A method, system and medium for efficient breeding of penaeus monodon larvae

Abstract

The present application relates to a kind of high-efficiency culture method, system and medium of Penaeus monodon fry, belong to Penaeus monodon aquaculture technical field, the present application is formulated relevant prevention and cure scheme according to the outbreak disease data information of each Penaeus monodon aquaculture area, and relevant prevention and cure scheme is transmitted to remote Penaeus monodon culture monitoring terminal.The present application is evaluated by environmental factor data in aquaculture area, so as to obtain the weight information of corresponding environmental factor data occurrence disease, so as to determine whether occurrence disease according to the weight information of occurrence disease and the stress behavior of Penaeus monodon within preset time, when determining that disease has occurred, relevant prevention and cure scheme is formulated according to disease type.By the method, various Penaeus monodon diseases can be identified, and the prevention and cure scheme of Penaeus monodon generated is more reasonable, and the survival rate of Penaeus monodon in the process of cultivation is further improved.

Description

Technical Field

[0001] This invention relates to the field of tiger prawn farming technology, and in particular to a method, system and medium for the efficient cultivation of tiger prawn larvae. Background Technology

[0002] The tiger prawn (Litopenaeus monodon) is characterized by rapid growth, large size, wide salinity tolerance, and strong disease resistance. In recent years, the area cultivated for tiger prawns has been increasing year by year, leading to a surge in demand for seedlings, making it crucial to improve the survival rate of seedlings. However, tiger prawns are susceptible to various diseases during cultivation, each corresponding to different environmental factors. These environmental factors vary; for example, some diseases require a temperature of 25 degrees Celsius and a corresponding ion concentration of 2 mol / L, while others require 30 degrees Celsius and a corresponding ion concentration of 3 mol / L. Current technologies are ineffective in monitoring and controlling various tiger prawn diseases, failing to detect them promptly, often resulting in irreversible damage and significant economic losses for farmers. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides a method, system and medium for the efficient cultivation of tiger prawn larvae.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: The first aspect of this invention provides a method for the efficient cultivation of tiger prawn larvae, comprising the following steps: By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the environmental factor data related to tiger prawn cultivation, the environmental factor data of each tiger prawn farming area within a preset time period is obtained. By acquiring environmental factor data related to diseases of tiger prawns through big data, the environmental factor data and environmental factor data related to tiger prawn diseases in each tiger prawn farming area within a preset time period are evaluated to obtain evaluation results. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data from the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Based on the disease outbreak data of each tiger prawn farming area, relevant prevention and control plans are formulated and transmitted to the remote tiger prawn farming monitoring terminal.

[0005] Furthermore, in a preferred embodiment of the present invention, environmental factor data related to the cultivation of tiger prawns is obtained through big data, and data is collected for each tiger prawn farming area based on the environmental factor data related to tiger prawn cultivation to obtain environmental factor data for each tiger prawn farming area within a preset time period. Specifically, this includes the following steps: By setting keyword information for the cultivation of tiger prawns and constructing search tags based on the keyword information, and then searching through big data networks according to the search tags, environmental factor data related to tiger prawn cultivation can be obtained. Wireless monitoring nodes were set up in each Penaeus monodon farming area based on environmental factor data related to Penaeus monodon farming, and a wireless monitoring network was constructed based on the wireless monitoring nodes. Environmental factor data for each tiger prawn farming area were acquired through a wireless monitoring network, and outlier data were filtered out from the environmental factor data for each tiger prawn farming area. Outlier data in the environmental factor data of each tiger prawn farming area are removed, and environmental factor data for each tiger prawn farming area within a preset time period are generated based on the environmental factor data of each tiger prawn farming area.

[0006] Furthermore, in a preferred embodiment of the present invention, environmental factor data related to diseases of Penaeus monodon are obtained through big data analysis. The environmental factor data and the environmental factor data related to diseases of Penaeus monodon in each Penaeus monodon farming area within a preset time period are evaluated to obtain evaluation results. Specifically, this includes the following steps: By acquiring environmental factor data related to diseases of tiger prawns through big data, and determining the hierarchical structure system of the evaluation, the hierarchical structure system is divided into the target layer, the criterion layer and the scheme layer. Environmental factor data related to diseases of tiger prawns are input into the criteria layer, and environmental factor data of each tiger prawn farming area within a preset time period are input into the scheme layer. Several tiger prawn diseases are preset, and tiger prawn diseases are input into the target layer to generate an evaluation index system. The factors within the criteria layer and the scheme layer are compared with each other, and their relative importance is quantified using a preset quantitative scale to generate scale values. A judgment matrix is ​​then constructed based on the scale values. The judgment matrix is ​​normalized by the square root method to obtain the eigenvector. The weight vector information of each disease corresponding to each tiger prawn is generated based on the eigenvector, and the weight vector information of each disease corresponding to each tiger prawn is output as the evaluation result.

[0007] Furthermore, in a preferred embodiment of the present invention, abnormal environmental factor data in the evaluation results are obtained, and predictions are made based on the abnormal environmental factor data in the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Specifically, this includes the following steps: Obtain the weight vector information of each disease corresponding to each tiger prawn in the evaluation results, and compare the weight vector information of each disease corresponding to each tiger prawn with the preset weight vector information to obtain the deviation rate. If the deviation rate is greater than the deviation rate threshold, obtain the environmental factor data corresponding to the current evaluation result and treat the environmental factor data as abnormal environmental factor data. Image information of the tiger prawn farming area corresponding to abnormal environmental factor data within a preset time period was collected, and a tiger prawn behavior recognition model was constructed based on a convolutional neural network, and a large amount of tiger prawn behavior image information was obtained. A large amount of behavioral image information of tiger prawns is input into the tiger prawn behavior recognition model for training to obtain the trained tiger prawn behavior recognition model. The image information of tiger prawn farming areas corresponding to abnormal environmental factor data within a preset time is input into the trained tiger prawn behavior recognition model for recognition to obtain tiger prawn behavior information. If the behavioral information contains stress-related behavioral information that generates diseases corresponding to tiger prawns, then the outbreak disease data information for each tiger prawn farming area is generated and output.

[0008] Furthermore, in a preferred embodiment of the present invention, a relevant prevention and control plan is formulated based on the outbreak disease data information of each tiger prawn farming area, specifically including the following steps: Historical disease management information for various diseases of tiger prawns was obtained, and a database was constructed based on the historical disease management information for various diseases of tiger prawns. Outbreak disease data information for each tiger prawn farming area was obtained, and disease management solutions related to the outbreak disease data information for each tiger prawn farming area were retrieved through big data retrieval. The disease control solutions related to the outbreak disease data of each tiger prawn farming area are input into the database for matching to obtain relevant candidate disease control solutions with a high degree of matching. Obtain historical disease treatment plans for each tiger prawn farming area, and determine whether there is any overlap between the historical disease treatment plans for each tiger prawn farming area and the relevant candidate disease treatment plans with a matching degree greater than the preset matching degree within a predetermined time period. If a historical disease treatment plan for each tiger prawn farming area overlaps with a candidate disease treatment plan with a matching degree greater than the preset matching degree within a predetermined time period, then that treatment plan will be removed from the candidate disease treatment plan to generate the final candidate disease treatment plan.

[0009] Furthermore, in a preferred embodiment of the present invention, wireless monitoring nodes are set up in each Penaeus monodon farming area based on environmental factor data related to Penaeus monodon farming, and a wireless monitoring network is constructed based on the wireless monitoring nodes, specifically including: Obtain planning map information for each tiger prawn farming area, construct a 3D model of the tiger prawn farming area based on the planning map information, and obtain the detection range data information of the wireless sensor to be installed. The three-dimensional model of the tiger prawn farming area and the detection range data of the wireless sensors to be installed are input into the particle swarm algorithm to plan the deployment of wireless monitoring points in order to obtain the initial wireless monitoring nodes of the wireless sensors. The location of the heat source device is obtained from the 3D model of the tiger prawn farming area, and it is determined whether there is a wireless sensor within a preset range of the location of the heat source device. If wireless sensors exist within a preset range of the location of the heat source device, then wireless sensors are redeployed outside the preset range of the heat source device to generate the final wireless monitoring node, and a wireless monitoring network is constructed based on the final wireless monitoring node.

[0010] A second aspect of this invention provides a high-efficiency breeding system for Penaeus monodon larvae. The system includes a memory and a processor. The memory contains a program for a high-efficiency breeding method for Penaeus monodon larvae. When the processor executes the program for the high-efficiency breeding method for Penaeus monodon larvae, it performs the following steps: By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the environmental factor data related to tiger prawn cultivation, the environmental factor data of each tiger prawn farming area within a preset time period is obtained. By acquiring environmental factor data related to diseases of tiger prawns through big data, the environmental factor data and environmental factor data related to tiger prawn diseases in each tiger prawn farming area within a preset time period are evaluated to obtain evaluation results. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data from the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Based on the disease outbreak data of each tiger prawn farming area, relevant prevention and control plans are formulated and transmitted to the remote tiger prawn farming monitoring terminal.

[0011] In this embodiment, environmental factor data related to the cultivation of tiger prawns is obtained through big data, and data is collected for each tiger prawn farming area based on the environmental factor data related to tiger prawn cultivation to obtain environmental factor data for each tiger prawn farming area within a preset time period. The specific steps include: By setting keyword information for the cultivation of tiger prawns and constructing search tags based on the keyword information, and then searching through big data networks according to the search tags, environmental factor data related to tiger prawn cultivation can be obtained. Wireless monitoring nodes were set up in each Penaeus monodon farming area based on environmental factor data related to Penaeus monodon farming, and a wireless monitoring network was constructed based on the wireless monitoring nodes. Environmental factor data for each tiger prawn farming area were acquired through a wireless monitoring network, and outlier data were filtered out from the environmental factor data for each tiger prawn farming area. Outlier data in the environmental factor data of each tiger prawn farming area are removed, and environmental factor data for each tiger prawn farming area within a preset time period are generated based on the environmental factor data of each tiger prawn farming area.

[0012] In this embodiment, a relevant prevention and control plan is formulated based on the outbreak disease data of each tiger prawn farming area, specifically including the following steps: Historical disease management information for various diseases of tiger prawns was obtained, and a database was constructed based on the historical disease management information for various diseases of tiger prawns. Outbreak disease data information for each tiger prawn farming area was obtained, and disease management solutions related to the outbreak disease data information for each tiger prawn farming area were retrieved through big data retrieval. The disease control solutions related to the outbreak disease data of each tiger prawn farming area are input into the database for matching to obtain relevant candidate disease control solutions with a high degree of matching. Obtain historical disease treatment plans for each tiger prawn farming area, and determine whether there is any overlap between the historical disease treatment plans for each tiger prawn farming area and the relevant candidate disease treatment plans with a matching degree greater than the preset matching degree within a predetermined time period. If a historical disease treatment plan for each tiger prawn farming area overlaps with a candidate disease treatment plan with a matching degree greater than the preset matching degree within a predetermined time period, then that treatment plan will be removed from the candidate disease treatment plan to generate the final candidate disease treatment plan.

[0013] A third aspect of the present invention provides a computer-readable storage medium containing a program for a method of efficient cultivation of Penaeus monodon larvae. When the program is executed by a processor, it implements the steps of any one of the methods for efficient cultivation of Penaeus monodon larvae.

[0014] This invention addresses the shortcomings of the prior art and has the following beneficial effects: This invention acquires environmental factor data related to Penaeus monodon cultivation through big data analysis. Based on this data, it collects data from each Penaeus monodon farming area to obtain environmental factor data for each area within a preset time period. Furthermore, it acquires environmental factor data related to Penaeus monodon diseases through big data analysis, evaluates the environmental factor data for each farming area within the preset time period, and obtains evaluation results. Abnormal environmental factor data from the evaluation results are then identified, and predictions are made based on these abnormal data to obtain disease outbreak data for each Penaeus monodon farming area. Finally, relevant prevention and control plans are formulated based on the disease outbreak data for each farming area, and these plans are transmitted to a remote Penaeus monodon farming monitoring terminal. This invention evaluates environmental factor data in farming areas to obtain the weight information of corresponding environmental factor data related to disease occurrence. Based on this disease weight information and the stress behavior of Penaeus monodon within a preset time period, it determines whether a disease has occurred. When a disease is confirmed to have occurred, relevant prevention and control plans are formulated according to the disease type. This method enables the identification of various diseases in Penaeus monodon, and the resulting prevention and control schemes for Penaeus monodon are more reasonable, further improving the survival rate of Penaeus monodon during the cultivation process. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the overall method for efficient cultivation of Penaeus monodon larvae is shown. Figure 2 A flowchart of the first method for the efficient cultivation of Penaeus monodon larvae is shown; Figure 3 A second method flowchart for a highly efficient method of cultivating tiger prawn larvae is shown; Figure 4A system block diagram of a high-efficiency shrimp larvae cultivation system for Penaeus monodon is shown. Detailed Implementation

[0017] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0018] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0019] like Figure 1 As shown, the first aspect of this invention provides a method for efficient cultivation of tiger prawn larvae, comprising the following steps: S102: Obtain environmental factor data related to the cultivation of tiger prawns through big data, and collect data for each tiger prawn farming area based on the environmental factor data related to the cultivation of tiger prawns, so as to obtain the environmental factor data of each tiger prawn farming area within a preset time. S104: Obtain environmental factor data related to diseases of tiger prawns through big data, evaluate the environmental factor data and environmental factor data related to tiger prawn diseases in each tiger prawn farming area within a preset time period, and obtain evaluation results. S106: Obtain abnormal environmental factor data from the evaluation results and make predictions based on the abnormal environmental factor data from the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. S108: Develop relevant prevention and control plans based on the outbreak disease data of each tiger prawn farming area, and transmit the relevant prevention and control plans to the remote tiger prawn farming monitoring terminal.

[0020] It should be noted that this invention evaluates environmental factor data in the aquaculture area to obtain the weight information of diseases occurring under corresponding environmental factors. Based on this disease weight information and the stress behavior of *Litopenaeus monodon* within a preset time period, it determines whether a disease has occurred. When a disease is confirmed, a relevant prevention and control plan is formulated according to the disease type. This method can identify various diseases in *Litopenaeus monodon*, and the generated prevention and control plans are more reasonable, further improving the survival rate of *Litopenaeus monodon* during the cultivation process.

[0021] It should be noted that environmental factor data related to tiger prawn farming is obtained through big data analysis, and data is collected for each tiger prawn farming area based on this data. This yields environmental factor data for each tiger prawn farming area within a preset time period, specifically: By setting keyword information for the cultivation of tiger prawns and constructing search tags based on the keyword information, and then searching through big data networks according to the search tags, environmental factor data related to tiger prawn cultivation can be obtained. Wireless monitoring nodes were set up in each Penaeus monodon farming area based on environmental factor data related to Penaeus monodon farming, and a wireless monitoring network was constructed based on the wireless monitoring nodes. Environmental factor data for each tiger prawn farming area were acquired through a wireless monitoring network, and outlier data were filtered out from the environmental factor data for each tiger prawn farming area. Outlier data in the environmental factor data of each tiger prawn farming area are removed, and environmental factor data for each tiger prawn farming area within a preset time period are generated based on the environmental factor data of each tiger prawn farming area.

[0022] It should be noted that the environmental factor data includes water temperature, water salinity, ambient temperature, and ambient humidity. Specifically, based on the environmental factor data related to tiger prawn cultivation, wireless monitoring nodes are set up in each tiger prawn farming area, and a wireless monitoring network is constructed based on these nodes. This includes: Obtain planning map information for each tiger prawn farming area, construct a 3D model of the tiger prawn farming area based on the planning map information, and obtain the detection range data information of the wireless sensor to be installed. The three-dimensional model of the tiger prawn farming area and the detection range data of the wireless sensors to be installed are input into the particle swarm algorithm to plan the deployment of wireless monitoring points in order to obtain the initial wireless monitoring nodes of the wireless sensors. The location of the heat source device is obtained from the 3D model of the tiger prawn farming area, and it is determined whether there is a wireless sensor within a preset range of the location of the heat source device. If wireless sensors exist within a preset range of the location of the heat source device, then wireless sensors are redeployed outside the preset range of the heat source device to generate the final wireless monitoring node, and a wireless monitoring network is constructed based on the final wireless monitoring node.

[0023] It should be noted that the heat source equipment refers to the heating devices deployed by aquaculture users in the aquaculture area. Since existing wireless sensors are easily affected by temperature, the temperature at a given location will be higher the closer it is to the heat source equipment. This method allows the wireless sensors to be placed within a preset range of the heat source equipment, making the sensor placement more rational. The 3D model of the tiger prawn aquaculture area can be created using 3D modeling software, AR technology, or other methods.

[0024] Furthermore, in a preferred embodiment of the present invention, environmental factor data related to diseases of Penaeus monodon are obtained through big data analysis. The environmental factor data and the environmental factor data related to diseases of Penaeus monodon in each Penaeus monodon farming area within a preset time period are evaluated to obtain evaluation results. Specifically, this includes the following steps: By acquiring environmental factor data related to diseases of tiger prawns through big data, and determining the hierarchical structure system of the evaluation, the hierarchical structure system is divided into the target layer, the criterion layer and the scheme layer. Environmental factor data related to diseases of tiger prawns are input into the criteria layer, and environmental factor data of each tiger prawn farming area within a preset time period are input into the scheme layer. Several tiger prawn diseases are preset, and tiger prawn diseases are input into the target layer to generate an evaluation index system. The factors within the criteria layer and the scheme layer are compared with each other, and their relative importance is quantified using a preset quantitative scale to generate scale values. A judgment matrix is ​​then constructed based on the scale values. The judgment matrix is ​​normalized by the square root method to obtain the eigenvector. The weight vector information of each disease corresponding to each tiger prawn is generated based on the eigenvector, and the weight vector information of each disease corresponding to each tiger prawn is output as the evaluation result.

[0025] It should be noted that the weight vectors corresponding to different diseases are not consistent. For example, the temperature required for a certain disease to occur is 25 degrees Celsius and the corresponding ion concentration is 2 mol / L, while the temperature required for another disease to occur is 30 degrees Celsius and the corresponding ion concentration is 3 mol / L. The weight vector information of the current environmental factor data for the occurrence of a certain disease is calculated using the analytic hierarchy process (AHP). The larger the weight vector information, the greater the probability of the occurrence of a certain disease. For example, for hepatic leukorrhea, the larger the weight vector information for hepatic leukorrhea calculated from the environmental factor data, the greater the probability of this type of disease occurring.

[0026] like Figure 2As shown, in a preferred embodiment of the present invention, abnormal environmental factor data in the evaluation results are obtained, and predictions are made based on the abnormal environmental factor data in the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Specifically, this includes the following steps: S202: Obtain the weight vector information of each disease corresponding to the evaluation results of each tiger prawn, and compare the weight vector information of each disease corresponding to the tiger prawn with the preset weight vector information to obtain the deviation rate. If the deviation rate is greater than the deviation rate threshold, obtain the environmental factor data corresponding to the current evaluation results and use the environmental factor data as abnormal environmental factor data. S204: Collect image information of the Penaeus monodon farming area corresponding to abnormal environmental factor data within a preset time, construct a Penaeus monodon behavior recognition model based on a convolutional neural network, and obtain a large amount of Penaeus monodon behavior image information. S206: Input a large amount of behavioral image information of tiger prawns into the tiger prawn behavior recognition model for training, so as to obtain the trained tiger prawn behavior recognition model. Input the image information of tiger prawn farming area corresponding to abnormal environmental factor data within a preset time into the trained tiger prawn behavior recognition model for recognition, and obtain the behavioral information of tiger prawns. S208: If the behavioral information contains stress-related behavioral information that generates diseases corresponding to Penaeus monodon, then generate outbreak disease data information for each Penaeus monodon farming area and output the outbreak disease data information for each Penaeus monodon farming area.

[0027] It should be noted that a larger weight vector indicates a higher probability of a certain disease, but it does not necessarily mean that the disease will definitely occur. When tiger prawns exhibit certain stress behaviors, such as liver turbidity disease (a digestive system disease caused by a primary viral infection), it is common in mysid larvae and early-stage juveniles. Affected larvae and juveniles often float on the surface and move sluggishly. When some tiger prawns exhibit this behavior, it indicates that the disease is in its early stages.

[0028] like Figure 3 As shown, in a preferred embodiment of the present invention, a relevant prevention and control plan is formulated based on the outbreak disease data information of each tiger prawn farming area, specifically including the following steps: S302: Obtain historical disease management information for each disease of Penaeus monodon, and construct a database based on the historical disease management information for each disease of Penaeus monodon, obtain outbreak disease data information for each Penaeus monodon farming area, and retrieve disease management solutions related to the outbreak disease data information for each Penaeus monodon farming area through big data retrieval. S304: Input the disease control plan related to the outbreak disease data information of each tiger prawn farming area into the database for matching, so as to obtain the relevant candidate disease control plan with a matching degree greater than the preset matching degree. S306: Obtain historical disease treatment plans for each tiger prawn farming area, and determine whether there is any overlap between the historical disease treatment plans for each tiger prawn farming area and the related candidate disease treatment plans with a matching degree greater than the preset matching degree within a predetermined time period. S308: If the historical disease treatment plan for each tiger prawn farming area overlaps with the relevant candidate disease treatment plan with a matching degree greater than the preset matching degree within a predetermined time period, then the treatment plan is removed from the candidate disease treatment plan to generate the final candidate disease treatment plan.

[0029] It should be noted that if historical disease control programs in each tiger prawn farming area overlap with candidate disease control programs with a high degree of matching within a predetermined time period, it indicates that users frequently use these drugs to control the disease. Due to long-term use, drug resistance has developed against the pathogens or viruses causing the disease. This method can further screen out drug-resistant control programs, thereby providing users with better control solutions and improving disease control effectiveness.

[0030] like Figure 4 As shown, a second aspect of the present invention provides a high-efficiency breeding system for Penaeus monodon larvae. This system includes a memory 41 and a processor 62. The memory 41 contains a program for a high-efficiency breeding method for Penaeus monodon larvae. When the processor 62 executes the program for the high-efficiency breeding method for Penaeus monodon larvae, it performs the following steps: By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the environmental factor data related to tiger prawn cultivation, the environmental factor data of each tiger prawn farming area within a preset time period is obtained. By acquiring environmental factor data related to diseases of tiger prawns through big data, the environmental factor data and environmental factor data related to tiger prawn diseases in each tiger prawn farming area within a preset time period are evaluated to obtain evaluation results. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data from the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Based on the disease outbreak data of each tiger prawn farming area, relevant prevention and control plans are formulated and transmitted to the remote tiger prawn farming monitoring terminal.

[0031] In this embodiment, environmental factor data related to the cultivation of tiger prawns is obtained through big data, and data is collected for each tiger prawn farming area based on the environmental factor data related to tiger prawn cultivation to obtain environmental factor data for each tiger prawn farming area within a preset time period. The specific steps include: By setting keyword information for the cultivation of tiger prawns and constructing search tags based on the keyword information, and then searching through big data networks according to the search tags, environmental factor data related to tiger prawn cultivation can be obtained. Wireless monitoring nodes were set up in each Penaeus monodon farming area based on environmental factor data related to Penaeus monodon farming, and a wireless monitoring network was constructed based on the wireless monitoring nodes. Environmental factor data for each tiger prawn farming area were acquired through a wireless monitoring network, and outlier data were filtered out from the environmental factor data for each tiger prawn farming area. Outlier data in the environmental factor data of each tiger prawn farming area are removed, and environmental factor data for each tiger prawn farming area within a preset time period are generated based on the environmental factor data of each tiger prawn farming area.

[0032] In this embodiment, a relevant prevention and control plan is formulated based on the outbreak disease data of each tiger prawn farming area, specifically including the following steps: Historical disease management information for various diseases of tiger prawns was obtained, and a database was constructed based on the historical disease management information for various diseases of tiger prawns. Outbreak disease data information for each tiger prawn farming area was obtained, and disease management solutions related to the outbreak disease data information for each tiger prawn farming area were retrieved through big data retrieval. The disease control solutions related to the outbreak disease data of each tiger prawn farming area are input into the database for matching to obtain relevant candidate disease control solutions with a high degree of matching. Obtain historical disease treatment plans for each tiger prawn farming area, and determine whether there is any overlap between the historical disease treatment plans for each tiger prawn farming area and the relevant candidate disease treatment plans with a matching degree greater than the preset matching degree within a predetermined time period. If a historical disease treatment plan for each tiger prawn farming area overlaps with a candidate disease treatment plan with a matching degree greater than the preset matching degree within a predetermined time period, then that treatment plan will be removed from the candidate disease treatment plan to generate the final candidate disease treatment plan.

[0033] A third aspect of the present invention provides a computer-readable storage medium containing a program for a method of efficient cultivation of Penaeus monodon larvae. When the program is executed by a processor, it implements the steps of any one of the methods for efficient cultivation of Penaeus monodon larvae.

[0034] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0035] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0036] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0037] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0038] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0039] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for efficient cultivation of Penaeus monodon larvae, characterized in that, Includes the following steps: By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the environmental factor data related to the cultivation of tiger prawns, the environmental factor data of each tiger prawn farming area within a preset time period is obtained. By acquiring environmental factor data related to diseases of tiger prawns through big data, the environmental factor data and environmental factor data related to tiger prawn diseases in each tiger prawn farming area within a preset time period are evaluated to obtain evaluation results. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data from the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Based on the outbreak disease data information of each tiger prawn farming area, relevant prevention and control plans are formulated and transmitted to the remote tiger prawn farming monitoring terminal. By acquiring environmental factor data related to diseases of tiger prawns through big data, and determining the hierarchical structure system of evaluation, the hierarchical structure system is divided into target layer, criterion layer and scheme layer. The environmental factor data related to the diseases of Penaeus monodon are input into the criterion layer, and the environmental factor data of each Penaeus monodon farming area within a preset time period are input into the scheme layer. Several Penaeus monodon diseases are preset, and the Penaeus monodon diseases are input into the target layer to generate an evaluation index system. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data in the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Specifically, this includes the following steps: Obtain the weight vector information of each disease corresponding to each tiger prawn in the evaluation results, and compare the weight vector information of each disease corresponding to each tiger prawn with the preset weight vector information to obtain the deviation rate. If the deviation rate is greater than the deviation rate threshold, obtain the environmental factor data corresponding to the current evaluation result and use the environmental factor data as abnormal environmental factor data. Image information of the tiger prawn farming area corresponding to abnormal environmental factor data within a preset time period was collected, and a tiger prawn behavior recognition model was constructed based on a convolutional neural network, and a large amount of tiger prawn behavior image information was obtained. The large amount of behavioral image information of tiger prawns is input into the tiger prawn behavior recognition model for training to obtain the trained tiger prawn behavior recognition model. The image information of tiger prawn farming areas corresponding to the abnormal environmental factor data within the preset time is input into the trained tiger prawn behavior recognition model for recognition to obtain the behavioral information of tiger prawns. If the behavioral information contains stress-related behavioral information that generates diseases corresponding to Penaeus monodon, then outbreak disease data information for each Penaeus monodon farming area is generated and output.

2. The method for high-efficiency cultivation of Penaeus monodon larvae according to claim 1, characterized in that, By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the aforementioned environmental factor data, the environmental factor data for each tiger prawn farming area within a preset time period is obtained. Specifically, this includes the following steps: By setting keyword information for the cultivation of tiger prawns and constructing search tags based on the keyword information, and then searching through a big data network according to the search tags, environmental factor data related to tiger prawn cultivation can be obtained. Based on the environmental factor data related to the cultivation of Penaeus monodon, wireless monitoring nodes are set up in each Penaeus monodon farming area, and a wireless monitoring network is constructed based on the wireless monitoring nodes. Environmental factor data for each tiger prawn farming area are acquired through a wireless monitoring network, and outlier data are filtered from the environmental factor data of each tiger prawn farming area. Outlier data are removed from the environmental factor data of each tiger prawn farming area, and environmental factor data for each tiger prawn farming area within a preset time period are generated based on the environmental factor data of each tiger prawn farming area.

3. The method for high-efficiency cultivation of Penaeus monodon larvae according to claim 1, characterized in that, By acquiring environmental factor data related to diseases in Penaeus monodon through big data, the environmental factor data and disease-related environmental factor data of each Penaeus monodon farming area within a preset time period are evaluated to obtain evaluation results. The specific steps include: The factors within the criteria layer and the scheme layer are compared with each other, and their relative importance is quantified using a preset quantitative scale to generate scale values. A judgment matrix is ​​then constructed based on the scale values. The judgment matrix is ​​normalized using the square root method to obtain the feature vector. Based on the feature vector, the weight vector information of each disease corresponding to each tiger prawn is generated, and the weight vector information of each disease corresponding to each tiger prawn is output as the evaluation result.

4. The method for high-efficiency cultivation of Penaeus monodon larvae according to claim 1, characterized in that, Based on the disease outbreak data for each tiger prawn farming area, relevant prevention and control plans are formulated, including the following steps: Historical disease management information for various diseases of tiger prawns was obtained, and a database was constructed based on the historical disease management information for various diseases of tiger prawns. Outbreak disease data information for each tiger prawn farming area was obtained, and disease management solutions related to the outbreak disease data information for each tiger prawn farming area were retrieved through big data retrieval. The disease control solutions related to the outbreak disease data information of each tiger prawn farming area are input into the database for matching, so as to obtain relevant candidate disease control solutions with a matching degree greater than the preset matching degree. Obtain historical disease treatment plans for each tiger prawn farming area, and determine whether there is any overlap between the historical disease treatment plans for each tiger prawn farming area and the relevant candidate disease treatment plans with a matching degree greater than a preset matching degree within a predetermined time period. If a historical disease treatment plan for each tiger prawn farming area overlaps with a related candidate disease treatment plan with a matching degree greater than a preset matching degree within a predetermined time period, then that treatment plan is removed from the candidate disease treatment plan to generate the final candidate disease treatment plan.

5. The method for efficient cultivation of Penaeus monodon larvae according to claim 2, characterized in that, Based on the environmental factor data related to the cultivation of Penaeus monodon, wireless monitoring nodes are set up in each Penaeus monodon farming area, and a wireless monitoring network is constructed based on the wireless monitoring nodes, specifically including: Obtain planning map information for each tiger prawn farming area, construct a three-dimensional model of the tiger prawn farming area based on the planning map information, and obtain the detection range data information of the wireless sensor to be installed. The three-dimensional model of the tiger prawn farming area and the detection range data of the wireless sensors to be installed are input into the particle swarm algorithm to plan the deployment of wireless monitoring points in order to obtain the initial wireless monitoring nodes of the wireless sensors. The location of the heat source device is obtained from the 3D model of the tiger prawn farming area, and it is determined whether there is a wireless sensor within a preset range of the location of the heat source device. If there are wireless sensors within a preset range of the location of the heat source device, then wireless sensors are redeployed outside the preset range of the heat source device to generate the final wireless monitoring node, and a wireless monitoring network is constructed based on the final wireless monitoring node.

6. A high-efficiency shrimp larvae cultivation system for Penaeus monodon, characterized in that, The system includes a memory and a processor. The memory contains a program for a high-efficiency cultivation method for tiger prawn larvae. When the processor executes the program for the high-efficiency cultivation method for tiger prawn larvae, it performs the following steps: By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the environmental factor data related to the cultivation of tiger prawns, the environmental factor data of each tiger prawn farming area within a preset time period is obtained. By acquiring environmental factor data related to diseases of tiger prawns through big data, the environmental factor data and environmental factor data related to tiger prawn diseases in each tiger prawn farming area within a preset time period are evaluated to obtain evaluation results. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data from the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Based on the outbreak disease data information of each tiger prawn farming area, relevant prevention and control plans are formulated and transmitted to the remote tiger prawn farming monitoring terminal. By acquiring environmental factor data related to diseases of tiger prawns through big data, and determining the hierarchical structure system of evaluation, the hierarchical structure system is divided into target layer, criterion layer and scheme layer. The environmental factor data related to the diseases of Penaeus monodon are input into the criterion layer, and the environmental factor data of each Penaeus monodon farming area within a preset time period are input into the scheme layer. Several Penaeus monodon diseases are preset, and the Penaeus monodon diseases are input into the target layer to generate an evaluation index system. Obtain abnormal environmental factor data from the evaluation results, and make predictions based on the abnormal environmental factor data in the evaluation results to obtain outbreak disease data information for each tiger prawn farming area. Specifically, this includes the following steps: Obtain the weight vector information of each disease corresponding to each tiger prawn in the evaluation results, and compare the weight vector information of each disease corresponding to each tiger prawn with the preset weight vector information to obtain the deviation rate. If the deviation rate is greater than the deviation rate threshold, obtain the environmental factor data corresponding to the current evaluation result and use the environmental factor data as abnormal environmental factor data. Image information of the tiger prawn farming area corresponding to abnormal environmental factor data within a preset time period was collected, and a tiger prawn behavior recognition model was constructed based on a convolutional neural network, and a large amount of tiger prawn behavior image information was obtained. The large amount of behavioral image information of tiger prawns is input into the tiger prawn behavior recognition model for training to obtain the trained tiger prawn behavior recognition model. The image information of tiger prawn farming areas corresponding to the abnormal environmental factor data within the preset time is input into the trained tiger prawn behavior recognition model for recognition to obtain the behavioral information of tiger prawns. If the behavioral information contains stress-related behavioral information that generates diseases corresponding to Penaeus monodon, then outbreak disease data information for each Penaeus monodon farming area is generated and output.

7. The high-efficiency shrimp larvae cultivation system for Penaeus monodon according to claim 6, characterized in that, By acquiring environmental factor data related to the cultivation of tiger prawns through big data, and collecting data on each tiger prawn farming area based on the aforementioned environmental factor data, the environmental factor data for each tiger prawn farming area within a preset time period is obtained. Specifically, this includes the following steps: By setting keyword information for the cultivation of tiger prawns and constructing search tags based on the keyword information, and then searching through a big data network according to the search tags, environmental factor data related to tiger prawn cultivation can be obtained. Based on the environmental factor data related to the cultivation of Penaeus monodon, wireless monitoring nodes are set up in each Penaeus monodon farming area, and a wireless monitoring network is constructed based on the wireless monitoring nodes. Environmental factor data for each tiger prawn farming area are acquired through a wireless monitoring network, and outlier data are filtered from the environmental factor data of each tiger prawn farming area. Outlier data are removed from the environmental factor data of each tiger prawn farming area, and environmental factor data for each tiger prawn farming area within a preset time period are generated based on the environmental factor data of each tiger prawn farming area.

8. The high-efficiency shrimp larvae cultivation system for Penaeus monodon according to claim 6, characterized in that, Based on the disease outbreak data for each tiger prawn farming area, relevant prevention and control plans are formulated, including the following steps: Historical disease management information for various diseases of tiger prawns was obtained, and a database was constructed based on the historical disease management information for various diseases of tiger prawns. Outbreak disease data information for each tiger prawn farming area was obtained, and disease management solutions related to the outbreak disease data information for each tiger prawn farming area were retrieved through big data retrieval. The disease control solutions related to the outbreak disease data information of each tiger prawn farming area are input into the database for matching, so as to obtain relevant candidate disease control solutions with a matching degree greater than the preset matching degree. Obtain historical disease treatment plans for each tiger prawn farming area, and determine whether there is any overlap between the historical disease treatment plans for each tiger prawn farming area and the relevant candidate disease treatment plans with a matching degree greater than a preset matching degree within a predetermined time period. If a historical disease treatment plan for each tiger prawn farming area overlaps with a related candidate disease treatment plan with a matching degree greater than a preset matching degree within a predetermined time period, then that treatment plan is removed from the candidate disease treatment plan to generate the final candidate disease treatment plan.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program for a method of high-efficiency cultivation of tiger prawn larvae. When the program is executed by a processor, it implements the steps of a method for high-efficiency cultivation of tiger prawn larvae as described in any one of claims 1-5.

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