An ehnv virus detection method and system based on dynamic fluorescence intensity monitoring

Abstract

The application 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, obtains water impurity information, carries out fluorescence detection under different impurity characteristics, analyzes fluorescence data to evaluate the influence of impurities on detection accuracy. According to the accuracy influence data, the best sampling area is determined, and the EHNV virus sample is collected, the fluorescence intensity change of the sample is monitored, and finally the EHNV virus information in the water area is obtained. This method can improve the detection sensitivity and accuracy under the interference of impurities, and is suitable for the fields of aquaculture and ecological environment monitoring. The application 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 capacity, and is helpful for preventing and controlling the spread of EHNV virus.

Description

Technical Field

[0001] This invention relates to the field of virus detection technology, and in particular to an EHNV virus detection method and system based on dynamic fluorescence intensity monitoring. Background Technology

[0002] Epizootic Hemorrhagic Necrosis Virus (EHNV) is a highly dangerous virus in aquaculture, primarily affecting fish, especially cyprinids, and is highly contagious. Therefore, developing a new method for accurate, rapid, and effective detection of EHNV has become a pressing issue for the aquaculture industry.

[0003] Existing virus detection methods largely rely on traditional techniques such as PCR or ELISA. While these methods offer high accuracy, they are cumbersome, time-consuming, and costly, and they struggle to monitor the virus status in aquatic environments in real time. With advancements in fluorescence detection technology, methods based on monitoring changes in fluorescence intensity are gradually becoming a new trend in virus detection. However, impurities in water (such as organic matter, inorganic matter, and suspended particles) can interfere with fluorescence detection results, affecting the accuracy of virus detection.

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

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

[0006] To address at least one of the aforementioned technical problems, this invention proposes a method and system for detecting EHNV virus based on dynamic fluorescence intensity monitoring.

[0007] The first aspect of this invention provides a method for detecting EHNV virus based on dynamic fluorescence intensity monitoring, comprising:

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

[0009] Based on the fluorescence detection data, the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection was determined, and the accuracy impact data was obtained.

[0010] Based on the accuracy impact data, determine the optimal sampling area for EHNV virus detection in the water body to be tested;

[0011] EHNV virus detection samples are obtained from the optimal sampling area for detection, and fluorescence detection is performed on the EHNV virus detection samples. The changes in fluorescence intensity during the fluorescence detection process are monitored to obtain fluorescence intensity change data.

[0012] The EHNV virus information of the water area to be tested 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 the characteristics of each water impurity to obtain the detection results. Specifically:

[0014] Obtain the geographic environment data of the water area to be detected for EHNV virus, construct a three-dimensional geographic model of the water area to be detected based on the geographic environment data, and divide the water area to be detected into several small areas based on the three-dimensional geographic model.

[0015] Acquire water impurity information for each small area, including the content of inorganic and organic matter, and the size and content of suspended particles. Determine the water impurity characteristics of each small area based on the water impurity information, perform deduplication on the water impurity characteristics, and construct a water impurity characteristic data table for fluorescence detection testing.

[0016] Based on the water impurity characteristic data table, water impurity samples for each small area are constructed. The same amount of EHNV virus is added to each water impurity sample to obtain fluorescent detection test water samples.

[0017] The fluorescence detection test water samples are subjected to fluorescence detection to obtain fluorescence detection data for each fluorescence detection test water sample. The fluorescence detection data includes fluorescence intensity and fluorescence spectrum data.

[0018] In this scheme, the step of determining the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection based on the fluorescence detection data, and obtaining accuracy impact data, specifically involves:

[0019] Standard fluorescence detection data of different EHNV virus contents were obtained under the condition of no water impurities. Data features of the standard fluorescence detection data were extracted, including fluorescence intensity features, peak value, trough value, mean value, and variance features of the fluorescence spectrum.

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

[0021] Feature extraction is performed on the fluorescence detection data to obtain fluorescence detection data features. The fluorescence detection data features are compared with the EHNV virus content-fluorescence feature database to determine the EHNV virus detection content under each water impurity feature in the water body to be tested.

[0022] The accuracy of EHNV virus detection in the fluorescent detection test water samples under each water body impurity characteristic was evaluated based on the EHNV virus detection content under each water body impurity characteristic, and the detection accuracy data of the fluorescent detection test water samples under each water body impurity characteristic were obtained.

[0023] A feature-accuracy data matrix is ​​constructed by combining the characteristics of each water body impurity with the detection accuracy data. Based on the feature-accuracy data matrix, the Pearson correlation coefficient between the characteristics of each water body impurity and the detection accuracy is calculated, and a correlation coefficient matrix is ​​constructed.

[0024] The accuracy impact of EHNV virus fluorescence detection under the interference of different water body impurity characteristics was determined based on the correlation coefficient matrix, and the accuracy impact data were obtained.

[0025] In this scheme, determining the optimal sampling area for EHNV virus detection in the water body to be tested based on the accuracy impact data specifically involves:

[0026] A detection accuracy evaluation model is constructed based on the decision tree algorithm. The model parameters of the detection accuracy evaluation model are set, including the maximum depth of the decision tree, the minimum number of split samples, and the minimum number of leaf node samples.

[0027] The accuracy impact data is imported into the detection accuracy evaluation model to construct a decision tree for each water body impurity feature. The detection accuracy evaluation model is then trained based on the decision tree to obtain the trained detection accuracy evaluation model.

[0028] Obtain water impurity information for each small area of ​​the water body to be tested at the current time point, import the water impurity information into the detection accuracy evaluation model, and evaluate the accuracy of EHNV virus detection for the water impurity information of each small area to obtain the accuracy evaluation result.

[0029] Based on the accuracy assessment results, the optimal sampling area for EHNV virus detection in the waters to be tested is determined.

[0030] In this scheme, obtaining EHNV virus detection samples from the optimal sampling area, performing fluorescence detection on the EHNV virus detection samples, monitoring the fluorescence intensity changes during the fluorescence detection process, and obtaining fluorescence intensity change data are specifically as follows:

[0031] Obtain EHNV virus detection samples from the optimal detection sample sampling area, perform fluorescence detection operation based on the EHNV virus detection samples, and obtain fluorescence spectral data for fluorescence monitoring at a preset frequency;

[0032] Based on the fluorescence spectral data, the fluorescence intensity changes for EHNV virus detection were determined, and fluorescence intensity change data were obtained.

[0033] In this scheme, determining the EHNV virus information of the water area to be tested based on the fluorescence intensity change data specifically involves:

[0034] Perform 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 for a consecutive preset number of time points is less than a preset value and the full width at half maximum (FWHM) of the spectral peak shape of the fluorescence spectrum data is greater than a second preset value, then the fluorescence intensity change data is labeled as spectral peak shape abnormal data.

[0035] The spectral peak anomaly data is initially fitted using a Gaussian function to determine initial fitting parameters, including peak position, peak height, and peak width fitting parameters. A second fitting is then performed on the spectral peak anomaly data based on these initial fitting parameters. The peak shape symmetry of the second-fitted spectral peak anomaly data is then determined. If asymmetric, the Lorentz function is introduced as a correction term. The peak shape asymmetry of the initially fitted spectral peak anomaly data is corrected using the Lorentz function to obtain spectral calibration data.

[0036] The theoretical fluorescence lifetime range of the fluorophore is obtained during the EHNV virus detection process. The actual fluorophore lifetime information is calculated based on the spectral calibration data. The theoretical fluorescence lifetime range is compared with the actual fluorophore lifetime information to determine whether the fluorophore exhibits an abnormal fluorescence lifetime state.

[0037] If the fluorescence lifetime does not show any abnormalities, the fluorescence intensity change data is updated based on the spectral calibration data to obtain the first fluorescence intensity change data update strategy;

[0038] If an abnormal fluorescence lifetime occurs, a fluorophore protectant is added to the EHNV virus detection sample, and the fluorescence spectrum data is acquired a second time. The acquired fluorescence spectrum data is then calibrated, and the fluorescence intensity change data is updated based on the calibrated fluorescence spectrum data to obtain a second fluorescence intensity change data update strategy.

[0039] The fluorescence intensity change data is updated according to the first fluorescence intensity change data update strategy and the second fluorescence intensity change data update strategy to obtain the fluorescence intensity change update data.

[0040] The concentration and activity of EHNV virus in the water body to be tested are determined by updating the fluorescence intensity data, and EHNV virus information is obtained.

[0041] A second aspect of the present invention also provides an EHNV virus detection system based on dynamic fluorescence intensity monitoring. The system includes a memory and a processor. The memory includes a program for an EHNV virus detection method based on dynamic fluorescence intensity monitoring. When the processor executes the EHNV virus detection method program based on dynamic fluorescence intensity monitoring, it performs the following steps:

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

[0043] Based on the fluorescence detection data, the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection was determined, and the accuracy impact data was obtained.

[0044] Based on the accuracy impact data, determine the optimal sampling area for EHNV virus detection in the water body to be tested;

[0045] EHNV virus detection samples are obtained from the optimal sampling area for detection, and fluorescence detection is performed on the EHNV virus detection samples. The changes in fluorescence intensity during the fluorescence detection process are monitored to obtain fluorescence intensity change data.

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

[0047] This invention discloses a method and system for detecting EHNV virus based on dynamic fluorescence intensity monitoring. The method divides the water area to be tested into small regions and acquires information on water impurities. Fluorescence detection is performed under different impurity characteristics, and the fluorescence data is analyzed to assess the impact of impurities on detection accuracy. Based on the accuracy impact data, the optimal sampling area is determined, and EHNV virus samples are collected. Fluorescence intensity changes in the samples are monitored to ultimately obtain EHNV virus information in the water area. This method can improve detection sensitivity and accuracy under impurity interference and is applicable to fields such as aquaculture and ecological environment monitoring. This 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, contributing to the prevention and control of EHNV virus spread. Attached Figure Description

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

[0049] Figure 2 The flowchart illustrating the present invention for determining the optimal sampling area for detection samples is shown.

[0050] Figure 3 A flowchart illustrating the fluorescence intensity change data obtained in this invention is shown;

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

[0052] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0053] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

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

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

[0056] S102, 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.

[0057] S104, Based on the fluorescence detection data, determine the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection, and obtain accuracy impact data;

[0058] S106, Determine the optimal sampling area for EHNV virus detection in the water body to be detected based on the accuracy impact data;

[0059] S108, Obtain EHNV virus detection samples from the optimal detection sample sampling area, perform fluorescence detection on the EHNV virus detection samples, monitor the fluorescence intensity changes during the fluorescence detection process, and obtain fluorescence intensity change data.

[0060] S110, Determine the EHNV virus information of the water area to be tested based on the fluorescence intensity change data.

[0061] It should be noted that dividing the water area to be tested into small regions and obtaining information on water impurities before conducting fluorescence detection not only allows for refined water area management but also provides a direct visual representation of the virus's fluorescence performance in different impurity environments. Next, based on the detection data, the impact of impurities on the accuracy of fluorescence detection is assessed, and the optimal sampling area is determined. This establishes standard data references, clarifies the complex relationship between impurities and detection accuracy, avoids sampling in areas with severe impurity interference, and improves detection efficiency and accuracy. Obtaining samples from the optimal area and monitoring changes in fluorescence intensity accurately reflects the virus's characteristics and dynamic behavior. Finally, anomalies are processed based on intensity change data, fluorescence lifetime is determined, and update strategies are established, thereby accurately determining virus concentration and activity, precisely quantifying virus content, and accurately assessing virus activity based on the pattern and rate of fluorescence intensity changes. This is beneficial for assessing the degree of virus contamination in the target water area and developing corresponding prevention and control measures, significantly improving the accuracy of EHNV virus fluorescence detection.

[0062] According to an embodiment of the present invention, 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 the detection result, specifically as follows:

[0063] Obtain the geographic environment data of the water area to be detected for EHNV virus, construct a three-dimensional geographic model of the water area to be detected based on the geographic environment data, and divide the water area to be detected into several small areas based on the three-dimensional geographic model.

[0064] Acquire water impurity information for each small area, including the content of inorganic and organic matter, and the size and content of suspended particles. Determine the water impurity characteristics of each small area based on the water impurity information, perform deduplication on the water impurity characteristics, and construct a water impurity characteristic data table for fluorescence detection testing.

[0065] Based on the water impurity characteristic data table, water impurity samples for each small area are constructed. The same amount of EHNV virus is added to each water impurity sample to obtain fluorescent detection test water samples.

[0066] The fluorescence detection test water samples are subjected to fluorescence detection to obtain fluorescence detection data for each fluorescence detection test water sample. The fluorescence detection data includes fluorescence intensity and fluorescence spectrum data.

[0067] It should be noted that information on water impurities, including the content of inorganic and organic matter, as well as the size and content of suspended particles, is obtained for each small area, and their characteristics are determined. A water impurity characteristic data table is constructed after deduplication, allowing detection to focus on key and representative impurity characteristics. Water impurity samples are constructed based on this table, and an equal amount of EHNV virus is added to obtain fluorescent detection test water samples. Fluorescence detection is performed to obtain detection 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 involves retaining only one instance of water impurity information with completely identical water quality. The water impurity data table contains water quality information for all sub-regions in the water area to be tested, with each row representing water impurity information for one or more sub-regions; each water impurity characteristic represents the water impurity information for each sub-region.

[0068] According to an embodiment of the present invention, the step of determining the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection based on the fluorescence detection data, and obtaining accuracy impact data, specifically involves:

[0069] Standard fluorescence detection data of different EHNV virus contents were obtained under the condition of no water impurities. Data features of the standard fluorescence detection data were extracted, including fluorescence intensity features, peak value, trough value, mean value, and variance features of the fluorescence spectrum.

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

[0071] Feature extraction is performed on the fluorescence detection data to obtain fluorescence detection data features. The fluorescence detection data features are compared with the EHNV virus content-fluorescence feature database to determine the EHNV virus detection content under each water impurity feature in the water body to be tested.

[0072] The accuracy of EHNV virus detection in the fluorescent detection test water samples under each water body impurity characteristic was evaluated based on the EHNV virus detection content under each water body impurity characteristic, and the detection accuracy data of the fluorescent detection test water samples under each water body impurity characteristic were obtained.

[0073] A feature-accuracy data matrix is ​​constructed by combining the characteristics of each water body impurity with the detection accuracy data. Based on the feature-accuracy data matrix, the Pearson correlation coefficient between the characteristics of each water body impurity and the detection accuracy is calculated, and a correlation coefficient matrix is ​​constructed.

[0074] The accuracy impact of EHNV virus fluorescence detection under the interference of different water body impurity characteristics was determined based on the correlation coefficient matrix, and the accuracy impact data were obtained.

[0075] It should be noted that during EHNV virus detection, impurities in the water can interfere with the fluorescence detection results, making it difficult to guarantee detection accuracy. Different water impurity characteristics (such as inorganic and organic matter content, suspended particulate matter, etc.) interact with the virus in different ways and to varying degrees, resulting in complex changes in the virus fluorescence detection signal. This makes it difficult to accurately determine the true virus situation directly from the detection data. Therefore, it is necessary to deeply analyze the impact of different water impurity characteristics on detection accuracy. Thus, by calculating the Pearson correlation coefficient between each water impurity characteristic and detection accuracy and constructing a correlation coefficient matrix, the Pearson correlation coefficient can accurately measure the strength and direction of the linear correlation between water impurity characteristics and detection accuracy. A positive value indicates a positive correlation, meaning that the impurity characteristic will, to some extent, promote an improvement in detection accuracy or change in the same direction; a negative value indicates a negative correlation, meaning that the impurity characteristic may interfere with detection accuracy, causing it to decrease or change in the opposite direction; values ​​close to 0 indicate a weak linear correlation. After constructing the correlation coefficient matrix, the differences in the impact of different water impurity characteristics on 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 represents the accuracy data of EHNV virus fluorescence detection under each water body impurity characteristic parameter.

[0076] Figure 2 The flowchart illustrating the present invention for determining the optimal sampling area for detection samples is shown.

[0077] According to an embodiment of the present invention, determining the optimal sampling area for EHNV virus detection in the water body to be detected based on the accuracy impact data specifically involves:

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

[0079] S204, The accuracy impact data is imported into the detection accuracy evaluation model to construct a decision tree for each water body impurity feature, and the detection accuracy evaluation model is trained based on the decision tree to obtain the trained detection accuracy evaluation model;

[0080] S206, Obtain water impurity information for each small area of ​​the water body to be detected at the current time point, import the water impurity information into the detection accuracy evaluation model, evaluate the accuracy of EHNV virus detection for the water impurity information of each small area, and obtain the accuracy evaluation result.

[0081] S208, Based on the accuracy assessment results, determine the optimal sampling area for EHNV virus detection in the water to be tested.

[0082] It's important to note that the decision tree algorithm is a model that makes decisions based on a tree structure. The growth scale and complexity of the decision tree are controlled by setting model parameters such as the maximum depth, minimum number of split samples, and minimum number of leaf node samples. After importing accuracy-influencing data into the model, water impurity characteristics are used as decision nodes, and detection accuracy data is used as the classification basis to progressively construct decision tree branches for each water impurity characteristic. During the construction process, the data is continuously divided and classified according to its feature distribution and accuracy, enabling the decision tree to learn the inherent logical relationships and patterns between different water impurity characteristics and detection accuracy, thus completing model training. Using the trained detection accuracy evaluation model, water impurity information in each small area of ​​the water body at the current time point 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 impurities on detection accuracy in different areas, thereby clarifying the optimal sampling area for detection.

[0083] Figure 3 A flowchart illustrating the fluorescence intensity change data obtained by this invention is shown.

[0084] According to an embodiment of the present invention, the step of obtaining EHNV virus detection samples from the optimal detection sample sampling area, performing fluorescence detection on the EHNV virus detection samples, monitoring the fluorescence intensity changes during the fluorescence detection process, and obtaining fluorescence intensity change data specifically includes:

[0085] S302, Obtain EHNV virus detection samples from the optimal detection sample sampling area, perform fluorescence detection operation based on the EHNV virus detection samples, and obtain fluorescence spectral data for fluorescence monitoring at a preset frequency;

[0086] S304, Based on the fluorescence spectral data, determine the fluorescence intensity change for EHNV virus detection, and obtain fluorescence intensity change data.

[0087] It should be noted that samples are obtained from the optimal sampling area for fluorescence detection. This area, determined through prior analysis, is less susceptible to interference from water impurities and offers higher detection accuracy. This minimizes the adverse effects of impurities on the detection, ensuring more reliable and accurate data. Acquiring fluorescence spectral data at a preset frequency allows for dynamic monitoring of the detection process, capturing subtle changes in the fluorescence spectrum in real time. Based on this fluorescence spectral data, the changes in fluorescence intensity for EHNV virus detection are determined, providing a direct reflection of the virus's activity status during the detection process. The fluorescence intensity change data includes both fluorescence intensity change data and fluorescence spectral change data.

[0088] According to an embodiment of the present invention, determining the EHNV virus information of the water area to be detected based on the fluorescence intensity change data specifically involves:

[0089] Perform 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 for a consecutive preset number of time points is less than a preset value and the full width at half maximum (FWHM) of the spectral peak shape of the fluorescence spectrum data is greater than a second preset value, then the fluorescence intensity change data is labeled as spectral peak shape abnormal data.

[0090] The spectral peak anomaly data is initially fitted using a Gaussian function to determine initial fitting parameters, including peak position, peak height, and peak width fitting parameters. A second fitting is then performed on the spectral peak anomaly data based on these initial fitting parameters. The peak shape symmetry of the second-fitted spectral peak anomaly data is then determined. If asymmetric, the Lorentz function is introduced as a correction term. The peak shape asymmetry of the initially fitted spectral peak anomaly data is corrected using the Lorentz function to obtain spectral calibration data.

[0091] It should be noted that the detection environment for EHNV virus based on dynamic fluorescence intensity monitoring is complex and variable. Various impurities in the water, performance fluctuations of the detection instrument, and external environmental factors (such as temperature and light) can all interfere with fluorescence spectral data, leading to inaccurate assessments of fluorescence intensity changes. 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 at a preset number of consecutive time points is less than a preset value, it means that the fluorescence intensity has hardly changed or has changed extremely little during this period. The full width at half maximum (FWHM) of the fluorescence spectral peak is an important parameter for measuring the width of the spectral peak. A larger FWHM indicates a wider spectral peak. When fluorescence intensity changes stagnate and abnormal broadening of the spectral peak occurs simultaneously, it is highly likely that an unexpected situation has occurred during the detection process, causing the fluorescence signal to deviate from the normal change pattern. Therefore, fluorescence intensity changes under such circumstances are calibrated as abnormal spectral peak data. By calibrating the abnormal spectral peak data using Gaussian and Lorentz functions, the accuracy and reliability of the data are improved. Calibration can effectively remove spectral data deviations caused by factors such as instrument errors, environmental interference, and the interaction between viruses and impurities, making the data more accurately reflect the true fluorescence characteristics of the EHNV virus.

[0092] The theoretical fluorescence lifetime range of the fluorophore is obtained during the EHNV virus detection process. The actual fluorophore lifetime information is calculated based on the spectral calibration data. The theoretical fluorescence lifetime range is compared with the actual fluorophore lifetime information to determine whether the fluorophore exhibits an abnormal fluorescence lifetime state.

[0093] If the fluorescence lifetime does not show any abnormalities, the fluorescence intensity change data is updated based on the spectral calibration data to obtain the first fluorescence intensity change data update strategy;

[0094] If an abnormal fluorescence lifetime occurs, a fluorophore protectant is added to the EHNV virus detection sample, and the fluorescence spectrum data is acquired a second time. The acquired fluorescence spectrum data is then calibrated, and the fluorescence intensity change data is updated based on the calibrated fluorescence spectrum data to obtain a second fluorescence intensity change data update strategy.

[0095] The fluorescence intensity change data is updated according to the first fluorescence intensity change data update strategy and the second fluorescence intensity change data update strategy to obtain the fluorescence intensity change update data.

[0096] The concentration and activity of EHNV virus in the water body to be tested are determined by updating the fluorescence intensity data, and EHNV virus information is obtained.

[0097] It is important to note that in EHNV virus detection, the fluorescence lifetime of the fluorophore is crucial for accurately determining virus information. However, the detection process is susceptible to interference from various factors, such as water impurities, environmental conditions, and interactions between the virus and other substances. These factors can cause the actual fluorescence lifetime of the fluorophore to deviate from its theoretical range, thus affecting the accuracy of EHNV virus concentration and activity assessment based on fluorescence intensity changes. By obtaining the theoretical fluorescence lifetime range and calculating the actual fluorophore lifetime for comparison, it is possible to accurately determine whether the fluorophore is in an abnormal state. If no abnormality is found, the first update strategy, which updates the fluorescence intensity change data based on spectral calibration data, effectively eliminates other interfering factors, ensuring that the data reflects the true state of the virus and improving the reliability of virus concentration and activity analysis. If an abnormality is found, the second strategy, which involves adding a fluorophore protectant and re-acquiring and calibrating the spectral data, corrects the detection deviation caused by abnormal fluorescence lifetime, restoring data accuracy. Finally, the fluorescence intensity change update data obtained according to the update strategy can accurately determine the EHNV virus concentration and activity in the tested water area, improving the accuracy of EHNV virus monitoring.

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

[0099] A second aspect of the present invention also provides an EHNV virus detection system 4 based on dynamic fluorescence intensity monitoring. The system includes a memory 41 and a processor 42. The memory includes a program for an EHNV virus detection method based on dynamic fluorescence intensity monitoring. When the processor executes the EHNV virus detection method program based on dynamic fluorescence intensity monitoring, it performs the following steps:

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

[0101] Based on the fluorescence detection data, the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection was determined, and the accuracy impact data was obtained.

[0102] Based on the accuracy impact data, determine the optimal sampling area for EHNV virus detection in the water body to be tested;

[0103] EHNV virus detection samples are obtained from the optimal sampling area for detection, and fluorescence detection is performed on the EHNV virus detection samples. The changes in fluorescence intensity during the fluorescence detection process are monitored to obtain fluorescence intensity change data.

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

[0105] This invention discloses a method and system for detecting EHNV virus based on dynamic fluorescence intensity monitoring. The method divides the water area to be tested into small regions and acquires information on water impurities. Fluorescence detection is performed under different impurity characteristics, and the fluorescence data is analyzed to assess the impact of impurities on detection accuracy. Based on the accuracy impact data, the optimal sampling area is determined, and EHNV virus samples are collected. Fluorescence intensity changes in the samples are monitored to ultimately obtain EHNV virus information in the water area. This method can improve detection sensitivity and accuracy under impurity interference and is applicable to fields such as aquaculture and ecological environment monitoring. This 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, contributing to the prevention and control of EHNV virus spread.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0107] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0108] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0109] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0110] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0111] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for detecting EHNV virus based on dynamic fluorescence intensity monitoring, characterized in that, Includes the following steps: The water area to be tested for EHNV virus was divided into several small regions, and the water impurity information of each small region was obtained. Fluorescence detection was then performed on the EHNV virus water samples under the characteristics of each water impurity to obtain fluorescence detection data. Specifically: Obtain the geographic environment data of the water area to be detected for EHNV virus, construct a three-dimensional geographic model of the water area to be detected based on the geographic environment data, and divide the water area to be detected into several small areas based on the three-dimensional geographic model. Acquire water impurity information for each small area, including the content of inorganic and organic matter, and the size and content of suspended particles. Determine the water impurity characteristics of each small area based on the water impurity information, perform deduplication on the water impurity characteristics, and construct a water impurity characteristic data table for fluorescence detection testing. Based on the water impurity characteristic data table, water impurity samples for each small area are constructed. The same amount of EHNV virus is added to each water impurity sample to obtain fluorescent detection test water samples. The fluorescence detection test water samples are subjected to fluorescence detection operation to obtain fluorescence detection data for each fluorescence detection test water sample. The fluorescence detection data includes fluorescence intensity and fluorescence spectrum data. Based on the fluorescence detection data, the impact of different water body impurity characteristics on the accuracy of EHNV virus fluorescence detection was determined, and the accuracy impact data were obtained as follows: Standard fluorescence detection data of different EHNV virus contents were obtained under the condition of no water impurities. Data features of the standard fluorescence detection data were extracted, including fluorescence intensity features, peak value, trough value, mean value, and variance features of the fluorescence spectrum. The data features of the standard fluorescence detection data were used to construct an EHNV virus content-fluorescence feature database; Feature extraction is performed on the fluorescence detection data to obtain fluorescence detection data features. The fluorescence detection data features are compared with the EHNV virus content-fluorescence feature database to determine the EHNV virus detection content under each water impurity feature in the water body to be tested. The accuracy of EHNV virus detection in the fluorescent detection test water samples under each water body impurity characteristic was evaluated based on the EHNV virus detection content under each water body impurity characteristic, and the detection accuracy data of the fluorescent detection test water samples under each water body impurity characteristic were obtained. A feature-accuracy data matrix is ​​constructed by combining the characteristics of each water body impurity with the detection accuracy data. Based on the feature-accuracy data matrix, the Pearson correlation coefficient between the characteristics of each water body impurity and the detection accuracy is calculated, and a correlation coefficient matrix is ​​constructed. The accuracy impact of EHNV virus fluorescence detection under the interference of different water body impurity characteristics was determined based on the correlation coefficient matrix, and the accuracy impact data were obtained. Based on the accuracy impact data, the optimal sampling area for EHNV virus detection in the water body to be tested was determined, specifically as follows: A detection accuracy evaluation model is constructed based on the decision tree algorithm. The model parameters of the detection accuracy evaluation model are set, including the maximum depth of the decision tree, the minimum number of split samples, and the minimum number of leaf node samples. The accuracy impact data is imported into the detection accuracy evaluation model to construct a decision tree for each water body impurity feature. The detection accuracy evaluation model is then trained based on the decision tree to obtain the trained detection accuracy evaluation model. Obtain water impurity information for each small area of ​​the water body to be tested at the current time point, import the water impurity information into the detection accuracy evaluation model, and evaluate the accuracy of EHNV virus detection for the water impurity information of each small area to obtain the accuracy evaluation result. Based on the accuracy assessment results, determine the optimal sampling area for EHNV virus detection in the water body to be tested; EHNV virus detection samples are obtained from the optimal sampling area for detection, and fluorescence detection is performed on the EHNV virus detection samples. The changes in fluorescence intensity during the fluorescence detection process are monitored to obtain fluorescence intensity change data. The EHNV virus information of the water area to be tested is determined based on the fluorescence intensity change data.

2. The method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, The process involves obtaining EHNV virus detection samples from the optimal sampling area, performing fluorescence detection on the EHNV virus detection samples, monitoring the changes in fluorescence intensity during the fluorescence detection process, and obtaining fluorescence intensity change data. Specifically: Obtain EHNV virus detection samples from the optimal detection sample sampling area, perform fluorescence detection on the EHNV virus detection samples, and obtain fluorescence spectral data of fluorescence monitoring at a preset frequency; Based on the fluorescence spectral data, the fluorescence intensity changes for EHNV virus detection were determined, and fluorescence intensity change data were obtained.

3. The method for detecting EHNV virus based on dynamic fluorescence intensity monitoring according to claim 1, characterized in that, The determination of EHNV virus information in the waters to be tested based on the fluorescence intensity change data specifically involves: Perform 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 for a consecutive preset number of time points is less than a preset value and the full width at half maximum (FWHM) of the spectral peak shape of the fluorescence spectrum data is greater than a second preset value, then the fluorescence intensity change data is labeled as spectral peak shape abnormal data. The spectral peak anomaly data is initially fitted using a Gaussian function to determine initial fitting parameters, including peak position, peak height, and peak width fitting parameters. A second fitting is then performed on the spectral peak anomaly data based on these initial fitting parameters. The peak shape symmetry of the second-fitted spectral peak anomaly data is then determined. If asymmetric, the Lorentz function is introduced as a correction term. The peak shape asymmetry of the initially fitted spectral peak anomaly data is corrected using the Lorentz function to obtain spectral calibration data. The theoretical fluorescence lifetime range of the fluorophore is obtained during the EHNV virus detection process. The actual fluorophore lifetime information is calculated based on the spectral calibration data. The theoretical fluorescence lifetime range is compared with the actual fluorophore lifetime information to determine whether the fluorophore exhibits an abnormal fluorescence lifetime state. If the fluorescence lifetime does not show any abnormalities, the fluorescence intensity change data is updated based on the spectral calibration data to obtain the first fluorescence intensity change data update strategy; If an abnormal fluorescence lifetime occurs, a fluorophore protectant is added to the EHNV virus detection sample, and the fluorescence spectrum data is acquired a second time. The acquired fluorescence spectrum data is then calibrated, and the fluorescence intensity change data is updated based on the calibrated fluorescence spectrum data to obtain a second fluorescence intensity change data update strategy. The fluorescence intensity change data is updated according to the first fluorescence intensity change data update strategy and the second fluorescence intensity change data update strategy to obtain the fluorescence intensity change update data. The concentration and activity of EHNV virus in the water body to be tested are determined by updating the fluorescence intensity data, and EHNV virus information is obtained.

4. 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 memory and a processor. The memory includes a computer program for the EHNV virus detection method based on dynamic fluorescence intensity monitoring. The processor is used to execute the computer program in the memory. When the computer program in the memory is executed by the processor, it implements the steps of the EHNV virus detection method based on dynamic fluorescence intensity monitoring as described in any one of claims 1 to 3.

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