A fishing vessel navigation operation safety monitoring method and early warning system

Abstract

The present invention discloses a fishing boat navigation operation safety monitoring method and early warning system, and relates to the field of fishery management. The present invention includes obtaining the sea conditions in the monitoring area and the forecast of the sea conditions in the future period; continuously obtaining the position data of the fishing boats in the monitoring area, the position data including the adjustable position data and the fixed position data; marking the position data of the fishing boats in the safe state and the unsafe state and the corresponding sea conditions; according to the position data of the fishing boats marked in the safe state and the unsafe state in the monitoring area and the corresponding sea conditions, obtaining the safety judgment basis of the fishing boats in the monitoring area under different position data and sea conditions; judging whether the fishing boat is in a safe state according to the position data of the fishing boats in the monitoring area at the current moment, and the sea conditions in the monitoring area and the forecast of the sea conditions in the future period; if so, no operation is performed, otherwise, a safety early warning indication is sent to the fishing boat. The present invention improves the safety of the fishing boat navigation operation process.

Description

Technical Field

[0001] The invention belongs to the technical field of fishery management, and in particular relates to a fishing vessel navigation operation safety monitoring method and an early warning system. Background Art

[0002] With the continuous advancement of fishery development, the safety of fishing boats at sea has received increasing attention. During the navigation and operation of fishing boats, safety accidents are prone to occur due to weather changes, complex sea conditions and equipment failures. Traditional safety monitoring methods usually rely on manual observation and experience judgment, which makes it difficult to predict and prevent risks in a timely and accurate manner.

[0003] In the existing technology, some fishing boats are equipped with simple navigation and communication equipment, but they cannot achieve comprehensive safety monitoring and early warning functions. This limitation leads to the lack of effective response measures for fishing boats in emergencies, increasing the risk of loss of life and property. Summary of the invention

[0004] The purpose of the present invention is to provide a fishing vessel navigation operation safety monitoring method and early warning system, by performing safety correlation analysis on the fishing vessel's position data and sea conditions, to improve the safety of the fishing vessel's navigation operation process.

[0005] 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 the safety of fishing vessel navigation operations, comprising: Obtain sea conditions in the monitoring area and sea condition forecasts for future periods; Continuously acquiring the position data of fishing vessels in the monitoring area, wherein the position data includes adjustable position data and fixed position data; Mark the position data of fishing vessels in safe and unsafe states and the corresponding sea conditions; Based on the position data of fishing vessels marked in safe and unsafe states in the monitoring area and the corresponding sea conditions, the safety judgment basis of fishing vessels in the monitoring area under different position data and sea conditions is obtained; Determine whether the fishing boat is in a safe state based on the current position data of the fishing boat in the monitoring area, the sea conditions in the monitoring area and the sea conditions forecast for the future period; If so, no action is taken; If not, a safety warning instruction is sent to the fishing vessel.

[0006] The present invention also discloses a method for monitoring the safety of fishing vessel navigation operations, comprising: Receive safety warning instructions; Execute the safety warning instruction.

[0007] The present invention also discloses a fishing vessel navigation operation safety monitoring and early warning system, comprising: Early warning management terminal, used to obtain sea conditions in the monitoring area and sea condition forecasts for future periods; Continuously acquiring the position data of fishing vessels in the monitoring area, wherein the position data includes adjustable position data and fixed position data; Mark the position data of fishing vessels in safe and unsafe states and the corresponding sea conditions; Based on the position data of fishing vessels marked in safe and unsafe states in the monitoring area and the corresponding sea conditions, the safety judgment basis of fishing vessels in the monitoring area under different position data and sea conditions is obtained; Determine whether the fishing boat is in a safe state based on the current position data of the fishing boat in the monitoring area, the sea conditions in the monitoring area and the sea conditions forecast for the future period; If so, no action is taken; If not, a safety warning instruction is sent to the fishing vessel; The fishing vessel end is used to receive the safety warning indication; Execute the safety warning instruction.

[0008] The present invention collects the position data of fishing boats and the sea conditions in the monitoring area through the early warning management terminal, and obtains the safety judgment basis of fishing boats in the monitoring area under different position data and sea conditions through pre-marking analysis. The safety status of the fishing boats is then judged to determine whether the fishing boats in the monitoring area are in a safe state, and a safety early warning indication is sent accordingly, thereby warning fishing boats in unsafe states to take countermeasures in time, thereby improving the safety of fishing boats during navigation operations.

[0009] 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

[0010] 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.

[0011] Figure 1 A schematic diagram of a functional interaction unit of a fishing vessel navigation operation safety monitoring and early warning system according to the present invention in one embodiment; Figure 2 A schematic diagram of an embodiment of the early warning management terminal 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 S4 in one embodiment of the present invention; Figure 5 This is a schematic diagram of a step flow chart of step S43 in an embodiment of the present invention; Figure 6 A schematic diagram of a step flow chart of step S434 in an embodiment of the present invention; Figure 7 This is a schematic diagram of the step flow of step S5 in one embodiment of the present invention; Figure 8 This is a schematic diagram of the step flow of step S7 in one embodiment of the present invention; Fig. 9 A schematic diagram of a step flow chart of step S74 in an embodiment of the present invention; In the accompanying drawings, the components represented by the reference numerals are listed as follows: 1- Early warning 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 vessel navigation operation safety monitoring and early warning system, which can be divided into an early warning management terminal 1 and a fishing vessel terminal 2 from the functional unit. The early warning management terminal 1 collects data for early warning analysis and sends a safety early warning instruction to instruct the fishing vessel to perform risk avoidance operations.

[0015] During the implementation of this solution, the early warning management terminal 1 can first execute step S1 to obtain the sea conditions in the monitoring area and the sea condition forecast for the future period. The sea conditions mainly refer to the following two situations: First, under the action of wind, the sea conditions are divided into 10 levels according to the sea surface conditions in the field of vision, the shape of the wave crest and its degree of breakage, and the amount of wave foam; second, the physical, chemical, biological and other properties of the sea area and their changes, including the distribution of temperature, salinity and density, the distribution of water masses and ocean circulation, etc. Next, step S2 can be executed to continuously obtain the position data of fishing vessels in the monitoring area. The position data includes adjustable position data and fixed position data, which usually includes ship name, call sign, IMO number, MMSI, ship type, length, ship width, molded depth, ship registration, ship age, gross tonnage, net tonnage, deadweight tonnage and other ship data.

[0016] In order to mark the safe and unsafe states, step S3 can be executed next to have the fishery staff mark the position data of the fishing boats in the safe and unsafe states and the corresponding sea conditions. Of course, the identification and marking can also be performed through the recognition model. Step S4 can be executed next to derive the safety judgment basis of the fishing boats in the monitoring area under different position data and sea conditions based on the position data of the fishing boats marked in the safe and unsafe states and the corresponding sea conditions in the monitoring area.

[0017] When performing early warning analysis on fishing boats in the monitoring area, step S5 can be first executed to determine whether the fishing boat is in a safe state based on the position data of the fishing boat in the monitoring area at the current moment, the sea conditions in the monitoring area, and the sea condition forecast in the future period. If yes, it means that the fishing boat is in a safe state, so step S6 can be executed next without any operation. If no, it means that the fishing boat is in an unsafe state, so step S7 can be executed next to send a safety warning indication to the fishing boat.

[0018] See also Figures 8 to 9 As shown, since fishing boats will suffer losses if they change their fishing plans and go to other sea areas, if the fishing boats can reach a safe state by adjusting the speed, changing the weight of ballast water, adjusting the draft depth, etc., then they can stay in the current sea area to avoid some losses. In view of this, when generating a safety warning indication, the early warning management terminal 1 can first execute step S71 to divide the types of ship position data into adjustable ship position data and fixed ship position data. Next, step S72 can be executed to simulate the adjustable ship position data of the fishing boat to determine whether the adjusted fishing boat can be in a safe state. If so, it means that the safety state can be achieved by adjusting some parameters of the fishing boat, then step S73 can be executed to generate and send a safety warning indication containing simulated adjustable ship position data. If not, it means that only migration can be carried out to avoid disasters, so step S74 can be executed to generate and send a safety warning indication requiring the fishing boat to leave the current sea area.

[0019] Of course, even if the fishing boat can only avoid disaster by leaving, it can also indicate the sea area it needs to go to. Therefore, step S741 can be executed next to simulate whether the fishing boat can be in a safe state in other sea areas. If so, step S742 can be executed next to package the target safe sea area location into the safety warning indication and send it. In addition, if other sea areas that can be visited are in bad sea conditions, step S743 can only be executed to package the return to the fishing port into the safety warning indication and send it.

[0020] See also Figure 1 and 3 As shown, in this solution, the fishing boat end 2 mounted on the fishing boat maintains a receiving state. If step S021 is triggered to receive a safety warning indication, step S022 needs to be immediately executed to execute the safety warning indication. It can be automatically executed by the automatic driving system, or it can be manually executed by the captain, first mate and other staff.

[0021] See also Figure 4 As shown, the safety of the route operation of fishing boats is closely related to the sea conditions and the position data of the fishing boats themselves. Under the same sea conditions, large-tonnage and deep-draft fishing boats may be safe, but some small fishing boats may be in danger, which requires comprehensive analysis and judgment of the relationship between sea conditions and fishing boats. Specifically, firstly, the various attributes of the fishing boats themselves can be analyzed, and step S41 is executed to extract the features of the position data of the fishing boats marked in a safe state and an unsafe state in the monitoring area to obtain the position features of the fishing boats in a safe state and an unsafe state; the types of the position data of the fishing boats include tonnage, speed, bow direction, track direction, longitude, latitude, length, ship width and / or draft depth, and the position data of various types of fishing boats are sorted and quantized to obtain the position features of each fishing boat. Next, the attributes of various types of sea conditions can be analyzed, and step S42 is executed to extract the features of the sea conditions in which the fishing boats marked in a safe state and an unsafe state are located in the monitoring area to obtain sea condition features corresponding to the position features of the fishing boats in the safe state and the unsafe state; the types of sea conditions include wind speed, wind direction, water temperature, air temperature, humidity, ocean current direction, ocean current speed and / or wave height, and the sea condition data of each type are sorted and quantized to obtain the sea condition features.

[0022] See also Figures 5 to 6As shown, safety analysis can be performed in combination with the sea conditions and the associated data of fishing boats in safe and unsafe states, and step S43 is executed to summarize the position characteristics of fishing boats in multiple safe and unsafe states and the corresponding sea condition characteristics to obtain multiple groups of position characteristics and sea condition characteristic combinations in safe states and multiple groups of position characteristics and sea condition characteristic combinations in unsafe states, which serve as a basis for safety judgment of fishing boats in the monitoring area under different position data and sea condition states. In the specific execution process, step S431 can be first executed to splice each group of position characteristics and sea condition characteristic combinations in safe and unsafe states into a combined feature vector. Next, step S432 can be executed to select two as standard combined feature vectors from all combined feature vectors. Next, step S433 can be executed to calculate and obtain the vector difference norm of each standard combined feature vector and other combined feature vectors.

[0023] In order to fully group the combined feature vectors of the safe state and the sub-safe state, step S434 can be performed next to classify the combined feature vector set in the safe state as the safe combined feature vector set and the combined feature vector set in the unsafe state as the unsafe combined feature vector set according to the vector difference norm of the standard combined feature vector and other combined feature vectors. In the specific execution process, step S4341 can be performed first for each other combined feature vector, it is combined with the standard combined feature vector with the smallest vector difference norm to form two combined feature vector sets respectively. Next, step S4342 can be performed to determine whether the safe state or unsafe state corresponding to the combined feature vectors in each combined feature vector set is consistent. If not, it means that the combined feature vectors of the safe state and the sub-safe state have not been fully grouped, so continue to select two as standard combined feature vectors and continue to generate two combined feature vector sets for judgment. If so, it means that the grouping is sufficient, so step S4343 can be performed next to use the combined feature vector set corresponding to the safe state of each combined feature vector contained therein as the safe combined feature vector set. Finally, step S4344 may be executed to take a set of combined feature vectors in which each combined feature vector corresponds to a non-safe state as a set of non-safe combined feature vectors.

[0024] In the subsequent data sorting stage, firstly, step S435 is executed to combine the ship position feature and sea condition feature corresponding to each combined feature vector in the safe combined feature vector set as multiple sets of ship position feature and sea condition feature combinations in a safe state. Finally, step S436 can be executed to combine the ship position feature and sea condition feature corresponding to each combined feature vector in the unsafe combined feature vector set as multiple sets of ship position feature and sea condition feature combinations in an unsafe state.

[0025] In order to supplement the implementation process of the above steps S431 to S436, 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.

[0026] #include <iostream> #include <vector> #include <cmath> #include <algorithm> #include <limits> / / Define the feature vector type typedef std::vector <double>FeatureVector; / / Calculate the Euclidean distance (norm) between two eigenvectors double calculateNorm(const FeatureVector&vec1, const FeatureVector&vec2) { double norm = 0.0; for (size_t i = 0; i <vec1.size(); ++i) { norm += std::pow(vec1[i] - vec2[i], 2); } return std::sqrt(norm); } / / Concatenate multiple feature vectors into a combined feature vector FeatureVector concatenateFeatureVectors(const FeatureVector&shipFeatures, const FeatureVector&seaFeatures) { FeatureVector combined = shipFeatures; combined.insert(combined.end(), seaFeatures.begin(), seaFeatures.end()); return combined; } / / Select two standard combination feature vectors std::pair<FeatureVector, FeatureVector> selectStandardFeatureVectors(const std::vector <featurevector>&combinedFeatures) { / / For simplicity, select the first and second eigenvectors as the standard return {combinedFeatures[0], combinedFeatures[1]}; } / / Classify the combined feature vectors according to the minimum difference norm with the standard vector std::pair <std::vector <featurevector>, std::vector <featurevector>>classifyFeatureVectors( const std::vector <featurevector>&combinedFeatures, const FeatureVector&standardVec1, const FeatureVector&standardVec2) { std::vector <featurevector>group1; std::vector <featurevector>group2; for (const auto&vec : combinedFeatures) { double norm1 = calculateNorm(vec, standardVec1); double norm2 = calculateNorm(vec, standardVec2); / / Group according to the standard vector with the smallest difference norm if (norm1 <norm2) { group1.push_back(vec); } else { group2.push_back(vec); } } return {group1, group2}; } / / Judge whether each combined feature vector set is in the same state (safe or unsafe) bool isConsistentState(const std::vector <featurevector>&group, conststd::vector <std::string>&statuses, const std::string&targetStatus) { for (const auto&vec : group) { size_t index =&vec -&group[0]; / / Get the index of the feature vector if (statuses[index] != targetStatus) { return false; } } return true; } / / Main logic: classify and generate safe and unsafe feature vector sets void processFeatureVectors( const std::vector <featurevector>&combinedFeatures, const std::vector <std::string>&statuses, std::vector <featurevector>&safeGroup, std::vector <featurevector>&unsafeGroup) { bool foundConsistentGroups = false; while (!foundConsistentGroups) { / / Select two standard combination feature vectors auto [standardVec1, standardVec2] = selectStandardFeatureVectors(combinedFeatures); / / Classify auto [group1, group2] = classifyFeatureVectors(combinedFeatures,standardVec1, standardVec2); / / Check whether the status of each group is consistent bool group1Consistent = isConsistentState(group1, statuses, "safe"); bool group2Consistent = isConsistentState(group2, statuses, "unsafe"); if (group1Consistent&&group2Consistent) { foundConsistentGroups = true; safeGroup = group1; unsafeGroup = group2; } } } / / Output feature vector void printFeatureVector(const FeatureVector&features) { for (double value : features) { std::cout< <value<<" "; } std::cout< <std::endl; } int main() { / / Simulate the combined feature vectors of some fishing boats and sea conditions std::vector <featurevector>shipFeatures = { {500, 12, 90, 85, 120.123, 30.456, 50, 10, 5}, {1000, 15, 45, 50, 121.123, 31.456, 60, 12, 6}, {800, 14, 70, 65, 122.123, 32.456, 55, 11, 5.5} }; std::vector <featurevector>seaFeatures = { {15, 120, 22, 25, 80, 130, 2, 1}, {30, 200, 18, 20, 70, 150, 3, 3}, {12, 100, 23, 27, 85, 140, 1.5, 0.5} }; / / The states are "safe" and "unsafe" respectively std::vector <std::string>statuses = {"safe", "unsafe", "safe"}; / / Concatenate the feature vectors std::vector <featurevector>combinedFeatures; for (size_t i = 0; i <shipFeatures.size(); ++i) { combinedFeatures.push_back(concatenateFeatureVectors(shipFeatures[i],seaFeatures[i])); } / / Store the result's secure and non-secure feature vector set std::vector <featurevector>safeGroup, unsafeGroup; / / Process and classify feature vectors processFeatureVectors(combinedFeatures, statuses, safeGroup,unsafeGroup); / / Output results std::cout<<"Safe Group Features:"< <std::endl; for (const auto&features : safeGroup) { printFeatureVector(features); } std::cout<<"Unsafe Group Features:"< <std::endl; for (const auto&features : unsafeGroup) { printFeatureVector(features); } return 0; } See also Figure 7 As shown, in the process of making safety judgments on fishing boats in the monitoring area at the previous moment, the real-time sea conditions in the monitoring area and the forecast of sea conditions in the future period can be used. The two are the same in judgment logic. The following only shows the safety judgment based on the real-time sea conditions in the monitoring area. First, step S51 can be executed to obtain the sea condition characteristics of different sea areas in the monitoring area according to the sea conditions in the monitoring area and the forecast of sea conditions in the future period. Next, step S52 can be executed to extract the characteristics of the position data of fishing boats in the monitoring area at the current moment and combine the sea condition characteristics of different sea areas in the monitoring area to obtain the position characteristics and sea condition characteristics combination of each fishing boat in the monitoring area at the current moment. Next, step S53 can be executed to splice the position characteristics and sea condition characteristics combination of each fishing boat in the monitoring area at the current moment into the current combined feature vector of each fishing boat. Next, step S54 can be executed for each fishing boat to calculate the minimum value of the vector difference norm of each combined feature vector in the safe state combined feature vector set and the unsafe combined feature vector set. If the combined feature vector corresponding to the minimum value of the vector difference norm of the current combined feature vector of the fishing boat belongs to the safe state combined feature vector set, then the step S55 is triggered to determine that the fishing boat is in a safe state. If the combined feature vector corresponding to the minimum value of the vector difference norm of the current combined feature vector of the fishing boat belongs to the unsafe state combined feature vector set, then the step S56 is triggered to determine that the fishing boat is in an unsafe state.

[0027] In order to provide supplementary explanation for the implementation process of the above-mentioned steps S51 to S56, source codes of some functional modules are provided, and comparative explanations are provided in the comments.

[0028] #include <iostream> #include <vector> #include <cmath> #include <limits> / / Define the feature vector type typedef std::vector <double>FeatureVector; / / Calculate the Euclidean distance (norm) between two eigenvectors double calculateNorm(const FeatureVector&vec1, const FeatureVector&vec2) { double norm = 0.0; for (size_t i = 0; i <vec1.size(); ++i) { norm += std::pow(vec1[i] - vec2[i], 2); } return std::sqrt(norm); } / / Extract the position features of the fishing boat FeatureVector extractShipFeature(double tonnage, double speed, doubleheading, double longitude, double latitude) { return { tonnage, speed, heading, longitude, latitude}; } / / Extract sea condition features FeatureVector extractSeaConditionFeature(double windSpeed, doublewindDirection, double waveHeight) { return { windSpeed, windDirection, waveHeight}; } / / Combine the fishing boat's position characteristics and sea condition characteristics to obtain the current combined feature vector FeatureVector getCombinedFeatureVector(const FeatureVector&shipFeatures, const FeatureVector&seaFeatures) { FeatureVector combined = shipFeatures; combined.insert(combined.end(), seaFeatures.begin(), seaFeatures.end()); return combined; } / / Judge whether the fishing boat is in a safe state bool isShipSafe(const FeatureVector¤tFeature, const std::vector <featurevector>&safeFeatureVectors, const std::vector <featurevector>&unsafeFeatureVectors) { double minSafeNorm = std::numeric_limits <double>::max(); double minUnsafeNorm = std::numeric_limits <double>::max(); / / Calculate the minimum difference norm of each vector in the safe state feature vector set for (const auto&safeVec : safeFeatureVectors) { double norm = calculateNorm(currentFeature, safeVec); if (norm <minSafeNorm) { minSafeNorm = norm; } } / / Calculate the minimum difference norm of each vector in the set of unsafe state feature vectors for (const auto&unsafeVec : unsafeFeatureVectors) { double norm = calculateNorm(currentFeature, unsafeVec); if (norm <minUnsafeNorm) { minUnsafeNorm = norm; } } / / Judge the safe state: If the minimum difference norm corresponds to the safe set, it is a safe state; otherwise, it is an unsafe state return minSafeNorm <minUnsafeNorm; } int main() { / / Simulate the combined feature vector set of historical security and non-security states std::vector <featurevector>safeFeatureVectors = { {500, 12, 90, 120.123, 30.456, 15, 120, 2.0}, {800, 14, 70, 122.123, 32.456, 10, 110, 1.5} }; std::vector <featurevector>unsafeFeatureVectors = { {1000, 20, 45, 123.123, 33.456, 25, 210, 3.0}, {600, 18, 65, 124.123, 34.456, 30, 180, 3.5} }; / / The current fishing boat's position data and sea conditions data double currentTonnage = 700; / / Current fishing boat tonnage double currentSpeed ​​= 16; / / Current speed double currentHeading = 80; / / Current heading double currentLongitude = 121.123; / / Current longitude double currentLatitude = 31.456; / / Current latitude double currentWindSpeed ​​= 20; / / Current wind speed double currentWindDirection = 130; / / Current wind direction double currentWaveHeight = 2.5; / / Current wave height / / Extract the current fishing boat's position features FeatureVector currentShipFeature = extractShipFeature(currentTonnage,currentSpeed, currentHeading, currentLongitude, currentLatitude); / / Extract current sea condition characteristics FeatureVector currentSeaFeature = extractSeaConditionFeature(currentWindSpeed, currentWindDirection, currentWaveHeight); / / Get the combined feature vector of the current fishing boat FeatureVector currentCombinedFeature = getCombinedFeatureVector(currentShipFeature, currentSeaFeature); / / Judge whether the fishing boat is in a safe state bool isSafe = isShipSafe(currentCombinedFeature, safeFeatureVectors,unsafeFeatureVectors); / / Output results if (isSafe) { std::cout<<"The fishing boat is in a safe state."< <std::endl; } else { std::cout<<"The fishing boat is in an unsafe state!"< <std::endl; } return 0; } 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.

[0029] 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.

[0030] 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.

[0031] 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.< / featurevector> < / featurevector> < / double> < / double> < / featurevector> < / featurevector> < / double> < / limits> < / cmath> < / vector> < / iostream> < / featurevector> < / featurevector> < / std::string> < / featurevector> < / featurevector> < / featurevector> < / featurevector> < / std::string> < / featurevector> < / std::string> < / featurevector> < / featurevector> < / featurevector> < / featurevector> < / featurevector> < / featurevector> < / featurevector> < / double> < / limits> < / algorithm> < / cmath> < / vector> < / iostream>

Claims

1. A method for monitoring the safety of fishing vessel navigation operations, characterized in that: include, Obtain sea conditions in the monitoring area and sea condition forecasts for future periods; Continuously obtain the position data of fishing vessels in the monitoring area; Mark the position data of fishing vessels in safe and unsafe states and the corresponding sea conditions; Based on the position data of fishing vessels marked in safe and unsafe states in the monitoring area and the corresponding sea conditions, the safety judgment basis of fishing vessels in the monitoring area under different position data and sea conditions is obtained; Determine whether the fishing boat is in a safe state based on the current position data of the fishing boat in the monitoring area, the sea conditions in the monitoring area and the sea conditions forecast for the future period; If so, no action is taken; If not, a safety warning instruction is sent to the fishing vessel.

2. The method according to claim 1, characterized in that: The step of obtaining a safety judgment basis for fishing vessels in the monitoring area under different position data and sea conditions based on the position data of fishing vessels marked in a safe state and an unsafe state in the monitoring area and the corresponding sea conditions, include, Feature extraction is performed on the position data of fishing vessels marked in a safe state and an unsafe state in the monitoring area to obtain the position features of fishing vessels in a safe state and an unsafe state; Extract the characteristics of the sea conditions where the fishing boats marked in safe and unsafe states are located in the monitoring area, and obtain the sea condition characteristics corresponding to the position characteristics of the fishing boats in safe and unsafe states; The position characteristics of fishing vessels in multiple safe and unsafe states and the corresponding sea condition characteristics are summarized to obtain multiple sets of combinations of position characteristics and sea condition characteristics in safe states and multiple sets of combinations of position characteristics and sea condition characteristics in unsafe states, which serve as the basis for safety judgment of fishing vessels in the monitoring area under different position data and sea conditions.

3. The method according to claim 2, characterized in that The types of fishing vessel position data include tonnage, speed, heading, track direction, longitude, latitude, length, width and / or draft depth. The position data of each type of fishing vessel are sorted and vectorized to obtain the position characteristics of each fishing vessel. The types of sea conditions include wind speed, wind direction, water temperature, air temperature, humidity, current direction, current speed and / or wave height. The sea condition data of each type are sorted and quantized to obtain the sea condition characteristics.

4. The method according to claim 3, characterized in that The step of summarizing the fishing boat position characteristics and corresponding sea condition characteristics in multiple safe and unsafe states to obtain multiple groups of combinations of position characteristics and sea condition characteristics in safe states, and multiple groups of combinations of position characteristics and sea condition characteristics in unsafe states includes: Combine each group of ship position features and sea condition features in safe and unsafe states and concatenate them into a combined feature vector; Select two from all the combined feature vectors as standard combined feature vectors; Calculate and obtain the vector difference norm between each standard combination eigenvector and other combination eigenvectors; According to the vector difference norm between the standard combined feature vector and other combined feature vectors, a combined feature vector set in a safe state and a combined feature vector set in an unsafe state are classified; The ship position feature and sea condition feature corresponding to each combined feature vector in the safety combined feature vector set are combined as multiple sets of ship position feature and sea condition feature combinations in a safe state; The ship position feature and sea condition feature combination corresponding to each combined feature vector in the unsafe combined feature vector set are used as multiple sets of ship position feature and sea condition feature combinations in the unsafe state.

5. The method according to claim 4, characterized in that The step of classifying the combined feature vector set in a safe state as a safe combined feature vector set and the combined feature vector set in an unsafe state as an unsafe combined feature vector set according to the vector difference norm between the standard combined feature vector and other combined feature vectors includes: For each other combined eigenvector, combine it with the standard combined eigenvector with the smallest vector difference norm to form two combined eigenvector sets respectively; Determine whether the safety states or unsafe states corresponding to the combined feature vectors in each combined feature vector set are consistent; If not, continue to select two as standard combined feature vectors, and continue to generate two sets of combined feature vectors for judgment; If so, each combined feature vector contained therein corresponds to a combined feature vector set of a safe state, as a safe combined feature vector set; A set of combined feature vectors in which each combined feature vector contained therein corresponds to an unsafe state is taken as an unsafe combined feature vector set.

6. The method according to claim 4, characterized in that The step of judging whether the fishing boat is in a safe state based on the position data of the fishing boat in the monitoring area at the current moment, the sea conditions in the monitoring area and the sea conditions forecast in the future period, include, According to the sea conditions in the monitoring area and the sea condition forecast in the future period, the sea condition characteristics of different sea areas in the monitoring area are obtained; After extracting the features of the current position data of fishing boats in the monitoring area, the features of the sea conditions in different sea areas in the monitoring area are combined to obtain the combination of the current position features and sea conditions features of each fishing boat in the monitoring area; The current position characteristics and sea condition characteristics of each fishing boat in the monitoring area are combined and spliced ​​into the current combined feature vector of each fishing boat; For each fishing boat, the minimum value of the vector difference norm between the current combined feature vector of the fishing boat and each combined feature vector in the safe state combined feature vector set and the unsafe combined feature vector set is calculated; If the combined feature vector corresponding to the minimum value of the vector difference norm of the current combined feature vector of the fishing boat belongs to the set of combined feature vectors of safe state, then the fishing boat is in a safe state; If the combined feature vector corresponding to the minimum value of the vector difference norm of the current combined feature vector of the fishing boat belongs to the set of combined feature vectors of unsafe state, the fishing boat is in an unsafe state.

7. The method according to claim 1, characterized in that The step of sending a safety warning instruction to the fishing vessel comprises: The types of ship position data are divided into adjustable ship position data and fixed ship position data; Simulate the adjustable position data of the fishing boat to determine whether the fishing boat after adjustment can be in a safe state; If so, a safety warning indication including simulated adjustable ship position data is generated and sent; If not, a safety warning instruction requiring the fishing boat to leave the current sea area is generated and sent.

8. The method according to claim 7, characterized in that: The step of generating and sending a safety warning instruction requiring the fishing boat to leave the current sea area includes: Simulate whether fishing boats can be in a safe state in other waters; If yes, the target safe sea area location is packaged into the safety warning indication and sent; If not, the return to the fishing port will be packed into the safety warning instructions and sent.

9. A method for monitoring the safety of fishing vessel navigation operations, characterized in that: include, Receiving a safety warning indication in a fishing vessel navigation operation safety monitoring method according to any one of claims 1 to 8; Execute the safety warning instruction.

10. A fishing vessel navigation safety monitoring and early warning system, characterized in that: include, Early warning management terminal, used to obtain sea conditions in the monitoring area and sea condition forecasts for future periods; Continuously acquiring the position data of fishing vessels in the monitoring area, wherein the position data includes adjustable position data and fixed position data; Mark the position data of fishing vessels in safe and unsafe states and the corresponding sea conditions; Based on the position data of fishing vessels marked in safe and unsafe states in the monitoring area and the corresponding sea conditions, the safety judgment basis of fishing vessels in the monitoring area under different position data and sea conditions is obtained; Determine whether the fishing boat is in a safe state based on the current position data of the fishing boat in the monitoring area, the sea conditions in the monitoring area and the sea conditions forecast for the future period; If so, no action is taken; If not, a safety warning instruction is sent to the fishing vessel; The fishing vessel end is used to receive the safety warning indication; Execute the safety warning instruction.

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