An overseas fishery safety early warning method and system based on big data, and a medium

Abstract

The application discloses a kind of over-ocean fishery safety early warning method, system and medium based on big data, by obtaining and analyzing the marine historical weather data of multiple marine fishing sub-regions and the marine environmental information of current region, obtain current warning information, determine the warning level of warning information, generate different navigation correction information for different warning levels, according to correction information, obtain suitable fishing route, thereby effectively improve fishing yield and economic efficiency of marine fishery.In addition, by analyzing the safe marine sub-region in the fishing area, a suitable return route is obtained, which can effectively reduce the economic loss caused by abnormal weather factors in marine production.

Description

Technical Field

[0001] This invention relates to the field of big data, and more specifically, to a method and system medium for early warning of transoceanic fishery safety based on big data. Background Technology

[0002] Currently, establishing a risk early warning system for distant-water fisheries and strengthening risk prevention and control capabilities are among the key tasks for the development of my country's distant-water fisheries. Therefore, it is necessary to objectively understand the problems in the development of such distant-water fisheries and strengthen the construction of early warning and support systems.

[0003] However, due to the lack of effective early warning mechanisms, some fishing vessels fail to adjust their routes appropriately in real time when encountering abnormal weather or marine environments, often leading to a decline in marine fishery production. Furthermore, severe weather conditions can result in significant economic losses for the fishing vessels. Therefore, an effective early warning method for fishery safety is urgently needed. Summary of the Invention

[0004] To address at least one of the aforementioned technical problems, this invention proposes a big data-based early warning system for transoceanic fisheries safety.

[0005] The first aspect of this invention provides a method for early warning of transoceanic fishery safety based on big data, comprising:

[0006] Obtain two-dimensional map information of the marine fishing area, divide the area according to the two-dimensional map information, and obtain multiple marine fishing sub-areas;

[0007] Historical marine weather data for marine fishing sub-regions are obtained, and marine weather forecast data is obtained by performing predictive analysis based on big data information on the historical marine weather data.

[0008] Marine weather forecast data is imported into an early warning analysis model for early warning analysis to obtain marine early warning information.

[0009] Obtain marine environmental information, update marine early warning information based on marine environmental information, and obtain a second marine early warning information;

[0010] Ocean route correction information is generated based on the second ocean early warning information, and the route is corrected based on the ocean route correction information.

[0011] In this solution, the step of obtaining two-dimensional map information of the marine fishing area and dividing the area based on the two-dimensional map information to obtain multiple marine fishing sub-areas is as follows:

[0012] Construct a two-dimensional ocean map model, mark the ocean fishing areas on the ocean two-dimensional map model, and obtain two-dimensional map information of the ocean fishing areas;

[0013] Obtain area information from the two-dimensional map information, and divide the marine fishing area into M marine fishing sub-regions according to the size of the area, with the area of ​​each region being smaller than the preset maximum area.

[0014] In this solution, the acquisition of historical marine weather data for the marine fishing sub-area, and the subsequent prediction and analysis based on big data information to obtain marine weather forecast data, specifically involves:

[0015] Establish a marine weather data training model, obtain a marine weather data training set from big data, and import the training set into the marine data training model for training;

[0016] Historical marine weather data from marine fishing sub-regions are imported into a trained marine data training model for data prediction, resulting in marine weather forecast data.

[0017] In this solution, the step of importing marine weather forecast data into an early warning analysis model for early warning analysis to obtain marine early warning information specifically involves:

[0018] Marine weather forecast data for each marine fishing sub-region is obtained, and the marine weather forecast data is imported into the early warning analysis model for data analysis to obtain the weather anomaly level information for each marine fishing sub-region.

[0019] The early warning level information for marine fishing sub-regions is obtained by calculating and analyzing the weather anomaly level information.

[0020] The marine early warning information is obtained by fusing the weather anomaly level information with the early warning level information.

[0021] The specific formula for calculating the warning level is as follows:

[0022]

[0023] Where P represents the warning level, M represents the total number of marine fishing sub-regions, and E represents the warning level. i Let K be the weather anomaly level for the i-th marine fishing sub-region, and K be the weather anomaly correction coefficient. The marine early warning information includes the weather anomaly level information and early warning level information for each marine fishing sub-region.

[0024] In this solution, the acquisition of marine environmental information and the updating of marine early warning information based on the marine environmental information to obtain a second marine early warning information are specifically as follows:

[0025] By analyzing and comparing wind speed, wind direction, temperature, and rainfall information from marine environmental information with marine weather forecast data, marine weather forecast deviation information can be obtained.

[0026] Marine weather forecast deviation information and marine early warning information are imported into the early warning analysis model for real-time early warning analysis to obtain the second marine early warning information.

[0027] In this scheme, the step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information specifically involves:

[0028] Obtain information on marine fishing routes;

[0029] Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information;

[0030] The warning level is compared with the first preset warning threshold and the second preset warning threshold;

[0031] If the warning level is greater than the first preset warning threshold and less than the second preset warning threshold, then the warning level of each marine fishing sub-area is analyzed based on the second marine warning information.

[0032] Marine fishing sub-areas with warning levels below the first preset warning threshold are marked as priority fishing sub-areas;

[0033] Based on the marine fishing route information, the marine fishing sub-area where the route is located is marked as the initial fishing sub-area;

[0034] Ocean route correction information is obtained based on the priority fishing sub-area and the initial fishing sub-area;

[0035] The second marine fishing route information is obtained by correcting the route based on the marine route correction information.

[0036] In this solution, the step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information further includes:

[0037] Obtain information on marine fishing routes;

[0038] Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information;

[0039] Compare the warning level with the second preset warning threshold;

[0040] If the warning level is greater than the second preset warning threshold, the warning level for each marine fishing sub-area is obtained based on the second marine warning information;

[0041] The warning level of each marine fishing sub-area is compared with the preset safety level, and marine fishing sub-areas with a lower than the preset safety level are selected as safe marine sub-areas.

[0042] Return route information is generated based on the aforementioned safe ocean sub-region.

[0043] A second aspect of the present invention also provides a big data-based early warning system for transoceanic fisheries safety. The system includes a memory and a processor. The memory includes a big data-based early warning method program for transoceanic fisheries safety. When executed by the processor, the big data-based early warning method program for transoceanic fisheries safety implements the following steps:

[0044] Obtain two-dimensional map information of the marine fishing area, divide the area according to the two-dimensional map information, and obtain multiple marine fishing sub-areas;

[0045] Historical marine weather data for marine fishing sub-regions are obtained, and marine weather forecast data is obtained by performing predictive analysis based on big data information on the historical marine weather data.

[0046] Marine weather forecast data is imported into an early warning analysis model for early warning analysis to obtain marine early warning information.

[0047] Obtain marine environmental information, update marine early warning information based on marine environmental information, and obtain a second marine early warning information;

[0048] Ocean route correction information is generated based on the second ocean early warning information, and the route is corrected based on the ocean route correction information.

[0049] In this solution, the step of importing marine weather forecast data into an early warning analysis model for early warning analysis to obtain marine early warning information specifically involves:

[0050] Marine weather forecast data for each marine fishing sub-region is obtained, and the marine weather forecast data is imported into the early warning analysis model for data analysis to obtain the weather anomaly level information for each marine fishing sub-region.

[0051] The early warning level information for marine fishing sub-regions is obtained by calculating and analyzing the weather anomaly level information.

[0052] The marine early warning information is obtained by fusing the weather anomaly level information with the early warning level information.

[0053] The specific formula for calculating the warning level is as follows:

[0054]

[0055] Where P represents the warning level, M represents the total number of marine fishing sub-regions, and E represents the warning level. i Let K be the weather anomaly level for the i-th marine fishing sub-region, and K be the weather anomaly correction coefficient. The marine early warning information includes the weather anomaly level information and early warning level information for each marine fishing sub-region.

[0056] A third aspect of the present invention also provides a computer-readable storage medium comprising a big data-based method program for early warning of safety in transoceanic fisheries, wherein when the big data-based method program for early warning of safety in transoceanic fisheries is executed by a processor, it implements the steps of the big data-based method for early warning of safety in transoceanic fisheries as described in any of the preceding claims.

[0057] This invention discloses a method, system, and medium for early warning of transoceanic fisheries safety based on big data. By acquiring and analyzing historical marine weather data and current marine environmental information from multiple marine fishing sub-regions, it obtains current early warning information, determines the warning level, generates different navigation correction information for different warning levels, and determines suitable fishing routes based on the correction information, thereby effectively improving catch yield and the economic efficiency of marine fisheries. Furthermore, by analyzing safe marine sub-regions within the fishing area, it obtains suitable return routes, effectively reducing economic losses caused by abnormal weather factors in marine production. Attached Figure Description

[0058] Figure 1 A flowchart of a big data-based early warning method for transoceanic fisheries is shown.

[0059] Figure 2 This invention illustrates a flowchart of the process for acquiring marine weather forecast data.

[0060] Figure 3 This invention illustrates a flowchart of the process for obtaining information on a second marine fishing route.

[0061] Figure 4 The diagram shows a block diagram of a big data-based early warning system for transoceanic fisheries according to the present invention. Detailed Implementation

[0062] 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.

[0063] 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.

[0064] Figure 1 A flowchart of a big data-based early warning method for transoceanic fisheries safety is shown.

[0065] like Figure 1As shown, the first aspect of this invention provides a method for early warning of transoceanic fishery safety based on big data, comprising:

[0066] S102, Obtain two-dimensional map information of the marine fishing area, divide the area according to the two-dimensional map information, and obtain multiple marine fishing sub-areas;

[0067] S104, Obtain historical marine weather data for the marine fishing sub-area, and perform predictive analysis based on big data information based on the historical marine weather data to obtain marine weather forecast data;

[0068] S106, Import marine weather forecast data into the early warning analysis model for early warning analysis to obtain marine early warning information;

[0069] S108: Obtain marine environmental information, update marine early warning information based on marine environmental information, and obtain a second marine early warning information;

[0070] S110, Generate marine route correction information based on the second marine early warning information, and make route corrections based on the marine route correction information.

[0071] Figure 2 The flowchart illustrating the process of acquiring marine weather forecast data according to the present invention is shown.

[0072] According to an embodiment of the present invention, the step of obtaining two-dimensional map information of the marine fishing area and dividing the area according to the two-dimensional map information to obtain multiple marine fishing sub-areas is specifically as follows:

[0073] S202, Construct a two-dimensional ocean map model, mark the ocean fishing area on the two-dimensional ocean map model, and obtain the two-dimensional map information of the ocean fishing area;

[0074] S204, obtain the area information from the two-dimensional map information, and divide the marine fishing area into M marine fishing sub-areas according to the size of the area, and the area of ​​each area is less than the preset maximum area.

[0075] It should be noted that, according to the size of the area, the marine fishing area is divided into M marine fishing sub-regions, where M is the total number of marine fishing sub-regions. The size of M is determined by the size of the area; the larger the area, the larger M is, and the more regions are divided. The division method is generally a grid system.

[0076] According to an embodiment of the present invention, the step of obtaining historical marine weather data for a marine fishing sub-region and performing predictive analysis based on big data information to obtain marine weather forecast data specifically involves:

[0077] Establish a marine weather data training model, obtain a marine weather data training set from big data, and import the training set into the marine data training model for training;

[0078] Historical marine weather data from marine fishing sub-regions are imported into a trained marine data training model for data prediction, resulting in marine weather forecast data.

[0079] It should be noted that the marine weather data training set obtained from the big data is a historically existing data training set, which includes marine weather change data. Specifically, the process of importing historical marine weather data from marine fishing sub-regions into a trained marine data training model for data prediction results in marine weather prediction data where each marine fishing sub-region possesses both historical marine weather data and marine weather prediction data.

[0080] According to an embodiment of the present invention, the step of importing marine weather forecast data into an early warning analysis model for early warning analysis to obtain marine early warning information specifically includes:

[0081] Marine weather forecast data for each marine fishing sub-region is obtained, and the marine weather forecast data is imported into the early warning analysis model for data analysis to obtain the weather anomaly level information for each marine fishing sub-region.

[0082] The early warning level information for marine fishing sub-regions is obtained by calculating and analyzing the weather anomaly level information.

[0083] The marine early warning information is obtained by fusing the weather anomaly level information with the early warning level information.

[0084] The specific formula for calculating the warning level is as follows:

[0085]

[0086] Where P represents the warning level, M represents the total number of marine fishing sub-regions, and E represents the warning level. i Let K be the weather anomaly level for the i-th marine fishing sub-region, and K be the weather anomaly correction coefficient. The marine early warning information includes the weather anomaly level information and early warning level information for each marine fishing sub-region.

[0087] It should be noted that each marine fishing sub-region has independent marine weather forecast data and weather anomaly level information. By subdividing the marine fishing region and obtaining specific marine information for the marine fishing sub-region, the accuracy of prediction and analysis of the marine fishing region can be improved.

[0088] According to an embodiment of the present invention, the step of obtaining marine environmental information and updating marine early warning information based on marine environmental information to obtain a second marine early warning information specifically includes:

[0089] By analyzing and comparing wind speed, wind direction, temperature, and rainfall information from marine environmental information with marine weather forecast data, marine weather forecast deviation information can be obtained.

[0090] Marine weather forecast deviation information and marine early warning information are imported into the early warning analysis model for real-time early warning analysis to obtain the second marine early warning information.

[0091] It should be noted that in the process of analyzing and comparing the wind speed, wind direction, temperature, and rainfall information from the marine environmental information with marine weather forecast data to obtain marine weather forecast deviation information, the wind speed, wind direction, temperature, and rainfall information are real-time acquired information about the current marine environment of the fishing vessel. In the process of importing the marine weather forecast deviation information and marine early warning information into the early warning analysis model for real-time early warning analysis, the early warning analysis model will compare and analyze the weather forecast deviation information and weather anomaly level information for each marine fishing sub-region to obtain new weather anomaly level information and new early warning level information. The new weather anomaly level information and the new early warning level information will then be fused to obtain a second marine early warning information.

[0092] Figure 3 The flowchart illustrating the process of obtaining information on a second marine fishing route according to the present invention is shown.

[0093] According to an embodiment of the present invention, the step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information specifically includes:

[0094] S302, Obtain information on marine fishing routes;

[0095] S304, based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information;

[0096] S306, compare the warning level with the first preset warning threshold and the second preset warning threshold;

[0097] S308, If the warning level is greater than the first preset warning threshold and less than the second preset warning threshold, then the warning level of each marine fishing sub-area is analyzed based on the second marine warning information.

[0098] S310, mark marine fishing sub-areas with warning levels lower than the first preset warning threshold as priority fishing sub-areas;

[0099] S312, based on marine fishing route information, mark the marine fishing sub-area where the route is located as the initial fishing sub-area;

[0100] S314, Ocean route correction information is obtained based on the priority fishing sub-area and the initial fishing sub-area;

[0101] S316, based on the marine route correction information, the second marine fishing route information is obtained by correcting the route.

[0102] According to an embodiment of the present invention, the step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information further includes:

[0103] Obtain information on marine fishing routes;

[0104] Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information;

[0105] Compare the warning level with the second preset warning threshold;

[0106] If the warning level is greater than the second preset warning threshold, the warning level for each marine fishing sub-area is obtained based on the second marine warning information;

[0107] The warning level of each marine fishing sub-area is compared with the preset safety level, and marine fishing sub-areas with a lower than the preset safety level are selected as safe marine sub-areas.

[0108] Return route information is generated based on the aforementioned safe ocean sub-region.

[0109] It should be noted that the warning levels are generally from 0 to 10, with the first preset warning threshold being 4, the second preset warning threshold being 7, and the preset safety level being 5. If the warning level is higher than the second preset warning threshold, it indicates that there are relatively severe weather conditions in the marine fishing sub-area where the current location is situated.

[0110] Figure 4 The diagram shows a block diagram of a big data-based early warning system for transoceanic fisheries according to the present invention.

[0111] Obtain two-dimensional map information of the marine fishing area, divide the area according to the two-dimensional map information, and obtain multiple marine fishing sub-areas;

[0112] Historical marine weather data for marine fishing sub-regions are obtained, and marine weather forecast data is obtained by performing predictive analysis based on big data information on the historical marine weather data.

[0113] Marine weather forecast data is imported into an early warning analysis model for early warning analysis to obtain marine early warning information.

[0114] Obtain marine environmental information, update marine early warning information based on marine environmental information, and obtain a second marine early warning information;

[0115] Ocean route correction information is generated based on the second ocean early warning information, and the route is corrected based on the ocean route correction information.

[0116] According to an embodiment of the present invention, the step of obtaining two-dimensional map information of the marine fishing area and dividing the area according to the two-dimensional map information to obtain multiple marine fishing sub-areas is specifically as follows:

[0117] Construct a two-dimensional ocean map model, mark the ocean fishing areas on the ocean two-dimensional map model, and obtain two-dimensional map information of the ocean fishing areas;

[0118] Obtain area information from the two-dimensional map information, and divide the marine fishing area into M marine fishing sub-regions according to the size of the area, with the area of ​​each region being smaller than the preset maximum area.

[0119] It should be noted that, according to the size of the area, the marine fishing area is divided into M marine fishing sub-regions, where M is the total number of marine fishing sub-regions. The size of M is determined by the size of the area; the larger the area, the larger M is, and the more regions are divided. The division method is generally a grid system.

[0120] According to an embodiment of the present invention, the step of obtaining historical marine weather data for a marine fishing sub-region and performing predictive analysis based on big data information to obtain marine weather forecast data specifically involves:

[0121] Establish a marine weather data training model, obtain a marine weather data training set from big data, and import the training set into the marine data training model for training;

[0122] Historical marine weather data from marine fishing sub-regions are imported into a trained marine data training model for data prediction, resulting in marine weather forecast data.

[0123] It should be noted that the marine weather data training set obtained from the big data is a historically existing data training set, which includes marine weather change data. Specifically, the process of importing historical marine weather data from marine fishing sub-regions into a trained marine data training model for data prediction results in marine weather prediction data where each marine fishing sub-region possesses both historical marine weather data and marine weather prediction data.

[0124] According to an embodiment of the present invention, the step of importing marine weather forecast data into an early warning analysis model for early warning analysis to obtain marine early warning information specifically includes:

[0125] Marine weather forecast data for each marine fishing sub-region is obtained, and the marine weather forecast data is imported into the early warning analysis model for data analysis to obtain the weather anomaly level information for each marine fishing sub-region.

[0126] The early warning level information for marine fishing sub-regions is obtained by calculating and analyzing the weather anomaly level information.

[0127] The marine early warning information is obtained by fusing the weather anomaly level information with the early warning level information.

[0128] The specific formula for calculating the warning level is as follows:

[0129]

[0130] Where P represents the warning level, M represents the total number of marine fishing sub-regions, and E represents the warning level. i Let K be the weather anomaly level for the i-th marine fishing sub-region, and K be the weather anomaly correction coefficient. The marine early warning information includes the weather anomaly level information and early warning level information for each marine fishing sub-region.

[0131] It should be noted that each marine fishing sub-region has independent marine weather forecast data and weather anomaly level information. By subdividing the marine fishing region and obtaining specific marine information for the marine fishing sub-region, the accuracy of prediction and analysis of the marine fishing region can be improved.

[0132] According to an embodiment of the present invention, the step of obtaining marine environmental information and updating marine early warning information based on marine environmental information to obtain a second marine early warning information specifically includes:

[0133] By analyzing and comparing wind speed, wind direction, temperature, and rainfall information from marine environmental information with marine weather forecast data, marine weather forecast deviation information can be obtained.

[0134] Marine weather forecast deviation information and marine early warning information are imported into the early warning analysis model for real-time early warning analysis to obtain the second marine early warning information.

[0135] It should be noted that in the process of analyzing and comparing the wind speed, wind direction, temperature, and rainfall information from the marine environmental information with marine weather forecast data to obtain marine weather forecast deviation information, the wind speed, wind direction, temperature, and rainfall information are real-time acquired information about the current marine environment of the fishing vessel. In the process of importing the marine weather forecast deviation information and marine early warning information into the early warning analysis model for real-time early warning analysis, the early warning analysis model will compare and analyze the weather forecast deviation information and weather anomaly level information for each marine fishing sub-region to obtain new weather anomaly level information and new early warning level information. The new weather anomaly level information and the new early warning level information will then be fused to obtain a second marine early warning information.

[0136] According to an embodiment of the present invention, the step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information specifically includes:

[0137] Obtain information on marine fishing routes;

[0138] Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information;

[0139] The warning level is compared with the first preset warning threshold and the second preset warning threshold;

[0140] If the warning level is greater than the first preset warning threshold and less than the second preset warning threshold, then the warning level of each marine fishing sub-area is analyzed based on the second marine warning information.

[0141] Marine fishing sub-areas with warning levels lower than the first preset warning threshold are marked as priority fishing sub-areas;

[0142] Based on the marine fishing route information, the marine fishing sub-area where the route is located is marked as the initial fishing sub-area;

[0143] Ocean route correction information is obtained based on the priority fishing sub-area and the initial fishing sub-area;

[0144] The second marine fishing route information is obtained by correcting the route based on the marine route correction information.

[0145] According to an embodiment of the present invention, the step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information further includes:

[0146] Obtain information on marine fishing routes;

[0147] Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information;

[0148] Compare the warning level with the second preset warning threshold;

[0149] If the warning level is greater than the second preset warning threshold, the warning level for each marine fishing sub-area is obtained based on the second marine warning information;

[0150] The warning level of each marine fishing sub-area is compared with the preset safety level, and marine fishing sub-areas with a lower than the preset safety level are selected as safe marine sub-areas.

[0151] Return route information is generated based on the aforementioned safe ocean sub-region.

[0152] It should be noted that the warning levels are generally from 0 to 10, with the first preset warning threshold being 4, the second preset warning threshold being 7, and the preset safety level being 5. If the warning level is higher than the second preset warning threshold, it indicates that there are relatively severe weather conditions in the marine fishing sub-area where the current location is situated.

[0153] A second aspect of the present invention also provides a big data-based early warning system 4 for transoceanic fisheries safety. The system includes a memory 41 and a processor 42. The memory includes a big data-based early warning method program for transoceanic fisheries safety. When executed by the processor, the big data-based early warning method program for transoceanic fisheries safety implements the following steps:

[0154] Obtain two-dimensional map information of the marine fishing area, divide the area according to the two-dimensional map information, and obtain multiple marine fishing sub-areas;

[0155] Historical marine weather data for marine fishing sub-regions are obtained, and marine weather forecast data is obtained by performing predictive analysis based on big data information on the historical marine weather data.

[0156] Marine weather forecast data is imported into an early warning analysis model for early warning analysis to obtain marine early warning information.

[0157] Obtain marine environmental information, update marine early warning information based on marine environmental information, and obtain a second marine early warning information;

[0158] Ocean route correction information is generated based on the second ocean early warning information, and the route is corrected based on the ocean route correction information.

[0159] According to an embodiment of the present invention, the step of importing marine weather forecast data into an early warning analysis model for early warning analysis to obtain marine early warning information specifically includes:

[0160] Marine weather forecast data for each marine fishing sub-region is obtained, and the marine weather forecast data is imported into the early warning analysis model for data analysis to obtain the weather anomaly level information for each marine fishing sub-region.

[0161] The early warning level information for marine fishing sub-regions is obtained by calculating and analyzing the weather anomaly level information.

[0162] The marine early warning information is obtained by fusing the weather anomaly level information with the early warning level information.

[0163] The specific formula for calculating the warning level is as follows:

[0164]

[0165] Where P represents the warning level, M represents the total number of marine fishing sub-regions, and E represents the warning level. i Let K be the weather anomaly level for the i-th marine fishing sub-region, and K be the weather anomaly correction coefficient. The marine early warning information includes the weather anomaly level information and early warning level information for each marine fishing sub-region.

[0166] It should be noted that each marine fishing sub-region has independent marine weather forecast data and weather anomaly level information. By subdividing the marine fishing region and obtaining specific marine information for the marine fishing sub-region, the accuracy of prediction and analysis of the marine fishing region can be improved.

[0167] A third aspect of the present invention also provides a computer-readable storage medium comprising a big data-based method program for early warning of safety in transoceanic fisheries, wherein when the big data-based method program for early warning of safety in transoceanic fisheries is executed by a processor, it implements the steps of the big data-based method for early warning of safety in transoceanic fisheries as described in any of the preceding claims.

[0168] This invention discloses a method, system, and medium for early warning of transoceanic fisheries safety based on big data. By acquiring and analyzing historical marine weather data and current marine environmental information from multiple marine fishing sub-regions, it obtains current early warning information, determines the warning level, generates different navigation correction information for different warning levels, and determines suitable fishing routes based on the correction information, thereby effectively improving catch yield and the economic efficiency of marine fisheries. Furthermore, by analyzing safe marine sub-regions within the fishing area, it obtains suitable return routes, effectively reducing economic losses caused by abnormal weather factors in marine production.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0174] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for early warning of transoceanic fishery safety based on big data, characterized in that, include: Obtain two-dimensional map information of the marine fishing area, divide the area according to the two-dimensional map information, and obtain multiple marine fishing sub-areas; Historical marine weather data for marine fishing sub-regions are obtained, and marine weather forecast data is obtained by performing predictive analysis based on big data information on the historical marine weather data. Marine weather forecast data is imported into an early warning analysis model for early warning analysis to obtain marine early warning information. Obtain marine environmental information, update marine early warning information based on marine environmental information, and obtain a second marine early warning information; Based on the second marine early warning information, marine route correction information is generated, and route correction is performed based on the marine route correction information. Specifically, the process of importing marine weather forecast data into an early warning analysis model for early warning analysis to obtain marine early warning information includes: Marine weather forecast data for each marine fishing sub-region is obtained, and the marine weather forecast data is imported into the early warning analysis model for data analysis to obtain the weather anomaly level information for each marine fishing sub-region. The early warning level information for marine fishing sub-regions is obtained by calculating and analyzing the weather anomaly level information. The marine early warning information is obtained by fusing the weather anomaly level information with the early warning level information. The specific formula for calculating the warning level is as follows: ， Where P represents the warning level, and M represents the total number of marine fishing sub-regions. Let K be the weather anomaly level for the i-th marine fishing sub-region, and K be the weather anomaly correction coefficient. The marine early warning information includes the weather anomaly level information and early warning level information for each marine fishing sub-region. Specifically, the acquisition of marine environmental information and the updating of marine early warning information based on the marine environmental information to obtain a second marine early warning information are as follows: By analyzing and comparing wind speed, wind direction, temperature, and rainfall information from marine environmental information with marine weather forecast data, marine weather forecast deviation information can be obtained. Marine weather forecast deviation information and marine early warning information are imported into an early warning analysis model for real-time early warning analysis to obtain a second marine early warning information. Specifically, the step of generating maritime route correction information based on the second maritime early warning information and correcting the route based on the maritime route correction information involves: Obtain information on marine fishing routes; Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information; The warning level is compared with the first preset warning threshold and the second preset warning threshold; If the warning level is greater than the first preset warning threshold and less than the second preset warning threshold, then the warning level of each marine fishing sub-area is analyzed based on the second marine warning information. Marine fishing sub-areas with warning levels below the first preset warning threshold are marked as priority fishing sub-areas; Based on the marine fishing route information, the marine fishing sub-area where the route is located is marked as the initial fishing sub-area; Ocean route correction information is obtained based on the priority fishing sub-area and the initial fishing sub-area; The second marine fishing route information is obtained by correcting the route based on the marine route correction information; The step of generating marine route correction information based on the second marine early warning information and correcting the route based on the marine route correction information further includes: Obtain information on marine fishing routes; Based on the second marine early warning information, obtain the early warning level of the marine fishing sub-area where the current location is in the marine fishing route information; Compare the warning level with the second preset warning threshold; If the warning level is greater than the second preset warning threshold, the warning level for each marine fishing sub-area is obtained based on the second marine warning information; The warning level of each marine fishing sub-area is compared with the preset safety level, and marine fishing sub-areas with a lower than the preset safety level are selected as safe marine sub-areas. Return route information is generated based on the aforementioned safe ocean sub-region.

2. The method for early warning of transoceanic fishery safety based on big data according to claim 1, characterized in that, The process involves obtaining two-dimensional map information of the marine fishing area, dividing the area based on the two-dimensional map information, and obtaining multiple marine fishing sub-areas, specifically as follows: Construct a two-dimensional ocean map model, mark the ocean fishing areas on the ocean two-dimensional map model, and obtain two-dimensional map information of the ocean fishing areas; Obtain area information from the two-dimensional map information, and divide the marine fishing area into M marine fishing sub-regions according to the size of the area, with the area of ​​each region being smaller than the preset maximum area.

3. The method for early warning of transoceanic fishery safety based on big data according to claim 1, characterized in that, The process of acquiring historical marine weather data for the marine fishing sub-region and performing predictive analysis based on big data information to obtain marine weather forecast data specifically involves: Establish a marine weather data training model, obtain a marine weather data training set from big data, and import the training set into the marine data training model for training; Historical marine weather data from marine fishing sub-regions are imported into a trained marine data training model for data prediction, resulting in marine weather forecast data.

4. A big data-based early warning system for transoceanic fisheries safety, characterized in that: The system includes: a memory and a processor. The memory includes a big data-based early warning method program for transoceanic fisheries safety. When the big data-based early warning method program for transoceanic fisheries safety is executed by the processor, it implements the steps of the big data-based early warning method for transoceanic fisheries safety as described in any one of claims 1 to 3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a big data-based method program for early warning of transoceanic fishery safety. When the big data-based method program for early warning of transoceanic fishery safety is executed by a processor, it implements the steps of the big data-based method for early warning of transoceanic fishery safety as described in any one of claims 1 to 3.

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