EHNV virus detection method and system based on dynamic fluorescence intensity monitoring

Abstract

The invention discloses an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring. According to the method, a to-be-detected water area is divided into small areas, water impurity information is obtained, fluorescence detection is performed under different impurity characteristics, and fluorescence data are analyzed to evaluate the influence of impurities on detection accuracy. According to the accuracy influence data, an optimal sampling area is determined, an EHNV virus sample is collected, fluorescence intensity change monitoring is conducted on the sample, and finally EHNV virus information in the water area is obtained. The method can improve the detection sensitivity and accuracy under impurity interference, and is suitable for the fields of aquaculture, ecological environment monitoring and the like. The invention also provides a corresponding detection system, which comprises a fluorescence detection device, a data analysis module and a sample processing unit, has automatic operation and efficient data processing capabilities, and is beneficial to prevention and control of EHNV virus diffusion.

Description

Technical Field

[0001] The present invention relates to the technical field of virus detection, and in particular to an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring. Background Art

[0002] EHNV (Epidemic Hemorrhage Necrosis Virus) is a highly harmful virus to the aquaculture industry, mainly affecting fish, especially carp, and is highly contagious. Therefore, developing a new method that can accurately, quickly and effectively detect EHNV has become an urgent problem to be solved in the aquaculture industry.

[0003] Existing virus detection methods mostly rely on traditional PCR detection or ELISA and other technologies. Although these methods have high accuracy, they are cumbersome to operate, take a long time, are costly, and are difficult to monitor the virus status in the water environment in real time. With the advancement of fluorescence detection technology, the method based on fluorescence intensity change monitoring has gradually become a new trend in virus detection. However, impurities in the water (such as organic matter, inorganic matter, suspended particles, etc.) may interfere with the fluorescence detection results and affect the accuracy of virus detection.

[0004] The characteristics of water impurities include the type and content of inorganic and organic matter in the water, the size of suspended particles and their concentration. Different water impurities have different effects on the absorption, scattering and changes in fluorescence spectra of fluorescent signals. For example, high concentrations of suspended particles may cause scattering of fluorescent signals, affecting the accurate reading of fluorescence intensity; and some organic matter in the water may interact with fluorescent probes and interfere with the generation of fluorescent signals. These interfering factors reduce the accuracy and reliability of fluorescence intensity data when performing virus detection in complex water environments, thereby affecting the sensitivity and accuracy of virus detection.

[0005] Therefore, how to perform efficient and accurate EHNV virus detection under different water impurity characteristics and reduce the interference of impurities on fluorescence signals has become an urgent problem to be solved in current virus detection technology. In response to this problem, the present invention proposes an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring, which dynamically analyzes the impact of water impurity characteristics on fluorescence detection and optimizes the detection process, thereby improving the accuracy and sensitivity of virus detection, and is suitable for real-time virus monitoring in complex water quality environments. Summary of the invention

[0006] In order to solve at least one of the above technical problems, the present invention proposes an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring.

[0007] The first aspect of the present invention provides an EHNV virus detection method based on dynamic fluorescence intensity monitoring, comprising:

[0008] The water area to be tested for EHNV virus is divided into several small areas, the water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under the characteristics of each water impurity to obtain fluorescence detection data;

[0009] Determine the influence of different water impurity characteristics on the accuracy of EHNV virus fluorescence detection according to the fluorescence detection data, and obtain accuracy influence data;

[0010] Determine the optimal sampling area for testing EHNV virus in the waters to be tested based on the accuracy impact data;

[0011] Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, monitoring a change in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data;

[0012] The EHNV virus information of the water area to be tested for EHNV virus is determined based on the fluorescence intensity change data.

[0013] In this scheme, the water area to be tested for EHNV virus is divided into several small areas, the water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under each water impurity characteristic to obtain the test results, specifically:

[0014] Acquire geographical environment data of the water area to be detected for EHNV virus, construct a three-dimensional geographical model of the water area to be detected according to the geographical environment data, and divide the water area to be detected into several small areas according to the three-dimensional geographical model;

[0015] Obtain water impurity information for each small area, the water impurity information including inorganic and organic matter content, suspended particle size and content, determine water impurity characteristics for each small area based on the water impurity information, perform deduplication operations on the water impurity characteristics, and construct a water impurity characteristic data table for fluorescence detection test;

[0016] Construct a water impurity sample for each small area according to the water impurity characteristic data table, add the same amount of EHNV virus to each water impurity sample, and obtain a fluorescence detection test water sample;

[0017] The fluorescence detection test water sample is subjected to a fluorescence detection operation to obtain fluorescence detection data of each fluorescence detection test water sample, wherein the fluorescence detection data includes fluorescence intensity and fluorescence spectrum data.

[0018] In this scheme, the accuracy impact data of the fluorescence detection data is determined based on the fluorescence detection data, and the accuracy impact data is obtained, specifically:

[0019] Obtain standard fluorescence detection data of different EHNV virus contents without the influence of water impurities, and extract data features of the standard fluorescence detection data, wherein the data features include fluorescence intensity features, peak values, troughs, mean values, and variance features of the fluorescence spectrum;

[0020] The data features of the standard fluorescence detection data are used to construct an EHNV virus content-fluorescence feature database;

[0021] Performing feature extraction on the fluorescence detection data to obtain fluorescence detection data features, comparing the fluorescence detection data features with the EHNV virus content-fluorescence feature database, and determining the EHNV virus detection content under each water body impurity feature in the water area to be detected;

[0022] Evaluate the EHNV virus detection accuracy of the fluorescence detection test water sample under each water impurity characteristic according to the EHNV virus detection content under each water impurity characteristic, and obtain detection accuracy data of the fluorescence detection test water sample under each water impurity characteristic;

[0023] Construct a feature-accuracy data matrix using each water body impurity feature and the detection accuracy data, calculate the Pearson correlation coefficient between each water body impurity feature and the detection accuracy based on the feature-accuracy data matrix, and construct a correlation coefficient matrix;

[0024] The accuracy impact of EHNV virus fluorescence detection under the interference of different water impurity characteristics is determined according to the correlation coefficient matrix to obtain accuracy impact data.

[0025] In this solution, the optimal detection sample sampling area of ​​the EHNV virus in the waters to be detected is determined according to the accuracy impact data, specifically:

[0026] Constructing a detection accuracy evaluation model based on a decision tree algorithm, and setting model parameters of the detection accuracy evaluation model, wherein the model parameters include a maximum depth of the decision tree, a minimum number of split samples, and a minimum number of leaf node samples;

[0027] Importing the accuracy influencing data into the detection accuracy evaluation model to construct a decision tree for each water impurity feature, and training the detection accuracy evaluation model according to the decision tree to obtain a trained detection accuracy evaluation model;

[0028] Obtain water impurity information of each small area at the current time point in the water area to be detected, import the water impurity information into the detection accuracy evaluation model to perform EHNV virus detection accuracy evaluation on the water impurity information of each small area, and obtain an accuracy evaluation result;

[0029] The optimal detection sample sampling area for the EHNV virus in the waters to be detected is determined based on the accuracy evaluation results.

[0030] In this solution, the EHNV virus detection sample is obtained from the optimal detection sample sampling area, a fluorescence detection operation is performed on the EHNV virus detection sample, and the fluorescence intensity change during the fluorescence detection process is monitored to obtain fluorescence intensity change data, specifically:

[0031] Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation according to the EHNV virus detection sample, and obtaining fluorescence spectrum data of fluorescence monitoring at a preset frequency;

[0032] The fluorescence intensity change of EHNV virus detection is determined according to the fluorescence spectrum data to obtain fluorescence intensity change data.

[0033] In this scheme, the EHNV virus information of the water area to be detected for EHNV virus is determined according to the fluorescence intensity change data, specifically:

[0034] Performing a first-order difference operation on the fluorescence intensity change data, if the absolute value of the first-order difference of the fluorescence intensity change data at a preset number of consecutive time points is less than a preset value and the half-width at half maximum of the spectrum peak of the fluorescence spectrum data is greater than a second preset value, calibrating the fluorescence intensity change data as spectrum peak abnormality data;

[0035] Performing a preliminary fitting on the spectral peak shape abnormality data based on a Gaussian function to determine preliminary fitting parameters, wherein the preliminary fitting parameters include peak position, peak height, and peak width fitting parameters, performing a secondary fitting on the spectral peak shape abnormality data according to the preliminary fitting parameters, judging the peak shape symmetry of the spectral peak shape abnormality data after the secondary fitting, and if it is asymmetric, introducing a Lorentz function as a correction term, and performing a peak shape asymmetry correction on the preliminary fitted spectral peak shape abnormality data according to the Lorentz function to obtain spectral calibration data;

[0036] Obtaining a theoretical fluorescence lifetime range of the fluorophore during the EHNV virus detection process, calculating actual fluorophore lifetime information of the fluorophore according to the spectral calibration data, comparing the theoretical fluorescence lifetime range with the actual fluorophore lifetime information, and determining whether the fluorophore has an abnormal fluorescence lifetime state;

[0037] If the fluorescence lifetime is not in an abnormal state, the fluorescence intensity change data is updated according to the spectrum calibration data to obtain a first fluorescence intensity change data update strategy;

[0038] If an abnormal state of fluorescence lifetime occurs, a fluorophore protective agent is added to the EHNV virus detection sample, and the fluorescence spectrum data is acquired for a second time, and the fluorescence spectrum data acquired for the second time is calibrated, and the fluorescence intensity change data is updated according to the calibrated fluorescence spectrum data to obtain a second fluorescence intensity change data update strategy;

[0039] Performing an updating operation on the fluorescence intensity change data according to a first fluorescence intensity change data updating strategy and a second fluorescence intensity change data updating strategy to obtain fluorescence intensity change update data;

[0040] The concentration and activity of the EHNV virus in the waters to be tested are determined by updating data based on the change in fluorescence intensity to obtain EHNV virus information.

[0041] The second aspect of the present invention also provides an EHNV virus detection system based on dynamic fluorescence intensity monitoring, the system comprising: a memory, a processor, the memory comprising an EHNV virus detection method program based on dynamic fluorescence intensity monitoring, and when the EHNV virus detection method program based on dynamic fluorescence intensity monitoring is executed by the processor, the following steps are implemented:

[0042] The water area to be tested for EHNV virus is divided into several small areas, the water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under the characteristics of each water impurity to obtain fluorescence detection data;

[0043] Determine the influence of different water impurity characteristics on the accuracy of EHNV virus fluorescence detection according to the fluorescence detection data, and obtain accuracy influence data;

[0044] Determine the optimal sampling area for testing EHNV virus in the waters to be tested based on the accuracy impact data;

[0045] Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, monitoring a change in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data;

[0046] The EHNV virus information of the water area to be tested for EHNV virus is determined based on the fluorescence intensity change data.

[0047] The present invention discloses an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring. The method divides the water area to be detected into small areas and obtains water impurity information, performs fluorescence detection under different impurity characteristics, and analyzes the fluorescence data to evaluate the impact of impurities on detection accuracy. According to the accuracy impact data, the optimal sampling area is determined and EHNV virus samples are collected, and the samples are monitored for fluorescence intensity changes, and finally the EHNV virus information in the water area is obtained. This method can improve the detection sensitivity and accuracy under impurity interference, and is suitable for fields such as aquaculture and ecological environment monitoring. The present invention also provides a corresponding detection system, including a fluorescence detection device, a data analysis module and a sample processing unit, which has automated operation and efficient data processing capabilities, and is helpful to prevent and control the spread of EHNV virus. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A flow chart of an EHNV virus detection method based on dynamic fluorescence intensity monitoring of the present invention is shown;

[0049] Figure 2 A flow chart of determining the optimal detection sample sampling area according to the present invention is shown;

[0050] Figure 3 The flowchart of obtaining fluorescence intensity variation data of the present invention is shown;

[0051] Figure 4 A block diagram of an EHNV virus detection system based on dynamic fluorescence intensity monitoring of the present invention is shown. DETAILED DESCRIPTION

[0052] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0053] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0054] Figure 1 A flow chart of an EHNV virus detection method based on dynamic fluorescence intensity monitoring of the present invention is shown.

[0055] like Figure 1 As shown, the first aspect of the present invention provides an EHNV virus detection method based on dynamic fluorescence intensity monitoring, comprising:

[0056] S102, dividing the water area to be tested for EHNV virus into several small areas, obtaining water impurity information of each small area, and performing fluorescence detection on the EHNV virus water sample under each water impurity characteristic to obtain fluorescence detection data;

[0057] S104, determining the influence of different water impurity characteristics on the accuracy of EHNV virus fluorescence detection according to the fluorescence detection data, and obtaining accuracy influence data;

[0058] S106, determining the optimal detection sample sampling area for EHNV virus in the waters to be detected according to the accuracy impact data;

[0059] S108, obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, monitoring a change in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data;

[0060] S110, determining the EHNV virus information of the water area to be detected for EHNV virus according to the fluorescence intensity change data.

[0061] It should be noted that by dividing the water area to be tested into small areas and obtaining water impurity information for fluorescence detection, it can not only finely manage the water area, but also intuitively reflect the fluorescence performance of the virus in different impurity environments through fluorescence detection data. Then, based on the detection data, the impact of impurities on the accuracy of fluorescence detection is judged and the optimal sampling area is determined. A standard data reference can be established to clarify the complex relationship between impurities and detection accuracy, avoid sampling in areas with severe impurity interference, and improve detection efficiency and accuracy. Obtaining the best area sample detection and monitoring the change in fluorescence intensity can truly reflect the characteristics and dynamic behavior of the virus. Finally, according to the intensity change data processing anomalies, judging the fluorescence life span and determining the update strategy, the virus concentration and activity and other information can be accurately determined, the virus content can be accurately quantitatively analyzed, and the virus activity can be accurately evaluated based on the pattern and rate of fluorescence intensity change, which is conducive to evaluating the degree of virus contamination in the target waters and formulating corresponding prevention and control measures, greatly improving the accuracy of EHNV virus fluorescence detection.

[0062] According to an embodiment of the present invention, the water area to be detected for EHNV virus is divided into several small areas, water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under each water impurity characteristic to obtain the detection results, specifically:

[0063] Acquire geographical environment data of the water area to be detected for EHNV virus, construct a three-dimensional geographical model of the water area to be detected according to the geographical environment data, and divide the water area to be detected into several small areas according to the three-dimensional geographical model;

[0064] Obtain water impurity information for each small area, the water impurity information including inorganic and organic matter content, suspended particle size and content, determine water impurity characteristics for each small area based on the water impurity information, perform deduplication operations on the water impurity characteristics, and construct a water impurity characteristic data table for fluorescence detection test;

[0065] Construct a water impurity sample for each small area according to the water impurity characteristic data table, add the same amount of EHNV virus to each water impurity sample, and obtain a fluorescence detection test water sample;

[0066] The fluorescence detection test water sample is subjected to a fluorescence detection operation to obtain fluorescence detection data of each fluorescence detection test water sample, wherein the fluorescence detection data includes fluorescence intensity and fluorescence spectrum data.

[0067] It should be noted that the water impurity information including inorganic matter, organic matter content, suspended particle size and content in each small area is obtained and its characteristics are determined. After deduplication, a water impurity characteristic data table is constructed so that the detection can focus on key and representative impurity characteristics. According to the table, a water impurity sample is constructed and an equal amount of EHNV virus is added to obtain a fluorescence detection test water sample. Fluorescence detection is performed to obtain the test results containing intensity and spectral data, which can comprehensively and systematically reflect the fluorescence characteristics of the virus under different water impurity conditions. The deduplication operation is to retain only one water impurity information with exactly the same water quality. The water impurity data table contains the water quality information of all sub-areas in the water area to be detected, and each row represents the water impurity information of one or more sub-areas; each water impurity feature is the water impurity information of each sub-area.

[0068] According to an embodiment of the present invention, the accuracy impact data of the fluorescence detection data is determined based on the fluorescence detection data, and the accuracy impact data is obtained, specifically:

[0069] Obtain standard fluorescence detection data of different EHNV virus contents without the influence of water impurities, and extract data features of the standard fluorescence detection data, wherein the data features include fluorescence intensity features, peak values, troughs, mean values, and variance features of the fluorescence spectrum;

[0070] The data features of the standard fluorescence detection data are used to construct an EHNV virus content-fluorescence feature database;

[0071] Performing feature extraction on the fluorescence detection data to obtain fluorescence detection data features, comparing the fluorescence detection data features with the EHNV virus content-fluorescence feature database, and determining the EHNV virus detection content under each water body impurity feature in the water area to be detected;

[0072] Evaluate the EHNV virus detection accuracy of the fluorescence detection test water sample under each water impurity characteristic according to the EHNV virus detection content under each water impurity characteristic, and obtain detection accuracy data of the fluorescence detection test water sample under each water impurity characteristic;

[0073] Construct a feature-accuracy data matrix using each water body impurity feature and the detection accuracy data, calculate the Pearson correlation coefficient between each water body impurity feature and the detection accuracy based on the feature-accuracy data matrix, and construct a correlation coefficient matrix;

[0074] The accuracy impact of EHNV virus fluorescence detection under the interference of different water impurity characteristics is determined according to the correlation coefficient matrix to obtain accuracy impact data.

[0075] It should be noted that during the EHNV virus detection process, impurities in the water body will interfere with the fluorescence detection results, making it difficult to ensure the accuracy of the detection. Different water impurity characteristics (such as inorganic and organic matter content, suspended particles, etc.) interact with the virus in different ways and degrees, causing complex changes in the fluorescence detection signal of the virus, making it difficult to accurately judge the true situation of the virus directly from the detection data. Therefore, it is necessary to deeply analyze the impact of different water impurity characteristics on the detection accuracy; therefore, by calculating the Pearson correlation coefficient between each water impurity characteristic and the detection accuracy and constructing a correlation coefficient matrix, the Pearson correlation coefficient can accurately measure the closeness and direction of the linear correlation between the water impurity characteristic and the detection accuracy. A positive value indicates a positive correlation, that is, the impurity characteristic will promote the improvement of detection accuracy to some extent or change in the same direction; a negative value indicates a negative correlation, which means that the impurity characteristic may interfere with the detection accuracy, causing it to decrease or change in the opposite direction; close to 0 indicates that the linear correlation between the two is weak. After constructing the correlation coefficient matrix, the differences in the impact of different water impurity characteristics on the detection accuracy can be comprehensively and intuitively compared. It can quickly identify key impurity characteristics that have a strong positive or negative correlation with detection accuracy. The accuracy impact data are accuracy data representing the EHNV virus fluorescence detection corresponding to each water impurity characteristic parameter.

[0076] Figure 2 A flow chart of determining the optimal detection sample sampling area according to the present invention is shown.

[0077] According to an embodiment of the present invention, the optimal detection sample sampling area of ​​the EHNV virus in the waters to be detected is determined according to the accuracy impact data, specifically:

[0078] S202, constructing a detection accuracy evaluation model based on a decision tree algorithm, and setting model parameters of the detection accuracy evaluation model, wherein the model parameters include a maximum depth of the decision tree, a minimum number of split samples, and a minimum number of leaf node samples;

[0079] S204, importing the accuracy influencing data into the detection accuracy evaluation model to construct a decision tree for each water impurity feature, and training the detection accuracy evaluation model according to the decision tree to obtain a trained detection accuracy evaluation model;

[0080] S206, obtaining water impurity information of each small area at the current time point in the water area to be detected, importing the water impurity information into the detection accuracy evaluation model to perform EHNV virus detection accuracy evaluation on the water impurity information of each small area, and obtaining an accuracy evaluation result;

[0081] S208, determining the best detection sample sampling area for the EHNV virus in the water area to be detected according to the accuracy evaluation result.

[0082] It should be noted that the decision tree algorithm is a model for making decisions based on a tree structure. The growth scale and complexity of the decision tree can be controlled by setting model parameters such as the maximum depth of the decision tree, the minimum number of split samples, and the minimum number of leaf node samples. After the accuracy-influencing data is imported into the model, the water impurity characteristics are used as decision nodes, and the detection accuracy data is used as the classification basis to gradually construct the decision tree branches for each water impurity characteristic. During the construction process, the data is continuously divided and classified according to the characteristic distribution and accuracy of the data, so that the decision tree can learn the inherent logical relationship and regular pattern between different water impurity characteristics and detection accuracy, thereby completing the model training. Using the trained detection accuracy evaluation model, the water impurity information of each small area at the current time point of the water area to be detected can be quickly processed and analyzed to obtain the corresponding EHNV virus detection accuracy evaluation results. It can accurately determine the degree of influence of water impurity interference on detection accuracy in different areas, thereby clarifying the optimal detection sample sampling area.

[0083] Figure 3 A flow chart of obtaining fluorescence intensity variation data according to the present invention is shown.

[0084] According to an embodiment of the present invention, the EHNV virus detection sample is obtained from the optimal detection sample sampling area, a fluorescence detection operation is performed on the EHNV virus detection sample, and the fluorescence intensity change during the fluorescence detection process is monitored to obtain fluorescence intensity change data, specifically:

[0085] S302, obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation according to the EHNV virus detection sample, and obtaining fluorescence spectrum data of fluorescence monitoring at a preset frequency;

[0086] S304, determining the fluorescence intensity change of EHNV virus detection according to the fluorescence spectrum data to obtain fluorescence intensity change data.

[0087] It should be noted that samples are obtained from the optimal detection sample sampling area for fluorescence detection. Since this area has been determined to be less disturbed by water impurities and has higher detection accuracy based on preliminary analysis, it can minimize the adverse effects of impurities on detection and ensure that the detection data is more reliable and authentic. Obtaining fluorescence spectrum data for fluorescence monitoring at a preset frequency can realize dynamic monitoring of the detection process and capture subtle changes in the fluorescence spectrum in real time. Determining the fluorescence intensity changes of EHNV virus detection based on these fluorescence spectrum data and obtaining corresponding data can intuitively reflect the changes in the activity state of the virus during the detection process. The fluorescence intensity change data includes fluorescence intensity change data and fluorescence spectrum change data.

[0088] According to an embodiment of the present invention, the EHNV virus information of the water area to be detected for EHNV virus is determined according to the fluorescence intensity change data, specifically:

[0089] Performing a first-order difference operation on the fluorescence intensity change data, if the absolute value of the first-order difference of the fluorescence intensity change data at a preset number of consecutive time points is less than a preset value and the half-width at half maximum of the spectrum peak of the fluorescence spectrum data is greater than a second preset value, calibrating the fluorescence intensity change data as spectrum peak abnormality data;

[0090] Performing a preliminary fitting on the spectral peak shape abnormality data based on a Gaussian function to determine preliminary fitting parameters, wherein the preliminary fitting parameters include peak position, peak height, and peak width fitting parameters, performing a secondary fitting on the spectral peak shape abnormality data according to the preliminary fitting parameters, judging the peak shape symmetry of the spectral peak shape abnormality data after the secondary fitting, and if it is asymmetric, introducing a Lorentz function as a correction term, and performing a peak shape asymmetry correction on the preliminary fitted spectral peak shape abnormality data according to the Lorentz function to obtain spectral calibration data;

[0091] It should be noted that in the detection process of EHNV virus monitoring based on dynamic fluorescence intensity, the detection environment is complex and changeable. Various impurities in the water body, performance fluctuations of the detection instrument, and external environmental factors (such as temperature, light, etc.) may interfere with the fluorescence spectrum data, resulting in inaccurate evaluation of the fluorescence intensity change data. The first-order difference is a method used to measure the rate of change of data. When the absolute value of the first-order difference of the fluorescence intensity change data at a preset number of time points is less than the preset value, it means that the fluorescence intensity has hardly changed or has changed very little during this period. The half-height full width of the spectral peak of the fluorescence spectrum data is an important parameter to measure the narrowness of the spectral peak width. The larger the half-height full width, the wider the spectral peak. When the fluorescence intensity change stagnates and the spectral peak shape is abnormally broadened at the same time, there is a high probability that an unexpected situation has occurred in the detection process, causing the fluorescence signal to deviate from the normal change pattern. Therefore, the fluorescence intensity change data in this case is calibrated as spectral peak shape abnormal data, and the spectral peak shape abnormal data is calibrated by Gaussian function and Lorentz function to improve the accuracy and reliability of the data. Calibration can effectively remove spectral data deviations caused by instrument errors, environmental interference, and interactions between viruses and impurities, so that the data can more accurately reflect the true fluorescence characteristics of the EHNV virus.

[0092] Obtaining a theoretical fluorescence lifetime range of the fluorophore during the EHNV virus detection process, calculating actual fluorophore lifetime information of the fluorophore according to the spectral calibration data, comparing the theoretical fluorescence lifetime range with the actual fluorophore lifetime information, and determining whether the fluorophore has an abnormal fluorescence lifetime state;

[0093] If the fluorescence lifetime is not in an abnormal state, the fluorescence intensity change data is updated according to the spectrum calibration data to obtain a first fluorescence intensity change data update strategy;

[0094] If an abnormal state of fluorescence lifetime occurs, a fluorophore protective agent is added to the EHNV virus detection sample, and the fluorescence spectrum data is acquired for a second time, and the fluorescence spectrum data acquired for the second time is calibrated, and the fluorescence intensity change data is updated according to the calibrated fluorescence spectrum data to obtain a second fluorescence intensity change data update strategy;

[0095] Performing an updating operation on the fluorescence intensity change data according to a first fluorescence intensity change data updating strategy and a second fluorescence intensity change data updating strategy to obtain fluorescence intensity change update data;

[0096] The concentration and activity of the EHNV virus in the waters to be tested are determined by updating data based on the change in fluorescence intensity to obtain EHNV virus information.

[0097] It should be noted that in the detection of EHNV virus, the fluorescence lifetime of the fluorophore is crucial to accurately judge the virus information. However, the detection process is susceptible to interference from a variety of factors, such as water impurities, detection environmental conditions, and the interaction between viruses and other substances. These factors may cause the actual fluorescence lifetime of the fluorophore to deviate from its theoretical range, thereby affecting the accuracy of the EHNV virus concentration and activity judgment based on the fluorescence intensity change data. By obtaining the theoretical fluorescence lifetime range and calculating the actual fluorophore lifetime information for comparison, it is possible to accurately determine whether the fluorophore is in an abnormal state. If it is not abnormal, the first update strategy of updating the fluorescence intensity change data based on the spectral calibration data can effectively eliminate other interfering factors, ensure that the data reflects the true situation of the virus, and improve the reliability of virus concentration and activity analysis. If an abnormality occurs, the second strategy of adding a fluorophore protective agent and updating after secondary acquisition and calibration of the spectral data can correct the detection deviation caused by the abnormal fluorescence lifetime and restore the data accuracy. Finally, according to the fluorescence intensity change update data obtained by the update strategy, the EHNV virus concentration and activity of the waters to be detected can be accurately determined, and the monitoring accuracy of the EHNV virus can be improved.

[0098] Figure 4 A block diagram of an EHNV virus detection system based on dynamic fluorescence intensity monitoring of the present invention is shown.

[0099] The second aspect of the present invention further provides an EHNV virus detection system 4 based on dynamic fluorescence intensity monitoring, the system comprising: a memory 41, a processor 42, the memory comprising an EHNV virus detection method program based on dynamic fluorescence intensity monitoring, and when the EHNV virus detection method program based on dynamic fluorescence intensity monitoring is executed by the processor, the following steps are implemented:

[0100] The water area to be tested for EHNV virus is divided into several small areas, the water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under the characteristics of each water impurity to obtain fluorescence detection data;

[0101] Determine the influence of different water impurity characteristics on the accuracy of EHNV virus fluorescence detection according to the fluorescence detection data, and obtain accuracy influence data;

[0102] Determine the optimal sampling area for testing EHNV virus in the waters to be tested based on the accuracy impact data;

[0103] Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, monitoring a change in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data;

[0104] The EHNV virus information of the water area to be tested for EHNV virus is determined based on the fluorescence intensity change data.

[0105] The present invention discloses an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring. The method divides the water area to be detected into small areas and obtains water impurity information, performs fluorescence detection under different impurity characteristics, and analyzes the fluorescence data to evaluate the impact of impurities on detection accuracy. According to the accuracy impact data, the optimal sampling area is determined and EHNV virus samples are collected, and the samples are monitored for fluorescence intensity changes, and finally the EHNV virus information in the water area is obtained. This method can improve the detection sensitivity and accuracy under impurity interference, and is suitable for fields such as aquaculture and ecological environment monitoring. The present invention also provides a corresponding detection system, including a fluorescence detection device, a data analysis module and a sample processing unit, which has automated operation and efficient data processing capabilities, and is helpful to prevent and control the spread of EHNV virus.

[0106] In the several embodiments provided in the present application, it should be understood that the disclosed devices 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.

[0107] 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; 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.

[0108] 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.

[0109] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments 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 embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0110] 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.

[0111] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring, characterized in that: The following steps are involved: The water area to be tested for EHNV virus is divided into several small areas, the water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under the characteristics of each water impurity to obtain fluorescence detection data; Determine the influence of different water impurity characteristics on the accuracy of EHNV virus fluorescence detection according to the fluorescence detection data, and obtain accuracy influence data; Determine the optimal sampling area for testing EHNV virus in the waters to be tested based on the accuracy impact data; Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, monitoring a change in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data; The EHNV virus information of the water area to be tested for EHNV virus is determined based on the fluorescence intensity change data.

2. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, The water area to be tested for EHNV virus is divided into several small areas, water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under each water impurity characteristic to obtain the detection results, which are specifically: Acquire geographical environment data of the water area to be detected for EHNV virus, construct a three-dimensional geographical model of the water area to be detected according to the geographical environment data, and divide the water area to be detected into several small areas according to the three-dimensional geographical model; Obtain water impurity information for each small area, the water impurity information including inorganic and organic matter content, suspended particle size and content, determine water impurity characteristics for each small area based on the water impurity information, perform deduplication operations on the water impurity characteristics, and construct a water impurity characteristic data table for fluorescence detection test; Construct a water impurity sample for each small area according to the water impurity characteristic data table, add the same amount of EHNV virus to each water impurity sample, and obtain a fluorescence detection test water sample; The fluorescence detection test water sample is subjected to a fluorescence detection operation to obtain fluorescence detection data of each fluorescence detection test water sample, wherein the fluorescence detection data includes fluorescence intensity and fluorescence spectrum data.

3. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, The accuracy impact data of the fluorescence detection data is determined based on the fluorescence detection data, and the accuracy impact data is obtained, specifically: Obtain standard fluorescence detection data of different EHNV virus contents without the influence of water impurities, and extract data features of the standard fluorescence detection data, wherein the data features include fluorescence intensity features, peak values, troughs, mean values, and variance features of the fluorescence spectrum; The data features of the standard fluorescence detection data are used to construct an EHNV virus content-fluorescence feature database; Performing feature extraction on the fluorescence detection data to obtain fluorescence detection data features, comparing the fluorescence detection data features with the EHNV virus content-fluorescence feature database, and determining the EHNV virus detection content under each water body impurity feature in the water area to be detected; Evaluate the EHNV virus detection accuracy of the fluorescence detection test water sample under each water impurity characteristic according to the EHNV virus detection content under each water impurity characteristic, and obtain detection accuracy data of the fluorescence detection test water sample under each water impurity characteristic; Construct a feature-accuracy data matrix using each water body impurity feature and the detection accuracy data, calculate the Pearson correlation coefficient between each water body impurity feature and the detection accuracy based on the feature-accuracy data matrix, and construct a correlation coefficient matrix; The accuracy impact of EHNV virus fluorescence detection under the interference of different water impurity characteristics is determined according to the correlation coefficient matrix to obtain accuracy impact data.

4. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, The optimal detection sample sampling area of ​​the EHNV virus in the waters to be detected is determined according to the accuracy impact data, specifically: Constructing a detection accuracy evaluation model based on a decision tree algorithm, and setting model parameters of the detection accuracy evaluation model, wherein the model parameters include a maximum depth of the decision tree, a minimum number of split samples, and a minimum number of leaf node samples; Importing the accuracy influencing data into the detection accuracy evaluation model to construct a decision tree for each water impurity feature, and training the detection accuracy evaluation model according to the decision tree to obtain a trained detection accuracy evaluation model; Obtain water impurity information of each small area at the current time point in the water area to be detected, import the water impurity information into the detection accuracy evaluation model to perform EHNV virus detection accuracy evaluation on the water impurity information of each small area, and obtain an accuracy evaluation result; The optimal detection sample sampling area for the EHNV virus in the waters to be detected is determined based on the accuracy evaluation results.

5. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, The step of obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, and monitoring a change in fluorescence intensity during the fluorescence detection process to obtain fluorescence intensity change data is specifically as follows: Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation according to the EHNV virus detection sample, and obtaining fluorescence spectrum data of fluorescence monitoring at a preset frequency; The fluorescence intensity change of EHNV virus detection is determined according to the fluorescence spectrum data to obtain fluorescence intensity change data.

6. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, Determining the EHNV virus information of the water area to be detected for EHNV virus according to the fluorescence intensity change data is specifically: Performing a first-order difference operation on the fluorescence intensity change data, if the absolute value of the first-order difference of the fluorescence intensity change data at a preset number of consecutive time points is less than a preset value and the half-width at half maximum of the spectrum peak of the fluorescence spectrum data is greater than a second preset value, calibrating the fluorescence intensity change data as spectrum peak abnormality data; Performing a preliminary fitting on the spectral peak shape abnormality data based on a Gaussian function to determine preliminary fitting parameters, wherein the preliminary fitting parameters include peak position, peak height, and peak width fitting parameters, performing a secondary fitting on the spectral peak shape abnormality data according to the preliminary fitting parameters, judging the peak shape symmetry of the spectral peak shape abnormality data after the secondary fitting, and if it is asymmetric, introducing a Lorentz function as a correction term, and performing a peak shape asymmetry correction on the preliminary fitted spectral peak shape abnormality data according to the Lorentz function to obtain spectral calibration data; Obtaining a theoretical fluorescence lifetime range of the fluorophore during the EHNV virus detection process, calculating actual fluorophore lifetime information of the fluorophore according to the spectral calibration data, comparing the theoretical fluorescence lifetime range with the actual fluorophore lifetime information, and determining whether the fluorophore has an abnormal fluorescence lifetime state; If the fluorescence lifetime is not in an abnormal state, the fluorescence intensity change data is updated according to the spectrum calibration data to obtain a first fluorescence intensity change data update strategy; If an abnormal state of fluorescence lifetime occurs, a fluorophore protective agent is added to the EHNV virus detection sample, and the fluorescence spectrum data is acquired for a second time, and the fluorescence spectrum data acquired for the second time is calibrated, and the fluorescence intensity change data is updated according to the calibrated fluorescence spectrum data to obtain a second fluorescence intensity change data update strategy; Performing an updating operation on the fluorescence intensity change data according to a first fluorescence intensity change data updating strategy and a second fluorescence intensity change data updating strategy to obtain fluorescence intensity change update data; The concentration and activity of the EHNV virus in the waters to be tested are determined by updating data based on the change in fluorescence intensity to obtain EHNV virus information.

7. An EHNV virus detection system based on dynamic fluorescence intensity monitoring, characterized in that: The EHNV virus detection system based on dynamic fluorescence intensity monitoring includes a storage device and a processor, wherein the storage device includes an EHNV virus detection method program based on dynamic fluorescence intensity monitoring, and when the EHNV virus detection method program based on dynamic fluorescence intensity monitoring is executed by the processor, the following steps are implemented: The water area to be tested for EHNV virus is divided into several small areas, the water impurity information of each small area is obtained, and the EHNV virus water sample is subjected to fluorescence detection under the characteristics of each water impurity to obtain fluorescence detection data; Determine the influence of different water impurity characteristics on the accuracy of EHNV virus fluorescence detection according to the fluorescence detection data, and obtain accuracy influence data; Determine the optimal sampling area for testing EHNV virus in the waters to be tested based on the accuracy impact data; Obtaining an EHNV virus detection sample from the optimal detection sample sampling area, performing a fluorescence detection operation on the EHNV virus detection sample, monitoring a change in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data; The EHNV virus information of the water area to be tested for EHNV virus is determined based on the fluorescence intensity change data.

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