Method for managing VNN-free fry farm for cynoglossus semilaevis cultivation

Abstract

The invention belongs to the technical field of cynoglossus semilaevis farm management, and discloses a management method of a VNN-free fry farm for cynoglossus semilaevis breeding. According to the method, the water quality anomaly evaluation condition and the fry anomaly condition are analyzed respectively, whether fry anomaly exists in each monitoring culture pond or not is comprehensively judged, the one-sidedness of single-factor analysis is avoided, information is obtained from the environment and object double view angles, and the judgment accuracy is greatly improved. After an abnormal source is determined, a prevention and control strategy can be formulated in a targeted manner, and the prevention and control effect is greatly improved. The method further identifies the cross-contamination risk culture ponds when determining that the offspring seeds are abnormal, can effectively block disease transmission, timely find out the possible cross-contamination culture ponds and perform isolation treatment, prevents diseases from spreading among the ponds, reduces the risk of disease infection and death of large-area offspring seeds, is beneficial to precise treatment of water quality, and improves the survival rate of the offspring seeds. An exclusive purification scheme is made for the polluted water body, and the adverse effect of water quality deterioration on seedling growth is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of Cynoglossus semilaevis aquaculture farm management and relates to a management method for a VNN-free seedling farm for Cynoglossus semilaevis aquaculture. Background Art

[0002] Cynoglossus semilaevis is an important marine aquaculture fish. During the Cynoglossus semilaevis aquaculture process, VNN (Viral Nervous Necrosis) can cause serious harm to various aspects of the health status, growth performance, reproductive ability, and aquaculture economic benefits of Cynoglossus semilaevis. The management of a VNN-free seedling farm for Cynoglossus semilaevis aquaculture is of great significance for ensuring seedling quality, stabilizing industrial development, improving economic benefits, and protecting the ecological environment, and is the key to the healthy and sustainable development of the Cynoglossus semilaevis aquaculture industry. Therefore, the research on the management of a VNN-free seedling farm for Cynoglossus semilaevis aquaculture is of great significance.

[0003] In the prior art, there are also related solutions for fishery aquaculture management technologies. For example, a Chinese invention patent application for a method and device for regulating the growth environment of cultured fish with the publication number CN114004433A includes: obtaining fish growth environment index data and fish behavior and growth index data, where the fish behavior and growth index data are fish behavior and growth index data obtained based on fish video image data. Inputting the fish growth environment index data and the fish behavior and growth index data into a trained environment regulation model to obtain a regulation plan for the corresponding fish growth environment.

[0004] In addition, a Chinese invention patent application for a fish aquaculture system and method for land-based recirculating water with the publication number CN118917959A includes: a data monitoring module, a strategy optimization module, a feed processing module, and a salvage module. The data monitoring module is used to collect in real time the fish population quantity data, the body weight data of individual fish, the body length data of individual fish, the internal bone structure of individual fish, and the water quality status in the aquaculture pond. The strategy optimization module updates in real time the growth situation of the fish and the water quality information in the aquaculture pond based on the fish population quantity data, the body weight data of individual fish, the body length data of individual fish, the internal bone structure, and the water quality status, and constructs a fish body growth prediction model and a water quality prediction model, and optimizes the aquaculture strategy according to the growth prediction model and the water quality prediction model. The feed processing module automatically adjusts the feeding amount based on the optimized aquaculture strategy and regularly detects and removes residual feed. The automatic salvage module is used to monitor the dead fish situation and locate and salvage the dead fish based on the dead fish situation.

[0005] Although the above two solutions propose some solutions for fishery aquaculture management technology, there are still certain limitations: on the one hand, when the existing technical solutions monitor the quality of aquaculture conditions, the water quality conditions and the growth conditions of fish are analyzed and abnormal identified independently. This analysis method ignores the interaction between the two. Water factors deeply affect the physiological activities of fish, and fish metabolites also act on the water in return. Independent analysis cannot comprehensively reflect the dynamic relationship between the two, making it difficult to accurately judge the causes of abnormal fish growth or diseases, lacking overall relevance, and wasting data correlation information, resulting in insufficient early warning ability, making it difficult to prevent aquaculture risks in a timely manner, and being unfavorable to the stability and sustainable development of aquaculture production.

[0006] On the other hand, the existing technical solutions lack the identification of aquaculture ponds with cross - contamination risks. This analysis method increases the difficulty of disease prevention and control, making it difficult to detect potential transmission routes. Once an epidemic breaks out, it spreads rapidly, the prevention and control cost soars, weakening the early warning ability of water quality deterioration. Cross - contamination causes the water quality to affect each other, endangering the growth and health of Cynoglossus semilaevis, reducing the aquaculture management efficiency, lacking pertinence in management, and causing resource waste. In the long run, it restricts the sustainable development of the aquaculture industry and is difficult to build a stable and healthy aquaculture model. Summary of the Invention

[0007] In view of this, to solve the problems raised in the above - mentioned background technology, a management method for a VNN - free seedling farm for Cynoglossus semilaevis aquaculture is proposed.

[0008] The object of the present invention can be achieved through the following technical solutions: A management method for a VNN - free seedling farm for Cynoglossus semilaevis aquaculture, including: S1. Monitoring the water quality of aquaculture ponds: Monitoring the water quality of each monitored aquaculture pond, obtaining the pH value, dissolved oxygen concentration, and ammonia nitrogen concentration of each monitored aquaculture pond, and analyzing the abnormal evaluation of the water quality of each monitored aquaculture pond.

[0009] S2. Judging the replacement of the water body in aquaculture ponds: Based on the abnormal evaluation of the water quality of each monitored aquaculture pond, judging whether each monitored aquaculture pond needs to replace the water body. If so, execute.

[0010] S3. Analyzing the behavioral characteristics of seedlings: Setting an underwater high - definition camera at the center point of each monitored aquaculture pond, collecting the images of the behavioral characteristics of seedlings in each monitored aquaculture pond, obtaining the data on the impact of abnormal swimming of seedlings and the data on the impact of abnormal vitality of seedlings in each monitored aquaculture pond, and analyzing the abnormal evaluation of the behavioral characteristics of seedlings in each monitored aquaculture pond.

[0011] S4. Analyzing the appearance characteristics of seedlings: Collecting the images of the appearance characteristics of seedlings in each monitored aquaculture pond, obtaining the evaluation data on the degree of body color change and the evaluation data on the degree of body surface damage of seedlings in each monitored aquaculture pond, and analyzing the abnormal evaluation of the appearance characteristics of seedlings in each monitored aquaculture pond.

[0012] S5. Seedling feeding characteristic analysis: Obtain the feeding weight of each feeding operation in each monitored aquaculture pond during a preset monitoring period based on the feeding log records of each monitored aquaculture pond. At the same time, use a high-definition camera to collect the feeding images corresponding to each feeding operation, obtain the weight of the uneaten feed, and analyze the abnormal evaluation of the seedling feeding characteristics in each monitored aquaculture pond.

[0013] S6. Judgment of seedling abnormal conditions: Judge whether there are abnormal seedlings in each monitored aquaculture pond, and record the monitored aquaculture ponds with abnormalities as abnormal aquaculture ponds.

[0014] S7. Cross-contamination risk identification: Obtain the cross-contamination risk distances between each abnormal monitored aquaculture pond and other monitored aquaculture ponds, and identify each cross-contamination risk aquaculture pond.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) By analyzing the abnormal evaluation of water quality and the abnormal conditions of seedlings respectively, the present invention obtains information from two perspectives: the environmental aspects such as pH, dissolved oxygen concentration, and ammonia nitrogen concentration, and the aspects of the behavior, appearance, and feeding characteristics of seedlings, and comprehensively judges whether there are abnormal seedlings in each monitored aquaculture pond. This method fully considers the influence of water body indicators on seedlings and the state of seedlings themselves, avoids the one-sidedness of single-factor analysis, and greatly improves the accuracy and comprehensiveness of judgment.

[0016] (2) When the present invention judges that there are abnormal seedlings, it further identifies each cross-contamination risk aquaculture pond. By calculating the cross-contamination risk distances between each abnormal monitored aquaculture pond and other monitored aquaculture ponds and comparing them with the safety monitoring distance, the cross-contamination risk index is determined, so as to find out the aquaculture ponds that may be cross-contaminated. Timely isolation treatment of these risk aquaculture ponds effectively blocks the transmission route of diseases between aquaculture ponds, reduces the risk of large-scale seedling infection and death, and ensures the healthy growth of seedlings.

[0017] (3) After the present invention clarifies the source of abnormality, it can formulate targeted prevention and control strategies. If it is determined that the abnormal seedlings are caused by water body problems, measures to improve water quality will be taken; if it is judged that the seedlings are infected with diseases, treatment will be carried out in a timely manner, greatly improving the prevention and control effect. At the same time, after identifying the cross-contamination risk aquaculture ponds, it is possible to accurately treat the water quality, clarify the source of cross-contamination, and formulate a dedicated purification plan for the polluted water body to avoid the adverse effects of water quality deterioration on the growth of seedlings. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0019] Figure 1 Schematic diagram of the implementation steps of the method of the present invention.

[0020] Figure 2 Flowchart for judging whether the water body of each monitored aquaculture pond needs to be replaced according to an embodiment provided by the present invention.

[0021] Figure 3 Flowchart for judging whether there are abnormal fry in each monitored aquaculture pond according to an embodiment provided by the present invention. Detailed implementation manners

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0023] Please refer to Figure 1 As shown, the present invention provides a management method for a VNN-free fry farm for Cynoglossus semilaevis culture, including: S1. Monitoring the water quality of aquaculture ponds: Monitoring the water quality of each monitored aquaculture pond, obtaining the pH value, dissolved oxygen concentration, and ammonia nitrogen concentration of each monitored aquaculture pond, and analyzing the abnormal evaluation of the water quality of each monitored aquaculture pond.

[0024] In a preferred embodiment of the present invention, to analyze the abnormal evaluation of the water quality of each monitored aquaculture pond, an abnormal evaluation index of the water quality of each monitored aquaculture pond needs to be constructed, and the specific method is as follows: Extract the pH value, dissolved oxygen concentration, and ammonia nitrogen concentration of each monitored aquaculture pond, and record them as , , , where represents the number of the monitored aquaculture pond, , represents the number of monitored aquaculture ponds.

[0025] It should be added that the pH value, dissolved oxygen concentration, and ammonia nitrogen concentration of each monitored aquaculture pond can be detected and obtained by using a pH sensor, an electrochemistry-type dissolved oxygen sensor, and a biosensor respectively.

[0026] Using the formula The water quality anomaly evaluation index of each monitored aquaculture pond is obtained through analysis , where represents the preset reference pH value, represents the preset reference dissolved oxygen concentration, represents the preset reference ammonia nitrogen concentration, represents the allowable difference between the preset pH value and the reference pH value, represents the allowable difference between the preset dissolved oxygen concentration and the reference dissolved oxygen concentration, represents the allowable difference between the preset ammonia nitrogen concentration and the reference ammonia nitrogen concentration.

[0027] It should be noted that the reasons for selecting pH value, dissolved oxygen concentration, and ammonia nitrogen concentration as the influencing factors of the water quality anomaly evaluation index are as follows: First, the pH value directly affects the living environment of organisms in water. An excessively high or low pH value will have an adverse impact on the physiological functions of aquatic organisms and even endanger their lives. Second, the dissolved oxygen concentration is necessary for the respiration of aquatic organisms. Insufficient dissolved oxygen will lead to the death of aquatic organisms due to hypoxia and affect the ecological balance. Finally, a high ammonia nitrogen concentration is toxic to aquatic organisms, which will damage their gill tissues and affect metabolism, etc. Moreover, the ammonia nitrogen concentration is also an important indicator for measuring the degree of water pollution. These three factors can comprehensively reflect the chemical properties and ecological health status of water bodies, and are crucial for judging water quality anomalies and ensuring the healthy growth of fry in aquaculture ponds.

[0028] It should be noted that the setting basis of the reference pH value, reference dissolved oxygen concentration, and reference ammonia nitrogen concentration: The reference pH value is set because different aquatic organisms have different suitable pH value ranges. For example, most fish are suitable for a neutral or slightly alkaline environment with a pH value of 6.5 - 8.5. Beyond this range, it will affect their physiological functions and also change the solubility of substances in water. The reference dissolved oxygen concentration is set considering the respiration needs of aquatic organisms. Most healthy aquaculture of fish and shrimp requires a dissolved oxygen concentration of more than 5.0 mg / L, and that of cold-water fish and shrimp is even higher. Sufficient dissolved oxygen can promote the degradation of organic matter by microorganisms. The reference ammonia nitrogen concentration is set because ammonia nitrogen is highly toxic to aquatic organisms, and even low concentrations of ammonia nitrogen may affect growth. Setting a reasonable reference value can ensure the health of aquatic organisms and also reflect the degree of water pollution.

[0029] It should be noted that the basis for setting the allowable difference between the pH value and the reference pH value, the allowable difference between the dissolved oxygen concentration and the reference dissolved oxygen concentration, and the allowable difference between the ammonia nitrogen concentration and the reference ammonia nitrogen concentration are as follows: Firstly, it is the survival requirements of the fry. Different fry have different adaptation ranges to water quality indicators, and the allowable difference needs to be set according to their physiological characteristics to ensure normal growth. Secondly, breeding experience and historical data are crucial. Through long-term practice and data analysis, the fluctuation range of water quality indicators when the fry grow well can be determined, and the allowable difference can be determined accordingly. Moreover, the stability of the breeding environment has a significant impact. The allowable difference of a stable breeding system can be narrow, while an open system requires a wider range. The accuracy of the detection equipment also has an impact. With high accuracy, the allowable difference can be small, and with low accuracy, it needs to be appropriately increased to avoid misjudgment. Finally, economic benefits are one of the considerations. It is necessary to balance the cost of water quality adjustment and the growth requirements of the fry, and reasonably set the allowable difference to take into account the breeding benefits while ensuring the health of the fry.

[0030] It should be added that the formula construction relationship corresponding to the water quality anomaly evaluation index of each monitored breeding pond obtained by the above analysis is as follows: 1. Index selection: Extract the pH value, dissolved oxygen concentration, and ammonia nitrogen concentration of each monitored breeding pond as key water quality parameters because these parameters have a significant impact on the survival and growth of the fry. At the same time, a reference value is set to represent the ideal water quality index suitable for the growth of the fry.

[0031] 2. Difference calculation: Calculate the absolute value of the difference between the water quality parameters of each monitored breeding pond and the reference value to measure the degree of deviation of the current water quality parameters from the ideal value.

[0032] 3. Normalization processing: Divide the above differences by the allowable difference respectively to normalize the degree of deviation, so that parameters with different dimensions are comparable.

[0033] 4. Comprehensive operation: Add the normalized degrees of deviation and put them into the hyperbolic tangent function for operation. Map the comprehensive degree of deviation to a reasonable interval to obtain the water quality anomaly evaluation index, so as to comprehensively reflect the degree of water quality anomaly of each monitored breeding pond.

[0034] S2. Judgment on water body replacement of breeding ponds: Based on the water quality anomaly evaluation of each monitored breeding pond, judge whether each monitored breeding pond needs to replace the water body. If necessary, execute it.

[0035] In a preferred embodiment of the present invention, please refer to Figure 2 As shown, the specific method for judging whether each monitored breeding pond needs to replace the water body is as follows: Extract the water quality anomaly evaluation index of each monitored breeding pond, and then compare it with the pre-set water quality anomaly evaluation index threshold. If the water quality anomaly evaluation index of a certain monitored breeding pond is greater than the water quality anomaly evaluation index threshold, it is judged that the monitored breeding pond needs to replace the water body; otherwise, it is judged that the monitored breeding pond does not need to replace the water body.

[0036] It should be noted that the basis for setting the threshold of the water quality anomaly evaluation index is as follows: Firstly, it is the survival needs of aquatic organisms. It is necessary to ensure their healthy growth based on indicators such as the suitable pH value, dissolved oxygen, and ammonia nitrogen concentration for the cultured fry. Secondly, it is the breeding experience and historical data. Long-term practice and monitoring data can reflect the water quality changes at different stages and under different conditions, providing a reference for determining the threshold. Thirdly, it is the self-purification ability of the water body. It is necessary to evaluate in combination with factors such as the size, water depth, and water flow of the aquaculture pond to ensure that the water body maintains the ecosystem under natural purification. Finally, it is the breeding goal and economic benefits. Setting the threshold requires balancing the breeding cost and output. If the threshold is too strict, frequent water changes will increase the cost, and if it is too loose, it will affect the growth of the fry. A reasonable setting can ensure the maximization of breeding benefits.

[0037] Exemplarily, the threshold of the water quality anomaly evaluation index is 。

[0038] S3. Analysis of fry behavior characteristics: Set underwater high-definition cameras at the center points of each monitored aquaculture pond, collect images of the fry behavior characteristics in each monitored aquaculture pond, obtain data on the influence of abnormal swimming conditions and abnormal vitality conditions of the fry in each monitored aquaculture pond, and analyze the abnormal evaluation of the fry behavior characteristics in each monitored aquaculture pond.

[0039] It should be noted that the reasons for analyzing the abnormal evaluation of fry behavior characteristics are as follows: Firstly, the behavior of fry is an intuitive reflection of its health status. By analyzing abnormal swimming (such as the roll angle, trajectory straightness, etc.) and abnormal vitality (such as swimming speed, proportion of fish leaving the group, etc.), potential diseases or discomfort can be detected in a timely manner, and early intervention and treatment can be carried out to reduce losses. Secondly, changes in the aquaculture environment will affect the behavior of fry. Evaluating abnormalities can grasp the influence of factors such as water quality and feed on fry, and optimize the aquaculture conditions. Thirdly, scientific evaluation and analysis can provide data support for aquaculture management, helping to reasonably plan aquaculture density, feeding strategies, etc., and improve aquaculture efficiency and economic benefits. In short, analyzing the abnormal fry behavior characteristics is crucial for ensuring the healthy growth of fry and promoting the development of the aquaculture industry.

[0040] In a preferred embodiment of the present invention, the data on the influence of abnormal swimming conditions of the fry includes monitoring the roll angle and the curvature of the swimming trajectory, and the data on the influence of abnormal vitality conditions of the fry includes monitoring the swimming speed, the proportion of fish leaving the group, and the feeding aggregation duration.

[0041] Specifically, the analysis methods for monitoring the rollover angle and the curvature of the swimming trajectory are as follows: 1. Monitoring the rollover angle: Extract the characteristic images of the fry behavior in each monitored aquaculture pond, and then locate the positions of each fry in the fry behavior images corresponding to each monitored aquaculture pond. Then, obtain the body reference horizontal direction of each fry. Denote the angle between the body reference horizontal direction of each fry and the horizontal direction as the monitored rollover angle of each fry corresponding to each monitored aquaculture pond. Then, perform an average calculation to obtain the monitored rollover angle of each monitored aquaculture pond.

[0042] 2. Curvature of the swimming trajectory: Extract the characteristic images of the fry behavior in each monitored aquaculture pond collected by a high-definition camera. Use model construction software to construct the swimming trajectories of each fry in each monitored aquaculture pond. Perform equally spaced point distribution on the swimming trajectories of each fry to obtain a number of monitoring points. Obtain the curvature of each monitoring point, and then perform an average calculation to obtain the curvature of the swimming trajectory of each fry in each monitored aquaculture pond. Perform an average calculation on the curvature of the swimming trajectory of each fry in each monitored aquaculture pond to obtain the curvature of the swimming trajectory of each monitored aquaculture pond.

[0043] Specifically, the analysis methods for monitoring the swimming speed, the outlier ratio, and the feeding aggregation duration are as follows: 1. Monitoring the swimming speed: Extract the characteristic images of the fry behavior in each monitored aquaculture pond collected by a high-definition camera. Obtain the positions of each fish in each monitored aquaculture pond at different times. Obtain the moving distance and the interval duration of each fry corresponding to each monitored aquaculture pond. Then, perform a ratio calculation to obtain the swimming speed of each fry in each monitored aquaculture pond. Perform an average calculation on the swimming speed of each fry in each monitored aquaculture pond to obtain the monitored swimming speed of each monitored aquaculture pond.

[0044] 2. Outlier ratio: Extract the characteristic images of the fry behavior in each monitored aquaculture pond collected by a high-definition camera, and then locate the positions of each fry in each monitored aquaculture pond. Obtain whether there are other fry in the monitoring area corresponding to the position of each fry in each monitored aquaculture pond. If so, judge that the fry is not an outlier; otherwise, judge that the fry is an outlier. Count the number of outlier fry and the total number of fry in each monitored aquaculture pond, and then perform a ratio calculation to obtain the corresponding outlier ratio of each monitored aquaculture pond.

[0045] 3. Feeding aggregation duration: Use a high-definition camera to collect the characteristic images of the fry behavior in each monitored aquaculture pond during the corresponding feeding period. Obtain the feeding time corresponding to each monitored aquaculture pond and the arrival time of each fry. Perform a difference calculation between the arrival time of each fry and the feeding time to obtain the aggregation duration of each fry, and then perform an average calculation to obtain the corresponding feeding aggregation duration of each monitored aquaculture pond.

[0046] It should be noted that the reasons for selecting the monitoring of the roll - over angle and the curvature of the swimming trajectory as the influencing data for abnormal swimming conditions of fry are as follows: First, the roll - over angle can intuitively reflect the body balance and health status of fry. Normal fry should maintain a stable posture, and abnormal angles may indicate diseases or environmental discomfort. Second, the curvature of the swimming trajectory can reflect the flexibility and coordination of fry. Abnormal curvature may mean that its motor ability is limited or there are problems with the nervous system. These data can comprehensively and accurately evaluate the swimming state of fry, detect abnormalities in a timely manner, and provide an important basis for ensuring the health of fry and optimizing aquaculture management.

[0047] It should be noted that the reasons for selecting the monitoring of swimming speed, the proportion of fish leaving the group, and the duration of aggregation during feeding as the influencing data for abnormal fry vitality are as follows: Swimming speed is an intuitive manifestation of fry vitality. Healthy and vigorous fry have a stable and appropriate swimming speed, and abnormal changes in speed may imply problems with their physical functions. The proportion of fish leaving the group can reflect the group behavior and social characteristics of fry. Under normal circumstances, fry mostly swim in groups, and a high proportion of fish leaving the group may mean that the fry are sick, stressed, or there is environmental discomfort. The duration of aggregation during feeding is closely related to the appetite and vitality of fry. Quick aggregation and normal duration indicate that the fry have good appetite and vitality. If the aggregation is slow or the duration is too short, it may be due to problems with the health status of the fry or the quality of the feed. These data can effectively evaluate the vitality of fry when combined.

[0048] In a preferred embodiment of the present invention, to analyze the abnormal evaluation of the fry behavior characteristics in each monitored aquaculture pond, it is necessary to construct an abnormal evaluation index of the fry behavior characteristics in each monitored aquaculture pond, and the specific method is as follows: Extract the monitored roll - over angle and the curvature of the swimming trajectory of each monitored aquaculture pond, and analyze to obtain the evaluation index of the abnormal swimming conditions of the fry in each monitored aquaculture pond.

[0049] It should be added that the specific method for analyzing and obtaining the evaluation index of the abnormal swimming conditions of the fry in each monitored aquaculture pond is as follows: Extract the monitored roll - over angle and the curvature of the swimming trajectory of each monitored aquaculture pond, and denote them as 、 , and use the formula to analyze and obtain the evaluation index of the abnormal swimming conditions of the fry in each monitored aquaculture pond , where represents the pre - set reference monitored roll - over angle, represents the pre - set reference curvature of the swimming trajectory, represents the permitted difference between the curvature of the swimming trajectory and the reference curvature of the swimming trajectory, represents the natural constant.

[0050] It should be noted that the basis for setting the reference monitoring roll angle and the curvature of the reference swimming trajectory is as follows: First, it is the biological characteristics of the fry. The normal swimming performances of fry of different species, varieties, and growth stages are different. There are differences between juveniles and adults, and the reference values can be determined accordingly. Second, the historical aquaculture data is of great significance. The data accumulated through long-term monitoring can be statistically analyzed to obtain the average level or the fluctuation range under normal conditions as the reference basis. Third, the behavior patterns of healthy fry are crucial. By observing their swimming in a suitable environment, standards can be set to judge whether they are healthy or not.

[0051] It should be supplemented that the construction relationship of the evaluation index corresponding formula for the abnormal swimming conditions of fry in each monitored aquaculture pond obtained through analysis is as follows: 1. Index selection: Extract the monitoring roll angle and the curvature of the swimming trajectory as the key indicators reflecting the swimming state of the fry. Because these two indicators can intuitively reflect the swimming posture and trajectory changes of the fry in the water, which is of great significance for judging whether their swimming is abnormal.

[0052] 2. Reference value setting: Set the reference monitoring roll angle and the reference curvature of the swimming trajectory, representing the corresponding index values under the normal swimming state of the fry. Based on this, the degree of deviation of the actual indicators of the fry in each monitored aquaculture pond from the normal state is measured.

[0053] 3. Difference and normalization processing: Calculate the absolute value of the difference between the actual curvature of the swimming trajectory and the reference value, and divide it by the pre-set allowable difference to normalize the degree of deviation of this index. At the same time, divide the monitoring roll angle by the reference monitoring roll angle for normalization processing, so that the two indexes with different dimensions are comparable.

[0054] 4. Comprehensive operation: Add the above two normalized values and put them into the exponential part of the exponential function. This function form can map the comprehensive degree of deviation to an interval, which is convenient for intuitively reflecting the abnormal degree of fry swimming. The closer the value is to, the lower the abnormal degree of swimming, and the closer it is to, the higher the abnormal degree.

[0055] Extract the monitored swimming speed, the proportion of out-of-group individuals, and the feeding aggregation duration of each monitored aquaculture pond, and analyze to obtain the evaluation index for the abnormal vitality of fry in each monitored aquaculture pond.

[0056] It should be supplemented that the specific method for analyzing and obtaining the evaluation index for the abnormal vitality of fry in each monitored aquaculture pond is as follows: Extract the monitored swimming speed, the proportion of out-of-group individuals, and the feeding aggregation duration of each monitored aquaculture pond, and record them as , , .

[0057] Use the formula to analyze and obtain the evaluation index for the abnormal vitality of fry in each monitored aquaculture pond , where represents the preset reference swimming speed, represents the preset reference outlier ratio, represents the preset reference feeding aggregation duration.

[0058] It should be noted that the settings of the reference swimming speed, reference outlier ratio, and reference feeding aggregation duration are based on: First, the biological characteristics of the fry. The physiological structures and habits of fry of different species and varieties are different, which determine different normal behavior performances. For example, lively fry swim fast, and gregarious fry have less outlier behavior. These characteristics are the basis for setting reference values. Second, there are significant differences in growth stages. Larval fry swim slowly and have weak group awareness, while adult fry are the opposite. The reference values need to vary according to the stage. Third, the historical aquaculture data provides important support. The data of the normal behavior of fry accumulated through long-term monitoring can be statistically analyzed to obtain a reasonable reference range.

[0059] It should be added that the construction relationship of the evaluation index corresponding formula for the abnormal situation of fry vitality in each monitored aquaculture pond obtained by the analysis: 1. Index selection: Extract the monitored swimming speed, outlier ratio, and feeding aggregation duration as the key indicators reflecting fry vitality. The monitored swimming speed can reflect the movement ability of fry, the outlier ratio can reflect their group behavior characteristics, and the feeding aggregation duration indicates the response and vitality of fry to food.

[0060] 2. Reference value setting: Set the reference swimming speed, reference outlier ratio, and reference feeding aggregation duration, which represent the corresponding index values of fry in the normal vitality state. These reference values provide a benchmark for measuring the degree of deviation of the actual index from the normal state.

[0061] 3. Ratio operation and normalization: Perform ratio operations on each index. Divide the reference swimming speed by the monitored swimming speed, the outlier ratio by the reference outlier ratio, and the feeding aggregation duration by the reference feeding aggregation duration. Through such operations, the indexes with different dimensions are normalized to make them comparable.

[0062] 4. Comprehensive operation: Add the above three normalized values to construct an evaluation index for the abnormal situation of fry vitality. This functional form can map the comprehensive deviation degree to an interval. The closer the value is, the closer the fry vitality is to the normal state and the lower the abnormal degree; the closer the value is, the higher the abnormal degree of fry vitality means.

[0063] Sum the evaluation index of the abnormal swimming situation of fry and the evaluation index of the abnormal situation of fry vitality in each monitored aquaculture pond according to the weight to obtain the evaluation index of the abnormal behavior characteristics of fry in each monitored aquaculture pond.

[0064] It should be noted that the basis for setting the corresponding weights of the abnormal seedling swimming evaluation index and the abnormal seedling vitality evaluation index is: first, the degree of impact on the health of the seedlings. The greater the threat to the health of the seedlings, the higher the corresponding index weight. Secondly, the needs are different at different breeding stages. Initial vitality is important, and the swimming state in the later stage is more critical, and the weights are adjusted accordingly. Furthermore, historical data and experience are also critical. By analyzing the frequency and consequences of previous abnormal situations, the weights can be reasonably determined. Finally, the breeding goals play a guiding role. With rapid growth as the goal, the weight of the abnormal vitality evaluation index increases; if quality and stress resistance are emphasized, the weight of the abnormal swimming evaluation index increases. Combining these factors, we can set weights scientifically and accurately evaluate abnormalities in seedling behavioral characteristics.

[0065] For example, the weights corresponding to the abnormal situation evaluation index of seedling movement and the abnormal situation evaluation index of seedling vitality are respectively .

[0066] S4. Analysis of seedling appearance characteristics: Collect images of seedling appearance characteristics in each monitored breeding pond, obtain evaluation data on the degree of body color change and the degree of body surface damage in each monitored breeding pond, and analyze the abnormal assessment of seedling appearance characteristics in each monitored breeding pond.

[0067] It should be noted that the reasons for analyzing the abnormalities in the appearance characteristics of seedlings in each monitored breeding pond are: first, it can achieve disease early warning. Changes in seedling body color and body surface damage are often precursors to disease. Early analysis can lead to early detection and prevention, thereby curbing the spread of the disease. Secondly, it helps to evaluate the breeding environment. An unfavorable environment can easily cause abnormal appearance of seedlings. Through analysis, it can be determined whether the environment is suitable and optimized. Furthermore, it can optimize breeding management. After identifying abnormal conditions, breeders can adjust density, feeding and other measures in a targeted manner to improve breeding efficiency and seedling survival rate. Finally, it is related to quality control. Appearance is a direct reflection of seedling quality. Evaluating abnormalities can screen high-quality seedlings, ensure the quality and market competitiveness of breeding products, and promote the healthy development of the breeding industry.

[0068] In a preferred embodiment of the present invention, the body color change degree evaluation data includes the number of monitored individual spots and the proportion of monitored spot areas, and the body surface damage degree evaluation data includes the number of monitored individual ulcer locations and the proportion of monitored ulcer areas.

[0069] Specifically, the analysis method for monitoring the number of individual spots and the proportion of the spot area: Extract the images of the appearance characteristics of the fry corresponding to each monitored aquaculture pond, further obtain the images of the appearance characteristics of each fry in each monitored aquaculture pond, grayscale the images of the appearance characteristics of each fry in each monitored aquaculture pond to obtain the grayscale images of the appearance characteristics of each fry in each monitored aquaculture pond, obtain the grayscale values of each pixel point in the corresponding image, and then compare with the pre-set mapping relationship between the grayscale value and the spot to determine whether each pixel point is in the spot area. If the grayscale value of a certain pixel point conforms to the pre-set mapping relationship between the grayscale value and the spot, then identify that the pixel point is located in the spot area, and then obtain each spot area, count the number of monitored individual spots of each fry, and obtain the area of each spot area, perform summation calculation to obtain the monitored spot area, and then perform proportion calculation with the surface area of the corresponding fry to obtain the proportion of the monitored spot area of each fry. Calculate the average value of the number of monitored individual spots and the proportion of the monitored spot area of each fry in each monitored aquaculture pond respectively to obtain the number of monitored individual spots and the proportion of the monitored spot area of each monitored aquaculture pond.

[0070] It should be noted that the analysis method for monitoring the number of individual ulcers and the proportion of the ulcer area refers to the analysis method for monitoring the number of individual spots and the proportion of the spot area.

[0071] It should be noted that the reasons for selecting the number of individual spots and the proportion of the spot area as the evaluation data for the degree of body color change: On the one hand, the number of spots can intuitively reflect the frequency and range of the body color change of the fry. An increase or decrease in the number may imply a change in the health status of the fry, such as being infected with diseases or being stimulated by the environment. On the other hand, the proportion of the spot area can measure the degree of body color change. A larger area proportion indicates a significant change, which may have a greater impact on the appearance and physiological functions of the fry. The combination of these two data can comprehensively and accurately evaluate the body color change of the fry. At the same time, they are easy to obtain through image acquisition and analysis, and are highly operable. In actual aquaculture monitoring, they can effectively provide a reliable basis for judging the health status and environmental adaptability of the fry, and help to adjust the aquaculture strategy in a timely manner.

[0072] It should be noted that the reasons for selecting the number of individual ulcers and the proportion of the ulcer area as the evaluation data for the degree of body surface injury: First of all, the number of ulcer positions can reflect the damaged range of the body surface of the fry. The more positions there are, the more scattered the injured parts are, and the fry may face more complex health problems. Secondly, the proportion of the ulcer area directly reflects the severity of the injury. The larger the proportion, the more serious the injury, and the greater the impact on the physiological functions and survival of the fry. The combination of the two can comprehensively evaluate the body surface injury situation, not only judge the damaged range, but also determine the severity. Moreover, these data are easy to obtain through image acquisition and analysis, and are highly operable in actual aquaculture monitoring. They can provide accurate basis for taking treatment measures in a timely manner, adjusting the aquaculture environment, etc., and ensure the healthy growth of the fry.

[0073] In a preferred embodiment of the present invention, in order to analyze the abnormal evaluation of the fry appearance characteristics of each monitored aquaculture pond, it is necessary to construct an abnormal evaluation index for the fry appearance characteristics of each monitored aquaculture pond. The specific method is as follows: Extract the number of spots of the monitored individuals in each monitored aquaculture pond and the proportion of the monitored spot area, and then calculate the difference from the pre-set reference number of spots of the reference individuals and the reference proportion of the spot area respectively to obtain the abnormal amount of the number of spots of the individuals in each monitored aquaculture pond and the abnormal amount of the proportion of the spot area. Then, calculate the ratio with the pre-set reference number of spots of the reference individuals and the reference proportion of the spot area respectively to obtain the abnormal index of the number of spots of the monitored individuals and the abnormal index of the proportion of the monitored spot area in each monitored aquaculture pond.

[0074] Calculate the average value of the abnormal index of the number of spots of the monitored individuals and the abnormal index of the proportion of the monitored spot area in each monitored aquaculture pond to obtain the evaluation index of the body color change degree of each monitored aquaculture pond.

[0075] Similarly, analyze and obtain the evaluation index of the body surface injury degree of each monitored aquaculture pond.

[0076] It should be noted that the specific analysis method of the evaluation index of the body surface injury degree of each monitored aquaculture pond: Extract the number of ulcer positions of the monitored individuals in each monitored aquaculture pond and the proportion of the monitored ulcer area, and then calculate the difference from the pre-set reference number of ulcer positions of the reference individuals and the reference proportion of the ulcer area respectively to obtain the abnormal amount of the number of ulcer positions of the individuals in each monitored aquaculture pond and the abnormal amount of the proportion of the ulcer area. Then, calculate the ratio with the pre-set reference number of ulcer positions of the reference individuals and the reference proportion of the ulcer area respectively to obtain the abnormal index of the number of ulcer positions of the monitored individuals and the abnormal index of the proportion of the monitored ulcer area in each monitored aquaculture pond.

[0077] Calculate the average value of the abnormal index of the number of ulcer positions of the monitored individuals and the abnormal index of the proportion of the monitored ulcer area in each monitored aquaculture pond to obtain the evaluation index of the body surface injury degree of each monitored aquaculture pond.

[0078] Based on the evaluation index of the body color change degree and the evaluation index of the body surface injury degree of each monitored aquaculture pond, analyze and obtain the abnormal evaluation index of the fry appearance characteristics of each monitored aquaculture pond.

[0079] Specifically, sum the evaluation index of the body color change degree and the evaluation index of the body surface injury degree of each monitored aquaculture pond according to the weight to obtain the abnormal evaluation index of the fry appearance characteristics of each monitored aquaculture pond.

[0080] It should be noted that the basis for setting the corresponding weights of the body color change degree evaluation index and the body surface injury degree evaluation index is as follows: First, it is the degree of influence on the health of the fry. If body surface injuries are likely to cause serious diseases and endanger survival, its weight will be higher than that of body color changes, which are mostly environmental adaptability responses. Second is the recoverability. The body color changes recover quickly, while body surface injuries recover slowly and may have sequelae. The body surface injuries with poor recoverability have a higher corresponding weight. Moreover, the difficulty of monitoring and evaluation also has an impact. Body color changes are easy to accurately monitor and quantify, while there are errors in the evaluation of body surface injuries due to various factors. The relatively accurately measurable body color changes will be considered in the weight setting. Finally, the breeding stages are different. Fry are sensitive to body color changes in the larval stage, and the problem of body surface injuries is more serious in the adult stage, and the weights will also be adjusted according to the stage differences. Considering these factors comprehensively, the weights of the two can be set scientifically and reasonably to accurately evaluate the abnormal appearance characteristics of the fry.

[0081] Exemplarily, the corresponding weights of the body color change degree evaluation index and the body surface injury degree evaluation index are respectively .

[0082] S5. Analysis of fry feeding characteristics: Based on the feeding log records of each monitored aquaculture pond, obtain the feeding weight of each feeding operation in each monitored aquaculture pond during the preset monitoring period. At the same time, use a high-definition camera to collect the feeding images corresponding to each feeding operation, obtain the corresponding weight of uneaten feed, and analyze the abnormal evaluation of the fry feeding characteristics in each monitored aquaculture pond.

[0083] Specifically, the specific method for obtaining the corresponding weight of uneaten feed: Extract the feeding images corresponding to each feeding operation collected by the high-definition camera, use image processing software to obtain the number of feed particles corresponding to each feeding operation in each monitored aquaculture pond, and multiply the number of feed particles corresponding to each feeding operation in each monitored aquaculture pond by the preset reference single weight of the feed to obtain the weight of uneaten feed corresponding to each feeding operation in each monitored aquaculture pond.

[0084] It should be noted that the reasons for analyzing the abnormal evaluation of the fry feeding characteristics in each monitored aquaculture pond are as follows: From a health perspective, abnormal feeding of fry may be due to illness or stress. Timely analysis can detect problems early and treat them. In terms of the aquaculture environment, factors such as water quality and water temperature affect feeding. Analysis can determine whether the environment is suitable for optimization. In terms of feed quality, abnormal feeding may be due to feed problems, and the formula can be adjusted or the feed can be replaced accordingly to ensure aquaculture benefits. For aquaculture management, mastering the abnormal feeding characteristics can reasonably adjust the feeding strategy, accurately feed, reduce waste, and lower costs. In short, the analysis of the abnormal evaluation of the fry feeding characteristics can comprehensively ensure the healthy growth of the fry, improve the aquaculture management level, and promote the efficient development of the aquaculture industry.

[0085] In a preferred embodiment of the present invention, to analyze the abnormal evaluation of the fry feeding characteristics in each monitored aquaculture pond, it is necessary to construct an abnormal evaluation index for the fry feeding characteristics in each monitored aquaculture pond, and the specific method is as follows: Extract the feeding weight of each feeding operation in each monitored aquaculture pond during the monitoring period, and at the same time extract the weight of the uneaten feed corresponding to each feeding operation in each monitored aquaculture pond.

[0086] Calculate the ratio of the weight of the uneaten feed to the corresponding feeding weight for each feeding operation in each monitored aquaculture pond to obtain the ratio of the weight of the uneaten feed for each feeding operation in each monitored aquaculture pond, and then calculate the average value to obtain the average ratio of the weight of the uneaten feed in each monitored aquaculture pond.

[0087] Take the average ratio of the weight of the uneaten feed in each monitored aquaculture pond as the abnormal evaluation index for the fry feeding characteristics in each monitored aquaculture pond.

[0088] S6. Judgment of fry abnormal situation: Judge whether there are fry abnormalities in each monitored aquaculture pond, and mark the monitored aquaculture ponds with abnormalities as abnormal aquaculture ponds.

[0089] In a preferred embodiment of the present invention, please refer to Figure 3 As shown, the specific method for judging whether there are fry abnormalities in each monitored aquaculture pond is as follows: Extract the abnormal evaluation index of the fry behavior characteristics, the abnormal evaluation index of the fry appearance characteristics, and the abnormal evaluation index of the fry feeding characteristics in each monitored aquaculture pond, and then calculate the sum according to the weights to obtain the abnormal evaluation index of the fry in each monitored aquaculture pond.

[0090] It should be noted that the basis for setting the corresponding weights of the abnormal evaluation index of the fry behavior characteristics, the abnormal evaluation index of the fry appearance characteristics, and the abnormal evaluation index of the fry feeding characteristics in each monitored aquaculture pond: First, it is the impact on the survival and growth of the fry. The evaluation index of the characteristic with a greater impact has a higher weight. For example, the feeding characteristic has a key impact and the weight can be set high. Second, there are differences in the breeding stages. When the fry are in the larval stage, the appearance characteristics are important for judging health and have a high weight; when they are in the adult stage, the behavior and feeding characteristics can better reflect health and the weights are increased. Third, according to the disease occurrence law, diseases are manifested through certain characteristics, and the weight of the evaluation index of this characteristic is increased during the high-incidence period. Finally, it is affected by the aquaculture environment factors. Poor water quality is likely to cause appearance abnormalities, and the weight of the appearance characteristic evaluation index is high; the environment is likely to cause stress, and the weight of the behavior characteristic evaluation index is high. Considering these factors comprehensively, set the weights scientifically to accurately evaluate the fry abnormalities.

[0091] Exemplarily, the corresponding weights of the abnormal evaluation index of the fry behavior characteristics, the abnormal evaluation index of the fry appearance characteristics, and the abnormal evaluation index of the fry feeding characteristics are respectively .

[0092] Calculate the sum of the water quality abnormal evaluation index and the fry abnormal evaluation index in each monitored aquaculture pond according to the weights to obtain the comprehensive evaluation index of the fry growth abnormality in each monitored aquaculture pond.

[0093] Compare the comprehensive evaluation index of abnormal seedling growth in each monitored aquaculture pond with the preset threshold of the comprehensive evaluation index of abnormal seedling growth. If the comprehensive evaluation index of abnormal seedling growth in a certain monitored aquaculture pond is greater than the threshold of the comprehensive evaluation index of abnormal seedling growth, it is determined that there is abnormal seedling in this monitored aquaculture pond; otherwise, it is determined that there is no abnormal seedling in this monitored aquaculture pond.

[0094] It should be noted that the setting basis of the threshold of the comprehensive evaluation index of abnormal seedling growth: First, it is the characteristics of the seedlings themselves. Different species and varieties have different growth characteristics, and different thresholds need to be set accordingly. Aquaculture experience and historical data are also crucial. They can show the normal growth fluctuation range and provide a reference for setting the threshold. Aquaculture environmental conditions, such as water quality and water temperature, have a great impact on seedling growth, and the threshold needs to be determined in combination with environmental factors. In terms of aquaculture goals and economic benefits, different goals have different requirements for seedling growth, and the cost and output need to be taken into account to reasonably set the threshold. In addition, the need for disease prevention and control cannot be ignored. Setting the threshold based on the growth performance of seedlings when common diseases occur can achieve early detection and prevention of diseases. Only by integrating these factors can the threshold of the comprehensive evaluation index of abnormal seedling growth be scientifically set.

[0095] Exemplarily, the threshold of the comprehensive evaluation index of abnormal seedling growth is .

[0096] S7. Cross - contamination risk identification: Obtain the cross - contamination risk distances between each abnormal monitored aquaculture pond and other monitored aquaculture ponds, and identify each cross - contamination risk aquaculture pond.

[0097] In a preferred embodiment of the present invention, the specific method for identifying each cross - contamination risk aquaculture pond is as follows: Extract the cross - contamination risk distances between each abnormal monitored aquaculture pond and other monitored aquaculture ponds, calculate the ratio of each to the preset safety monitoring distance respectively, and then take the reciprocal to obtain the cross - contamination risk index between each abnormal monitored aquaculture pond and other monitored aquaculture ponds.

[0098] It should be noted that the cross - contamination risk distance between each abnormal monitored aquaculture pond and other monitored aquaculture ponds refers to the distance between the center points of each monitored aquaculture pond.

[0099] Statistically obtain the cross - contamination risk index between each monitored aquaculture pond and each abnormal monitored aquaculture pond, and then make a comparison, and select the maximum cross - contamination risk index as the cross - contamination risk index of each monitored aquaculture pond.

[0100] Compare the cross - contamination risk index of each monitored aquaculture pond with the preset cross - contamination risk index threshold. If the cross - contamination risk index of a certain monitored aquaculture pond is greater than the cross - contamination risk index threshold, identify this monitored aquaculture pond as a cross - contamination risk aquaculture pond.

[0101] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of the present technology can make various modifications, supplements, or use similar methods for substitution to the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall fall within the protection scope of the present invention.

Claims

1. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis aquaculture, characterized in that: include: S1. Monitoring of water quality in aquaculture ponds: Monitor the water quality of each monitored aquaculture pond, obtain the pH, dissolved oxygen concentration and ammonia nitrogen concentration of each monitored aquaculture pond, and analyze the abnormal evaluation of water quality in each monitored aquaculture pond; S2. Judgment on water replacement in aquaculture ponds: Based on the abnormal water quality evaluation of each monitored aquaculture pond, it is determined whether water replacement is required in each monitored aquaculture pond, and if necessary, it is carried out; S3. Analysis of seedling behavior characteristics: Underwater high-definition cameras are set at the center of each monitoring breeding pond to collect seedling behavior characteristic images of each monitoring breeding pond, obtain data on the impact of abnormal seedling swimming and abnormal seedling vitality in each monitoring breeding pond, and analyze the abnormal evaluation of seedling behavior characteristics in each monitoring breeding pond; S4. Analysis of seedling appearance characteristics: Collect images of seedling appearance characteristics of each monitored breeding pond, obtain evaluation data on the degree of body color change and the degree of body surface damage of each monitored breeding pond, and analyze the abnormal evaluation of seedling appearance characteristics of each monitored breeding pond; S5. Analysis of seedling feeding characteristics: Based on the feeding log records of each monitored breeding pond, the feeding weight of each feeding operation in each monitored breeding pond within the preset monitoring period is obtained. At the same time, a high-definition camera is used to collect the feeding images corresponding to each feeding operation, and the corresponding uneaten feed weight is obtained, and the abnormal evaluation of seedling feeding characteristics of each monitored breeding pond is analyzed; S6. Seedling abnormality judgment: judge whether there is seedling abnormality in each monitoring breeding pond, and record the monitoring breeding pond with abnormality as an abnormal breeding pond; S7. Cross-contamination risk identification: Obtain the cross-contamination risk distance between each abnormal monitoring breeding pond and other monitoring breeding ponds, and identify each cross-contamination risk breeding pond.

2. The method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding according to claim 1, characterized in that: The analysis of the abnormal water quality evaluation of each monitored aquaculture pond requires the construction of an abnormal water quality evaluation index for each monitored aquaculture pond, and the specific method is as follows: The pH, dissolved oxygen concentration and ammonia nitrogen concentration of each monitoring aquaculture pond were extracted and recorded as , , ,in Indicates the number of the monitored breeding pond. , Indicates the number of monitored breeding ponds; Using the formula The water quality abnormality evaluation index of each monitored breeding pond was obtained by analysis. ,in Indicates the preset reference pH. Indicates the preset reference dissolved oxygen concentration. Indicates the preset reference ammonia nitrogen concentration. Indicates the allowable difference between the preset pH and the reference pH. Indicates the allowable difference between the preset dissolved oxygen concentration and the reference dissolved oxygen concentration. Indicates the allowable difference between the preset ammonia nitrogen concentration and the reference ammonia nitrogen concentration.

3. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 2, characterized in that: The specific method for judging whether each monitored breeding pond needs to be replaced with water is as follows: The water quality abnormality evaluation index of each monitored breeding pond is extracted, and then compared with the pre-set water quality abnormality evaluation index threshold. If the water quality abnormality evaluation index of a monitored breeding pond is greater than the water quality abnormality evaluation index threshold, it is judged that the monitored breeding pond needs to replace the water body. Otherwise, it is judged that the monitored breeding pond does not need to replace the water body.

4. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 3, characterized in that: The data affecting abnormal swimming conditions of the seedlings include monitoring the rollover angle and the curvature of the swimming trajectory, and the data affecting abnormal vitality of the seedlings include monitoring the swimming speed, the proportion of outliers and the feeding gathering time.

5. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 4, characterized in that: The analysis of abnormal behavior characteristics of seedlings in each monitoring breeding pond requires the construction of abnormal behavior characteristics evaluation index of seedlings in each monitoring breeding pond, and the specific method is as follows: The monitoring rollover angle and swimming trajectory curvature of each monitoring aquaculture pond are extracted and recorded as , , using the formula The abnormal movement evaluation index of seedlings in each monitoring breeding pond was obtained by analysis. ,in Indicates the preset reference monitoring rollover angle, represents the preset reference swimming trajectory curvature, Indicates the permissible difference between the preset swimming trajectory curvature and the reference swimming trajectory curvature. represents a natural constant; The monitored swimming speed, outlier ratio and feeding aggregation time of each monitoring aquaculture pond were extracted and recorded as , , , using the formula The abnormal situation evaluation index of seedling vitality in each monitored breeding pond was obtained by analysis ,in Indicates the preset reference swimming speed. represents the preset reference outlier ratio, Indicates the preset reference feeding aggregation time; The abnormal evaluation index of seedling swimming and the abnormal evaluation index of seedling vitality in each monitored breeding pond are summed up according to the weights to obtain the abnormal evaluation index of seedling behavior characteristics in each monitored breeding pond.

6. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 5, characterized in that: The data for evaluating the degree of body color change include the number of monitored individual spots and the proportion of monitored spot areas, and the data for evaluating the degree of body surface damage include the number of monitored individual ulcer locations and the proportion of monitored ulcer areas.

7. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 6, characterized in that: The analysis of abnormal appearance characteristics of the seedlings in each monitored breeding pond requires the construction of an abnormal appearance characteristics evaluation index for the seedlings in each monitored breeding pond, and the specific method is as follows: Extract the number of individual monitored spots and the proportion of monitored spot areas of each monitored breeding pool, and then perform difference calculations with the preset reference number of individual spots and the proportion of reference spot areas to obtain the abnormal number of individual spots and the abnormal number of spot areas of each monitored breeding pool, and then perform ratio calculations with the preset reference number of individual spots and the proportion of reference spot areas to obtain the abnormal index of the number of individual monitored spots and the abnormal index of the proportion of monitored spot areas of each monitored breeding pool; The average of the abnormal index of the number of individual spots and the abnormal index of the area ratio of the monitored spots in each monitored breeding pond was calculated to obtain the evaluation index of the degree of body color change in each monitored breeding pond; Similarly, the evaluation index of the degree of body surface damage of each monitored breeding pond was obtained; Based on the evaluation index of body color change degree and body surface damage degree of each monitored breeding pond, the abnormal evaluation index of seedling appearance characteristics of each monitored breeding pond was obtained.

8. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 7, characterized in that: The analysis of the abnormal feeding characteristics of the seedlings in each monitored breeding pond requires the construction of an abnormal feeding characteristics assessment index for the seedlings in each monitored breeding pond, and the specific method is as follows: Extract the weight of feed for each feeding operation in each monitoring aquaculture pool during the monitoring period, and extract the weight of uneaten feed for each feeding operation in each monitoring aquaculture pool; The weight of uneaten feed in each feeding operation of each monitored breeding pool is calculated by comparing the weight of uneaten feed with the corresponding feeding weight to obtain the weight ratio of uneaten feed in each feeding operation of each monitored breeding pool, and then the average is calculated to obtain the average weight ratio of uneaten feed in each monitored breeding pool; The average proportion of uneaten feed weight in each monitored breeding pond was used as the abnormal evaluation index of seedling feeding characteristics in each monitored breeding pond.

9. A method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding as claimed in claim 8, characterized in that: The specific method for judging whether there is abnormal seedling in each monitored breeding pond is as follows: Extract the abnormal evaluation index of seedling behavior characteristics, the abnormal evaluation index of seedling appearance characteristics and the abnormal evaluation index of seedling feeding characteristics of each monitored breeding pond, and then sum them up according to the weight to obtain the abnormal evaluation index of seedlings in each monitored breeding pond; The water quality abnormality evaluation index and seedling abnormality evaluation index of each monitored breeding pond are summed up according to the weights to obtain the seedling growth abnormality comprehensive evaluation index of each monitored breeding pond; The comprehensive evaluation index of seedling growth abnormality of each monitored breeding pond is compared with the pre-set comprehensive evaluation index threshold of seedling growth abnormality. If the comprehensive evaluation index of seedling growth abnormality of a monitored breeding pond is greater than the comprehensive evaluation index threshold of seedling growth abnormality, it is judged that there is seedling abnormality in the monitored breeding pond; otherwise, it is judged that there is no seedling abnormality in the monitored breeding pond.

10. The method for managing a VNN-free seedling farm for Cynoglossus semilaevis breeding according to claim 1, characterized in that: The specific method of identifying each cross-contamination risk breeding pond is as follows: The cross-contamination risk distance between each abnormal monitoring aquaculture pool and other monitoring aquaculture pools is extracted, and the ratio is calculated with the preset safety monitoring distance, and then the reciprocal is taken to obtain the cross-contamination risk index between each abnormal monitoring aquaculture pool and other monitoring aquaculture pools; The cross-contamination risk index of each monitored aquaculture pond and each abnormal monitored aquaculture pond is obtained by statistics, and then compared, and the maximum cross-contamination risk index is selected as the cross-contamination risk index of each monitored aquaculture pond; The cross-contamination risk index of each monitored breeding pond is compared with the preset cross-contamination risk index threshold. If the cross-contamination risk index of a monitored breeding pond is greater than the cross-contamination risk index threshold, the monitored breeding pond is identified as a cross-contamination risk breeding pond.

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