Methods and systems for supervising fishing operations within the marine ecological protection red line

Abstract

The present invention provides a method and system for supervising fishing operations within the marine ecological protection red line, including: obtaining AIS data, extracting key data from the AIS data; wherein the key data includes MMSI, position, speed, heading and sailing time; dividing sub-trajectory segments by MMSI; segmenting the ship trajectory according to the sub-trajectory segments; based on the ship trajectory segmentation, determining whether there is fishing behavior by a fishing vessel; if there is fishing behavior by a fishing vessel, determining whether it is a fishing ban period; if it is not a fishing ban period, making full use of the ship's AIS data to establish an integrated judgment system for trajectory, speed, and sailing time. Under the conditions of speed and sailing time restrictions, the trajectory feature judgment based on the direction distribution algorithm can more accurately and quickly locate the trawling fishing vessel at sea, thereby improving the management efficiency of human activities at sea within the marine ecological protection red line and facilitating the timely formulation of management and control decisions by functional departments.

Description

Technical Field

[0001] The present invention relates to the field of marine ecological environment technology, and in particular to a method and system for supervising fishing operations within the marine ecological protection red line. Background Art

[0002] The Automatic Identification System (AIS) is a shipborne broadcast transponder system that allows ships to monitor traffic conditions in nearby waters. AIS data primarily includes dynamic vessel information such as position, speed, and heading, as well as static attributes entered by crew members, such as the ship's name, call sign, type, and captain, and vessel identification information, primarily the unique MMSI (Medium Shipment Size) code.

[0003] Automatic Identification Systems (AISs) should maintain normal operation to avoid collisions between vessels. Research on the maritime applications of AIS data started relatively late in China, primarily focusing on ship collision avoidance and submarine cable management. Analysis of ship samples is relatively rare, and more mature research focuses on determining fishing trajectories.

[0004] Based on AIS data, we identify vessel trajectories and activity levels, determine the type and behavior of fishing vessels, and analyze the patterns of vessel activity. The analysis primarily utilizes data fusion, empirical rules, statistical results, and machine learning methods: 1) We directly identify vessel behaviors such as navigation, stopping, anchoring, and fishing through frequent mining of AIS trajectory data. We analyze the trajectory characteristics of AIS data from different voyages, specifically interlaced trajectories to identify fishing activity. 2) Through spatiotemporal alignment of radar and AIS data, we indirectly determine whether a vessel is engaging in illegal fishing by detecting that its AIS data has been stopped, indicating that the vessel's AIS equipment may be disabled.

[0005] The determination of fishing vessel behavior requires alignment with radar data analysis and cannot be made solely through AIS data. This increases the difficulty of data processing and requires high model computing capabilities, making it difficult to implement operational redline supervision for marine ecological protection. Secondly, the use of AIS data has limitations. Determining vessel behavior solely through single trajectory data or historical voyage data mining does not adequately analyze characteristic information such as the vessel's real-time speed and direction, resulting in ineffective judgment results. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a method and system for supervising fishing operations within the marine ecological protection red line. From the perspective of time and space, the present invention proposes management rules for fishing operations within the red line, which greatly improves the pertinence and applicability of identifying abnormal fishing behaviors at sea; makes full use of ship AIS data, establishes an integrated identification system of trajectory, speed, and sailing time, and under the conditions of speed and sailing time restrictions, through trajectory feature judgment based on directional distribution algorithm, it can more accurately and quickly locate trawling fishing vessels at sea, improve the efficiency of management of human activities at sea within the marine ecological protection red line, and facilitate functional departments to make timely management and control decisions.

[0007] In a first aspect, an embodiment of the present invention provides a method for supervising fishing operations within the marine ecological protection red line, the method comprising:

[0008] Acquiring AIS data and extracting key data from the AIS data; wherein the key data includes MMSI, position, speed, heading, and voyage time;

[0009] Divide the sub-trajectory segments by the MMSI;

[0010] performing ship trajectory segmentation according to the sub-trajectory segments;

[0011] Based on the ship trajectory segmentation, determining whether there is fishing behavior by the fishing vessel;

[0012] If the fishing vessel is fishing, determine whether it is a closed fishing season;

[0013] If it is not a closed fishing season, then if the fishing vessel is fishing in the core protection area, general control area of the nature reserve, and other species distribution areas within the marine ecological protection red line, the vessel identity information will be output;

[0014] Among them, the distribution areas of other species include coral reefs, seagrass beds, seaweed fields, oyster reefs, and rare and endangered species.

[0015] Furthermore, based on the ship trajectory segmentation, determining whether there is fishing behavior by a fishing vessel includes:

[0016] Traversing the target tracks and speeds of all fishing vessels in the detection area within the detection cycle time;

[0017] Tracing the continuous trajectory of the target within one detection cycle according to the target trajectory of all fishing vessels;

[0018] When the navigation speed and the navigation time are within a set range and the continuous trajectories are in a cross-overlapping form, the fishing boat is engaged in fishing.

[0019] Furthermore, the judgment of overlap is achieved in the following way:

[0020] Obtaining timestamps and geographic coordinates of trajectory points from the continuous trajectory;

[0021] Calculating the ship track direction distribution according to the timestamps of the track points and the geographic coordinates;

[0022] Calculating a position rotation angle according to the ship track direction distribution;

[0023] Calculate the standard deviation of the x-axis and the standard deviation of the y-axis according to the position rotation angle;

[0024] Calculating a ratio based on the standard deviation of the x-axis and the standard deviation of the y-axis;

[0025] When the ratio is within a first preset threshold range, there is a cross-overlap.

[0026] Furthermore, dividing the sub-trajectory segments by the MMSI includes:

[0027] Intercept any area within the marine ecological protection red line and mark the area as the target waters;

[0028] If multiple points in the trajectory appear continuously in the target water area, the trajectory is marked as target water area data;

[0029] Obtain all trajectories passing through the target water area;

[0030] Counting the number of track points in each track passing through the target water area;

[0031] When the number of the trajectory points is less than the first value, the current trajectory is eliminated;

[0032] When the number of the trajectory points is greater than the first value, the navigation time is greater than the second value, and the navigation speed is within a second preset threshold range, the MMSI is divided into the sub-trajectory segments.

[0033] Furthermore, based on the ship trajectory segmentation, determining whether there is fishing behavior by a fishing vessel includes:

[0034] Build a big data computing and storage framework;

[0035] Taking the key data as input parameters;

[0036] Based on predefined anomaly rules, it uses threshold comparison, spatial operations, feature matching, and comparative analysis, combined with multi-threading technology to perform concurrent detection of multiple types of anomaly features.

[0037] Determine the type of abnormal event based on the classification of abnormal trawling behavior;

[0038] Issue early warnings based on the abnormal event type and abnormal management requirements;

[0039] Outputting abnormal event detection results in an array format, wherein the abnormal event detection results include an ordered combination of current time, target location, target identifier, and abnormal event;

[0040] The presence of the fishing behavior by the fishing vessel is determined based on the abnormal event detection result.

[0041] Furthermore, calculating the position rotation angle according to the ship track direction distribution includes:

[0042] The position rotation angle is calculated according to the following formula:

[0043]

[0044] in, is the difference between the mean center and the x-coordinate, is the difference between the mean center and the y coordinate, is the position rotation angle, and the timestamp of the trajectory point is used as the element i of the sample, is the horizontal coordinate of the element i, is the vertical coordinate of the element i.

[0045] Furthermore, calculating the standard deviation of the x-axis and the standard deviation of the y-axis according to the position rotation angle includes:

[0046] The standard deviation of the x-axis and the standard deviation of the y-axis are calculated according to the following formula:

[0047]

[0048] in, is the standard deviation of the x-axis, is the standard deviation of the y-axis.

[0049] In a second aspect, an embodiment of the present invention provides a system for supervising fishing operations within the marine ecological protection red line, the system comprising:

[0050] An acquisition module, configured to acquire AIS data and extract key data from the AIS data; wherein the key data includes MMSI, position, speed, heading, and voyage time;

[0051] a division module, configured to divide sub-trajectory segments according to the MMSI;

[0052] a segmentation module, configured to segment the ship trajectory according to the sub-trajectory segments;

[0053] A first judgment module is used to judge whether there is fishing behavior of a fishing vessel based on the ship trajectory segmentation;

[0054] The second judgment module is used to judge whether it is a closed fishing period when the fishing vessel is fishing;

[0055] An output module is used to output the vessel identity information when the fishing vessel is fishing in the core protection area, general control area of the nature reserve and other species distribution areas within the marine ecological protection red line, when it is not a closed fishing season;

[0056] Among them, the distribution areas of other species include coral reefs, seagrass beds, seaweed fields, oyster reefs, and rare and endangered species.

[0057] In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the above-mentioned method when executing the computer program.

[0058] In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having a non-volatile program code executable by a processor, wherein the program code enables the processor to execute the method as described above.

[0059] The embodiments of the present invention provide a method and system for supervising fishing operations within the marine ecological protection red line, including: obtaining AIS data, extracting key data from the AIS data; wherein the key data includes MMSI, position, speed, heading, and sailing time; dividing the sub-trajectory segments according to the MMSI;

[0060] The ship trajectory is segmented according to the sub-trajectory segments; based on the ship trajectory segmentation, it is determined whether there is fishing behavior by fishing vessels; if there is fishing behavior by fishing vessels, it is determined whether it is a fishing ban period; if it is not a fishing ban period, the ship identity information is output when there is fishing behavior by fishing vessels in the core protection areas, general control areas of nature reserves and other species distribution areas within the marine ecological protection red line; among them, other species distribution areas include coral reefs, seagrass beds, seaweed fields, oyster reefs, rare and endangered species; from the perspective of time and space, management rules for fishing behavior within the red line are proposed, which greatly improves the pertinence and applicability of the identification of abnormal fishing behavior at sea; fully utilizes the ship AIS data to establish an integrated identification system of trajectory, speed, and sailing time. Under the conditions of speed and sailing time restrictions, the trajectory feature judgment based on the direction distribution algorithm can more accurately and quickly locate the trawling fishing vessels at sea, improve the efficiency of the management of human activities at sea within the marine ecological protection red line, and facilitate the functional departments to make timely management and control decisions.

[0061] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood through implementation of the present invention. The purposes and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0062] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0064] Figure 1 A flow chart of a method for supervising fishing operations within the marine ecological protection red line provided in Example 1 of the present invention;

[0065] Figure 2 A schematic diagram of the fishing boat trajectory characteristics provided in Example 1 of the present invention;

[0066] Figure 3 A schematic diagram of the trajectory of a suspicious vessel provided in Example 1 of the present invention;

[0067] Figure 4 Schematic diagram of the supervision system for fishing operations within the marine ecological protection red line provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0068] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0069] This application implements the ecological protection red line system. Effective red line supervision measures are critical to protecting important marine ecosystems within the red line. Sea trawling is one of the important factors affecting the habitat of marine organisms. Through the collection and analysis of relevant technical documents, a method for determining sea trawling behavior based on AIS data within the marine ecological protection red line has not yet been formed.

[0070] This method is suitable for identifying trawling activities at sea and within marine ecological protection red lines. It primarily uses statistical discrimination of vessel AIS data, combined with basic knowledge analysis of trawling behavior, to identify the behavioral characteristics of trawling vessels. In conjunction with the regulatory requirements for various types of marine ecological protection red lines, it provides a method and system for monitoring fishing operations within marine ecological protection red lines.

[0071] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0072] Example 1:

[0073] Figure 1 Flowchart of the method for supervising fishing operations within the marine ecological protection red line provided in Example 1 of the present invention.

[0074] Reference Figure 1 , the method comprises the following steps:

[0075] Step S101, obtaining AIS data and extracting key data from the AIS data; wherein the key data includes MMSI, position, speed, heading and sailing time;

[0076] Key data here also includes latitude and longitude, ship type, and signal reception time. First, quality control is performed on the AIS data, incomplete and unreasonable data records are deleted, and the MMSI (Maritime Mobile Service Identify) is extracted.

[0077] Step S102, dividing the sub-trajectory segments by MMSI;

[0078] Step S103, segmenting the ship trajectory according to the sub-trajectory segments;

[0079] Step S104: Based on the ship trajectory segmentation, determine whether there is any fishing behavior; if there is any fishing behavior, execute step S105; if there is no fishing behavior, end the process;

[0080] Step S105, determining whether it is a closed fishing season; if it is not a closed fishing season, executing step S106; if it is a closed fishing season, executing step S107;

[0081] Step S106: Outputting vessel identity information if fishing occurs in core protected areas, general control areas of nature reserves, and other species distribution areas within the marine ecological protection red line; other species distribution areas include coral reefs, seagrass beds, kelp beds, oyster reefs, and rare and endangered species;

[0082] Step S107: directly output the ship identity information.

[0083] Specifically, based on the characteristics of fishing vessel fishing behavior, trawling fishing behavior within the marine ecological protection red line is identified. This is mainly done through the vessel's identity information, combined with the location, speed, heading, time and other information provided in the AIS data, to identify abnormal fishing vessel behavior within the marine ecological protection red line, that is, the fishing behavior of the vessel at a certain time or in a certain area does not comply with navigation laws or management regulations. For example, according to relevant regulations, it is prohibited to engage in fishing activities during the closed fishing season, in the closed fishing area, and in nature reserves.

[0084] The ecological protection red line refers to an area within the ecological space that has special and important ecological functions and requires mandatory and strict protection. It is the bottom line and lifeline for ensuring and maintaining national ecological security.

[0085] The marine ecological protection red line mainly includes mangroves, coral reefs, seagrass beds, coastal salt marshes, important estuaries, important mudflats and shallow waters, distribution areas of rare and endangered species, and spawning grounds of important fishery resources. According to the red line management requirements, human development and utilization activities are not allowed in the core protection areas of the red line, and only limited human activities that do not cause damage to ecological functions are allowed in other areas.

[0086] Based on the impact of trawling on protected areas within the red line, and in combination with relevant national regulations on fishery harvesting and marine ecological protection red line management, rules for trawling within the marine ecological protection red line are formulated in terms of time and space:

[0087] 1. Time rules: Vessels are prohibited from trawling within the red line during the fishing ban period.

[0088] 2. Spatial rules outside the closed fishing season:

[0089] 1) Vessels are prohibited from conducting trawling in the red line core protection area and the general control area of the nature reserve;

[0090] 2) Trawling is prohibited in coral reefs, seagrass beds, kelp beds, oyster reefs, and areas where rare and endangered species are found.

[0091] If there is spatial overlap in the above red line areas, the areas with larger distribution range will be controlled.

[0092] Furthermore, step S104 includes the following steps:

[0093] Step S201, within the detection cycle time, traverse the target tracks and speeds of all fishing vessels in the detection area;

[0094] Step S202, tracing back the continuous trajectory of the target within a detection cycle time according to the target trajectory of all fishing vessels;

[0095] Step S203: When the speed and the sailing time are within the set range and the continuous trajectories are in the form of cross-overlapping, there is fishing behavior of the fishing boat.

[0096] Specifically, the purpose of anomaly detection of trawling fishing behavior at sea is mainly to detect the abnormal information hidden in the target data through computer means, so as to provide decision-making basis for management departments at all levels of marine ecological protection red lines.

[0097] This application mainly combines data and knowledge-driven, integrates field background knowledge and literature research knowledge to identify and classify abnormal behaviors of maritime targets, and on this basis matches the maritime target perception data with predefined rules to identify targets with abnormal behaviors.

[0098] During the detection cycle, the target trajectory and speed of all fishing vessels in the detection area are traversed; the continuous trajectory of the target within a detection cycle is traced back. When the speed and sailing time are within the set range and the continuous trajectory is cross-overlapping, it is determined that there is fishing behavior. Figure 2 ,In the fishing vessel trajectory features, the sequence of serial numbers represents the ,time order.

[0099] Furthermore, the judgment of overlap is achieved in the following way:

[0100] Step S301, obtaining the timestamps and geographic coordinates of the track points from the continuous track;

[0101] Step S302, calculating the ship track direction distribution based on the timestamps and geographic coordinates of the track points;

[0102] Step S303, calculating the position rotation angle according to the ship track direction distribution;

[0103] Step S304, calculating the standard deviation of the x-axis and the standard deviation of the y-axis according to the position rotation angle;

[0104] Step S305, calculating a ratio based on the standard deviation of the x-axis and the standard deviation of the y-axis;

[0105] Step S306: When the ratio is within a first preset threshold range, there is a cross-overlap.

[0106] Specifically, in order to extract the shape features of the trajectory in the time and space dimensions, the timestamps and geographic coordinates of the trajectory points are used as input to calculate the spatial distribution of the ship trajectory points, and whether the ship is performing operational behavior is determined based on the ship's trajectory characteristics.

[0107] The timestamp T of the track point is used as the element i of the sample, and the geographic coordinates (latitude and longitude coordinates) are used as the sample values to calculate the direction distribution of the ship track, that is, the standard deviation ellipse form refers to formula (1):

[0108] (1)

[0109] in, and is the coordinate of feature i, is the mean center of the features, and n is equal to the total number of features.

[0110] Furthermore, step S303 includes:

[0111] Calculate the position rotation angle according to formula (2):

[0112] (2)

[0113] in, is the difference between the mean center and the x-coordinate, is the difference between the mean center and the y coordinate, is the position rotation angle, and the timestamp of the trajectory point is used as the element i of the sample, is the horizontal coordinate of feature i, is the vertical coordinate of feature i.

[0114] Furthermore, step S304 includes:

[0115] Calculate the standard deviation of the x-axis and the standard deviation of the y-axis according to formula (3):

[0116] (3)

[0117] in, is the standard deviation of the x-axis, is the standard deviation of the y-axis.

[0118] When the standard deviation ellipse of the fishing boat trajectory distribution , it can be preliminarily determined that the vessel is carrying out trawling activities. The first preset threshold range can be (2.97, 10.91).

[0119] Furthermore, step S102 includes the following steps:

[0120] Step S401: intercept any area within the marine ecological protection red line and mark the area as the target water area;

[0121] Step S402: if multiple points in the trajectory appear continuously in the target water area, the trajectory is marked as target water area data;

[0122] Step S403, obtaining all trajectories passing through the target water area;

[0123] Step S404, counting the number of track points in each track passing through the target water area;

[0124] Step S405: when the number of trajectory points is less than the first value, the current trajectory is eliminated;

[0125] Step S406: When the number of trajectory points is greater than the first value, the navigation time is greater than the second value, and the speed is within the second preset threshold range, the sub-trajectory segments are divided according to the MMSI.

[0126] Specifically, the acquired data is processed initially, including AIS data deduplication, dynamic and static data fusion, and data sorting. Incorrect data in the original data, such as field data range errors, format errors, track point data drift, and speed changes, are resolved and maintained, and target waters data is extracted. The processing flow is as follows:

[0127] Cut off any area within the marine ecological protection red line and mark the area as the target water area S; for the trajectory Tr={p1,p2,...,pn}, if some points in the trajectory appear continuously in the target area S, then mark the entire trajectory Tr as the target water area data; integrate all trajectories passing through the area S, and count the number of trajectory points in each trajectory separately; when the number of trajectory points is less than the first value, it proves that the trajectory is incomplete and the current trajectory is discarded.

[0128] The trajectory generated by a normal ship's navigation is relatively simple, with a more concentrated distribution of track points. However, fishing tracks often have more complex shape features. Therefore, we use the ship's navigation and fishing tracks for shape feature extraction. We filter track points (greater than the first value) for speed within a second preset threshold range (set to 2 kn to 6 kn), and for navigation time greater than a second value (set to 2 hours). After preliminary processing, the track is divided into sub-segments based on the ship's MMSI number to achieve ship track segmentation. The first value can be set to 5.

[0129] Furthermore, step S104 includes the following steps:

[0130] Step S501: building a big data computing and storage framework;

[0131] Step S502, taking key data as input parameters;

[0132] Step S503, based on predefined anomaly rules, using threshold comparison, spatial operations, feature matching and comparative analysis, combined with multi-threading technology, performs concurrent detection of multiple types of anomaly features;

[0133] Step S504, determining the abnormal event type based on the abnormal trawling behavior classification;

[0134] Here, based on the classification of abnormal trawling behavior, the abnormality of each type of abnormal event is determined and behavioral abnormalities are distinguished.

[0135] Step S505: issuing an early warning based on the abnormal event type and abnormal management requirements;

[0136] Step S506: Outputting the abnormal event detection result in an array format, where the abnormal event detection result includes an ordered combination of the current time, the target location, the target identifier, and the abnormal event;

[0137] Step S507: determining the presence of fishing behavior by a fishing vessel based on the abnormal event detection result.

[0138] Specifically, the offshore trawling identification model is aimed at real-time / quasi-real-time analysis scenarios of massive targets. Based on the big data computing and storage architecture, it adopts a distributed real-time computing framework to stream data of offshore fishing vessels, detect predefined target abnormal events in real time, and store abnormal information in the database in real time to support dynamic display and monitoring and early warning on the front-end interface.

[0139] In the process of building a big data computing and storage framework, a three-level storage architecture of real-time database-historical database-thematic database is adopted for the scenarios of real-time perception of abnormal fishing vessels in status and quasi-real-time perception of abnormal fishing behavior. The real-time database uses MySQL and Redis, the historical database uses HBase and Hive, and the thematic database uses MySQL.

[0140] Based on the big data storage and computing framework, event-level anomaly detection algorithm integration is carried out, and the data input and result output modules are integrated to form the overall process of the abnormal fishing behavior identification model at sea.

[0141] The main advantages of this application include: 1) It presents the first method for identifying trawling activities within marine ecological protection red lines, effectively supporting decision-making for at-sea fishing and marine ecological protection management. 2) The method is simple and easy to implement. By fully utilizing ship AIS data, it eliminates the need for additional marine monitoring equipment and human patrol costs. Using data and models, it enables automated, real-time identification of trawling activities at sea, reducing both technical barriers and labor costs.

[0142] The application of the red line supervision of marine ecological protection in the distribution area of rare and endangered species includes the following steps:

[0143] Step S601: Process the AIS data of fishing vessels, extract the AIS data of fishing vessels in April in the marine ecological protection red line of the rare and endangered species distribution area of Dalian spotted seals in Liaoning Province, perform quality control on the data, delete incomplete and unreasonable data records, and extract the MMSI code.

[0144] Step S602: abnormal fishing vessel behavior determination. According to the red line control requirements, in order to protect the habitat environment of rare and endangered species, fishing operations are prohibited within the red line of their distribution area. The processed fishing vessel AIS data is used to determine trawling fishing behavior.

[0145] According to the trawling behavior recognition model, based on the analysis of single-day data, the target trajectories, speeds (2kn to 6kn) and flight times (T ≥ 2 hours) of all fishing vessels in the detection area within T time were traversed, and three suspicious trawling vessels were screened out. The track points and track distribution are as follows Figure 3 shown.

[0146] Step S603: Complete red line supervision warning and processing based on the result of the suspicious violation of the vessel within the red line.

[0147] Example 2:

[0148] Figure 4 Schematic diagram of the supervision system for fishing operations within the marine ecological protection red line provided in Example 2 of the present invention.

[0149] Reference Figure 4 , the system comprises:

[0150] The acquisition module is used to acquire AIS data and extract key data from the AIS data; the key data includes MMSI, position, speed, heading and sailing time;

[0151] A division module, used to divide the sub-trajectory segments by MMSI;

[0152] A segmentation module, used to segment the ship trajectory according to sub-trajectory segments;

[0153] The first judgment module is used to judge whether there is fishing behavior of the fishing vessel based on the segmentation of the ship trajectory;

[0154] The second judgment module is used to judge whether it is a closed fishing season when there is fishing behavior;

[0155] The output module is used to output vessel identity information when fishing vessels are fishing in core protected areas, general control areas of nature reserves, and other species distribution areas within the marine ecological protection red line, when fishing is not prohibited;

[0156] Among them, other species distribution areas include coral reefs, seagrass beds, seaweed fields, oyster reefs, and rare and endangered species.

[0157] The beneficial effects of this application are:

[0158] 1) Management System Division. This application, based on practical considerations and the actual needs of managing fishing within the marine ecological protection redline, proposes management rules for fishing within the redline from a temporal and spatial perspective, greatly improving the pertinence and applicability of identifying abnormal fishing behavior at sea.

[0159] 2) Model Algorithm Construction. This application, based on real-time and operability considerations, fully utilizes vessel AIS data to establish an integrated trajectory, speed, and duration identification system. Under speed and duration constraints, trajectory feature determination based on a directional distribution algorithm can more accurately and rapidly locate trawling vessels at sea, improving the efficiency of managing human activities at sea within the marine ecological protection redline and facilitating timely management and control decisions by relevant departments.

[0160] An embodiment of the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for supervising fishing operations within the marine ecological protection red line provided in the above embodiment are implemented.

[0161] An embodiment of the present invention also provides a computer-readable medium having a non-volatile program code executable by a processor, wherein a computer program is stored on the computer-readable medium, and when the computer program is run by the processor, the steps of the method for supervising fishing operations within the marine ecological protection red line of the above embodiment are executed.

[0162] The computer program product provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the previous method embodiments. For specific implementation, please refer to the method embodiments and will not be repeated here.

[0163] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and devices can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0164] In addition, in the description of the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to mechanical connections or electrical connections; they may refer to direct connections or indirect connections through an intermediate medium; and they may refer to internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0165] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0166] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0167] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the above-described embodiments, those skilled in the art should understand that any person skilled in the art can modify or easily conceive of changes to the technical solutions described in the above-described embodiments within the technical scope disclosed by the present invention, or replace some of the technical features therein with equivalents. Such modifications, changes, or replacements do not deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for supervising fishing operations within the marine ecological protection red line, characterized in that: The method comprises: Acquiring AIS data and extracting key data from the AIS data; wherein the key data includes MMSI, position, speed, heading, and voyage time; Divide the sub-trajectory segments by the MMSI; performing ship trajectory segmentation according to the sub-trajectory segments; Based on the ship trajectory segmentation, determining whether there is fishing behavior by the fishing vessel; If the fishing vessel is fishing, determine whether it is a closed fishing season; If it is not a closed fishing season, then if the fishing vessel is fishing in the core protection area, general control area of the nature reserve, and other species distribution areas within the marine ecological protection red line, the vessel identity information will be output; The distribution areas of other species include coral reefs, seagrass beds, seaweed fields, oyster reefs, and rare and endangered species; Based on the vessel trajectory segmentation, determining whether there is fishing behavior by the fishing vessel includes: Traversing the target tracks and speeds of all fishing vessels in the detection area within the detection cycle time; Tracing the continuous trajectory of the target within one detection cycle according to the target trajectory of all fishing vessels; When the speed and the sailing time are within the set range, and the continuous trajectories are in a cross-overlapping state, the fishing vessel is engaged in fishing. The judgment of overlap is achieved in the following way: Obtaining timestamps and geographic coordinates of trajectory points from the continuous trajectory; Calculating the ship track direction distribution according to the timestamps of the track points and the geographic coordinates; Calculating a position rotation angle according to the ship track direction distribution; Calculate the standard deviation of the x-axis and the standard deviation of the y-axis according to the position rotation angle; Calculating a ratio based on the standard deviation of the x-axis and the standard deviation of the y-axis; When the ratio is within a first preset threshold range, there is a cross-overlap; The sub-trajectory segments are divided by the MMSI, including: Intercept any area within the marine ecological protection red line and mark the area as the target waters; If multiple points in the trajectory appear continuously in the target water area, the trajectory is marked as target water area data; Obtain all trajectories passing through the target water area; Counting the number of track points in each track passing through the target water area; When the number of the trajectory points is less than the first value, the current trajectory is eliminated; When the number of the trajectory points is greater than the first value, the navigation time is greater than the second value, and the navigation speed is within a second preset threshold range, the MMSI is divided into the sub-trajectory segments.

2. The method for supervising fishing operations within the marine ecological protection red line according to claim 1 is characterized in that: Based on the vessel trajectory segmentation, determining whether there is fishing behavior by the fishing vessel includes: Build a big data computing and storage framework; Taking the key data as input parameters; Based on predefined anomaly rules, it uses threshold comparison, spatial operations, feature matching, and comparative analysis, combined with multi-threading technology to perform concurrent detection of multiple types of anomaly features. Determine the type of abnormal event based on the classification of abnormal trawling behavior; Issue early warnings based on the abnormal event type and abnormal management requirements; Outputting abnormal event detection results in an array format, wherein the abnormal event detection results include an ordered combination of current time, target location, target identifier, and abnormal event; The presence of the fishing behavior by the fishing vessel is determined based on the abnormal event detection result.

3. The method for supervising fishing operations within the marine ecological protection red line according to claim 1 is characterized in that: Calculating the position rotation angle according to the ship track direction distribution includes: The position rotation angle is calculated according to the following formula: in, is the difference between the mean center and the x-coordinate, is the difference between the mean center and the y coordinate, is the position rotation angle, and the timestamp of the trajectory point is used as the element i of the sample, is the horizontal coordinate of the element i, is the vertical coordinate of the element i.

4. The method for supervising fishing operations within the marine ecological protection red line according to claim 1 is characterized in that: Calculating the standard deviation of the x-axis and the standard deviation of the y-axis according to the position rotation angle includes: The standard deviation of the x-axis and the standard deviation of the y-axis are calculated according to the following formula: in, is the standard deviation of the x-axis, is the standard deviation of the y-axis.

5. A monitoring system for fishing operations within the marine ecological protection red line, characterized by: The system comprises: An acquisition module, configured to acquire AIS data and extract key data from the AIS data; wherein the key data includes MMSI, position, speed, heading, and voyage time; a division module, configured to divide sub-trajectory segments according to the MMSI; a segmentation module, configured to segment the ship trajectory according to the sub-trajectory segments; A first judgment module is used to judge whether there is fishing behavior of a fishing vessel based on the ship trajectory segmentation; The second judgment module is used to judge whether it is a closed fishing period when the fishing vessel is fishing; An output module is used to output the vessel identity information when the fishing vessel is fishing in the core protection area, general control area of the nature reserve and other species distribution areas within the marine ecological protection red line, when it is not a closed fishing season; The distribution areas of other species include coral reefs, seagrass beds, seaweed fields, oyster reefs, and rare and endangered species; The first judgment module is specifically configured to: Traversing the target tracks and speeds of all fishing vessels in the detection area within the detection cycle time; Tracing the continuous trajectory of the target within one detection cycle according to the target trajectory of all fishing vessels; When the speed and the sailing time are within the set range, and the continuous trajectories are in a cross-overlapping state, the fishing vessel is engaged in fishing. The judgment of overlap is achieved in the following way: Obtaining timestamps and geographic coordinates of trajectory points from the continuous trajectory; Calculating the ship track direction distribution according to the timestamps of the track points and the geographic coordinates; Calculating a position rotation angle according to the ship track direction distribution; Calculate the standard deviation of the x-axis and the standard deviation of the y-axis according to the position rotation angle; Calculating a ratio based on the standard deviation of the x-axis and the standard deviation of the y-axis; When the ratio is within a first preset threshold range, there is a cross-overlap; The partitioning module is specifically used for: Intercept any area within the marine ecological protection red line and mark the area as the target waters; If multiple points in the trajectory appear continuously in the target water area, the trajectory is marked as target water area data; Obtain all trajectories passing through the target water area; Counting the number of track points in each track passing through the target water area; When the number of the trajectory points is less than the first value, the current trajectory is eliminated; When the number of the trajectory points is greater than the first value, the navigation time is greater than the second value, and the navigation speed is within a second preset threshold range, the MMSI is divided into the sub-trajectory segments.

6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A computer-readable medium having a non-volatile program code executable by a processor, characterized in that The program code causes the processor to execute the method according to any one of claims 1 to 4.

Images