Monitoring method and management system for abnormity of fishing boats in fishing port

Abstract

The invention discloses a fishing boat abnormity monitoring method and management system in a fishing port, and relates to the technical field of port management. The method comprises the steps that after a fishing boat enters a port, boat position data of the fishing boat in the fishing port are continuously obtained, and the boat position data comprise MMSI codes and positions of the fishing boat; marking the fishing boats which are confirmed to be abnormal, obtaining the position data of the fishing boats in an abnormal state in the fishing port, and extracting the abnormal features of the fishing boats; comparing the normal state features with the abnormal state features of the fishing boats to obtain a fishing boat abnormal behavior feature vector set; and judging whether the fishing boats in the fishing port at the current moment are in an abnormal state or not according to the abnormal feature vector set, if so, sending a warning instruction to the fishing boats in the abnormal state and / or adjacent fishing boats, and if not, not responding. Risk factors in the fishing port can be effectively reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of port management, and in particular relates to an abnormal monitoring method and management system for fishing vessels in a fishing port. Background Art

[0002] Traditional fishing vessel monitoring systems usually rely on equipment such as the Automatic Identification System (AIS), the Satellite Positioning System (GPS) and radar to monitor the location and movement trajectory of fishing vessels. Although existing positioning technologies can provide real-time location data of fishing vessels, GPS signals may be interfered with in fishing ports due to factors such as port terrain and building obstructions, resulting in reduced positioning accuracy, inability to accurately determine abnormal behavior of fishing vessels, and prone to abnormal conditions such as collisions. Summary of the invention

[0003] The purpose of the present invention is to provide a method and management system for abnormal monitoring of fishing boats in a fishing port. By analyzing the position data of the fishing boat, it is predicted whether the fishing boat is in a risky state, so as to give timely warnings and effectively reduce the risk factors in the fishing port.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention provides a method for monitoring abnormal fishing boats in a fishing port, comprising: After the fishing boat enters the port, continuously obtain the position data of the fishing boat in the fishing port, wherein the position data includes the MMSI code and position of the fishing boat; Mark the confirmed abnormal fishing boats and obtain the position data of the fishing boats in abnormal state in the fishing port during the abnormal period; Extracting data features of the fishing vessel position data in the fishing port as the fishing vessel position features; According to the position characteristics of the fishing vessels in abnormal state in the fishing port during the abnormal period and the position characteristics of the fishing vessels in abnormal state, the abnormal position characteristic range is obtained; According to the abnormal ship position feature range, it is judged whether the fishing boats in the fishing port are in an abnormal state at the current moment; If so, a warning instruction is sent to the fishing vessel in the abnormal state and / or the adjacent fishing vessels; If not, no response is made.

[0005] The present invention also discloses a method for monitoring abnormal fishing boats in a fishing port, comprising: Receive alert instructions; Enter collision alert state.

[0006] The present invention also discloses a fishing boat abnormality monitoring and management system in a fishing port, comprising: The port management terminal is used to continuously obtain the position data of the fishing boats in the fishing port after the fishing boats enter the port, wherein the position data includes the MMSI code and position of the fishing boats; Mark the confirmed abnormal fishing boats to obtain the position data of the fishing boats in abnormal state in the fishing port during the abnormal period; Extracting data features of the fishing vessel position data in the fishing port as the fishing vessel position features; According to the position characteristics of the fishing vessels in abnormal state in the fishing port during the abnormal period and the position characteristics of the fishing vessels in abnormal state, the abnormal position characteristic range is obtained; According to the abnormal ship position feature range, it is judged whether the fishing boats in the fishing port are in an abnormal state at the current moment; If so, a warning instruction is sent to the fishing vessel in the abnormal state and / or the adjacent fishing vessels; If not, no response is made; The fishing boat side is used to receive warning instructions; Enter collision alert state.

[0007] The present invention collects the position data reported by fishing boats in the fishing port through the port management end, and analyzes the abnormal position feature range after marking the status of the fishing boats, and determines whether the fishing boats in the fishing port are in an abnormal state at the current moment, thereby issuing warning notices to fishing boats in abnormal states, effectively reducing risk factors in the fishing port.

[0008] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0010] Figure 1 A schematic diagram of the functional units and information flow of an abnormal monitoring and management system for fishing vessels in a fishing port according to the present invention in one embodiment; Figure 2 A schematic diagram of a process flow of a port administration terminal according to an embodiment of the present invention; Figure 3 It is a schematic diagram of the steps of a fishing boat end according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the step flow of step S1 in one embodiment of the present invention; Figure 5 This is a schematic diagram of a step flow chart of step S13 in an embodiment of the present invention; Figure 6 This is a schematic diagram of the step flow of step S3 in one embodiment of the present invention; Figure 7 This is a schematic diagram of the step flow of step S4 in one embodiment of the present invention; Figure 8 This is a schematic diagram of a step flow chart of step S41 in an embodiment of the present invention; Fig. 9 This is a schematic diagram of a step flow chart of step S414 in an embodiment of the present invention; Fig.10 FIG. 4 is a schematic diagram of a step flow chart of step S5 according to an embodiment of the present invention.

[0011] In the accompanying drawings, the components represented by the reference numerals are listed as follows: 1-Port management end, 2-Fishing vessel end. DETAILED DESCRIPTION

[0012] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0013] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0014] See also Figures 1 to 3 As shown, the present invention provides a fishing boat abnormal monitoring and management system in a fishing port. From the functional unit, it includes a port management terminal 1 and a fishing boat terminal 2. The port management terminal 1 can be a virtual functional unit, and in practice it can be a dedicated device. The fishing boat terminal 2 can be a communication device set in the fishing boat.

[0015] After the fishing boat enters the port, the port management terminal 1 in this scheme continues to work during the working process, and executes step S1 through VTS (vessel traffic management system) and ship AIS (automatic identification system) to continuously obtain the position data of the fishing boat in the fishing port. The position data includes the MMSI code, position, speed, tonnage and other information of the fishing boat. The MMSI code is a nine-digit code sent by the ship radio communication system on its radio channel, which can uniquely identify various stations and group call stations, commonly known as the "nine-digit code". Next, step S2 can be executed to mark the confirmed abnormal fishing boats to obtain the position data of the fishing boats in abnormal state in the fishing port during the abnormal period, because the fishery administration, traffic control or port staff perform manual marking.

[0016] See also Figure 6 As shown, since the position data of fishing boats is complex and it is not convenient to compare and analyze different fishing boats, step S3 can be executed next to extract the data features of the position data of fishing boats in the fishing port as the position features of fishing boats. Considering that the present scheme mainly considers the risk of collision between fishing boats in the fishing port, the factors closely related to the collision between fishing boats are mainly the quality of fishing boats and the parameters related to navigation. Therefore, in the process of extracting the position features of fishing boats, step S31 can be first executed to extract the tonnage, speed, heading and position of fishing boats in the position data. In order to facilitate the comparison and calculation, step S32 can be executed next to arrange the tonnage, speed, heading and position of each fishing boat in the same order for vectorization as the position features of each fishing boat.

[0017] Since fishing vessels in abnormal states usually have common characteristics, for example, fishing vessels in a collision danger state usually have high speed, large tonnage, abnormal navigation, etc. In order to calculate the abnormal ship position characteristic range, step S4 can be first performed to obtain the abnormal ship position characteristic range according to the ship position characteristics of fishing vessels in abnormal states in the fishing port during the abnormal period and the ship position characteristics of fishing vessels in normal states.

[0018] In the process of the port management terminal 1 in this scheme making a real-time abnormal judgment on the fishing boats in the fishing port, step S5 can be first executed to judge whether the fishing boats in the fishing port are in an abnormal state at the current moment according to the abnormal ship position feature range. If so, it means that the fishing boat is in danger of collision, so step S6 can be executed next to send a warning instruction to the fishing boat in the abnormal state, and of course, a warning instruction can also be sent to the fishing boats adjacent to the fishing boat in the abnormal state to serve as a warning. If not, it means that no abnormality is found, so step S7 can be executed next to not respond.

[0019] After executing step S021 to receive the warning instruction, the fishing boat terminal 2 set in the fishing boat can then execute step S022 to enter the collision warning state, reminding the captain, the captain and other staff in the fishing boat to pay attention to navigation safety in the fishing port.

[0020] See also Figures 4 to 5 As shown, the most important thing in the abnormal state of a fishing boat is whether it is in a state of danger of collision, which is usually closely related to the distance between fishing boats. The position of fishing boats obtained by VTS and ship AIS systems is not very accurate. Especially when the distance between fishing boats is close, accurate fishing boat positions are more needed. In view of this, the port management terminal 1 in this scheme needs to appropriately improve the position accuracy of some fishing boats in combination with the position conditions between fishing boats during operation. Specifically, the rough distance between fishing boats can be obtained first, that is, step S11 is executed to retrieve the abnormal condition records of fishing boats in the fishing port according to the MMSI code in the fishing boat's position data, and the fishing boats with abnormal records are marked as high-risk fishing boats. Next, step S12 can be executed to obtain the initial distance between multiple high-risk fishing boats and the nearest adjacent fishing boats under abnormal conditions according to the position data of fishing boats in abnormal conditions in the fishing port during the abnormal period as the critical distance.

[0021] Afterwards, step S13 may be executed to obtain the safety distance within the fishing port based on the critical distances of multiple high-risk fishing vessels. Specifically, step S131 may be executed first to use the maximum value among the critical distances of multiple high-risk fishing vessels as the benchmark safety distance. Next, step S132 may be executed to use the difference between the maximum value and the minimum value among the critical distances of multiple high-risk fishing vessels as the window width of the safety distance. Next, step S133 may be executed to use the ratio of the window width of the safety distance to the number of high-risk fishing vessels as the floating difference distance. Finally, step S134 may be executed to use the cumulative value of the benchmark safety distance and the floating difference distance as the safety distance within the fishing port.

[0022] In order to determine whether the distance between a fishing boat and an adjacent fishing boat is too close, step S14 can be executed next to determine whether the distance between fishing boats in the fishing port is less than the safe distance based on the position in the fishing boat's position data. If not, it means that the distance is safe enough, so step S15 can be executed next to not respond. If yes, it means that the distance is too close, so step S16 can be executed next to mark the fishing boats whose distances are less than the safe distance as key fishing boats. Finally, step S17 can be executed to obtain a more accurate position of the key fishing boat through several other methods, and the position in the position data of the key fishing boat can be corrected.

[0023] Other ways to obtain the location of fishing boats in this solution can be panoramic high-definition camera image positioning, and the accurate location of fishing boats in the fishing port can be monitored through video linkage tracking. The video linkage tracking module conducts on-site full-time supervision when key ships enter key waters for water safety control, and realizes automatic full-time tracking by monitoring cameras to ensure the navigation safety of such ships. The video linkage tracking module is linked with the ship dynamic monitoring system, and the video screen quickly locates the target ship, and automatically controls the front-end equipment according to the direction and speed of the ship's movement to keep the target ship in the center of the picture.

[0024] In order to supplement the implementation process of the above steps S11 to S17, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0025] #include <iostream> #include <vector> #include <string> #include <cmath> #include<unordered_set> #include <limits> / / Define the fishing boat's position data structure struct ShipPosition { std::string MMSI; / / MMSI code of fishing boat double latitude; / / The latitude of the fishing boat double longitude; / / Longitude of the fishing boat bool isHighRisk; / / Is it a high-risk fishing vessel? bool isKeyShip; / / Is it a key fishing vessel? }; / / Simulate obtaining the fishing boat's position data std::vector <shipposition>getShipPositions() { return { {"123456789", 34.5, 120.3, false, false}, {"987654321", 34.51, 120.32, false, false} / / Add more data }; } / / Simulate obtaining the MMSI list of high-risk fishing vessels std::unordered_set <std::string>getHighRiskMMSI() { return {"987654321"}; / / Example of MMSI of high-risk fishing vessel } / / Calculate the distance between the two fishing boats double calculateDistance(const ShipPosition&ship1, constShipPosition&ship2) { return std::sqrt(std::pow(ship1.latitude - ship2.latitude, 2) + std::pow(ship1.longitude - ship2.longitude, 2)); } / / Get the critical distance of high-risk fishing vessels std::vector <double>getCriticalDistances(const std::vector <shipposition>&ships) { std::vector <double>criticalDistances; for (const auto&ship : ships) { if (ship.isHighRisk) { double minDistance = std::numeric_limits <double>::max(); for (const auto& otherShip : ships) { if (ship.MMSI != otherShip.MMSI) { double distance = calculateDistance(ship, otherShip); if (distance < minDistance) { minDistance = distance; } } } criticalDistances.push_back(minDistance); } } return criticalDistances; } / / Calculate the safety distance double calculateSafetyDistance(const std::vector <double>&criticalDistances, size_t highRiskCount) { if (criticalDistances.empty() || highRiskCount == 0) return 0.0; double maxCriticalDistance = *std::max_element(criticalDistances.begin(), criticalDistances.end()); double minCriticalDistance = *std::min_element(criticalDistances.begin(), criticalDistances.end()); double windowWidth = maxCriticalDistance - minCriticalDistance; double floatingDifference = windowWidth / highRiskCount; return maxCriticalDistance + floatingDifference; } / / Correct the position of the key fishing boat void correctPosition(ShipPosition&ship) { / / Simulate the corrected position. In actual applications, data should be obtained from more accurate sensors. ship.latitude += 0.0001; ship.longitude += 0.0001; } int main() { / / Get the fishing boat's position data std::vector <shipposition>shipPositions = getShipPositions(); / / Get the MMSI code list of high-risk fishing vessels auto highRiskMMSI = getHighRiskMMSI(); / / Mark high-risk fishing vessels for (auto&ship : shipPositions) { if (highRiskMMSI.find(ship.MMSI) != highRiskMMSI.end()) { ship.isHighRisk = true; } } / / Get the critical distance of high-risk fishing vessels std::vector <double>criticalDistances = getCriticalDistances(shipPositions); / / Calculate the safe distance double safetyDistance = calculateSafetyDistance(criticalDistances,highRiskMMSI.size()); / / Judge whether the distance between fishing boats is less than the safe distance for (auto&ship : shipPositions) { for (auto&otherShip : shipPositions) { if (ship.MMSI != otherShip.MMSI) { double distance = calculateDistance(ship, otherShip); if (distance <safetyDistance) { ship.isKeyShip = true; otherShip.isKeyShip = true; } } } } / / Correct the position of the key fishing boat for (auto&ship : shipPositions) { if (ship.isKeyShip) { correctPosition(ship); } } / / Output results for (const auto&ship : shipPositions) { std::cout<<"MMSI: "< <ship.MMSI <<", Latitude: "< <ship.latitude <<", Longitude: "< <ship.longitude <<", HighRisk: "< <ship.isHighRisk <<", KeyShip: "< <ship.isKeyShip<<std::endl; } return 0; } This code enables continuous monitoring of fishing boats in fishing ports. It identifies high-risk fishing boats through MMSI coding, calculates critical distance and safety distance, determines whether the distance between fishing boats is less than the safety distance, and marks them as key fishing boats, and further corrects their location data. This process helps effectively manage and monitor the anti-collision safety of fishing boats in fishing ports.

[0026] See also Figure 7 As shown, in the process of calculating the abnormal position feature range in the above step S4, the position feature of the fishing boat in the normal state can be referred to the position feature of the abnormal state. Specifically, step S41 can be first executed to divide the position features of the fishing boats in the abnormal state and the normal state into multiple internally correlated position feature sets according to the difference between the position features. Next, step S42 can be executed to take the position feature set composed of all the position features of the fishing boats in the abnormal state as the pure abnormal position feature set, and obtain the vector range corresponding to each pure abnormal position feature set as the pure abnormal position feature range. Next, step S43 can be executed to take the position feature set composed of all the position features of the fishing boats in the normal state as the pure normal position feature set, and obtain the vector range corresponding to each pure normal position feature set as the pure normal position feature range. Next, step S44 can be executed to take the position feature set composed of the position features of the fishing boats in the normal state and the abnormal state as the mixed position feature set, and obtain the vector range corresponding to each mixed position feature set as the mixed position feature range.

[0027] Since a collision of fishing boats may cause inestimable losses, in order to avoid collisions of fishing boats in the fishing port as much as possible, the range of the abnormal ship position characteristic range can be appropriately widened. Specifically, step S45 can be executed to subtract the overlapped part of the mixed ship position characteristic range with the pure normal ship position characteristic range from the remaining part as the supplementary abnormal ship position characteristic range. Finally, step S46 can be executed to take both the pure abnormal ship position characteristic range and the supplementary abnormal ship position characteristic range as the abnormal ship position characteristic range.

[0028] See also Figures 8 to 9 As shown, in the process of operation and analysis, the internally correlated position features can firstly execute step S411 to select multiple marked position features from all the position features, which can be randomly selected or pre-selected by the staff. Next, step S412 can be executed to use the vector difference norm between the position features as the difference between the position features, and calculate and obtain the vector difference norm between each marked position feature and each other position feature. Next, step S413 can be executed to classify each other position feature and the marked position feature with the smallest vector difference norm into the same position feature set.

[0029] In the process of classifying the position features, step S414 can be executed to determine whether the position feature set has sufficient correlation according to the vector difference norm between the position features in each position feature set. Specifically, step S4141 can be executed first to calculate the mean vector of all the position features contained in the position feature set. Next, step S4142 can be executed to use the position feature with the smallest vector difference norm between the mean vector in the position feature set as the updated marked position feature. Next, step S4143 can be executed to determine whether the updated marked position feature has changed. If all the updated marked position features do not change, it can be determined that step S4144 is executed to determine that there is sufficient correlation between the position features in each position feature set. If the existing updated marked position features change, it can be determined that step S4145 is executed to determine that there is not sufficient correlation between the position features in each position feature set.

[0030] If it is determined that there is no sufficient correlation between the position features in the position feature set, then steps S412 to S414 can be executed to iteratively generate updated marked position features and position feature sets and continue to determine whether there is sufficient correlation. If there is sufficient correlation between the position features in the position feature set, then step S415 can be executed to output the position feature set with correlation inside.

[0031] In order to supplement the implementation process of the above steps S411 to S415, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0032] #include <iostream> #include <vector> #include <cmath> #include <limits> / / Define the ship position feature structure struct FeatureVector { std::string MMSI; std::vector <double>features; }; / / Calculate the vector difference norm double calculateNorm(const std::vector <double>&v1, const std::vector <double>&v2) { double sum = 0.0; for (size_t i = 0; i <v1.size(); ++i) { sum += std::pow(v1[i] - v2[i], 2); } return std::sqrt(sum); } / / Calculate the mean vector std::vector <double>calculateMeanVector(const std::vector <featurevector>&featureSet) { std::vector <double>mean(featureSet[0].features.size(), 0.0); for (const auto&fv : featureSet) { for (size_t i = 0; i <fv.features.size(); ++i) { mean[i] += fv.features[i]; } } for (double&val : mean) { val / = featureSet.size(); } return mean; } / / Main function int main() { / / Example extracted ship position features std::vector <featurevector>allFeatures = { {"123456789", {500.0, 15.5, 180.0, 34.5, 120.3}}, {"987654321", {600.0, 14.0, 90.0, 34.51, 120.32}}, {"111222333", {550.0, 16.0, 175.0, 34.6, 120.4}}, / / Add more data }; / / Initialize the mark position feature (the first two in the example are marks) std::vector <featurevector>markedFeatures = {allFeatures[0],allFeatures[1]}; std::vector <featurevector>updatedMarkedFeatures = markedFeatures; bool hasChanged; do { hasChanged = false; / / Create a feature set std::vector <std::vector <featurevector>>featureSets(markedFeatures.size()); / / Classify ship position characteristics for (const auto&feature : allFeatures) { double minNorm = std::numeric_limits <double>::max(); size_t bestIndex = 0; for (size_t i = 0; i < markedFeatures.size(); ++i) { double norm = calculateNorm(feature.features, markedFeatures[i].features); if (norm < minNorm) { minNorm = norm; bestIndex = i; } } featureSets[bestIndex].push_back(feature); } / / Update the marked ship position features for (size_t i = 0; i < featureSets.size(); ++i) { if (!featureSets[i].empty()) { std::vector <double>meanVector = calculateMeanVector(featureSets[i]); double minNorm = std::numeric_limits <double>::max(); FeatureVector bestFeature; for (const auto&feature : featureSets[i]) { double norm = calculateNorm(feature.features, meanVector); if (norm <minNorm) { minNorm = norm; bestFeature = feature; } } if (bestFeature.MMSI != updatedMarkedFeatures[i].MMSI) { updatedMarkedFeatures[i] = bestFeature; hasChanged = true; } } } / / Update the marked ship position feature markedFeatures = updatedMarkedFeatures; } while (hasChanged); / / Output a set of ship position features with sufficient correlation for (size_t i = 0; i <featureSets.size(); ++i) { std::cout<<"Feature Set "< for (const auto&feature : featureSets[i]) { std::cout<<"MMSI: "< <feature.MMSI<<", Features: "; for (const auto&f : feature.features) { std::cout< <f<<" "; } std::cout< <std::endl;​ } } return 0; } This code implements the clustering process of the ship position features. It classifies each ship position feature by calculating the difference between it and the marker feature, and updates the marker feature until it no longer changes. This method ensures that the features within each feature set are sufficiently correlated, thus forming an internally correlated feature set.

[0033] See also Fig.10 As shown, since the abnormal state is judged by the ship position characteristics in the above step S4, in the process of judging whether the fishing boats in the fishing port at the current moment are in an abnormal state, step S51 can be first executed to extract the ship position characteristics of the fishing boats in the fishing port at the current moment according to the ship position data of the fishing boats in the fishing port at the current moment. Next, step S52 can be executed to judge whether the ship position characteristics of the fishing boats in the fishing port at the current moment are within the abnormal ship position characteristic range. If so, step S53 is triggered to determine that the fishing boat is in an abnormal state, otherwise, step S54 is triggered to determine that the fishing boat is not in an abnormal state.

[0034] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0035] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0036] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0037] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.< / double> < / double> < / double> < / featurevector> < / featurevector> < / featurevector> < / featurevector> < / double> < / featurevector> < / double> < / double> < / double> < / double> < / limits> < / cmath> < / vector> < / iostream> < / double> < / shipposition> < / double> < / double> < / double> < / shipposition> < / double> < / std::string> < / shipposition> < / limits> < / cmath> < / string> < / vector> < / iostream>

Claims

1. A method for monitoring abnormal fishing boats in a fishing port, characterized in that: include, After the fishing boat enters the port, continuously obtain the position data of the fishing boat in the fishing port, wherein the position data includes the MMSI code and position of the fishing boat; Mark the confirmed abnormal fishing boats and obtain the position data of the abnormal fishing boats in the fishing port during the abnormal period; Extracting data features of the fishing vessel position data in the fishing port as the fishing vessel position features; According to the position characteristics of fishing vessels in abnormal status in the fishing port during abnormal period and the position characteristics of fishing vessels in normal status, the abnormal position characteristic range is obtained; According to the abnormal ship position feature range, it is judged whether the fishing boats in the fishing port are in an abnormal state at the current moment; If so, a warning instruction is sent to the fishing vessel in the abnormal state and / or the adjacent fishing vessels; If not, no response is made.

2. The method according to claim 1, characterized in that The step of continuously acquiring the position data of fishing vessels in the fishing port comprises: Retrieve abnormal status records of fishing vessels in fishing ports based on the MMSI codes in the fishing vessel position data, and mark fishing vessels with abnormal records as high-risk fishing vessels; According to the position data of fishing boats in abnormal state in the fishing port during the abnormal period, the initial distance between multiple high-risk fishing boats and the nearest adjacent fishing boats under abnormal conditions is obtained as the critical distance; The safe distance within the fishing port is obtained based on the critical distance of multiple high-risk fishing vessels; Determining whether the distance between fishing boats in the fishing port is less than the safety distance according to the positions in the fishing boat position data; If not, no response is made; If so, the fishing vessels whose distances to each other are less than the safety distance are marked as key fishing vessels; A more accurate position of the key fishing vessel is obtained through several other methods, and the position in the position data of the key fishing vessel is corrected.

3. The method according to claim 2, characterized in that The step of obtaining the safe distance within the fishing port according to the critical distance of multiple high-risk fishing vessels, include, The maximum value among the critical distances of multiple high-risk fishing vessels is used as the benchmark safety distance; The difference between the maximum and minimum critical distances of multiple high-risk fishing vessels is used as the window width of the safety distance; The ratio of the safety distance window width to the number of high-risk fishing vessels is used as the floating difference distance; The accumulated value of the benchmark safety distance and the floating difference distance is taken as the safety distance within the fishing port.

4. The method according to claim 1, characterized in that The step of extracting data features of the position data of fishing boats in the fishing port as the position features of the fishing boats comprises: Extracting the tonnage, speed, heading and position of the fishing vessel from the vessel position data; The tonnage, speed, heading and position of each fishing vessel are arranged in the same order and vectorized as the position characteristics of each fishing vessel.

5. The method according to claim 4, characterized in that The step of obtaining the abnormal ship position characteristic range according to the ship position characteristics of the fishing boats in abnormal state in the fishing port during the abnormal period and the ship position characteristics of the fishing boats in normal state includes: According to the difference between the position characteristics, the position characteristics of fishing vessels in abnormal state and normal state are divided into multiple position characteristic sets with internal correlation; A ship position feature set consisting of all the ship position features of fishing boats in abnormal states is taken as a pure abnormal ship position feature set, and a vector range corresponding to each pure abnormal ship position feature set is obtained as a pure abnormal ship position feature range; A ship position feature set consisting of all the ship position features of fishing boats in normal state is taken as a pure normal ship position feature set, and a vector range corresponding to each pure normal ship position feature set is obtained as a pure normal ship position feature range; A ship position feature set composed of a mixture of ship position features of fishing boats in a normal state and an abnormal state is used as a mixed ship position feature set, and a vector range corresponding to each mixed ship position feature set is obtained as a mixed ship position feature range; The remainder after deducting the overlapped part with the pure normal ship position characteristic range from the mixed ship position characteristic range is used as the supplementary abnormal ship position characteristic range; Both the pure abnormal ship position characteristic range and the supplementary abnormal ship position characteristic range are regarded as the abnormal ship position characteristic range.

6. The method according to claim 5, characterized in that The step of dividing the position characteristics of fishing boats in abnormal state and normal state into a plurality of internally correlated position characteristic sets according to the difference between the position characteristics comprises: Select multiple marked ship position features from all ship position features; The vector difference norm between the ship position features is used as the difference between the ship position features, and the vector difference norm between each marked ship position feature and each other ship position feature is calculated; For each other ship position feature, classify it and the marked ship position feature with the smallest vector difference norm into the same ship position feature set; Determining whether the ship position feature set has sufficient correlation according to the vector difference norm between the ship position features in each of the ship position feature sets; If so, iteratively generate updated marked position features and position feature sets and continue to determine whether they have sufficient correlation; If not, a set of internally correlated ship position features is obtained.

7. The method according to claim 5, characterized in that The step of judging whether the ship position feature set has sufficient correlation according to the vector difference norm between the ship position features in each of the ship position feature sets comprises: For each of the ship position feature sets, The mean vectors of all the ship position features contained in the ship position feature set are calculated respectively, The ship position feature with the smallest vector difference norm between the ship position feature set and the mean vector is used as the updated marked ship position feature, Determine whether the updated marked ship position characteristics have changed; If all the updated marked ship position features do not change, then there is sufficient correlation between the ship position features in each of the ship position feature sets; If the existing updated marked ship position features do not change, there is no sufficient correlation between the ship position features in each of the ship position feature sets.

8. The method according to claim 5, characterized in that The step of judging whether the fishing boat in the fishing port is in an abnormal state at the current moment according to the abnormal ship position characteristic range includes: The position characteristics of the fishing boats in the fishing port at the current moment are obtained according to the position data of the fishing boats in the fishing port at the current moment; Determine whether the position characteristics of the fishing boats in the fishing port at the current moment are within the range of abnormal position characteristics; If so, it is in an abnormal state; If not, it is not in an abnormal state.

9. A method for monitoring abnormal fishing boats in a fishing port, characterized in that: include, Receiving a warning instruction in a method for monitoring abnormal fishing boats in a fishing port as described in any one of claims 1 to 8; Enter collision alert state.

10. An abnormal monitoring and management system for fishing vessels in a fishing port, characterized in that: include, The port management terminal is used to continuously obtain the position data of the fishing boats in the fishing port after the fishing boats enter the port, wherein the position data includes the MMSI code and position of the fishing boats; Mark the confirmed abnormal fishing boats and obtain the position data of the fishing boats in abnormal state in the fishing port during the abnormal period; Extracting data features of the position data of fishing vessels in the fishing port as position features of fishing vessels; According to the position characteristics of fishing vessels in abnormal state in the fishing port during abnormal period and the position characteristics of fishing vessels in normal state, the abnormal position characteristic range is obtained; According to the abnormal ship position feature range, it is judged whether the fishing boats in the fishing port are in an abnormal state at the current moment; If so, a warning instruction is sent to the fishing vessel in the abnormal state and / or the adjacent fishing vessels; If not, no response is made; The fishing boat side is used to receive warning instructions; Enter collision alert state.

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