A method and system for efficiently gathering and capturing fertilized eggs of yellowfin tuna

Abstract

The present invention discloses a method and system for efficiently gathering and capturing fertilized eggs of yellowfin tuna. The present invention aims at yellowfin tuna farming, constructs a three-dimensional visualized water area model, and sets monitoring points to monitor water characteristics, fish activities and fertilized egg distribution. Through multi-period monitoring, the flow field of the water body, the activities of the fish school and the distribution characteristics of the fertilized eggs are serialized, and the gray correlation method is used to analyze the change correlation therebetween to form water body-activity correlation data, and then a fertilized egg loss prevention system is constructed, the breeding area is monitored in real time and data is imported, and the distribution of fertilized eggs is predicted by comparing with the correlation data. At the same time, the breeding environment is regulated in real time and a gathering and capturing scheme is generated to optimize breeding management. Through the present invention, it is possible to effectively analyze and mine the correlation data of the distribution of fertilized eggs and information such as water bodies and fish activities, realize precise regulation of the water body, so that the number and distribution of fertilized eggs tend to be expected, and at the same time, the gathering and capturing efficiency of fertilized eggs is improved, and the breeding benefits are further improved.

Description

Technical Field

[0001] The present invention relates to the field of fish farm data analysis, and more specifically, to a method and system for efficiently gathering and capturing fertilized eggs of yellowfin tuna. Background Art

[0002] Yellowfin tuna is an important economic fish with extremely high commercial value worldwide. In the cage-based tuna farming process, the efficient collection of fertilized eggs is a key link in improving farming efficiency and economic benefits. Traditional fertilized egg collection methods have many shortcomings, such as easy escape of fertilized eggs, cannibalism by foreign organisms, and low collection efficiency. In addition, changes in water flow field characteristics, fish activity patterns, and the spatial distribution characteristics of fertilized eggs have a great correlation with the effect of fertilized egg aggregation, but existing methods fail to fully consider these factors, resulting in low aggregation efficiency. Summary of the invention

[0003] The invention overcomes the defects of the prior art and proposes a method and system for efficiently gathering and capturing fertilized eggs of yellowfin tuna.

[0004] The first aspect of the present invention provides a method for efficiently gathering and capturing fertilized eggs of yellowfin tuna, comprising:

[0005] Construct a three-dimensional visual water model based on the target breeding area of ​​yellowfin tuna;

[0006] Through the water area model, multiple monitoring points are set. During multiple breeding cycles, the water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points to obtain the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics;

[0007] Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data;

[0008] Construct a fertilized egg loss prevention system, monitor the target breeding area in real time within a real-time cycle, obtain real-time water characteristics and real-time fish activity characteristics, and import them into the fertilized egg loss prevention system, serialize the real-time water characteristics and real-time fish activity characteristics, compare them with water body-activity correlation data, and predict the distribution of fertilized eggs. By comparing the predicted distribution of fertilized eggs with the expected distribution, the water breeding environment can be adjusted in real time and a capture plan can be generated.

[0009] In this solution, a three-dimensional visual water model is constructed according to the target breeding area of ​​yellowfin tuna, specifically:

[0010] Obtain basic information on the target breeding area;

[0011] The basic information includes the area of ​​the breeding area, the area outline, monitoring points, and cage layout information;

[0012] A three-dimensional visual water area model is constructed based on basic information, and water area monitoring is visualized through the water area model.

[0013] In this scheme, the water area model is used to set multiple monitoring points. During multiple breeding cycles, the water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points to obtain water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics, specifically:

[0014] In the water model, multiple monitoring points are set to ensure that the distance between adjacent monitoring points is within the preset interval, and high-resolution cameras and underwater sensors are installed at each monitoring point;

[0015] During multiple breeding cycles, water characteristics are monitored through monitoring points, fish activities and fertilized eggs are identified and marked, and water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are obtained;

[0016] The water flow field characteristics include water flow velocity, water flow direction and water temperature information; the fish activity characteristics include the activity frequency, residence time and high-frequency activity range information of the fish at different monitoring points; the fertilized egg distribution characteristics include the location, quantity and distribution information of fertilized eggs under different breeding cycles.

[0017] In this solution, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are serialized based on the time sequence of multiple breeding cycles to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data, which is specifically:

[0018] Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are dimensionlessly processed and time-seriesized, and the first serialized data, the second serialized data and the third serialized data are obtained respectively;

[0019] The serialized data includes multiple segments of sequence data, each of which corresponds to a breeding cycle;

[0020] Performing a change analysis on each segment of sequence data in the third serialized data and the previous segment of sequence data, wherein the change analysis calculates the difference between the sequence data by using the Euclidean distance, and based on a preset difference value, selects the sequence data segments that meet the change characteristics and marks them as changed sequence data;

[0021] Taking the breeding cycle of the change sequence data as the change cycle, the sequence segments between the first breeding cycle and the change cycle are respectively intercepted from the first serialized data and the second serialized data to obtain the first intercepted serialized data and the second intercepted serialized data;

[0022] Based on the grey correlation method, the change sequence data is used as the reference sequence, and the first intercepted serialized data and the second intercepted serialized data are used as the comparison sequence. The absolute difference between the reference sequence and each comparison sequence is calculated to obtain the maximum difference and the minimum difference. According to the maximum difference and the minimum difference, and the resolution coefficient is set to 0.5, the grey correlation coefficient is calculated. Through the correlation coefficient and the preset threshold, the sequence segments with correlation relationship are screened out from the first intercepted serialized data and the second intercepted serialized data, and the water body-activity data associated with the change of fertilized egg distribution is obtained;

[0023] The change series data are associated with the water body-activity data to form water body-activity associated data.

[0024] In this scheme, the fertilized egg loss prevention system is constructed, and within a real-time cycle, the target breeding area is monitored in real time, and the real-time water body characteristics and real-time fish activity characteristics are obtained and imported into the fertilized egg loss prevention system, and the real-time water body characteristics and real-time fish activity characteristics are serialized and compared with the water body-activity correlation data, and the fertilized egg distribution is predicted, and the predicted distribution of the fertilized eggs is compared with the expected distribution, and the water body breeding environment is regulated in real time and the aggregation and capture scheme is generated, specifically:

[0025] In a real-time cycle, the target breeding area is monitored in real time and the real-time water characteristics and real-time fish activity characteristics are imported into the fertilized egg loss prevention system;

[0026] The real-time water body characteristics and the real-time fish school activity characteristics are serialized, and data similarity analysis based on Euclidean distance is performed with the water body-activity association data, and the water body characteristic data and the fish school activity characteristics with similarity greater than a preset similarity are extracted to obtain real-time association data. In the similarity analysis process, the change sequence data in the water body-activity association data is introduced to predict the distribution change of fertilized eggs, and the predicted distribution status of fertilized eggs is obtained;

[0027] Compare the predicted distribution of fertilized eggs with the expected distribution, and conduct water area regulation analysis based on water characteristics and fish activity dimensions based on real-time correlation data to regulate the aquaculture environment in real time;

[0028] The distribution status of fertilized eggs is predicted, the distribution density of fertilized eggs is calculated, and a capture plan is generated based on the corresponding distribution density.

[0029] In this solution, the fertilized egg loss prevention system includes a data acquisition module and a data analysis module. The data acquisition module is used to collect and preprocess relevant data, including water bodies, fish activities, and fertilized egg distribution data. The data analysis module is used to perform real-time analysis of the collected data, fertilized egg distribution prediction and water body regulation analysis, and save the analysis results to the system database.

[0030] In this solution, the fertilized egg loss prevention system is interconnected with the visual water area model, and the corresponding fertilized egg analysis results are displayed through the water area model.

[0031] The second aspect of the present invention further provides a system for efficiently gathering and capturing fertilized yellowfin tuna eggs, the system comprising: a memory and a processor, the memory comprising a program for efficiently gathering and capturing fertilized yellowfin tuna eggs, and the program for efficiently gathering and capturing fertilized yellowfin tuna eggs when executed by the processor implements the following steps:

[0032] Construct a three-dimensional visual water model based on the target breeding area of ​​yellowfin tuna;

[0033] Through the water area model, multiple monitoring points are set. During multiple breeding cycles, the water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points to obtain the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics;

[0034] Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data;

[0035] Construct a fertilized egg loss prevention system, monitor the target breeding area in real time within a real-time cycle, obtain real-time water characteristics and real-time fish activity characteristics, and import them into the fertilized egg loss prevention system, serialize the real-time water characteristics and real-time fish activity characteristics, compare them with water body-activity correlation data, and predict the distribution of fertilized eggs. By comparing the predicted distribution of fertilized eggs with the expected distribution, the water breeding environment can be adjusted in real time and a capture plan can be generated.

[0036] The third aspect of the present invention further provides a computer-readable storage medium, which includes a program for efficiently aggregating and capturing fertilized eggs of yellowfin tuna. When the program for efficiently aggregating and capturing fertilized eggs of yellowfin tuna is executed by a processor, the steps of the method for efficiently aggregating and capturing fertilized eggs of yellowfin tuna as described in any one of the above items are implemented.

[0037] The present invention discloses a method and system for efficiently gathering and capturing fertilized eggs of yellowfin tuna. The present invention aims at yellowfin tuna farming, constructs a three-dimensional visualized water area model, and sets monitoring points to monitor water characteristics, fish activities and fertilized egg distribution. Through multi-period monitoring, the flow field of the water body, the activities of the fish school and the distribution characteristics of the fertilized eggs are serialized, and the gray correlation method is used to analyze the change correlation therebetween to form water body-activity correlation data, and then a fertilized egg loss prevention system is constructed, the breeding area is monitored in real time and data is imported, and the distribution of fertilized eggs is predicted by comparing with the correlation data. At the same time, the breeding environment is regulated in real time and a gathering and capturing scheme is generated to optimize breeding management. Through the present invention, it is possible to effectively analyze and mine the correlation data of the distribution of fertilized eggs and information such as water bodies and fish activities, realize precise regulation of the water body, so that the number and distribution of fertilized eggs tend to be expected, and at the same time, the gathering and capturing efficiency of fertilized eggs is improved, and the breeding benefits are further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flow chart showing a method for efficiently gathering and capturing fertilized eggs of yellowfin tuna according to the present invention is shown;

[0039] Figure 2 A block diagram of a system for efficiently gathering and capturing fertilized eggs of yellowfin tuna according to the present invention is shown. DETAILED DESCRIPTION

[0040] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0042] Figure 1 The present invention shows a flow chart of a method for efficiently gathering and capturing fertilized eggs of yellowfin tuna.

[0043] like Figure 1 As shown, the first aspect of the present invention provides a method for efficiently gathering and capturing fertilized eggs of yellowfin tuna, comprising:

[0044] S102, constructing a three-dimensional visual water model according to the target breeding area of ​​yellowfin tuna;

[0045] S104, setting a plurality of monitoring points through the water area model, monitoring water characteristics, fish activity characteristics and fertilized egg distribution through the monitoring points during a plurality of breeding cycles, and obtaining water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics;

[0046] S106, based on the time sequence of multiple breeding cycles, the water flow field characteristics, the fish activity characteristics and the fertilized egg distribution characteristics are serialized to obtain first serialized data, second serialized data and third serialized data, and the change correlation between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the correlation relationship is marked to obtain water body-activity correlation data;

[0047] S108, constructing a fertilized egg loss prevention system, monitoring the target breeding area in real time within a real-time cycle, obtaining real-time water characteristics and real-time fish activity characteristics and importing them into the fertilized egg loss prevention system, serializing the real-time water characteristics and real-time fish activity characteristics and comparing them with water body-activity correlation data and predicting the distribution of fertilized eggs, comparing the predicted distribution of fertilized eggs with the expected distribution, and regulating the water breeding environment in real time and generating a capture plan.

[0048] According to an embodiment of the present invention, the three-dimensional visualized water area model is constructed according to the target breeding area of ​​yellowfin tuna, specifically:

[0049] Obtain basic information on the target breeding area;

[0050] The basic information includes the area of ​​the breeding area, the area outline, monitoring points, and cage layout information;

[0051] A three-dimensional visual water area model is constructed based on basic information, and water area monitoring is visualized through the water area model.

[0052] It should be noted that the monitoring points are artificially set parameters, including quantity and location. The water area model is used for data visualization, which can visualize the activities of fish schools and the distribution of fertilized eggs, making it easier for aquaculture personnel to regulate the water area.

[0053] According to an embodiment of the present invention, a plurality of monitoring points are set through the water area model, and water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points during multiple breeding cycles to obtain water body flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics, specifically:

[0054] In the water model, multiple monitoring points are set to ensure that the distance between adjacent monitoring points is within the preset interval, and high-resolution cameras and underwater sensors are installed at each monitoring point;

[0055] During multiple breeding cycles, water characteristics are monitored through monitoring points, fish activities and fertilized eggs are identified and marked, and water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are obtained;

[0056] The water flow field characteristics include water flow velocity, water flow direction and water temperature information; the fish activity characteristics include the activity frequency, residence time and high-frequency activity range information of the fish at different monitoring points; the fertilized egg distribution characteristics include the location, quantity and distribution information of fertilized eggs under different breeding cycles.

[0057] It should be noted that the multiple breeding cycles are analysis cycles within a preset historical time period. The breeding cycle can be set to the spawning cycle of yellowfin tuna. In each breeding cycle, the correlation between the distribution characteristics of fertilized eggs and the characteristics of the water body, fish activities, etc. in the corresponding period can be analyzed.

[0058] According to an embodiment of the present invention, based on the time sequence of multiple breeding cycles, the water flow field characteristics, the fish activity characteristics and the fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data, which is specifically:

[0059] Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are dimensionlessly processed and time-seriesized, and the first serialized data, the second serialized data and the third serialized data are obtained respectively;

[0060] The serialized data includes multiple segments of sequence data, each of which corresponds to a breeding cycle;

[0061] Performing a change analysis on each segment of sequence data in the third serialized data and the previous segment of sequence data, wherein the change analysis calculates the difference between the sequence data by using the Euclidean distance, and based on a preset difference value, selects the sequence data segments that meet the change characteristics and marks them as changed sequence data;

[0062] Taking the breeding cycle of the change sequence data as the change cycle, the sequence segments between the first breeding cycle and the change cycle are respectively intercepted from the first serialized data and the second serialized data to obtain the first intercepted serialized data and the second intercepted serialized data;

[0063] Based on the grey correlation method, the change sequence data is used as the reference sequence, and the first intercepted serialized data and the second intercepted serialized data are used as the comparison sequence. The absolute difference between the reference sequence and each comparison sequence is calculated to obtain the maximum difference and the minimum difference. According to the maximum difference and the minimum difference, and the resolution coefficient is set to 0.5, the grey correlation coefficient is calculated. Through the correlation coefficient and the preset threshold, the sequence segments with correlation relationship are screened out from the first intercepted serialized data and the second intercepted serialized data, and the water body-activity data associated with the change of fertilized egg distribution is obtained;

[0064] The change series data are associated with the water body-activity data to form water body-activity associated data.

[0065] It should be noted that the first, second, and third serialized data all include multiple segments of sequence data. The change analysis calculates the difference between the sequence data through the Euclidean distance, and screens out the sequence data segments that meet the change characteristics based on the preset difference value. Specifically, the Euclidean distance is used to measure the degree of change of the sequence data of the two, and the preset difference value is introduced to compare with the actual Euclidean distance to screen out the sequence data segments that exceed a certain degree of change, that is, the change sequence data. The change sequence data may include multiple, and the data reflects the distribution change of fertilized eggs, and one change sequence data corresponds to a breeding cycle. Subsequently, the change sequence data is used as a reference sequence, and the sequence data on water bodies and fish activities before the change time point is intercepted and used as a comparison sequence for correlation analysis to screen out the influencing factors associated with the change sequence data (i.e., the fertilized egg distribution change data).

[0066] The first serialized data, the second serialized data, and the third serialized data correspond to the water flow field characteristics, the fish school activity characteristics, and the fertilized egg distribution characteristics data, respectively.

[0067] The first intercepted serialized data and the second intercepted serialized data both include multiple data sequences. When calculating the correlation coefficient with the reference sequence, the correlation coefficient of each intercepted sequence can be calculated with the reference sequence, that is, the sequence can be split for correlation analysis, and multiple correlation values ​​can be obtained respectively. The sequence segments with correlation are further compared and judged one by one with the preset threshold value, and the sequence segments with correlation are screened to obtain water body-activity correlation data. The water body-activity data is the water body characteristics and the fish school activity characteristics data, and the water body-activity data is correlated with the corresponding change sequence data.

[0068] It should be explained in detail that, in the process of screening the change sequence data, there may be multiple change data segments that meet the expectations, and they need to be associated with the corresponding water body-activity data respectively. For example, in a certain breeding cycle, the corresponding sequence data segment in the third serialized data is judged to be a sequence data segment that meets the change characteristics, so it is marked as the change sequence data. Further, based on this change sequence data, the gray correlation method is analyzed to find the water body-activity data (i.e., influencing factors and parameters) related to this change sequence data. In another breeding cycle, another sequence segment corresponding to the third serialized data also has a sequence data segment that meets the change characteristics, so it is necessary to repeat the association analysis step to obtain the water body-activity data, and associate the change sequence data with the water body-activity data. Therefore, the water body-activity association data includes multiple association records, each of which includes the change sequence data and the water body-activity data. In the subsequent real-time cycle analysis and prediction, the water body-activity association data can be used to determine the current distribution of fertilized eggs and predict the distribution change.

[0069] It is worth mentioning that in the process of breeding yellowfin tuna, the distribution, quantity and fertilization rate of its fertilized eggs are closely related to the water environment and fish activities, but the correlation analysis process is relatively complicated. Traditional technology lacks effective mining of the correlation between the two and the prediction of the distribution of fertilized eggs, making it difficult to improve the efficiency of fertilized egg capture and to achieve precise regulation of aquaculture waters.

[0070] Based on this, the present invention introduces the Grey Relation Analysis (GRA), which is a multi-factor statistical analysis method. In the present invention, the Grey Relation Analysis (GRA) is used to mine the correlation data of the fertilized egg distribution and predict the distribution of the fertilized eggs. In the present invention, the water body and activity monitoring data under the historical breeding cycle are serialized, and the correlation analysis between the fertilized egg distribution and the water body and activity data is performed in a serialized form, which can effectively mine the change relationship and correlation relationship between the two, and extract the monitoring data with correlation relationship, and then carry out targeted water area regulation and breeding optimization.

[0071] According to an embodiment of the present invention, the fertilized egg loss prevention system is constructed, and within a real-time cycle, the target breeding area is monitored in real time, and the real-time water body characteristics and real-time fish school activity characteristics are obtained and imported into the fertilized egg loss prevention system, and the real-time water body characteristics and real-time fish school activity characteristics are serialized and compared with the water body-activity correlation data, and the fertilized egg distribution is predicted, and the predicted distribution of the fertilized eggs is compared with the expected distribution, and the water body breeding environment is regulated in real time and the aggregation and capture scheme is generated, specifically:

[0072] In a real-time cycle, the target breeding area is monitored in real time and the real-time water characteristics and real-time fish activity characteristics are imported into the fertilized egg loss prevention system;

[0073] The real-time water body characteristics and the real-time fish school activity characteristics are serialized, and data similarity analysis based on Euclidean distance is performed with the water body-activity association data, and the water body characteristic data and the fish school activity characteristics with similarity greater than a preset similarity are extracted to obtain real-time association data. In the similarity analysis process, the change sequence data in the water body-activity association data is introduced to predict the distribution change of fertilized eggs, and the predicted distribution status of fertilized eggs is obtained;

[0074] Compare the predicted distribution of fertilized eggs with the expected distribution, and conduct water area regulation analysis based on water characteristics and fish activity dimensions based on real-time correlation data to regulate the aquaculture environment in real time;

[0075] The distribution status of fertilized eggs is predicted, the distribution density of fertilized eggs is calculated, and a capture plan is generated based on the corresponding distribution density.

[0076] It should be noted that in the process of serializing the real-time water characteristics and the real-time fish activity characteristics, and performing data similarity analysis based on Euclidean distance with the water-activity association data, and extracting the water characteristics data and the fish activity characteristics with a similarity greater than a preset similarity, the real-time water characteristics and the real-time fish activity characteristics are specifically compared with each record in the water-activity association data, and the compared data is the water-activity data, and then the influencing factors and parameters associated with the distribution change are mined, that is, the real-time association data, and the corresponding water body and fish school regulation is carried out through the real-time association data. At the same time, based on the real-time monitoring data and the most similar records, the change sequence data of the corresponding records is obtained. Based on the change sequence data of the most similar records, the predicted distribution state of the fertilized eggs in the current monitoring period can be effectively judged, and accurate aggregation and capture schemes, water area regulation and other analyses can be carried out. In addition, through the predicted distribution, the aggregation and capture equipment and aggregation and capture conditions can be effectively prepared in advance to improve the efficiency of aquaculture regulation.

[0077] Through the present invention, it is possible to effectively analyze and mine the correlation data between the distribution of fertilized eggs and information such as water bodies and fish activities, achieve precise regulation of water bodies, make the number and distribution of fertilized eggs tend to be expected, and at the same time, improve the efficiency of fertilized egg aggregation and capture, and further improve breeding benefits.

[0078] It should be noted that the real-time correlation data includes water body correlation data and fish activity correlation data, which are extracted from real-time water body characteristics and real-time fish activity characteristics data. Real-time regulation of the aquaculture environment is specifically based on the expected distribution to make the yellowfin tuna farming situation tend to be expected. The regulation includes water temperature regulation, salinity regulation, water flow state regulation and other means. There may be many types of aggregation schemes, such as trawl method, scoop net method, water flow method, etc. Based on different fertilized egg distribution densities, different schemes or combined schemes can be adopted, which are specifically determined by the distribution status and density, and at the same time, the purpose of effectively improving the aggregation efficiency is achieved.

[0079] According to an embodiment of the present invention, the fertilized egg loss prevention system includes a data acquisition module and a data analysis module. The data acquisition module is used to collect and pre-process relevant data, and the relevant data includes water bodies, fish activities, and fertilized egg distribution data. The data analysis module is used to perform real-time analysis of the collected data, fertilized egg distribution prediction and water body regulation analysis, and save the analysis results to the system database.

[0080] According to an embodiment of the present invention, the fertilized egg loss prevention system is interconnected with the visualized water area model, and the corresponding fertilized egg analysis results are displayed through the water area model.

[0081] It should be noted that data can be transmitted between the fertilized egg loss prevention system and the visualized water area model to achieve visualization of the analysis results.

[0082] Figure 2 A block diagram of a system for efficiently gathering and capturing fertilized eggs of yellowfin tuna according to the present invention is shown.

[0083] The second aspect of the present invention further provides a yellowfin tuna fertilized egg efficient aggregation capture system 2, the system comprising: a memory 21, a processor 22, the memory 21 comprising a yellowfin tuna fertilized egg efficient aggregation capture program, the yellowfin tuna fertilized egg efficient aggregation capture program when executed by the processor 22 to implement the following steps:

[0084] Construct a three-dimensional visual water model based on the target breeding area of ​​yellowfin tuna;

[0085] Through the water area model, multiple monitoring points are set. During multiple breeding cycles, the water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points to obtain the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics;

[0086] Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data;

[0087] Construct a fertilized egg loss prevention system, monitor the target breeding area in real time within a real-time cycle, obtain real-time water characteristics and real-time fish activity characteristics, and import them into the fertilized egg loss prevention system, serialize the real-time water characteristics and real-time fish activity characteristics, compare them with water body-activity correlation data, and predict the distribution of fertilized eggs. By comparing the predicted distribution of fertilized eggs with the expected distribution, the water breeding environment can be adjusted in real time and a capture plan can be generated.

[0088] According to an embodiment of the present invention, the three-dimensional visualized water area model is constructed according to the target breeding area of ​​yellowfin tuna, specifically:

[0089] Obtain basic information on the target breeding area;

[0090] The basic information includes the area of ​​the breeding area, the area outline, monitoring points, and cage layout information;

[0091] A three-dimensional visual water area model is constructed based on basic information, and water area monitoring is visualized through the water area model.

[0092] It should be noted that the monitoring points are artificially set parameters, including quantity and location. The water area model is used for data visualization, which can visualize the activities of fish schools and the distribution of fertilized eggs, making it easier for aquaculture personnel to regulate the water area.

[0093] According to an embodiment of the present invention, a plurality of monitoring points are set through the water area model, and water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points during multiple breeding cycles to obtain water body flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics, specifically:

[0094] In the water model, multiple monitoring points are set to ensure that the distance between adjacent monitoring points is within the preset interval, and high-resolution cameras and underwater sensors are installed at each monitoring point;

[0095] During multiple breeding cycles, water characteristics are monitored through monitoring points, fish activities and fertilized eggs are identified and marked, and water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are obtained;

[0096] The water flow field characteristics include water flow velocity, water flow direction and water temperature information; the fish activity characteristics include the activity frequency, residence time and high-frequency activity range information of the fish at different monitoring points; the fertilized egg distribution characteristics include the location, quantity and distribution information of fertilized eggs under different breeding cycles.

[0097] It should be noted that the multiple breeding cycles are analysis cycles within a preset historical time period. The breeding cycle can be set to the spawning cycle of yellowfin tuna. In each breeding cycle, the correlation between the distribution characteristics of fertilized eggs and the characteristics of the water body, fish activities, etc. in the corresponding period can be analyzed.

[0098] According to an embodiment of the present invention, based on the time sequence of multiple breeding cycles, the water flow field characteristics, the fish activity characteristics and the fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data, which is specifically:

[0099] Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are dimensionlessly processed and time-seriesized, and the first serialized data, the second serialized data and the third serialized data are obtained respectively;

[0100] The serialized data includes multiple segments of sequence data, each of which corresponds to a breeding cycle;

[0101] Performing a change analysis on each segment of sequence data in the third serialized data and the previous segment of sequence data, wherein the change analysis calculates the difference between the sequence data by using the Euclidean distance, and based on a preset difference value, selects the sequence data segments that meet the change characteristics and marks them as changed sequence data;

[0102] Taking the breeding cycle of the change sequence data as the change cycle, the sequence segments between the first breeding cycle and the change cycle are respectively intercepted from the first serialized data and the second serialized data to obtain the first intercepted serialized data and the second intercepted serialized data;

[0103] Based on the grey correlation method, the change sequence data is used as the reference sequence, and the first intercepted serialized data and the second intercepted serialized data are used as the comparison sequence. The absolute difference between the reference sequence and each comparison sequence is calculated to obtain the maximum difference and the minimum difference. According to the maximum difference and the minimum difference, and the resolution coefficient is set to 0.5, the grey correlation coefficient is calculated. Through the correlation coefficient and the preset threshold, the sequence segments with correlation relationship are screened out from the first intercepted serialized data and the second intercepted serialized data, and the water body-activity data associated with the change of fertilized egg distribution is obtained;

[0104] The change series data are associated with the water body-activity data to form water body-activity associated data.

[0105] It should be noted that the first, second, and third serialized data all include multiple segments of sequence data. The change analysis calculates the difference between the sequence data through the Euclidean distance, and screens out the sequence data segments that meet the change characteristics based on the preset difference value. Specifically, the Euclidean distance is used to measure the degree of change of the sequence data of the two, and the preset difference value is introduced to compare with the actual Euclidean distance to screen out the sequence data segments that exceed a certain degree of change, that is, the change sequence data. The change sequence data may include multiple, and the data reflects the distribution change of fertilized eggs, and one change sequence data corresponds to a breeding cycle. Subsequently, the change sequence data is used as a reference sequence, and the sequence data on water bodies and fish activities before the change time point is intercepted and used as a comparison sequence for correlation analysis to screen out the influencing factors associated with the change sequence data (i.e., the fertilized egg distribution change data).

[0106] The first serialized data, the second serialized data, and the third serialized data correspond to the water flow field characteristics, the fish school activity characteristics, and the fertilized egg distribution characteristics data, respectively.

[0107] The first intercepted serialized data and the second intercepted serialized data both include multiple data sequences. When calculating the correlation coefficient with the reference sequence, the correlation coefficient of each intercepted sequence can be calculated with the reference sequence, that is, the sequence can be split for correlation analysis, and multiple correlation values ​​can be obtained respectively. The sequence segments with correlation are further compared and judged one by one with the preset threshold value, and the sequence segments with correlation are screened to obtain water body-activity correlation data. The water body-activity data is the water body characteristics and the fish school activity characteristics data, and the water body-activity data is correlated with the corresponding change sequence data.

[0108] It should be explained in detail that, in the process of screening the change sequence data, there may be multiple change data segments that meet the expectations, and they need to be associated with the corresponding water body-activity data respectively. For example, in a certain breeding cycle, the corresponding sequence data segment in the third serialized data is judged to be a sequence data segment that meets the change characteristics, so it is marked as the change sequence data. Further, based on this change sequence data, the gray correlation method is analyzed to find the water body-activity data (i.e., influencing factors and parameters) related to this change sequence data. In another breeding cycle, another sequence segment corresponding to the third serialized data also has a sequence data segment that meets the change characteristics, so it is necessary to repeat the association analysis step to obtain the water body-activity data, and associate the change sequence data with the water body-activity data. Therefore, the water body-activity association data includes multiple association records, each of which includes the change sequence data and the water body-activity data. In the subsequent real-time cycle analysis and prediction, the water body-activity association data can be used to determine the current distribution of fertilized eggs and predict the distribution change.

[0109] It is worth mentioning that in the process of breeding yellowfin tuna, the distribution, quantity and fertilization rate of its fertilized eggs are closely related to the water environment and fish activities, but the correlation analysis process is relatively complicated. Traditional technology lacks effective mining of the correlation between the two and the prediction of the distribution of fertilized eggs, making it difficult to improve the efficiency of fertilized egg capture and to achieve precise regulation of aquaculture waters.

[0110] Based on this, the present invention introduces Grey Relation Analysis (GRA), which is a multi-factor statistical analysis method. In the present invention, it is used to mine the correlation data of fertilized egg distribution and predict the distribution of fertilized eggs. In the present invention, the water body and activity monitoring data under the historical breeding cycle are serialized, and the correlation analysis between the fertilized egg distribution and the water body and activity data is performed in a serialized form, which can effectively mine the change relationship and correlation relationship between the two, and extract the monitoring data with correlation relationship, and then carry out targeted water area regulation and breeding optimization.

[0111] According to an embodiment of the present invention, the fertilized egg loss prevention system is constructed, and within a real-time cycle, the target breeding area is monitored in real time, and the real-time water body characteristics and real-time fish school activity characteristics are obtained and imported into the fertilized egg loss prevention system, and the real-time water body characteristics and real-time fish school activity characteristics are serialized and compared with the water body-activity correlation data, and the fertilized egg distribution is predicted, and the predicted distribution of the fertilized eggs is compared with the expected distribution, and the water body breeding environment is regulated in real time and the aggregation and capture scheme is generated, specifically:

[0112] In a real-time cycle, the target breeding area is monitored in real time and the real-time water characteristics and real-time fish activity characteristics are imported into the fertilized egg loss prevention system;

[0113] The real-time water body characteristics and the real-time fish school activity characteristics are serialized, and data similarity analysis based on Euclidean distance is performed with the water body-activity association data, and the water body characteristic data and the fish school activity characteristics with similarity greater than a preset similarity are extracted to obtain real-time association data. In the similarity analysis process, the change sequence data in the water body-activity association data is introduced to predict the distribution change of fertilized eggs, and the predicted distribution status of fertilized eggs is obtained;

[0114] Compare the predicted distribution of fertilized eggs with the expected distribution, and conduct water area regulation analysis based on water characteristics and fish activity dimensions based on real-time correlation data to regulate the aquaculture environment in real time;

[0115] The distribution status of fertilized eggs is predicted, the distribution density of fertilized eggs is calculated, and a capture plan is generated based on the corresponding distribution density.

[0116] It should be noted that in the process of serializing the real-time water characteristics and the real-time fish activity characteristics, and performing data similarity analysis based on Euclidean distance with the water-activity association data, and extracting the water characteristics data and the fish activity characteristics with a similarity greater than a preset similarity, the real-time water characteristics and the real-time fish activity characteristics are specifically compared with each record in the water-activity association data, and the compared data is the water-activity data, and then the influencing factors and parameters associated with the distribution change are mined, that is, the real-time association data, and the corresponding water body and fish school regulation is carried out through the real-time association data. At the same time, based on the real-time monitoring data and the most similar records, the change sequence data of the corresponding records is obtained. Based on the change sequence data of the most similar records, the predicted distribution state of the fertilized eggs in the current monitoring period can be effectively judged, and accurate aggregation and capture schemes, water area regulation and other analyses can be carried out. In addition, through the predicted distribution, the aggregation and capture equipment and aggregation and capture conditions can be effectively prepared in advance to improve the efficiency of aquaculture regulation.

[0117] Through the present invention, it is possible to effectively analyze and mine the correlation data between the distribution of fertilized eggs and information such as water bodies and fish activities, achieve precise regulation of water bodies, make the number and distribution of fertilized eggs tend to be expected, and at the same time, improve the efficiency of fertilized egg aggregation and capture, and further improve breeding benefits.

[0118] It should be noted that the real-time correlation data includes water body correlation data and fish activity correlation data, which are extracted from real-time water body characteristics and real-time fish activity characteristics data. Real-time regulation of the aquaculture environment is specifically based on the expected distribution to make the yellowfin tuna farming situation tend to be expected. The regulation includes water temperature regulation, salinity regulation, water flow state regulation and other means. There may be many types of aggregation schemes, such as trawl method, scoop net method, water flow method, etc. Based on different fertilized egg distribution densities, different schemes or combined schemes can be adopted, which are specifically determined by the distribution status and density, and at the same time, the purpose of effectively improving the aggregation efficiency is achieved.

[0119] According to an embodiment of the present invention, the fertilized egg loss prevention system includes a data acquisition module and a data analysis module. The data acquisition module is used to collect and pre-process relevant data, and the relevant data includes water bodies, fish activities, and fertilized egg distribution data. The data analysis module is used to perform real-time analysis of the collected data, fertilized egg distribution prediction and water body regulation analysis, and save the analysis results to the system database.

[0120] According to an embodiment of the present invention, the fertilized egg loss prevention system is interconnected with the visualized water area model, and the corresponding fertilized egg analysis results are displayed through the water area model.

[0121] It should be noted that data can be transmitted between the fertilized egg loss prevention system and the visualized water area model to achieve visualization of the analysis results.

[0122] The third aspect of the present invention further provides a computer-readable storage medium, which includes a program for efficiently aggregating and capturing fertilized eggs of yellowfin tuna. When the program for efficiently aggregating and capturing fertilized eggs of yellowfin tuna is executed by a processor, the steps of the method for efficiently aggregating and capturing fertilized eggs of yellowfin tuna as described in any one of the above items are implemented.

[0123] The present invention discloses a method and system for efficiently gathering and capturing fertilized eggs of yellowfin tuna. The present invention aims at yellowfin tuna farming, constructs a three-dimensional visualized water area model, and sets monitoring points to monitor water characteristics, fish activities and fertilized egg distribution. Through multi-period monitoring, the flow field of the water body, the activities of the fish school and the distribution characteristics of the fertilized eggs are serialized, and the gray correlation method is used to analyze the change correlation therebetween to form water body-activity correlation data, and then a fertilized egg loss prevention system is constructed, the breeding area is monitored in real time and data is imported, and the distribution of fertilized eggs is predicted by comparing with the correlation data. At the same time, the breeding environment is regulated in real time and a gathering and capturing scheme is generated to optimize breeding management. Through the present invention, it is possible to effectively analyze and mine the correlation data of the distribution of fertilized eggs and information such as water bodies and fish activities, realize precise regulation of the water body, so that the number and distribution of fertilized eggs tend to be expected, and at the same time, the gathering and capturing efficiency of fertilized eggs is improved, and the breeding benefits are further improved.

[0124] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0125] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0126] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0127] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0128] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0129] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for efficiently gathering and capturing fertilized eggs of yellowfin tuna, characterized in that: include: Construct a three-dimensional visual water model based on the target breeding area of ​​yellowfin tuna; Through the water area model, multiple monitoring points are set. During multiple breeding cycles, the water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points to obtain the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics; Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data; Construct a fertilized egg loss prevention system. Within a real-time cycle, monitor the target breeding area in real time and obtain real-time water characteristics and real-time fish activity characteristics and import them into the fertilized egg loss prevention system. Serialize the real-time water characteristics and real-time fish activity characteristics and compare them with water-activity correlation data to predict the distribution of fertilized eggs. By comparing the predicted distribution of fertilized eggs with the expected distribution, adjust the water breeding environment in real time and generate a capture plan; The three-dimensional visual water area model is constructed according to the target breeding area of ​​yellowfin tuna, specifically: Obtain basic information on the target breeding area; The basic information includes the area of ​​the breeding area, the area outline, monitoring points, and cage layout information; Build a three-dimensional visual water area model based on basic information, and visualize water area monitoring through the water area model; Among them, the water area model is used to set multiple monitoring points, and in multiple breeding cycles, the water body characteristics, fish activity characteristics and fertilized egg distribution are monitored through the monitoring points to obtain water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics, specifically: In the water model, multiple monitoring points are set to ensure that the distance between adjacent monitoring points is within the preset interval, and high-resolution cameras and underwater sensors are installed at each monitoring point; During multiple breeding cycles, water characteristics are monitored through monitoring points, fish activities and fertilized eggs are identified and marked, and water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are obtained; The water flow field characteristics include water flow velocity, water flow direction and water temperature information; the fish school activity characteristics include the activity frequency, residence time and high-frequency activity range information of the fish school at different monitoring points; the fertilized egg distribution characteristics include the location, quantity and distribution information of the fertilized eggs under different breeding cycles; Among them, based on the time sequence of multiple breeding cycles, the water flow field characteristics, the fish activity characteristics and the fertilized egg distribution characteristics are serialized to obtain the first serialized data, the second serialized data and the third serialized data. The change association between the first serialized data, the second serialized data and the third serialized data is analyzed by the grey correlation method, and the data with the association relationship is marked to obtain the water body-activity association data, which is specifically: Based on the time sequence of multiple breeding cycles, the water flow field characteristics, fish activity characteristics and fertilized egg distribution characteristics are dimensionlessly processed and time-seriesized, and the first serialized data, the second serialized data and the third serialized data are obtained respectively; The serialized data includes multiple segments of sequence data, each of which corresponds to a breeding cycle; Performing a change analysis on each segment of sequence data in the third serialized data and the previous segment of sequence data, wherein the change analysis calculates the difference between the sequence data by using the Euclidean distance, and based on a preset difference value, selects the sequence data segments that meet the change characteristics and marks them as changed sequence data; Taking the breeding cycle of the change sequence data as the change cycle, the sequence segments between the first breeding cycle and the change cycle are respectively intercepted from the first serialized data and the second serialized data to obtain the first intercepted serialized data and the second intercepted serialized data; Based on the grey correlation method, the change sequence data is used as the reference sequence, and the first intercepted serialized data and the second intercepted serialized data are used as the comparison sequence. The absolute difference between the reference sequence and each comparison sequence is calculated to obtain the maximum difference and the minimum difference. According to the maximum difference and the minimum difference, and the resolution coefficient is set to 0.5, the grey correlation coefficient is calculated. Through the correlation coefficient and the preset threshold, the sequence segments with correlation relationship are screened out from the first intercepted serialized data and the second intercepted serialized data, and the water body-activity data associated with the change of fertilized egg distribution is obtained; Correlating the change series data with the water body-activity data to form water body-activity correlation data; The construction of the fertilized egg loss prevention system monitors the target breeding area in real time within a real-time cycle, obtains real-time water characteristics and real-time fish activity characteristics, and imports them into the fertilized egg loss prevention system. The real-time water characteristics and real-time fish activity characteristics are serialized and compared with water-activity correlation data to predict the distribution of fertilized eggs. The predicted distribution of fertilized eggs is compared with the expected distribution, and the water breeding environment is regulated in real time to generate a capture scheme. Specifically, In a real-time cycle, the target breeding area is monitored in real time and the real-time water characteristics and real-time fish activity characteristics are imported into the fertilized egg loss prevention system; The real-time water body characteristics and the real-time fish school activity characteristics are serialized, and data similarity analysis based on Euclidean distance is performed with the water body-activity association data, and the water body characteristic data and the fish school activity characteristics with similarity greater than a preset similarity are extracted to obtain real-time association data. In the similarity analysis process, the change sequence data in the water body-activity association data is introduced to predict the distribution change of fertilized eggs, and the predicted distribution status of fertilized eggs is obtained; Compare the predicted distribution of fertilized eggs with the expected distribution, and conduct water area regulation analysis based on water characteristics and fish activity dimensions based on real-time correlation data to regulate the aquaculture environment in real time; The distribution status of fertilized eggs is predicted, the distribution density of fertilized eggs is calculated, and a capture plan is generated based on the corresponding distribution density.

2. The method for efficiently gathering and capturing fertilized eggs of yellowfin tuna according to claim 1, characterized in that: The fertilized egg loss prevention system includes a data acquisition module and a data analysis module. The data acquisition module is used to collect and preprocess relevant data, and the relevant data includes water body, fish activity, and fertilized egg distribution data. The data analysis module is used to perform real-time analysis of the collected data, fertilized egg distribution prediction and water body regulation analysis, and save the analysis results to the system database.

3. The method for efficiently gathering and capturing fertilized eggs of yellowfin tuna according to claim 1, characterized in that: The fertilized egg loss prevention system is interconnected with the visualized water area model, and the corresponding fertilized egg analysis results are displayed through the water area model.

4. A highly efficient yellowfin tuna fertilized egg gathering and capturing system, characterized in that: The system comprises: a memory and a processor, wherein the memory comprises a yellowfin tuna fertilized egg efficient aggregation and capture program, and when the yellowfin tuna fertilized egg efficient aggregation and capture program is executed by the processor, the steps of the yellowfin tuna fertilized egg efficient aggregation and capture method according to any one of claims 1 to 3 are implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a program for efficiently aggregating and capturing fertilized yellowfin tuna eggs. When the program for efficiently aggregating and capturing fertilized yellowfin tuna eggs is executed by a processor, the steps of the method for efficiently aggregating and capturing fertilized yellowfin tuna eggs according to any one of claims 1 to 3 are implemented.

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