CN115995165A - Ship navigation risk management method and system - Google Patents
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Abstract
The embodiment of the specification provides a ship navigation risk management method and system. The method is performed by a processing device, comprising: acquiring first track information of a current ship based on a user terminal; acquiring position information of other ships relative to the current ship at a plurality of time points in preset time based on a ship radar, and determining the position information at the plurality of time points in the preset time; determining position information of a plurality of future time points of other ships based on the position information of a plurality of time points in preset time and environmental characteristics, and determining second track information according to the position information of the plurality of future time points of other ships; judging whether the current ship has risk behaviors based on the relation between the first track information and the second track information, wherein the risk behaviors comprise at least one of a pursuit behavior and a ship meeting behavior; and sending prompt information to the user terminal in response to the risk behavior.
Description
The application is a divisional application which is proposed by China application with the application date of 2022, 10-month 31, the application number of 202211341514.4 and the invention name of a ship safe navigation management method and system.
Technical Field
The specification relates to the field of ship management, in particular to a ship navigation risk management method and system.
Background
In the navigation process of ships, particularly at a port of the ship, a large number of ships are often arranged in a limited water channel space, and when the ships meet or cross, the probability of collision accidents is greatly increased due to complex navigation water channel conditions. In order to avoid the ship accident, the risk condition needs to be judged according to the actual sailing state, and the judgment of the risk condition only depends on the crew, so that the self experience of the crew is required to be high, and the influence of the working state of the crew can be avoided.
It is therefore desirable to provide a ship voyage risk management method and system to better ensure safe voyage of a ship.
Disclosure of Invention
One or more embodiments of the present specification provide a ship navigation risk management method, the method being performed by a processing device, comprising: acquiring first track information of a current ship based on a user terminal; acquiring position information of other ships relative to the current ship at a plurality of time points in preset time based on a ship radar, and determining the position information at the plurality of time points in the preset time; determining position information of a plurality of future time points of the other ships based on the position information of the plurality of time points in the preset time and the environmental characteristics, and determining second track information according to the position information of the plurality of future time points of the other ships; judging whether the current ship has risk behaviors based on the relation between the first track information and the second track information, wherein the risk behaviors comprise at least one of a pursuit behavior and a ship meeting behavior; and sending prompt information to the user terminal in response to the risk behavior.
One or more embodiments of the present specification also provide a ship navigation risk management system, including: the first acquisition module is used for acquiring the position information of other ships relative to the current ship at a plurality of time points in the preset time based on the ship radar, and determining the position information at the plurality of time points in the preset time; the judging module is used for judging whether the current ship has risk behaviors or not based on the relation between the first track information and the second track information; and the prompt module is used for responding to the risk behaviors and sending prompt information to the user terminal.
One or more embodiments of the present specification also provide a ship navigation risk management apparatus, including a processor for performing the above ship navigation risk management method.
One or more embodiments of the present specification also provide a computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, perform the above-described ship navigation risk management method.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic illustration of an application scenario of a marine vessel safe voyage management system according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a method of marine vessel safe voyage management according to some embodiments of the present description;
FIG. 3 is a schematic illustration of a track prediction model shown in accordance with some embodiments of the present description;
FIG. 4 is a schematic diagram illustrating the transmission of a hint message to a user terminal according to some embodiments of the present disclosure;
fig. 5 is an exemplary block diagram of a marine vessel safe voyage management system according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic view of an application scenario of a ship safe navigation management system according to some embodiments of the present disclosure. As illustrated in fig. 1, the application scenario 100 of the ship safe navigation management system may include a current ship 110, at least one other ship (e.g., ship 111, ship 112), a radar 120, a processing device 130, a waterway 140.
The current vessel 110 may be a vessel that requires safe voyage management. For example, the current vessel may be a vessel that is about to launch or is underway. At least one other vessel (e.g., vessel 111, vessel 112) may be a vessel having a distance from the current vessel within a preset range, typically within a similar range as the current vessel traveling on the same waterway or waterway.
The current vessel and at least one other vessel navigate in a waterway 140, the waterway having at least one leg, each leg having a different width, curvature, and vessel density.
In some embodiments, the current vessel may include a radar 120 and a processing device 130.
The radar 120 is a device that detects and locates by radio, and may be used to determine the relative position, e.g., relative distance, direction, etc., of at least one other vessel and the current vessel 110.
The processing device 130 is a device having a computing capability, for example, a CPU or the like, which can be used to process ship information, determine the risk of ship navigation, or the like. In some embodiments, the processing device 130 may be disposed in a server, e.g., a computer, a computing cloud platform, etc.
It should be understood that the above scenario is by way of example only, and that various forms of transformations are possible in practical applications.
Fig. 2 is an exemplary flow chart of a method of marine vessel safe voyage management according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by the processing device 130.
Step 210, acquiring first track information of a current ship based on a user terminal. In some embodiments, step 210 may be performed by the first acquisition module 510.
The first track information refers to track information of the current ship when the ship is sailing. In some embodiments, the first track information includes a current direction of travel and speed of the vessel. For example, the first track information may include a route from port a to port at a speed of 2 knots/hour.
In some embodiments, the first obtaining module 510 may obtain, based on the user terminal, the first track information of the current ship through the positioning device on the current ship. For example, the first track information may be acquired by GPS positioning, coastal CDMA network positioning, base station positioning, network IP positioning, AIS positioning, etc.
Step 220, determining second track information of the other vessels based on parameters of the other vessels relative to the current vessel, the parameters being acquired by vessel radar mounted on the current vessel. In some embodiments, step 220 may be performed by the second acquisition module 520.
The second track information refers to track information of other ships when sailing. Similar to the first track information, the second track information includes the traveling direction and speed of other vessels.
In some embodiments, the second acquisition module 520 may determine the second track information for the other vessel based on parameters of the other vessel relative to the current vessel.
The parameters of the other vessels with respect to the current vessel may refer to navigational parameters of the other vessels with respect to the current vessel. For example, location information (e.g., other vessels are located at 30 ° east-north, 2km, of the current vessel), speed information (e.g., 1 knots/hour), and the like.
In some embodiments, the second obtaining module 520 may obtain parameters of other vessels relative to the current vessel by sea chart, GPS, radar, etc.
In some embodiments, the parameters of the other vessels relative to the current vessel may be the position information of the other vessels relative to the current vessel at a plurality of time points within the preset time.
In some embodiments, the second obtaining module 520 may determine the location information 330 of the plurality of time points within the preset time based on the ship radar obtaining the location information of the other ship relative to the current ship at the plurality of time points within the preset time; and processing the position information 330 of a plurality of time points in the preset time based on the track prediction model to obtain position information of a plurality of time points in the future, and determining second track information according to the position information of the plurality of time points in the future.
The plurality of time points within the preset time may include a plurality of time points in a period of time before the current time point. For example, the current time point is 19:56, and the preset time may be 19:00-19:56. Accordingly, the plurality of time points within the preset time may include 19:00, 19:10, 19:20, 19:30, 19:40, 19:50.
In some embodiments, the ship radar may acquire the position information of other ships relative to the current ship in real time, and store the acquired position information in a database and/or a storage device for ship safe navigation management. The second obtaining module 520 may obtain the position information of the other vessels relative to the current vessel at a plurality of time points within a preset time based on the position information stored in the database and/or the storage device, so as to determine the position information 330 at a plurality of time points within the preset time.
In some embodiments, the second obtaining module 520 may perform modeling or perform analysis processing on the location information 330 of multiple time points within the preset time by using various data analysis algorithms, such as regression analysis, discriminant analysis, and the like, to obtain location information of multiple time points in the future.
In some embodiments, the second obtaining module 520 may process the location information 330 of a plurality of time points in a preset time based on the track prediction model to obtain location information of a plurality of time points in the future. For further description of the above embodiments, see fig. 3 and its associated description.
In some embodiments, the second acquisition module 520 may take the location information of the future points in time directly as the second track information. In some embodiments, the processing device may also select partial location information from among location information from a plurality of points in time as the second track information. Wherein, the second acquisition module 520 may determine part of the position information based on a conventional course, reef situation, water course situation, etc.
Step 230, based on the relation between the first track information and the second track information, judging whether the current ship has risk behaviors. In some embodiments, step 230 may be performed by decision module 530.
Risk behavior refers to behavior that affects the safety of the vessel as well as sailing. In some embodiments, the risk behavior may include a pursuit and/or a ship-crossing behavior.
In some embodiments, the determination module 530 may determine whether the current vessel has risky behavior based on a relationship between the first track information and the second track information.
For example only, in the preset time, when the distance between at least one position information in the first track information and at least one position information in the second track information is smaller than the preset threshold value, and the average speed of the current ship and/or other ships exceeds the speed threshold value, it may be determined that the overtaking risk behavior exists. For example, a preset threshold value of 3km is set, a preset time is within 20 minutes before the current time point, and a speed threshold value is 2 knots/hour. When it is determined that the distance between one piece of position information in the first track information and one piece of position information in the second track information is 2.4km, and the current speed of the ship M is 5 knots/hour, in the case that the current speed of the ship N is 1.5 knots/hour, that is, the ship M may overtake the ship N, there is a overtaking risk behavior.
As another example, it may be determined that there is a risk behavior of the meeting ship in a case where the current ship is opposite to the traveling direction of at least one other ship and the distance of at least one of the first track information and at least one of the second track information is less than a preset threshold. For example, a preset threshold value is set to be 2km, there are a ship N driven to a port a by a port B and a ship M driven to a port B by a port a, and when it is determined that a distance between a certain position information in the first track information of the ship N and a certain position information in the second track information of the ship N is 1.4km, there is a risk behavior of a ship crossing.
And step 240, in response to the risk behavior, a prompt message is sent to the user terminal. In some embodiments, step 240 may be performed by prompt module 540.
The prompt information is information for prompting that the current ship possibly has risks. The prompt information may be in the form of one or more of a combination of forms including, but not limited to, a short message, a text push, an image, a video, a voice, a broadcast, etc.
In some embodiments, the prompt module 540 may send prompt information to the user terminal in response to the presence of risk activity. Wherein the hint information may include a risk type. For example, the hint may be that there is a risk of a meeting. The hint information may also be that there is a risk of overtaking.
In some embodiments, the hint module 540 may determine a risk value at which the risk behavior occurs in response to the presence of the risk behavior; and sending prompt information to the user terminal, wherein the prompt information comprises a risk value. For further description of the above embodiments, see fig. 4 and its associated description.
According to the methods disclosed by the embodiments of the specification, the risk behaviors such as overtaking or meeting can be determined according to the track information of the ship, the ship management staff or the sea man of the ship can be helped to determine the potential risk of current sailing, and the user is prompted, so that the sailing safety is ensured, and accidents are avoided.
FIG. 3 is a schematic diagram of a track prediction model shown in accordance with some embodiments of the present description. As shown in fig. 3, the track prediction model 300 may include the following.
In some embodiments, the position information of a plurality of time points in a preset time can be processed based on the track prediction model, so as to obtain the position information of a plurality of time points in the future.
The track prediction model may be used to determine location information for a plurality of points in time in the future. The track prediction model may be a model including, but not limited to, a convolutional neural network model, a deep neural network model, and the like.
In some embodiments, the inputs to the track prediction model may include location information 330, future weather information, current sea state information at a plurality of points in time within a preset time, and the outputs may be location information at a plurality of points in time in the future. The future weather information may include, among other things, weather information for the current time and a future period of time (e.g., 2 hours in the future, 1 day in the future), e.g., cloudiness, heavy rain, etc. The current sea state information may refer to current sea state, for example, wind speed (e.g., 10.8-12.4 m/s), wave height (e.g., 1.0-1.5 m), etc.
In some embodiments, the track prediction model may be trained from a plurality of labeled training samples. The initial track prediction model may be trained based on a plurality of sets of labeled training samples, the training samples may include sample position information for a plurality of points in time of the sample vessel during a first sample period, sample weather information for a second sample period, and sample sea state information, and the labels of the training samples may be position information for the plurality of points in time of the sample vessel during the second sample period, wherein the first sample period is earlier than the second sample period. The label can be obtained by manually marking the sample position information of the sample ship in the second sample time period, and can also be obtained by chart records in the second sample time period. It should be understood that the sample vessel and the vessel to be predicted should be of the same or of the same general class, e.g. of the type of the sample vessel and the vessel to be predicted are all of the ten thousand ton class of cargo vessels. Inputting a plurality of training samples into an initial track prediction model, constructing a loss function based on the output of the initial track prediction model and a label, iteratively updating parameters of the initial track prediction model based on the loss function, and obtaining a trained track prediction model after training is finished when the trained model meets preset conditions. The preset conditions may include, but are not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold, etc.
As shown in FIG. 3, the track prediction model 340 may include an environmental feature 341 extraction layer 340-1 and a prediction layer 340-2.
In some embodiments, the environmental feature 341 extraction layer 340-1 may process the future weather information 310 and the current sea state information 320 to determine the environmental features 341. The environmental feature 341 may refer to a feature obtained after feature extraction of the future weather information 310 and the current sea state information 320. The environmental feature 341 extraction layer 340-1 may be a deep neural network model. As shown in fig. 3, the input of the environmental feature 341 extraction layer 340-1 may be future weather information 310 and current sea state information 320, and the output of the environmental feature 341 extraction layer 340-1 may be the environmental feature 341.
In some embodiments, the prediction layer 340-2 may process the location information 330 and the environmental characteristics 341 at a plurality of time points within a preset time to determine the location information 350 at a plurality of time points in the future. Prediction layer 340-2 may be a recurrent neural network model. As shown in fig. 3, the input of the prediction layer 340-2 may include the location information 330 and the environmental characteristics 341 at a plurality of time points within a preset time, and the output of the prediction layer 340-2 may be the location information 350 at a plurality of time points in the future.
In some embodiments, environmental feature 341 extraction layer 340-1 and prediction layer 340-2 may be derived by co-training. The training samples may include sample position information of the sample vessel at a plurality of time points in a third sample period and sample weather information and sample sea state information in a fourth sample period, and the tag may be position information of the sample vessel at a plurality of time points in the fourth sample period, wherein the third sample period is earlier than the fourth sample period. The label can be obtained by manually marking the sample position information of the sample ship in the fourth sample time period, and can also be obtained by chart records in the fourth sample time period. It should be understood that the sample vessel and the vessel to be predicted should be of the same or of the same general class, e.g. of the type of the sample vessel and the vessel to be predicted are all of the ten thousand ton class of cargo vessels. Sample weather information and sample sea state information in the training samples are input into an initial environmental feature extraction layer. The output of the initial environmental feature extraction layer is then input into the initial prediction layer along with the sample position information, and a loss function is constructed based on the output of the initial prediction layer and the labels. And iteratively updating parameters of each layer in the initial track prediction model based on the loss function until a preset condition is met, so as to obtain the trained track prediction model. The preset conditions may include, but are not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold.
In some embodiments, the track prediction model 340 may also include an embedded layer 340-3. In some embodiments, embedded layer 340-3 may process waterway information 360 to determine waterway characteristics 342. Wherein the embedded layer 340-3 may be a deep neural network model. As shown in FIG. 3, the input to embedded layer 340-3 may be waterway information 360 and the output of embedded layer 340-3 may be waterway characteristics 342.
The water channel information refers to information of a water channel through which a ship sails. The waterway information may include a width (e.g., 30 meters), a curvature (e.g., 0.05), a depth (e.g., 25 meters) of the waterway, and a density of the ship (e.g., 62/km) 2 ) Etc.
In some embodiments, waterway information may be obtained based on waterway information collection devices (e.g., aerial remote sensing devices, sonar, etc.) and/or ship navigation systems.
The waterway characteristics 342 are characteristics obtained by extracting characteristics of waterway information. In some embodiments, the waterway characteristics may be characterized by a waterway characteristic sequence. A waterway feature sequence refers to a sequence of waterway features.
Illustratively, the embedded layer 340-3 may determine a feature vector of an average width and a feature vector of a minimum width according to the width of each segment of the waterway, determine a feature vector of the number of curves, a feature vector of an average radian, and a feature vector of a radius of curvature according to the curvature, determine a feature vector of the number of ships and a feature vector of a minimum distance between the ships within a preset range (e.g., within 2km of the ship M) according to the density information of the ships, and combine the feature vectors to determine the waterway feature sequence.
In some embodiments, environmental feature 341 extraction layer 340-1, prediction layer 340-2, and embedding layer 340-3 may be derived by co-training. The training sample may include sample position information of the sample vessel at a plurality of time points in a fifth sample period and sample weather information, sample sea state information, and waterway information in a sixth sample period, and the tag may be position information of the sample vessel at a plurality of time points in the sixth sample period, wherein the fifth sample period is earlier than the sixth sample period. The label can be obtained by manually marking the sample position information of the sample ship in the sixth sample time period, and can also be obtained by chart records in the sixth sample time period. It should be understood that the sample vessel and the vessel to be predicted should be of the same or of the same general class, e.g. of the type of the sample vessel and the vessel to be predicted are all of the ten thousand ton class of cargo vessels. Sample weather information and sample sea state information in the training sample are input into an initial environmental characteristic extraction layer, and sample water channel information in the training sample is input into an initial embedding layer. And then, inputting the output of the initial environmental characteristic extraction layer, the output of the initial embedding layer and the sample position information into an initial prediction layer together, and constructing a loss function based on the output of the initial prediction layer and the label. And iteratively updating parameters of each layer in the initial track prediction model based on the loss function until a preset condition is met, so as to obtain the trained track prediction model. The preset conditions may include, but are not limited to, the loss function converging, the loss function value being less than a preset value, or the number of training iterations reaching a threshold.
According to the method, through analysis of the water channel information, the method can be suitable for actual situations of different water channels, and therefore position information of a plurality of time points in preset time can be accurately determined.
According to the method disclosed by some embodiments of the specification, the future tracks of other ships are accurately predicted by combining the environmental conditions, so that the risk behaviors of the ships can be conveniently and subsequently judged, and the navigation safety of the ships and the smooth passage of a navigation water area are ensured.
Fig. 4 is a schematic diagram illustrating sending a hint to a user terminal according to some embodiments of the present disclosure. As shown in fig. 4, the flow 400 of sending a prompt message to a user terminal includes the following steps. In some embodiments, the process 400 may be performed by a processing device.
In step 410, in response to the presence of the risk activity, a risk value at which the risk activity occurs is determined.
In some embodiments, the processing device may process the waterway feature sequence, the vessel information of the current vessel, and the vessel information of other vessels based on the risk prediction model, and determine a risk value at which the risk behavior occurs. Wherein the risk value may include at least one of a meeting risk value and a pursuit risk value, and the risk value may be a value between 0 and 1, with a larger value indicating a higher risk. The risk prediction model is a machine learning model, for example, a neural network model, or the like.
In some embodiments, the input of the risk prediction model may include vessel information of the current vessel and vessel information of other vessels; the output data of the risk prediction model may include a chase risk value and a ship meeting risk value. The vessel information may include, among other things, the type, length, load, speed, distance between the current vessel and the other vessels, etc.
In some embodiments, the input of the risk prediction model further comprises a waterway feature sequence. The water channel characteristic sequence can comprise the characteristic vectors of average width and minimum width according to the width of each section of water channel, the characteristic vectors of curve number, average radian and curvature radius according to the curvature, the characteristic vectors of the number of ships and minimum distance in the extraction range according to the density information of the ships, and the characteristic vectors can be obtained by extracting the water channel information.
In some embodiments, the waterway feature sequence may also be obtained by an embedded layer of the track prediction model, and a detailed description of the track prediction model and its embedded layer may be found in the associated description of FIG. 3.
In some embodiments, the risk prediction model may include a first ship feature extraction layer, a second ship feature extraction layer, a first risk determination layer, and a second risk determination layer. The output of the first ship feature extraction layer and the second ship feature extraction layer can be used as the input of a first risk judgment layer and a second risk judgment layer, and the output of the first risk judgment layer and the second risk judgment layer can be used as the final output of a risk prediction model.
The first vessel feature extraction layer may be used to obtain a first feature vector for the current vessel, in some embodiments, the input data of the first vessel feature extraction layer may include information of the current vessel, and the output of the first vessel feature extraction layer may include the first feature vector of the current vessel.
In some embodiments, the first marine feature extraction layer may be a Neural network model (NN).
The second vessel feature extraction layer may be used to obtain a second feature vector for the other vessels, in some embodiments, the input data of the second vessel feature extraction layer may include information for the other vessels, and the output of the second vessel feature extraction layer may include the second feature vector for the other vessels.
In some embodiments, the second marine feature extraction layer may be a Neural network model (NN).
In some embodiments, parameters of the first marine feature extraction layer and the second marine feature extraction layer may be shared.
The first risk assessment layer may be used to determine a crossover risk value, in some embodiments, the input data of the first risk assessment layer may include a first feature vector, a second feature vector, and a waterway feature sequence; the output data of the first risk determination layer may include the chase risk value and its confidence.
In some embodiments, the first risk assessment layer may be a Neural network model (NN).
The second risk assessment layer may be used to determine a meeting risk value, in some embodiments, the input data for the second risk assessment layer may include a first feature vector, a second feature vector, and a waterway feature sequence; the output data of the second risk determination layer may include a meeting risk value and its confidence level.
In some embodiments, the second risk assessment layer may be a Neural network model (NN).
In some embodiments, the first ship feature extraction layer, the second ship feature extraction layer, the first risk assessment layer, and the second risk assessment layer in the risk prediction model may be obtained by joint training. The training samples may include multiple sets of training data, each set of training data including: information of historical current ships, information of historical other ships and corresponding historical water channel characteristic sequences.
The trained tags may be corresponding pursuit risk values and ship meeting risk values. In some embodiments, the trained tags may be determined based on historical data or manually noted. For example, 1 indicates that there is a high risk that it is not appropriate to chase or to meet, and 0 indicates that it is safe that it is possible to chase or meet.
In some embodiments of the present description, the processing device obtains the risk prediction model based on a combined training of the first ship extraction layer, the second ship extraction layer, the first risk assessment layer, and the second risk assessment layer. The risk value when the risk behaviors such as ship crossing and overtaking occur can be predicted through the risk prediction model, and on the basis, whether the risk exists can be prompted to the user terminal, so that relevant personnel for ship navigation can make appropriate decisions in time, and the ship navigation safety is guaranteed.
In some embodiments, when the risk value is greater than the threshold, the processing device may determine a recommended meeting location or a crossover location and send the recommended meeting location or crossover location to the user terminal.
In some embodiments, the processing device may process the first track information, the second track information, the ship information of the current ship, the ship information of other ships, and determine the recommended pursuit location or the meeting location based on the location recommendation model. Wherein, the chasing position or the meeting position can be represented by longitude and latitude.
In some embodiments, the input data of the location recommendation model may include first track information, second track information, vessel information of the current vessel, vessel information of other vessels; the output data of the location recommendation model may include a recommended location. The vessel information may include, among other things, the type, length, load, speed, distance between the current vessel and the other vessels, etc.
In some embodiments, the location recommendation model may include a marine feature extraction layer and a recommendation layer. The output of the first ship feature extraction layer can be used as input of a recommendation layer, and the output of the recommendation layer can be used as final output of the position recommendation model.
The ship feature extraction layer may be used to extract features of the ship. In some embodiments, the input data of the ship feature extraction layer may include ship information of the current ship, ship information of other ships. The output data of the ship feature extraction layer may include a first ship feature of the current ship and a second ship feature of the other ships.
In some embodiments, the marine feature extraction layer may have the same parameters as the first marine feature extraction layer and the second marine feature extraction layer in the risk prediction model.
The recommendation layer may be used to determine a recommendation location. In some embodiments, the input data of the recommendation layer may include a first vessel feature, a second vessel feature, first track information, second track information, and a waterway feature sequence. The output data of the recommendation layer may include a recommendation location. The water channel feature sequence may be the water channel feature sequence in the risk prediction model, and the acquisition of the first track information and the second track information may be described with reference to fig. 2.
In some embodiments, the location recommendation model may be obtained through multiple labeled sample training. The training samples may include historical first track information, historical second track information, historical current vessel information, and vessel information for other vessels.
The training tags may include locations where the ship history is safe and dangerous to ship at a meeting or a pursuit, and may be obtained based on historical ship voyage data.
In some embodiments of the present disclosure, when the risk value of the ship meeting or crossing is greater than the threshold value, the recommended ship meeting or crossing position is obtained by using the position recommendation model, and the user is prompted to better meet or cross the ship when the risk value is higher, but the ship has to meet or cross the ship, so that the ship can be guaranteed to smoothly meet or cross the ship to some extent, and the ship can safely sail.
In some embodiments, the prompt may include at least one of a ship risk value, a pursuit risk value, a recommended ship location, or a recommended pursuit location.
The risk value can be determined by the output result of the risk prediction model, and the recommended meeting ship position or the recommended overtaking position can be determined by the output result of the position recommendation model.
In some embodiments, the prompting message further includes displaying an early warning at the user terminal. The early warning may be performed in various manners, for example, text early warning, graphic early warning, voice early warning, etc. The early warning content can comprise the risk level of the risk behavior, for example, the early warning content can be 'high current overtaking risk', 'low ship meeting risk', and the like, and the early warning content can be set according to actual requirements.
In some embodiments, the processing device may determine a risk level corresponding to the risk behavior based on the risk value output by the risk prediction model. For example, the risk level is low in the interval of the risk value [0,0.4 ], medium in the interval of the risk value [0.4,0.7 ], and high in the interval of the risk value [0.7,1 ].
In some embodiments, the processing device may determine a manner of displaying the pre-warning at the user terminal based on the risk level of the risk behavior. For example, when the risk level is low risk, the early warning can be displayed in a text early warning or graphic early warning mode, and when the risk level is medium risk or high risk, the early warning can be performed in a sound early warning mode.
In some embodiments, the processing device may also determine a manner of pre-warning based on the risk level of the risk behavior and the confidence level of the risk prediction model. When the confidence coefficient of the risk prediction model is lower than a preset threshold value, the result of the risk prediction model is possibly provided with a certain error, so that the user can be simultaneously prompted by sound when early warning is carried out that the confidence coefficient of the risk prediction result is lower than a preset value; when the confidence coefficient of the risk prediction model is higher than a preset threshold value, the result of the risk prediction model is reliable, and early warning can be performed in different modes according to different risk grades, for example, early warning is performed in a text or graphic mode when the risk grade is low risk, and early warning is performed in a sound mode when the risk grade is medium risk or high risk.
In some embodiments, the mode of displaying the early warning may also include other possible modes, and may be specifically set according to actual ship management requirements.
Fig. 5 is an exemplary block diagram of a marine vessel safe voyage management system according to some embodiments of the present description. As shown in fig. 5, in some embodiments, the marine vessel safe voyage management system 500 may include a first acquisition module 510, a second acquisition module 520, a judgment module 530, and a prompt module 540.
In some embodiments, the first obtaining module 510 is configured to obtain, based on the user terminal, first track information of the current ship. For more on the first track information see fig. 2 and its associated description.
In some embodiments, the second acquisition module 520 is configured to determine the second track information of the other vessel based on parameters of the other vessel relative to the current vessel, the parameters being acquired by vessel radar installed on the current vessel. For more on the second track information see fig. 2 and its associated description. In some embodiments, the second determining module 520 is further configured to determine the location information 330 of the plurality of time points in the preset time based on the ship radar obtaining the location information of the other ship relative to the current ship at the plurality of time points in the preset time; and processing the position information 330 of a plurality of time points in the preset time based on the track prediction model to obtain position information of a plurality of time points in the future, and determining second track information according to the position information of the plurality of time points in the future. See fig. 3 and its associated description for more details regarding the track prediction model.
In some embodiments, the determining module 530 is configured to determine whether the current vessel has risk behavior based on a relationship between the first track information and the second track information. For more on judging whether the current vessel has risk behaviour see fig. 2 and its related description.
In some embodiments, the prompt module 540 is configured to determine a risk value at which the risk behavior occurs in response to the presence of the risk behavior; and sending prompt information to the user terminal, wherein the prompt information comprises the risk value. See fig. 4 and its associated description for more details regarding risk values. In some embodiments, the prompting module 540 is further configured to display the early warning on the user terminal, and the early warning on the user terminal, including: determining a risk level of the risk behavior based on the risk value; and determining a mode for displaying the early warning on the user terminal based on the risk level. See fig. 4 and its associated description for more details regarding the display of an early warning at a user terminal.
It should be understood that the system shown in fig. 5 and its modules may be implemented in a variety of ways. It should be noted that the above description of the ship safe navigation management system and the modules thereof is for convenience of description only, and the present description is not limited to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, various modules may be combined arbitrarily or a subsystem may be constructed in connection with other modules without departing from such principles. In some embodiments, the first acquiring module, the second acquiring module, the judging module and the prompting module disclosed in fig. 5 may be different modules in one system, or may be one module to implement the functions of two or more modules. For example, each module may share one memory module, or each module may have a respective memory module. Such variations are within the scope of the present description.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (10)
1. A method of marine vessel voyage risk management, the method performed by a processing device, comprising:
Acquiring first track information of a current ship based on a user terminal;
acquiring position information of other ships relative to the current ship at a plurality of time points in preset time based on a ship radar, and determining the position information at the plurality of time points in the preset time;
determining position information of a plurality of future time points of the other ships based on the position information of the plurality of time points in the preset time and the environmental characteristics, and determining second track information according to the position information of the plurality of future time points of the other ships;
judging whether the current ship has risk behaviors based on the relation between the first track information and the second track information, wherein the risk behaviors comprise at least one of a pursuit behavior and a ship meeting behavior;
and sending prompt information to the user terminal in response to the risk behavior.
2. The method of claim 1, the environmental characteristics being acquired based on future weather information and current sea state information.
3. The method of claim 1, the method further comprising:
and processing the position information of a plurality of time points in the preset time and the environmental characteristics based on a track prediction model, and determining the position information of a plurality of time points in the future of the other ships.
4. The method of claim 3, wherein the input of the track prediction model further comprises waterway information, the waterway information comprising at least one of an average width, a minimum width, a number of turns, an average radian, a radius of curvature, a number of vessels within a predetermined range, a minimum distance between vessels of at least one segment of waterway;
the determining location information for future points in time of the other vessel comprises:
and processing the position information of a plurality of time points in the preset time, the environmental characteristics and the water channel information based on the track prediction model, and determining the position information of a plurality of time points in the future of the other ships.
5. A marine vessel voyage risk management system, comprising:
the first acquisition module is used for acquiring first track information of the current ship based on the user terminal;
the second acquisition module is used for acquiring the position information of other ships relative to the current ship at a plurality of time points in the preset time based on the ship radar and determining the position information at the plurality of time points in the preset time;
the determining module is used for determining the position information of a plurality of time points in the future of the other ships based on the position information of a plurality of time points in the preset time and the environmental characteristics, and determining second track information according to the position information of a plurality of time points in the future of the other ships;
The judging module is used for judging whether the current ship has risk behaviors based on the relation between the first track information and the second track information, wherein the risk behaviors comprise at least one of a pursuit behavior and a ship meeting behavior;
and the prompt module is used for responding to the risk behaviors and sending prompt information to the user terminal.
6. The system of claim 5, the environmental characteristics are acquired based on future weather information and current sea state information.
7. The system of claim 5, the determination module further to:
and processing the position information of a plurality of time points in the preset time and the environmental characteristics based on a track prediction model, and determining the position information of a plurality of time points in the future of the other ships.
8. The system of claim 7, the input of the track prediction model further comprising waterway information, the waterway information comprising at least one of an average width, a minimum width, a number of turns, an average arc, a radius of curvature, a number of vessels within a predetermined range, a minimum distance between vessels of at least one segment of waterway;
the determination module is further to:
and processing the position information of a plurality of time points in the preset time, the environmental characteristics and the water channel information based on the track prediction model, and determining the position information of a plurality of time points in the future of the other ships.
9. A ship navigation risk management apparatus comprising a processor for performing the ship navigation risk management method of any one of claims 1 to 4.
10. A computer-readable storage medium storing computer instructions that, when read by a computer in the storage medium, the computer performs the ship voyage risk management method according to any one of claims 1 to 4.
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| CN115880950B (en) * | 2023-02-27 | 2023-05-05 | 中国船舶集团有限公司第七一九研究所 | Data processing method of automatic ship identification system |
| CN117037089A (en) * | 2023-10-09 | 2023-11-10 | 亿海蓝（北京）数据技术股份公司 | Method and device for detecting ship unauthorized exit behavior and readable storage medium |
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| JP3970415B2 (en) * | 1998-03-24 | 2007-09-05 | 株式会社トキメック | Ship collision prevention assistance apparatus and vessel collision prevention assistance method |
| CN105390029B (en) * | 2015-11-06 | 2019-04-26 | 武汉理工大学 | Ship collision prevention aid decision-making method and system based on Track Fusion and Trajectory Prediction |
| JP6998576B2 (en) * | 2017-03-31 | 2022-01-18 | 国立研究開発法人 海上・港湾・航空技術研究所 | Navigation support method and navigation support system adapted to the risks on the route |
| JP6806242B2 (en) * | 2017-04-20 | 2021-01-06 | 富士通株式会社 | Collision risk calculation program, collision risk calculation method and collision risk calculation device |
| JP7364521B2 (en) * | 2020-03-31 | 2023-10-18 | 株式会社光電製作所 | Avoidance route search device, avoidance route search method, program |
| CN112965475A (en) * | 2021-01-22 | 2021-06-15 | 青岛科技大学 | Obstacle collision prevention method based on dynamic navigation ship domain and collision prevention rule |
| CN113744570B (en) * | 2021-11-03 | 2022-03-25 | 武汉理工大学 | Anti-collision early warning method and device for ships in water area of bridge area |
| CN113901951A (en) * | 2021-11-06 | 2022-01-07 | 重庆数智三万智能装备有限公司 | Unmanned ship self-identification and obstacle avoidance method and device |
| CN114550501A (en) * | 2022-04-20 | 2022-05-27 | 迪泰（浙江）通信技术有限公司 | AIS-based ship danger early warning system and method |
| CN114895673A (en) * | 2022-04-26 | 2022-08-12 | 武汉理工大学 | Ship collision avoidance decision method based on deep reinforcement learning under rule constraint |
| CN115050214B (en) * | 2022-06-07 | 2023-08-29 | 兰州大学 | AIS data-based ship collision risk prediction method |
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