophilic component region are divided according to the difference in retention behavior of different organic components on the chromatographic column. The specific implementation mode of the division is to determine a time division point according to the retention time of known standard substances or the statistical rules established by analyzing a large number of aquaculture effluent samples, for example, the chromatographic signal region with a retention time between, for example, 8 minutes and, for example, 15 minutes is defined as the hydrophobic component region, which usually contains humic acid, fulvic acid and other components with large molecular weight and weak polarity; at the same time, the chromatographic signal region with a retention time between, for example, 2 minutes and, for example, 8 minutes is defined as the hydrophilic component region, which mainly includes small molecular organic acids, carbohydrates, protein degradation products and other components with strong polarity. The basis of this division is that under the conditions of the hydrophilic interaction chromatographic column used and the specific mobile phase, the stronger the polarity of the substance, the weaker the interaction with the stationary phase, and thus the shorter the retention time, and vice versa. When determining the range of the region, it is necessary to ensure that the baseline between the two regions is flat and stable to ensure the accuracy of subsequent integral calculation.

[0057] After the hydrophobic component region and the hydrophilic component region are clearly divided, the chromatographic peaks in the two regions are integrated to obtain quantitative data. The integral process is usually completed by the data processing software matched with the instrument, and the core is to calculate the area surrounded by the chromatographic peak curve and the baseline. The specific operation is to manually or automatically set the integral starting point and the integral ending point of each region in the software, and the software will automatically identify and track the baseline of the chromatographic peak according to the setting, and then accumulate and sum the organic carbon response values of all data points in the selected region to obtain a value representing the total amount of hydrophobic components, i.e. the hydrophobic component organic carbon response value, and a value representing the total amount of hydrophilic components, i.e. the hydrophilic component organic carbon response value. The organic carbon response value here is a dimensionless relative response intensity value, which is proportional to the organic carbon content of the corresponding component. When integrating, attention should be paid to the processing of possible peak overlap, such as setting a vertical dividing line or using the peak splitting algorithm provided by the software to ensure that the signal of each component is accurately attributed and measured. The obtained hydrophobic component organic carbon response value and hydrophilic component organic carbon response value will be directly input parameters for the next calculation.

[0058] After obtaining the hydrophobic component organic carbon response value and the hydrophilic component organic carbon response value, the hydrophilic-hydrophobic component ratio is calculated according to the two values. The hydrophilic-hydrophobic component ratio is an index for quantifying the relative abundance relationship between the hydrophilic component and the hydrophobic component in the sample. The specific implementation for calculating the ratio is to take the hydrophilic component organic carbon response value as the numerator and the hydrophobic component organic carbon response value as the denominator, and then perform division operation. The calculation can be described as: the hydrophilic-hydrophobic component ratio is equal to the hydrophilic component organic carbon response value divided by the hydrophobic component organic carbon response value. Through this calculation, if the obtained ratio is greater than 1, it indicates that the hydrophilic component dominates in the current sample; if the ratio is less than 1, it indicates that the hydrophobic component dominates. The calculation process of this ratio does not involve complex iteration conditions or weight setting, and the core logic is to directly compare the relative response intensities of the two types of components. In actual operation, in order to ensure the reliability of the calculation result, it is necessary to confirm that the hydrophobic component organic carbon response value used for calculation is not zero. If the hydrophobic component organic carbon response value is extremely low, for example, lower than the detection limit of the instrument, it can be set to a very small positive number much smaller than the hydrophilic component organic carbon response value, for example, 0.001, and then calculated to avoid mathematical calculation errors. The finally calculated hydrophilic-hydrophobic component ratio will be used as a key feature parameter for the subsequent comprehensive evaluation of the biodegradation potential of dissolved organic matter.

[0059] S3, according to the three-dimensional fluorescence spectrum data, obtaining the ratio of the tyrosine-like fluorescence component and the humus-like fluorescence component of the dissolved organic matter in the sample, which is specifically implemented as:

[0060] First, the three-dimensional fluorescence spectrum data obtained in step S1 and subjected to blank correction and normalization processing is acquired. This data is a data matrix containing three dimensions of excitation wavelength, emission wavelength, and fluorescence intensity. Based on this data matrix, a fluorescence contour plot can be drawn, in which, according to the characteristic excitation and emission wavelength positions of different fluorescent groups in dissolved organic matter, a tyrosine-like fluorescent component region and a humus-like fluorescent component region are identified. The characteristic peak of the tyrosine-like fluorescent component is usually in a low excitation and emission wavelength range, and its identification method is to define the enclosed region surrounded by an excitation wavelength range of, for example, 220 nm to 250 nm and an emission wavelength range of, for example, 300 nm to 330 nm on the contour plot as the tyrosine-like fluorescent component region, and the fluorescence signal in this region is mainly derived from the fluorescence characteristics of tyrosine amino acid residues in protein-like substances. The characteristic peak of the humus-like fluorescent component is in a high excitation and emission wavelength range, and its identification method is to define the enclosed region surrounded by an excitation wavelength range of, for example, 330 nm to 350 nm and an emission wavelength range of, for example, 420 nm to 450 nm on the contour plot as the humus-like fluorescent component region, and the fluorescence signal in this region is usually related to humus-like complex macromolecular organic matter. The specific boundary values of the region definition are determined based on a large number of spectral databases of known standard samples or recognized fluorescence region division maps in the art, and in actual operation, the boundaries can be accurately determined by using the default region template built in professional spectrum analysis software or the boundaries defined by the user according to the characteristics of the sample.

[0061] After the tyrosine-like fluorescence component region and the humus-like fluorescence component region are accurately identified, the fluorescence intensity signals in the two three-dimensional space regions need to be volume integrated respectively to obtain quantitative response values. Volume integration is a triple summation or integration operation of a function value in a three-dimensional space domain in mathematics, which in this case specifically means that the fluorescence intensity values of all data points in the selected region are accumulated. The specific implementation is to use a three-dimensional fluorescence spectrum data processing software to first manually or automatically set the excitation wavelength and emission wavelength boundaries of the identified region, and then the software will automatically extract the fluorescence intensity values corresponding to all discrete data points within the boundary range. Then these fluorescence intensity values are multiplied by a small area unit formed by the excitation wavelength step and the emission wavelength step. Then double summation is performed along the excitation and emission wavelength directions, and finally the value representing the total fluorescence signal intensity of the region, i.e. the tyrosine-like fluorescence component response value and the humus-like fluorescence component response value, is calculated. The step here is the excitation wavelength scanning interval and the emission wavelength scanning interval set during data acquisition, for example, both are 5 nanometers. The volume integration process is completely performed by the software according to the set algorithm, and the core is to accumulate all effective signals in a specific spectral region, thereby avoiding accidental errors that may be caused by a single peak and more comprehensively reflecting the overall abundance of the fluorescence component. The obtained tyrosine-like fluorescence component response value and humus-like fluorescence component response value are two comparable values with the same dimension and unit.

[0062] After obtaining the tyrosine-like fluorescence component response value and the humus-like fluorescence component response value, the ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component is calculated according to the two values. The ratio is an important indicator for quantifying the relative abundance of the humus-like component with respect to the tyrosine-like component in the sample. The specific implementation of calculating the ratio is to take the humus-like fluorescence component response value as the numerator and the tyrosine-like fluorescence component response value as the denominator, and then perform division operation. The calculation relationship is described as: the ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component is equal to the humus-like fluorescence component response value divided by the tyrosine-like fluorescence component response value. Through this calculation, if the obtained ratio is large, it indicates that the relative abundance of humus-like substances in the current sample is high; if the ratio is small, it indicates that the relative abundance of protein-derived substances is high. This calculation is a direct arithmetic operation and does not involve iteration or complex weight coefficients. In actual operation, to ensure the feasibility of mathematical calculation, it is necessary to confirm that the tyrosine-like fluorescence component response value used for calculation is not zero. In extreme cases, if the tyrosine-like fluorescence component response value is extremely low, for example, lower than the detection limit of the instrument, the value can be set to a very small positive number much smaller than the humus-like fluorescence component response value before calculation to prevent invalid calculation with a denominator of zero. The final calculated ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component will be used as a key spectral feature parameter and directly used in the subsequent steps to determine whether there is a significant humus-like component.

[0063] S4, determining whether the ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component indicates the presence of significant humus-like component, specifically implemented as:

[0064] First, the ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component calculated in step S3 is obtained. This ratio is a specific numerical value obtained by region identification and volume integration calculation of the three-dimensional fluorescence spectrum data through the aforementioned steps. After obtaining the ratio value, it is compared with a preset determination threshold. The preset determination threshold is a pre-set critical value for distinguishing whether there is significant humus-like component. The threshold is not arbitrarily set, but is determined based on the analysis data of a large number of historical aquaculture effluent samples and the correlation analysis of the operating efficiency of related biological treatment units. The specific implementation of its acquisition and setting is as follows: first, a representative aquaculture effluent sample library is collected, which covers effluent samples of different seasons, different aquaculture species and different treatment stages; then, the ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component of each sample is determined according to the method of steps S1 to S3; at the same time, the actual biodegradability data of the dissolved organic matter represented by each sample is obtained by parallel biological degradation experiments or long-term monitoring of the actual removal efficiency of organic matter by corresponding biological treatment units, such as activated sludge system; finally, through statistical analysis, the correlation between the ratio of the tyrosine-like fluorescence component to the humus-like fluorescence component and the actual biodegradability is found, and the critical value that can clearly distinguish between easily degradable samples and difficult degradable samples is determined as the preset determination threshold. For example, through analysis, it may be found that when the ratio value is less than 1.2, the organic matter in the sample shows the characteristics of slow degradation and easy accumulation in subsequent biological treatment, and 1.2 can be set as an exemplary preset determination threshold. The threshold can be adjusted according to the specific aquaculture process, water quality characteristics and treatment target, but the core basis for its setting is always the correlation between the ratio value and the biodegradation potential.

[0065] After the preset determination threshold is determined, the comparison operation is performed. The comparison is to compare the ratio value of the tyrosine-like fluorescence component to the humus-like fluorescence component obtained with the preset determination threshold value. This is a simple mathematical comparison process, which aims to determine whether the ratio value of the current sample is greater than, less than or equal to the preset determination threshold. In implementation, it can be completed in data processing software or simple comparison logic, with two numerical values as input parameters and a logical judgment as output result, that is, yes or no, corresponding to whether the ratio value is less than or equal to the threshold. This step does not involve complex iterative calculation, but requires that the dimensions of the two comparison objects are consistent, both being dimensionless ratios.

[0066] According to the comparison result, a final determination is made. If the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component is less than or equal to a preset determination threshold, it is determined that there is a significant humus-like component. Here, significant is a technical term, which specifically refers to the relative abundance of the humus-like component in the current sample reaching a critical level, which indicates that it may pose a potential risk to the stability and degradation efficiency of subsequent biological treatment processes and needs to be given additional attention. On the contrary, if the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component is greater than the preset determination threshold, it is determined that there is no significant humus-like component. This means that the dissolved organic matter in the sample is dominated by tyrosine-like and other easily degradable components, and its biodegradation potential is expected to be high. This determination step is the concluding link of the entire judgment process, which converts a continuous numerical ratio into a discrete classification decision, thereby providing a clear basis for selecting different analysis paths for subsequent steps.

[0067] S5, if not, determining the biodegradation potential trend of the dissolved organic matter in the sample based on the ratio of the hydrophilic and hydrophobic components and the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component; if yes, analyzing the molecular aggregation state of the humus-like fluorescent component, and determining the biodegradation potential trend of the dissolved organic matter in the sample based on the ratio of the hydrophilic and hydrophobic components, the ratio of the tyrosine-like fluorescent component to the humus-like fluorescent component, and the molecular aggregation state, which is specifically implemented as:

[0068] First, the subsequent analysis path is selected according to the determination conclusion made in step S4. The determination conclusion of step S4 is divided into two cases, i.e. there is a significant humus-like component or there is no significant humus-like component. The execution logic of the entire step S5 completely depends on this determination conclusion, and is divided into two parallel and mutually exclusive technical branches accordingly.

[0069] When step S4 determines that the humic-like component is not significant, the first analysis path is executed. Under this path, the hydrophilic-hydrophobic component ratio calculated in step S2 and the ratio of tyrosine-like fluorescence component to humic-like fluorescence component calculated in step S3 are first obtained. After obtaining these two ratio parameters, the biological degradation potential trend of the dissolved organic matter in the sample is determined based on them. The determination here is a comprehensive evaluation process, and its specific implementation is not a simple mathematical operation, but is based on pre-established empirical rules or decision matrices. The establishment of the rules or matrices is based on statistical analysis of a large number of aquaculture effluent samples with known biological degradation behavior. For example, the following determination logic can be set: when the hydrophilic-hydrophobic component ratio is greater than an empirical value, and the ratio of tyrosine-like fluorescence component to humic-like fluorescence component is also greater than another empirical value, the biological degradation potential trend is determined to be high; if the hydrophilic-hydrophobic component ratio is between 1.0 and 1.5, for example, and the ratio of tyrosine-like fluorescence component to humic-like fluorescence component is greater than 1.0, the trend is determined to be medium; if the hydrophilic-hydrophobic component ratio is less than 1.0, regardless of the ratio of tyrosine-like fluorescence component to humic-like fluorescence component, the trend is determined to be low. The empirical values here are the pre-set benchmarks, and these benchmark values are set based on the correlation between different parameter ranges in historical data and the actual observed biological treatment efficiency. The determination process is to compare the two measured values of the ratios of the current sample with the corresponding pre-set benchmarks, according to the value interval combination in which they fall, and according to the pre-defined rule table, to finally obtain a qualitative or semi-quantitative conclusion about the biological degradation potential trend, such as high, medium, low, or increasing, flat, decreasing. Under this path, since it has been determined that the humic-like component is not significant, there is no need to consider its molecular state, simplifying the analysis model and focusing on the relative advantage evaluation of the easily degradable component.

[0070] When step S4 determines that there is a significant humic-like component, a second analysis path is performed. Under this path, the analysis process is more in-depth. First, additional analysis of the molecular aggregation state of the humic-like fluorescent component is required. As further limited by the dependent claims, the analysis of the molecular aggregation state of the humic-like fluorescent component is specifically achieved by detecting whether the humic-like fluorescent component has a fluorescence self-quenching effect of non-linear decrease in fluorescence intensity with increasing concentration based on three-dimensional fluorescence spectral data. The specific implementation is as follows: the same portion of the filtered water sample obtained in step S1 is diluted to different degrees, for example, prepared into a stock solution and a series of concentration gradient samples diluted by 2 times, 4 times, 8 times, etc. Then, strictly according to the three-dimensional fluorescence spectrum acquisition conditions in step S1, the fluorescence intensity of each dilution sample at the characteristic excitation and emission wavelengths of the humic-like fluorescent component is measured respectively. Subsequently, the change rule of fluorescence intensity with the increase of sample dilution multiple, i.e. the decrease of concentration, is observed and analyzed. If the fluorescence intensity presents a significant, super-proportional increase with the decrease of concentration, i.e. the intensity-concentration relationship deviates from linearity, which is manifested as the intensity being inhibited at a higher concentration, it is determined that there is a fluorescence self-quenching effect, and it is further inferred that the humic-like fluorescent component is in a stronger molecular aggregation state or there is intermolecular interaction. Conversely, if the fluorescence intensity and the concentration have a good linear relationship, it is determined that there is no obvious self-quenching effect, indicating that the molecules are mainly in a dispersed state. The judgment of non-linear decrease can be achieved by calculating whether the ratio of fluorescence intensity and concentration at different concentration points is constant, or by drawing the fluorescence intensity-concentration curve and observing whether it deviates from the straight line passing through the origin. The final judgment result of the molecular aggregation state can be a qualitative conclusion, such as significant aggregation state or insignificant aggregation state, or a semi-quantitative index, such as grading according to the degree of deviation of the correlation coefficient of linear regression of concentration and fluorescence intensity from 1.0.

[0071] After the analysis of the molecular aggregation state of the humus-like fluorescent component, three key parameters are needed to be obtained in this second path for comprehensive judgment. The three parameters are: the proportion of hydrophilic and hydrophobic components obtained in step S2, the proportion of tyrosine-like fluorescent component and humus-like fluorescent component obtained in step S3, and the information of the molecular aggregation state of the humus-like fluorescent component obtained by the analysis just now. Based on the three parameters, the trend of the biodegradability potential of the dissolved organic matter in the sample is judged. As further limited by the dependent claims, the judgment is specifically to compare the proportion of hydrophilic and hydrophobic components, the proportion of tyrosine-like fluorescent component and humus-like fluorescent component, and the molecular aggregation state with the preset benchmarks respectively, and to obtain the judgment conclusion by comprehensively analyzing the comparison results. The preset benchmarks here include the benchmark for the proportion of hydrophilic and hydrophobic components, the benchmark for the proportion of tyrosine-like fluorescent component and humus-like fluorescent component, and the benchmark for the molecular aggregation state. The specific implementation mode of the comprehensive judgment can be a weighted scoring or a hierarchical decision tree. For example, different levels and scores can be set for each parameter: the proportion of hydrophilic and hydrophobic components is scored high if it is higher than a certain value, and scored low if it is lower than a certain value; the proportion of tyrosine-like fluorescent component and humus-like fluorescent component is scored high if it is higher than a certain value, and scored low if it is lower than a certain value; the molecular aggregation state is scored low if it is determined to be significantly aggregated, and scored high if it is determined to be not significantly aggregated. Then, the scores of the three parameters are added or weighted according to the preset weight to obtain a total score. Finally, the total score is compared with the preset total score threshold, so as to obtain the final trend judgment, for example, the total score is higher than a certain threshold to judge that the trend is acceptable, and lower than a certain threshold to judge that the trend is worrying. Another implementation mode is to use decision rules, for example: as long as the molecular aggregation state is determined to be significantly aggregated, the biodegradability potential trend is directly judged to be low regardless of the other two parameters; if the molecular aggregation state is not significantly aggregated, the proportions of the other two parameters are referred to, and the judgment is made according to the similar rules in the first path. All the preset benchmarks and thresholds, including the level boundary values of each parameter, the weight coefficients, the total score thresholds, etc., need to be determined through a large number of sample calibration experiments, that is, to establish the mapping relationship between the parameter combination and the actual biodegradability database, so as to extract effective classification rules or scoring standards. Through this multi-parameter and hierarchical comprehensive judgment, the additional degradation resistance that may be caused by the aggregation state of the humus-like component can be more accurately evaluated when the humus-like component is significantly present, so as to give a more reliable and more robust early warning conclusion on the biodegradability potential trend. The output of the whole step S5 is a clear trend judgment on the degradation behavior tendency of the dissolved organic matter in the sample to be tested in the subsequent biological treatment process, which directly serves the stability early warning and regulation decision of the treatment process.

[0072] Example 2: Figure 2The application discloses a structure diagram of a breeding tail water dissolved organic matter characteristic analysis system, and the breeding tail water dissolved organic matter characteristic analysis system comprises the following modules.

[0073] The data acquisition module is used for acquiring three-dimensional fluorescence spectrum data and liquid chromatography-organic carbon detection combined analysis data of the breeding tail water sample to be measured.

[0074] The first calculation module is used for obtaining the hydrophilic-hydrophobic component ratio of the dissolved organic matter in the sample according to the liquid chromatography-organic carbon detection combined analysis data.

[0075] The second calculation module is used for obtaining the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component of the dissolved organic matter in the sample according to the three-dimensional fluorescence spectrum data.

[0076] The significant judgment module is used for judging whether the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component indicates the existence of the significant humus-like component.

[0077] The trend judgment module is used for judging the biodegradation potential trend of the dissolved organic matter in the sample based on the hydrophilic-hydrophobic component ratio and the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component if the answer is no, and is used for analyzing the molecular aggregation state of the humus-like fluorescent component, judging the biodegradation potential trend of the dissolved organic matter in the sample based on the hydrophilic-hydrophobic component ratio, the ratio of the tyrosine-like fluorescent component and the humus-like fluorescent component and the molecular aggregation state if the answer is yes.

[0078] The calculation in the embodiments is all de-dimensioned to obtain numerical values, and preset parameters and threshold values in the calculation are set by a person skilled in the art according to actual conditions.

[0079] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially.

[0080] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed in the present application can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software depends on the specific application and the constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0081] In addition, the functional modules in each embodiment of the present application can be integrated in one processing module, or each module can be physically present alone, or two or more modules can be integrated in one module.

[0082] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is only a logical function division, and there can be another division manner for actual implementation, for example, multiple devices or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different parts can be indirect couplings or communication connections through some interfaces, devices or modules, and can be in electrical, mechanical or other forms.

[0083] The above describes only the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any modification or replacement within the technical range disclosed by the present application can be easily thought by any person skilled in the art, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0084] Finally: the above described only the preferred embodiments of the present application, and not for limiting the present application, any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application, should be included in the protection scope of the present application.

Claims

1. A method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater, characterized in that, Includes the following steps: S1. Obtain the three-dimensional fluorescence spectral data of the aquaculture wastewater sample to be tested and analyze the data using liquid chromatography-organic carbon detection coupled with the analysis data; S2. Based on the analysis data from liquid chromatography-organic carbon detection, the proportions of hydrophilic and hydrophobic components of soluble organic matter in the sample are obtained; S3. Based on the three-dimensional fluorescence spectral data, obtain the ratio of tyrosine-like fluorescent components to humic-like fluorescent components of dissolved organic matter in the sample; S4. Determine whether the ratio of tyrosine-like fluorescent components to humic substance-like fluorescent components indicates the presence of significant humic substance-like components. S5. If not, the biodegradation potential trend of dissolved organic matter in the sample is judged based on the ratio of hydrophilic and hydrophobic components and the ratio of tyrosine-like fluorescent components to humic-like fluorescent components. If so, the molecular aggregation state of the humic-like fluorescent components is analyzed, and the biodegradation potential trend of dissolved organic matter in the sample is judged based on the ratio of hydrophilic and hydrophobic components, the ratio of tyrosine-like fluorescent components to humic-like fluorescent components, and the molecular aggregation state.

2. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 1, characterized in that, S1 includes: The aquaculture wastewater sample to be tested was filtered to obtain the filtered water sample; The filtered water sample was scanned at all wavelengths using a fluorescence spectrophotometer to obtain three-dimensional fluorescence spectral data; The filtered water sample was analyzed using a liquid chromatography-organic carbon detector coupled with a liquid chromatography-organic carbon detector to obtain the analytical data.

3. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 1, characterized in that, S2 include: In the chromatograms corresponding to the data from liquid chromatography-organic carbon detection, the regions of hydrophobic components and hydrophilic components are divided according to the retention time. The chromatographic peaks of the hydrophobic and hydrophilic component regions were integrated separately to obtain the organic carbon response values ​​of the hydrophobic and hydrophilic components. The ratio of hydrophilic to hydrophobic components is calculated based on the organic carbon response values ​​of the hydrophobic and hydrophilic components.

4. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 1, characterized in that, S3 include: In the contour plot corresponding to the three-dimensional fluorescence spectral data, regions of tyrosine-like fluorescent components and regions of humic substances-like fluorescent components were identified. The fluorescence intensity of the tyrosine-like fluorescent component region and the humic substance-like fluorescent component region were integrated by volume to obtain the response values ​​of the tyrosine-like fluorescent component and the humic substance-like fluorescent component. The ratio of tyrosine-like fluorescent components to humic-like fluorescent components was calculated based on their response values.

5. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 1, characterized in that, S4 include: Obtain the ratio of tyrosine-like fluorescent components to humic substance-like fluorescent components; The ratio of tyrosine-like fluorescent components to humic substance-like fluorescent components is compared with a preset judgment threshold. If the ratio of tyrosine-like fluorescent components to humic substance-like fluorescent components is less than or equal to the judgment threshold, it is determined that there are significant humic substance-like components. If the ratio of tyrosine-like fluorescent components to humic substance-like fluorescent components is greater than the judgment threshold, it is determined that there are no significant humic substance-like components.

6. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 1, characterized in that, When step S4 determines that there are no significant humic substances, the following steps are performed: obtain the ratio of hydrophilic and hydrophobic components obtained in step S2 and the ratio of tyrosine-like fluorescent components to humic substances fluorescent components obtained in step S3; based on the ratio of hydrophilic and hydrophobic components and the ratio of tyrosine-like fluorescent components to humic substances fluorescent components, determine the biodegradation potential trend of dissolved organic matter in the sample.

7. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 1, characterized in that, When step S4 determines that there is a significant humic substance-like component, the following steps are performed: analyze the molecular aggregation state of the humic substance-like fluorescent component; obtain the ratio of hydrophilic and hydrophobic components obtained in step S2, the ratio of tyrosine-like fluorescent component to humic substance-like fluorescent component obtained in step S3, and the molecular aggregation state of the analyzed humic substance-like fluorescent component. Based on the ratio of hydrophilic and hydrophobic components, the ratio of tyrosine-like fluorescent components to humic-like fluorescent components, and the molecular aggregation state, the biodegradation potential trend of dissolved organic matter in the sample is determined.

8. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 6, characterized in that, In step S5, the molecular aggregation state of the humic substance-like fluorescent component is analyzed. Specifically, based on three-dimensional fluorescence spectral data, the presence of a fluorescence self-quenching effect in which the fluorescence intensity decreases nonlinearly with increasing concentration is detected in order to determine the molecular aggregation state of the humic substance-like fluorescent component.

9. The method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater according to claim 7, characterized in that, In step S5, the biodegradation potential trend of dissolved organic matter in the sample is determined by comparing the ratio of hydrophilic and hydrophobic components, the ratio of tyrosine-like fluorescent components to humic-like fluorescent components, and the molecular aggregation state with preset benchmarks, and drawing a conclusion on the biodegradation potential trend by combining the comparison results.

10. A system for analyzing the characteristics of dissolved organic matter in aquaculture wastewater, used to implement the method for analyzing the characteristics of dissolved organic matter in aquaculture wastewater as described in any one of claims 1-9, characterized in that, Includes the following modules: The data acquisition module is used to acquire the three-dimensional fluorescence spectral data and the liquid chromatography-organic carbon detection coupled analysis data of the aquaculture wastewater sample to be tested; The first calculation module is used to obtain the proportion of hydrophilic and hydrophobic components of dissolved organic matter in the sample based on the analysis data of liquid chromatography-organic carbon detection coupled analysis; The second calculation module is used to obtain the ratio of tyrosine-like fluorescent components to humic-like fluorescent components of dissolved organic matter in the sample based on the three-dimensional fluorescence spectral data. The significant judgment module is used to determine whether the ratio of tyrosine-like fluorescent components to humic substance-like fluorescent components indicates the presence of significant humic substance-like components. The trend judgment module is used to determine the biodegradation potential trend of dissolved organic matter in the sample based on the ratio of hydrophilic and hydrophobic components and the ratio of tyrosine-like fluorescent components to humic-like fluorescent components if not. If so, the molecular aggregation state of the humic-like fluorescent components is analyzed, and the biodegradation potential trend of dissolved organic matter in the sample is judged based on the ratio of hydrophilic and hydrophobic components, the ratio of tyrosine-like fluorescent components to humic-like fluorescent components, and the molecular aggregation state.

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