A method for predicting ship track when passing a bridge

Abstract

The present invention discloses a method for predicting the track of a ship passing a bridge. The method uses the Gram angle field to convert the time series of the track of each ship into an RGB image, and then uses the RGB images to form a training sample set. A convolutional neural network and an attention mechanism are introduced into the current mature GAN network structure to construct a ship crossing the bridge track prediction model. The convolutional neural network enables the ship crossing the bridge track prediction model to automatically learn the features of the RGB image that characterizes the ship track. The attention mechanism enables the ship crossing the bridge track prediction model to fully utilize the structural information in the RGB image, so that the ship crossing the bridge track prediction model can focus on important long-distance dependencies in the RGB image and more accurately extract key features in the RGB image. The method has the advantages of high prediction accuracy and low requirements for model design and optimization.

Description

Technical Field

[0001] The present invention relates to a track prediction method, in particular to a track prediction method for ships passing a bridge. Background Art

[0002] With the continued growth of global trade and the increase in water transport volume, the risk of ship-bridge collisions is gradually increasing, especially in narrow or busy waterways. Ship-bridge collisions are a serious type of maritime traffic accident, potentially resulting in not only significant casualties and environmental pollution, but also costly infrastructure damage and long-term traffic disruptions. With the increasing global shipping traffic, the risk of ships crossing complex waters such as bridge areas has increased accordingly. Traditional navigation safety warning systems are often inadequate in handling complex and dynamic scenarios, making research on the risk management and prevention of ship-bridge collisions particularly important. Due to the complex navigation environment and high traffic volume in bridge areas, traffic management departments need to use vessel traffic management systems (VTS) to understand the future paths of ships crossing bridges long in advance. This allows them to identify potential risks in advance, provide ample time for early warning and prevention, and improve decision-making support capabilities. In this context, long-term prediction of ship paths crossing bridges is crucial for traffic management departments to proactively detect collision risks and plan traffic flows.

[0003] The rapid development of artificial intelligence technology has promoted the widespread application of deep learning technology in the field of ship track prediction. Among them, representative ship track prediction methods are based on long short-term memory networks, gated recurrent units, etc. However, these methods are still unable to fully explore the motion characteristics between ship tracks and are only applicable to track prediction scenarios within a few minutes or shorter time range, such as ship collision avoidance. With the further development of deep learning technology and machine vision, generative adversarial networks (GANs), as an emerging technology in deep learning methods, are gradually being applied to ship track prediction. Generative adversarial networks optimize between the generative network and the discriminative network through an adversarial process. This adversarial training process enables the generator to continuously improve the quality of generated data, thereby improving the accuracy of long-term ship track prediction.

[0004] At present, Wang et al. published a GAN with an attention module and an interaction module to predict the trajectories of multiple ships in the paper "A prediction model of vessel trajectory based ongenerative adversarial network" to improve the accuracy of ship trajectory prediction in complex situations; Jia et al. proposed a ship warning area track prediction model based on conditional temporal GAN ​​in the paper "Conditional temporal GAN ​​for intent-aware vessel trajectory prediction in the precautionary area" to solve the uncertainty of navigation intentions of different route branches. However, the above two methods still have the following problems when predicting ship tracks: (1) When using the time series of ship tracks as training samples of the GAN model, it is difficult to capture the complex correlation between the temporal features in the time series and its related (invariant) attributes, which makes the GAN model unable to fully explore the motion features in the time series of ship tracks, and the prediction accuracy is difficult to break through; (2) There are long-term dependencies in the time series of ship tracks. The GAN model may face the challenge of difficulty in capturing long-term memory and long-term trends when dealing with long-term dependency modeling, which puts higher requirements on the design and optimization of the model. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for predicting the track of a ship passing a bridge with high prediction accuracy and low requirements for model design and optimization.

[0006] The technical solution adopted by the present invention to solve the above technical problems is: a method for predicting the track of a ship passing a bridge, comprising the following steps:

[0007] Step 1: Based on the AIS data of ships in the target bridge area, a time series of the tracks of several ships from entering the channel of the target bridge area to leaving the channel of the target bridge area is obtained. The time series of the track of each ship is converted into an RGB image using the Gramian Angular Field (GAF). The RGB image is called a track RGB image. At this time, several track RGB images are obtained by conversion. The obtained track RGB images are used to form a training sample set, where each track RGB image is used as a training sample in the training sample set;

[0008] Step 2: Introduce convolutional neural networks and attention mechanisms into both the generator and discriminator of the GAN model to obtain an improved GAN model, which is used as a ship bridge track prediction model;

[0009] Step 3: Use the training sample set to train the ship bridge track prediction model until the prediction accuracy of the ship bridge track prediction model meets the preset accuracy requirements, thereby obtaining a trained ship bridge track prediction model;

[0010] Step 4: When predicting a ship's bridge crossing track, the time series of the ship's historical track from the time it enters the channel where the target bridge area is located to the current prediction time is converted into an RGB image. This RGB image is the ship's track RGB image. The ship's track RGB image is then used as input data for the trained ship bridge crossing track prediction model. The trained ship bridge crossing track prediction model is used to predict the ship's track RGB image, and the predicted track RGB image output is obtained.

[0011] Step 5: The RGB image of the predicted track of the ship is reversely converted into a time series. The time series is the predicted result of the ship's bridge crossing track, completing the prediction of the ship's bridge crossing track.

[0012] Compared with the existing technology, the advantage of the present invention is that by using the Gramian Angular Field (GAF) to convert the time series of each ship's track into RGB images and then use the RGB images to form a training sample set, this conversion can help the bridge track prediction model capture the dynamic and periodic characteristics of each ship's track in the time series, laying the foundation for subsequently improving the efficiency of the ship bridge track prediction model in extracting navigation features in the ship track; the convolutional neural network (Convolutional neural network) is introduced into the current mature GAN network structure. The ship crossing bridge track prediction model is constructed by using convolutional neural networks (CNN) and attention mechanism. The convolutional neural network enables the ship crossing bridge track prediction model to automatically learn the features of the RGB image that characterizes the ship track, and can make full use of the structural information in the RGB image, laying the foundation for fully mining the ship track features. The attention module calculates the correlation between the various parts of the RGB image and assigns different weights, so that the ship crossing bridge track prediction model can focus on the important long-distance dependencies in the RGB image. This weight assignment helps to more accurately extract the key features in the RGB image, thereby improving the details and overall quality of the predicted RGB image generated by the ship crossing bridge track prediction model and improving the prediction accuracy of the ship crossing bridge track. Therefore, the prediction accuracy of the present invention is high and the design and optimization requirements of the model are low.

[0013] Furthermore, in step 1, based on the AIS data of ships in the target bridge area, a time series of the tracks of several ships from entering the channel where the target bridge area is located to leaving the channel where the target bridge area is located is obtained. The specific process of using GAF to convert the time series of each ship's track into an RGB image is as follows:

[0014] Step A1: Obtain AIS data of M ships in the target bridge area. M is determined based on actual forecast requirements. Theoretically, a larger value is better. The M ships are randomly numbered from 1 to M. The ship numbered i is called the i-th ship, where i = 1, 2, ..., M.

[0015] The longitude, latitude, heading, speed and timestamp in the AIS data of each ship are used to describe the navigation characteristics of each track point of each ship. Specifically, each track point in the AIS data of each ship is called the original track point, and the i-th ship is called the j-th track point. i The ship navigation characteristics of the original track point are recorded as Among them, j i =1,2,...,n i , n i represents the number of original track points passed by the i-th ship, As shown in formula (1):

[0016]

[0017] in, Indicates that the i-th ship is at its j-th i The longitude of the original track point, Indicates that the i-th ship is at its j-th i The latitude of the original track point, Indicates that the i-th ship is at its j-th i The heading of the original track point, Indicates that the i-th ship is at its j-th i The speed of the original track point, Indicates that the i-th ship is at its j-th i Timestamp of each original track point;

[0018] The original navigation behavior sequence of the i-th ship is recorded as S i ,

[0019] Step A2: Use Lagrange interpolation method to calculate the original navigation behavior sequence S of the i-th ship i Interpolation is performed to obtain the interpolated navigation behavior sequence of the i-th ship, which is recorded as C i , Among them, m iRepresents the original navigation behavior sequence S of the i-th ship i The number of track points that the i-th ship passes through after interpolation will affect the original navigation behavior sequence S of the i-th ship. i After interpolation, the track point that the i-th ship passes through is called the interpolation track point, that is, the i-th ship passes through m i interpolated track points, Indicates that the i-th ship is in its k-th i The ship navigation characteristics of k interpolation track points, i =1,2,...,m i , Indicates that the i-th ship is in its k-th i The longitude of the interpolated track points, Indicates that the i-th ship is in its k-th i The latitude of the interpolated track points, Indicates that the i-th ship is in its k-th i The heading of the interpolated track points, Indicates that the i-th ship is in its k-th i The speed of the interpolated track points, Indicates that the i-th ship is in its k-th i The timestamps of the interpolated track points;

[0020] Step A3: Set the track point number threshold to 60, and determine whether the number of interpolated track points passed by each ship is less than 60. If so, delete the interpolated navigation behavior sequence of the ship; otherwise, retain it.

[0021] The number of interpolated navigation behavior sequences of all ships retained at this time is counted and recorded as W, that is, the interpolated navigation behavior sequences of W ships are retained at this time, and these W ships are called retained ships, that is, W retained ships;

[0022] The interpolated navigation behavior sequence of the wth retained ship is recorded as D w , s w represents the number of interpolation track points passed by the w-th retained ship, w=1,2,...,W, Indicates that Article w reserves the ship in its Article e w The ship navigation characteristics of the interpolation track points, e w =1,2,...,s w , s w represents the number of interpolated track points of the wth retained ship, Indicates that Article w reserves the ship in its Article e w The longitude of the interpolated track points, Indicates that Article w reserves the ship in its Article ew The latitude of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The heading of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The speed of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The timestamps of the interpolated track points;

[0023] Step A4: Interpolated navigation behavior sequence D of the retained ship based on the wth item w Construct 5 subsequences for the wth reserved ship. The first subsequence is the longitude subsequence, the second subsequence is the latitude subsequence, the third subsequence is the heading subsequence, the fourth subsequence is the speed subsequence, and the fifth subsequence is the timestamp subsequence. The longitude subsequence of the wth reserved ship is recorded as The latitude subsequence is recorded as The heading subsequence is recorded as The speed subsequence is recorded as The timestamp subsequence is recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as

[0024] Step A5: Use formula (2) to calculate the αth subsequence of the wth reserved ship. The e w data Scaling is performed, where α=1,2,...,5, to obtain the αth subsequence of the wth retained ship The e w data Scaling data

[0025]

[0026] in, express The maximum data in express The minimum data in ;

[0027] Adopt the obtained to The αth scaled subsequence that constitutes the wth reserved ship is the αth scaling subsequence The e w Data:

[0028] Step A6: Use formula (3) to calculate the αth scaled subsequence of the wth reserved ship The e w data Perform the transformation to obtain the αth scaled subsequence of the wth reserved ship The e w data The polar coordinate angle

[0029]

[0030] Among them, arccos is the inverse cosine symbol;

[0031] Adopt the obtained to The αth scaled subsequence that constitutes the wth reserved ship The angle sequence of polar coordinates for The e w data, That is, the αth time series of the wth retained ship’s track;

[0032] Step A7: Use the Gramian Angular Summation Fields (GASF) or Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) to convert the time series of each retained ship track into an RGB image, i.e., a track RGB image. In this case, W track RGB images are obtained.

[0033] Furthermore, in step A7, the specific process of converting the time series of the wth retained ship into the track RGB image using the Gramian Angular Sum Fields (GASF) of the Gramian Angular Field (GAF) is as follows:

[0034] Step B1: Use formula (4) to calculate the RGB image pixel matrix of the αth time series of the wth retained ship track:

[0035]

[0036] Step B2: Use formula (5) to splice the RGB image pixel matrices of the five time series of the w-th retained ship track to obtain the RGB image pixel matrix of the w-th retained ship track:

[0037]

[0038] Step B3: Generate an RGB image of the wth retained ship track based on the RGB image pixel matrix of the wth retained ship track.

[0039] Furthermore, in step A7, the specific process of converting the time series of the wth retained ship into the track RGB image using the Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) is as follows:

[0040] Step C1: Use formula (6) to calculate the RGB image pixel matrix of the αth time series of the wth retained ship track:

[0041]

[0042] Step C2: Use formula (7) to splice the RGB image pixel matrices of the five time series of the w-th retained ship track to obtain the RGB image pixel matrix of the w-th retained ship track:

[0043]

[0044] Step C3: Generate an RGB image of the wth retained ship track based on the RGB image pixel matrix of the wth retained ship track.

[0045] Furthermore, the ship bridge track prediction model includes a generator and a discriminator. The generator includes a one-dimensional convolutional neural network layer, an attention layer and a fully connected layer. Its one-dimensional convolutional neural network layer is called the first one-dimensional convolutional neural network layer, its attention layer is called the first attention layer, and its fully connected layer is called the first fully connected layer; the first one-dimensional convolutional neural network layer is used to access a pre-constructed random noise vector that satisfies the normal distribution, and perform preliminary feature extraction and dimensionality expansion on it, construct potential features containing the information required to generate the image, and output it to the first attention layer. The first attention layer is used to screen and weight the potential features output by the first one-dimensional convolutional neural network layer to focus on key features and integrate feature information. The first fully connected layer is used to integrate and map the weighted image features output by the first attention layer to the image space, generate a final RGB image, that is, a predicted track RGB image, and output it; the discriminator includes a one-dimensional convolutional neural network layer, an attention layer and a fully connected layer, wherein the one-dimensional convolutional neural network layer is referred to as the second one-dimensional convolutional neural network layer, the attention layer is referred to as the second attention layer, and the fully connected layer is referred to as the second fully connected layer; the second one-dimensional convolutional neural network layer is used to access the predicted RGB image and the real RGB image output by the first fully connected layer, wherein the real RGB image is a Gramian angle field (Gramian angle field) Angular Field (GAF) converts the time series of the ship's track into an RGB image, and performs preliminary image feature extraction and feature difference comparison on the two to obtain preliminary image features that are output to the second attention layer. The second attention layer is used to further focus on the features that are more valuable for authenticity judgment in the preliminary image features output by the second one-dimensional convolutional neural network. The preliminary image features output by the second one-dimensional convolutional neural network are screened and weighted to obtain screened weighted image features that are output to the second fully connected layer. The second fully connected layer is used to perform a comprehensive analysis of the screened weighted image features output by the second attention layer to obtain a judgment result, which is a probability estimate of whether the two types of RGB images, the predicted RGB image and the real RGB image, connected to the second one-dimensional convolutional neural network layer, are real images or generated images.

[0046] Furthermore, when predicting the bridge crossing track of a certain ship in step 4, the specific process of converting the time series of the historical track of the ship from the time it enters the channel where the target bridge area is located to the current prediction time into an RGB image is as follows: first, obtain the AIS data of the ship from the time it enters the channel where the target bridge area is located to the current prediction time, process the AIS data of the ship according to steps A1 to A2 to obtain the interpolated navigation behavior sequence of the ship, and then directly treat the ship as a reserved ship and process it according to steps A4 to A7 to obtain the RGB image of the ship's track.

[0047] Furthermore, the specific process of step 5 of inversely converting the predicted track RGB image of the ship into a time series is as follows:

[0048] Step D1: The RGB pixel matrix corresponding to the RGB image of the predicted track of the ship is recorded as GAF. GAF is composed of the RGB image pixel matrices of the five time series of the predicted track of the ship. The RGB image pixel matrix of the αth time series of the predicted track of the ship is recorded as GAF. α , GAF α The number of rows and columns are both denoted as d, that is, GAF α is a d-order square matrix, and the number of interpolated track points of the predicted ship's track is d; GAF α The pixel value of the bth row and bth column in is recorded as (GAF α ) b,b , b=1,2,...,d;

[0049] Step D2: Use formula (8) to convert the RGB image pixel matrix GAF of the predicted ship's track in the αth time series into α The pixel value of the bth row and the bth column in GAF α ) b,b Perform reverse transformation to obtain the corresponding time data

[0050]

[0051] Step D3: Perform denormalization and obtain || is the absolute value symbol;

[0052] Step D4: Use to The αth time subsequence of the ship is predicted in, is the predicted time data of the αth time subsequence of the ship;

[0053] Step D5: Sequentially convert the first time subsequence To the 5th time subsequence The time series is formed by splicing, which is the prediction result of the ship's bridge crossing track, completing the prediction of the ship's bridge crossing track. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a prediction flow chart of the ship bridge crossing track prediction method of the present invention;

[0055] Figure 2 This is a ship trajectory diagram in the northbound traffic flow extracted from the waters near the Jintang Bridge in Example 2 of the present invention;

[0056] Figure 3 This is a schematic diagram of an RGB image obtained by converting the time series of a ship's track using GASF in Example 2 of the present invention;

[0057] Figure 4 This is a schematic diagram of the RGB images generated by the trained ship bridge track prediction model (hereinafter referred to as GASF-GAN) using GASF to transform the time series of ship tracks in Example 2 of the present invention, using the obtained RGB images as training samples;

[0058] Figure 5 Schematic diagram comparing the ship's bridge crossing track predicted by the GASF-GAN model in Example 2 of the present invention, the ship's bridge crossing track predicted by a ship's bridge crossing track prediction model trained using a simple time series as training samples (hereinafter referred to as TM-GAN), and the actual ship's bridge crossing track;

[0059] Figure 6 This is a schematic diagram of an RGB image obtained by converting the time series of a ship's track using GADF in Example 3 of the present invention;

[0060] Figure 7 This is a schematic diagram of the RGB images generated by the trained ship bridge track prediction (hereinafter referred to as GADF-GAN) using GADF to transform the time series of the ship's track as a training sample in Example 3 of the present invention;

[0061] Figure 8 This is a schematic diagram comparing the ship's bridge crossing track predicted by the GADF-GAN model and the ship's bridge crossing track predicted by the TM-GAN model in Example 3 of the present invention with the actual track of the ship. DETAILED DESCRIPTION

[0062] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0063] Example 1: Figure 1 As shown, a method for predicting the track of a ship passing a bridge includes the following steps:

[0064] Step 1: Based on the AIS data of ships in the target bridge area, a time series of the tracks of several ships from entering the channel of the target bridge area to leaving the channel of the target bridge area is obtained. The time series of the track of each ship is converted into an RGB image using the Gramian Angular Field (GAF). The RGB image is called a track RGB image. At this time, several track RGB images are obtained by conversion. The obtained track RGB images are used to form a training sample set, where each track RGB image is used as a training sample in the training sample set;

[0065] Step 2: Introduce convolutional neural networks and attention mechanisms into both the generator and discriminator of the GAN model to obtain an improved GAN model, which is used as a ship bridge track prediction model;

[0066] Step 3: Use the training sample set to train the ship bridge track prediction model until the prediction accuracy of the ship bridge track prediction model meets the preset accuracy requirements, thereby obtaining a trained ship bridge track prediction model;

[0067] Step 4: When predicting a ship's bridge crossing track, the time series of the ship's historical track from the time it enters the channel where the target bridge area is located to the current prediction time is converted into an RGB image. This RGB image is the ship's track RGB image. The ship's track RGB image is then used as input data for the trained ship bridge crossing track prediction model. The trained ship bridge crossing track prediction model is used to predict the ship's track RGB image, and the predicted track RGB image output is obtained.

[0068] Step 5: The RGB image of the predicted track of the ship is reversely converted into a time series. The time series is the predicted result of the ship's bridge crossing track, completing the prediction of the ship's bridge crossing track.

[0069] In this embodiment, the time series of each ship's track is converted into RGB images using Gramian Angular Field (GAF), and then the RGB images are used to form a training sample set. This conversion can help the bridge track prediction model capture the dynamic and periodic characteristics of each ship's track in the time series, laying the foundation for subsequently improving the efficiency of the ship bridge track prediction model in extracting navigation features in the ship track; the convolutional neural network (Convolutional neural network) is introduced into the current mature GAN network structure. The ship crossing bridge track prediction model is constructed by using convolutional neural networks (CNN) and attention mechanism. The convolutional neural network enables the ship crossing bridge track prediction model to automatically learn the features of the RGB image that characterizes the ship track, and can make full use of the structural information in the RGB image, laying the foundation for fully mining the ship track features. The attention module calculates the correlation between the various parts of the RGB image and assigns different weights, so that the ship crossing bridge track prediction model can focus on the important long-distance dependencies in the RGB image. This weight assignment helps to more accurately extract the key features in the RGB image, thereby improving the details and overall quality of the predicted RGB image generated by the ship crossing bridge track prediction model and improving the prediction accuracy of the ship crossing bridge track. Therefore, the prediction accuracy of the present invention is high and the design and optimization requirements of the model are low.

[0070] Example 2: This example is basically the same as Example 1, except that: in this example, in step 1, based on the AIS data of ships in the target bridge area waters, a time series of the tracks of several ships from entering the channel where the target bridge area is located to leaving the channel where the target bridge area is located is obtained. The specific process of using GAF to convert the time series of each ship's track into an RGB image is as follows:

[0071] Step A1: Obtain AIS data of M ships in the target bridge area. M is determined based on actual forecast requirements. Theoretically, a larger value is better. The M ships are randomly numbered from 1 to M. The ship numbered i is called the i-th ship, where i = 1, 2, ..., M.

[0072] The longitude, latitude, heading, speed and timestamp in the AIS data of each ship are used to describe the navigation characteristics of each track point of each ship. Specifically, each track point in the AIS data of each ship is called the original track point, and the i-th ship is called the j-th track point. i The ship navigation characteristics of the original track point are recorded as Among them, j i =1,2,...,n i , n i represents the number of original track points passed by the i-th ship, As shown in formula (1):

[0073]

[0074] in, Indicates that the i-th ship is at its j-th i The longitude of the original track point, Indicates that the i-th ship is at its j-th i The latitude of the original track point, Indicates that the i-th ship is at its j-th i The heading of the original track point, Indicates that the i-th ship is at its j-th i The speed of the original track point, Indicates that the i-th ship is at its j-th i Timestamp of each original track point;

[0075] The original navigation behavior sequence of the i-th ship is recorded as S i ,

[0076] Step A2: Use Lagrange interpolation method to calculate the original navigation behavior sequence S of the i-th ship i Interpolation is performed to obtain the interpolated navigation behavior sequence of the i-th ship, which is recorded as C i , Among them, m i Represents the original navigation behavior sequence S of the i-th ship i The number of track points that the i-th ship passes through after interpolation will affect the original navigation behavior sequence S of the i-th ship. i After interpolation, the track point that the i-th ship passes through is called the interpolation track point, that is, the i-th ship passes through m i interpolated track points, Indicates that the i-th ship is in its k-th i The ship navigation characteristics of k interpolation track points, i =1,2,...,m i , Indicates that the i-th ship is in its k-th i The longitude of the interpolated track points, Indicates that the i-th ship is in its k-th i The latitude of the interpolated track points, Indicates that the i-th ship is in its k-th i The heading of the interpolated track points, Indicates that the i-th ship is in its k-th i The speed of the interpolated track points, Indicates that the i-th ship is in its k-th i The timestamps of the interpolated track points;

[0077] Step A3: Set the track point number threshold to 60, and determine whether the number of interpolated track points passed by each ship is less than 60. If so, delete the interpolated navigation behavior sequence of the ship; otherwise, retain it.

[0078] The number of interpolated navigation behavior sequences of all ships retained at this time is counted and recorded as W, that is, the interpolated navigation behavior sequences of W ships are retained at this time, and these W ships are called retained ships, that is, W retained ships;

[0079] The interpolated navigation behavior sequence of the wth retained ship is recorded as D w , s w represents the number of interpolation track points passed by the w-th retained ship, w=1,2,...,W, Indicates that Article w reserves the ship in its Article e w The ship navigation characteristics of the interpolation track points, e w =1,2,...,s w , s w represents the number of interpolated track points of the wth retained ship, Indicates that Article w reserves the ship in its Article e w The longitude of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The latitude of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The heading of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The speed of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The timestamps of the interpolated track points;

[0080] Step A4: Interpolated navigation behavior sequence D of the retained ship based on the wth item w Construct 5 subsequences for the wth reserved ship. The first subsequence is the longitude subsequence, the second subsequence is the latitude subsequence, the third subsequence is the heading subsequence, the fourth subsequence is the speed subsequence, and the fifth subsequence is the timestamp subsequence. The longitude subsequence of the wth reserved ship is recorded as The latitude subsequence is recorded as The heading subsequence is recorded as The speed subsequence is recorded as The timestamp subsequence is recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as

[0081] Step A5: Use formula (2) to calculate the αth subsequence of the wth reserved ship. The e w data Scaling is performed, where α=1,2,...,5, to obtain the αth subsequence of the wth retained ship The e w data Scaling data

[0082]

[0083] in, express The maximum data in express The minimum data in ;

[0084] Adopt the obtained to The αth scaled subsequence that constitutes the wth reserved ship is the αth scaling subsequence The e w Data:

[0085] Step A6: Use formula (3) to calculate the αth scaled subsequence of the wth reserved ship The e w data Perform the transformation to obtain the αth scaled subsequence of the wth reserved ship The e w data The angle of the polar coordinates

[0086]

[0087] Among them, arccos is the inverse cosine symbol; using the obtained to The αth scaled subsequence that constitutes the wth reserved ship The angle sequence of polar coordinates for The e w data, That is, the αth time series of the wth retained ship’s track;

[0088] Step A7: Use the Gramian Angular Summation Fields (GASF) or Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) to convert the time series of each retained ship track into an RGB image, i.e., a track RGB image. In this case, W track RGB images are obtained.

[0089] In this embodiment, in step A7, the specific process of converting the time series of the wth retained ship into the track RGB image using the Gramian Angular Sum Fields (GASF) of the Gramian Angular Field (GAF) is as follows:

[0090] Step B1: Use formula (4) to calculate the RGB image pixel matrix of the αth time series of the wth retained ship track:

[0091]

[0092] Step B2: Use formula (5) to splice the RGB image pixel matrices of the five time series of the w-th retained ship track to obtain the RGB image pixel matrix of the w-th retained ship track:

[0093]

[0094] Step B3: Generate an RGB image of the wth retained ship track based on the RGB image pixel matrix of the wth retained ship track.

[0095] In this embodiment, GASF is used to convert the time series of ship tracks into RGB images. This conversion can help capture the dynamic and periodic features in the time series data, laying the foundation for subsequently improving the efficiency of extracting navigation features in ship tracks.

[0096] In this embodiment, the ship bridge track prediction model includes a generator and a discriminator. The generator includes a one-dimensional convolutional neural network layer, an attention layer, and a fully connected layer. Its one-dimensional convolutional neural network layer is called the first one-dimensional convolutional neural network layer, its attention layer is called the first attention layer, and its fully connected layer is called the first fully connected layer; the first one-dimensional convolutional neural network layer is used to access a pre-constructed random noise vector that satisfies the normal distribution, and perform preliminary feature extraction and dimensionality expansion on it, construct potential features containing the information required to generate the image, and output it to the first attention layer. The first attention layer is used to screen and weight the potential features output by the first one-dimensional convolutional neural network layer to focus on key features and integrate feature information , the image features after feature weighting are output to the first fully connected layer, the first fully connected layer is used to integrate and map the image features after feature weighting outputted by the first attention layer to the image space, generate the final RGB image, that is, the predicted track RGB image, and output it; the discriminator includes a one-dimensional convolutional neural network layer, an attention layer and a fully connected layer, the one-dimensional convolutional neural network layer is called the second one-dimensional convolutional neural network layer, the attention layer is called the second attention layer, and the fully connected layer is called the second fully connected layer; the second one-dimensional convolutional neural network layer is used to access the predicted RGB image and the real RGB image outputted by the first fully connected layer, where the real RGB image is the Gramian angle field (Gramian angle field). Angular Field (GAF) converts the time series of the ship's track into an RGB image, and performs preliminary image feature extraction and feature difference comparison on the two to obtain preliminary image features that are output to the second attention layer. The second attention layer is used to further focus on the features that are more valuable for authenticity judgment in the preliminary image features output by the second one-dimensional convolutional neural network. The preliminary image features output by the second one-dimensional convolutional neural network are screened and weighted to obtain screened weighted image features that are output to the second fully connected layer. The second fully connected layer is used to perform a comprehensive analysis of the screened weighted image features output by the second attention layer to obtain a judgment result, which is a probability estimate of whether the two types of RGB images, the predicted RGB image and the real RGB image, connected to the second one-dimensional convolutional neural network layer, are real images or generated images.

[0097] In this embodiment, the ship-bridge-passing track prediction model incorporates a one-dimensional convolutional neural network into the original GAN ​​structure (generator and discriminator). This allows the one-dimensional convolutional neural network to automatically learn the features that characterize the RGB image of the ship's track and fully utilize the structural information in the image, laying the foundation for the ship-bridge-passing track prediction model to fully explore the ship's track features. In addition, the ship-bridge-passing track prediction model introduces an attention mechanism into the original GAN ​​structure (generator and discriminator). This allows the attention module to calculate the correlation between the various parts of the RGB image and assign different weights. This allows the ship-bridge-passing track prediction model to focus on important long-range dependencies in the RGB image through the attention module. This weight distribution method helps to more accurately extract key features in the RGB image, thereby improving the details and overall quality of the generated RGB image and improving the accuracy of the long-term prediction of the ship's bridge-passing track.

[0098] In this embodiment, when predicting a ship's bridge crossing track in step 4, the specific process of converting the time series of the ship's historical track from the time the ship enters the channel where the target bridge area is located to the current prediction time into an RGB image is as follows: first, obtain the AIS data of the ship from the time the ship enters the channel where the target bridge area is located to the current prediction time; process the AIS data of the ship according to steps A1 to A2 to obtain the interpolated navigation behavior sequence of the ship; then, directly treat the ship as a reserved ship and process it according to steps A4 to A7 to obtain the RGB image of the ship's track.

[0099] In this embodiment, the specific process of step 5 inversely converting the predicted track RGB image of the ship into a time series is as follows:

[0100] Step D1: The RGB pixel matrix corresponding to the RGB image of the predicted track of the ship is recorded as GAF. GAF is composed of the RGB image pixel matrices of the five time series of the predicted track of the ship. The RGB image pixel matrix of the αth time series of the predicted track of the ship is recorded as GAF. α , GAF α The number of rows and columns are both denoted as d, that is, GAF α is a d-order square matrix, and the number of interpolated track points of the predicted ship's track is d; GAF α The pixel value of the bth row and bth column in is recorded as (GAF α ) b,b , b=1,2,...,d;

[0101] Step D2: Use formula (8) to convert the RGB image pixel matrix GAF of the predicted ship's track in the αth time series into α The pixel value of the bth row and the bth column in GAFα ) b,b Perform reverse transformation to obtain the corresponding time data

[0102]

[0103] Step D3: Perform denormalization and obtain || is the absolute value symbol;

[0104] Step D4: Use to The αth time subsequence of the ship is predicted in, is the predicted time data of the αth time subsequence of the ship;

[0105] Step D5: Sequentially convert the first time subsequence To the 5th time subsequence The time series is formed by splicing, which is the prediction result of the ship's bridge crossing track, completing the prediction of the ship's bridge crossing track.

[0106] In this example, the AIS data of the waters near the Jintang Bridge in Ningbo-Zhoushan Port from June 1, 2020 to December 31, 2020 were selected as the research object. The ship trajectory diagram of the northbound traffic flow after extraction in the waters near the Jintang Bridge is shown in the figure below. Figure 2 shown. Figure 2 In the figure, points A and B are the locations of the two piers of Jintang Bridge, with specific coordinates of (30°03′39.16″N, 121°48′5.08″E) and (30°03′39.7″N, 121°48′27.95″E) respectively. There is a traffic separation system between the two piers of Jintang Bridge, separating the traffic flows in two opposite directions (the left side of the channel is the southbound traffic flow, and the right side of the channel is the northbound traffic flow). In order to better analyze the changes in ship traffic flow, only the northbound traffic flow is selected for analysis. A total of 1091 ships' AIS tracks (i.e., ship AIS data) in this water area are extracted, as shown below: Figure 2 The AIS tracks are processed using steps A1 to A4. The processed AIS track data are converted into RGB images using GADF. The RGB image converted from a track data is shown in the figure below. Figure 3 As shown in Figure 1, these RGB images are divided into a training set and a validation set. The training set is fed into the ship bridge track prediction model proposed in this invention to train the ship bridge track prediction model (the trained ship bridge track prediction model is referred to as GASF-GAN). Six RGB images generated from the validation set are randomly selected for visualization, as shown in Figure 1. Figure 4 As shown in the figure, the inception score (IS), Frechet Inception distance (FID) score, and maximum mean discrepancy (MMD) of the ship bridge track prediction model in the validation set were recorded as error indicators of the ship bridge track prediction model, which were 2.3016, 5.0318, and 1.4734, respectively. Subsequently, these RGB images were reverse-converted into time series.

[0107] In order to verify the prediction effect of the ship crossing bridge track prediction model trained after the GASF is used for graph conversion, the ship crossing bridge track prediction model is trained using a simple time series (the trained ship crossing bridge track prediction model is referred to as the TM-GAN model) and the GASF-GAN model for ship track prediction. The prediction results of 6 navigation tracks are randomly selected for comparison. Figure 5 As shown. Figure 5 It can be seen that compared with the TM-GAN model, the predicted trajectory of the GASF-GAN model proposed in this invention has a higher degree of fit with the actual trajectory and a better prediction effect. In order to further quantify the prediction accuracy of the two track prediction models, the TM-GAN model and the GASF-GAN model, the mean square error (MSE), final displacement error (FDE), Flamenche distance (FD), and average Euclidean distance (AED) are selected as error indicators, and the four average error indicators of the predicted trajectory are calculated, as shown in Table 1:

[0108] Table 1 Error indicators of trajectories predicted using TM-GAN model and GASF-GAN model

[0109]

[0110] From the analysis of the data in Table 1, it can be seen that compared with the TM-GAN model, the prediction effect of the GASF-GAN model proposed in the present invention is significantly improved.

[0111] Example 3: This example is basically the same as Example 2, except that: in this example, in step A7, instead of using the Gramian Angular Summation Fields (GASF) of the Gramian Angular Field (GAF) to convert the time series of the w-th retained ship into a track RGB image, the Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) is used to convert the time series of the w-th retained ship into a track RGB image.

[0112] In this embodiment, in step 7, the specific process of converting the time series of the wth retained ship into the track RGB image using the Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) is as follows:

[0113] Step C1: Use formula (6) to calculate the RGB image pixel matrix of the αth time series of the wth retained ship track:

[0114]

[0115] Step C2: Use formula (7) to splice the RGB image pixel matrices of the five time series of the w-th retained ship track to obtain the RGB image pixel matrix of the w-th retained ship track:

[0116]

[0117] Step C3: Generate an RGB image of the wth retained ship track based on the RGB image pixel matrix of the wth retained ship track.

[0118] In this embodiment, GADF is used to convert the time series of ship tracks into RGB images. This conversion can help capture the dynamic and periodic features in the time series data, laying the foundation for subsequently improving the efficiency of extracting navigation features in ship tracks.

[0119] In this example, the AIS data of the waters near the Jintang Bridge of Ningbo-Zhoushan Port from June 1, 2020 to December 31, 2020 were selected as the research object. The ship trajectory diagram of the northbound traffic flow after extraction in the waters near the Jintang Bridge is shown in the figure below. Figure 2 shown. Figure 2In the figure, points A and B are the locations of the two piers of Jintang Bridge, with specific coordinates of (30°03′39.16″N, 121°48′5.08″E) and (30°03′39.7″N, 121°48′27.95″E) respectively. There is a traffic separation system between the two piers of Jintang Bridge, separating the traffic flows in two opposite directions (the left side of the channel is the southbound traffic flow, and the right side of the channel is the northbound traffic flow). In order to better analyze the changes in ship traffic flow, only the northbound traffic flow is selected for analysis. A total of 1091 ships' AIS tracks (i.e., ship AIS data) in this water area are extracted, as shown below: Figure 2 These AIS tracks are processed using steps A1 to A4.

[0120] Use GADF to convert the processed trajectory data into RGB images. The RGB image converted from a certain trajectory data is as follows: Figure 6 As shown in Figure 2, these RGB images are divided into a training set and a validation set. The training set is fed into the ship bridge track prediction model proposed in the present invention to train the ship bridge track prediction model (the trained ship bridge track prediction model is referred to as the GADF-GAN model). Six RGB images generated from the validation set are randomly selected for visualization, as shown in Figure 2. Figure 7 As shown. The initial score (IS), Frechet Inception distance (FID) score and maximum mean discrepancy (MMD) of the ship bridge track prediction model in the validation set are recorded as the error indicators of the ship bridge track prediction model, which are 0.4324, 16.8178 and 2.0120 respectively. They are compared with the relevant error indicators of the prediction results of the GASF-GAN model in Example 2, as shown in Table 2:

[0121] Table 2 Performance of GASF-GAN model and GADF-GAN model

[0122]

[0123] From Table 2, we can see that the prediction error of the GADF-GAN model is larger than that of the GASF-GAN model. Subsequently, these RGB images are inversely converted into time series.

[0124] In order to verify the prediction effect of the ship bridge track prediction model proposed in this invention after using GADF for graph conversion, the TM-GAN model and the GADF-GAN model were used for track prediction, and the prediction results of 16 navigation tracks were randomly selected for comparison. Figure 8 As shown, from Figure 8It can be seen that compared with the TM-GAN model, the predicted trajectory of the GADF-GAN model proposed in this invention has a higher degree of fit with the actual trajectory and a better prediction effect. In order to further quantify the prediction accuracy of the two trajectory prediction models, the TM-GAN model and the GADF-GAN model, the mean square error (MSE), final displacement error (FDE), Flamenche distance (FD), and average Euclidean distance (AED) are selected as error indicators, and the four average error indicators of the predicted trajectory of the validation set are calculated, as shown in Table 3:

[0125] Table 3 Error indicators of trajectories predicted using TM-GAN model and GADF-GAN model

[0126]

[0127] From the analysis of Table 3, we can see that although the prediction accuracy of the GADF-GAN model is not as good as that of the GASF-GAN model, the prediction effect of the GADF-GAN model is still improved compared with the TM-GAN model.

[0128] In summary, the ship bridge track prediction method of the present invention has high prediction accuracy and low requirements for model design and optimization.

Claims

1. A method for predicting the track of a ship passing a bridge, characterized in that The following steps are involved: Step 1: Based on the AIS data of ships in the target bridge area, a time series of the tracks of several ships from entering the channel of the target bridge area to leaving the channel of the target bridge area is obtained. The time series of the track of each ship is converted into an RGB image using the Gramian Angular Field (GAF). The RGB image is called a track RGB image. At this time, several track RGB images are obtained by conversion. The obtained track RGB images are used to form a training sample set, where each track RGB image is used as a training sample in the training sample set; Step 2: Introduce convolutional neural networks and attention mechanisms into both the generator and discriminator of the GAN model to obtain an improved GAN model, which is used as a ship bridge track prediction model; Step 3: Use the training sample set to train the ship bridge track prediction model until the prediction accuracy of the ship bridge track prediction model meets the preset accuracy requirements, thereby obtaining a trained ship bridge track prediction model; Step 4: When predicting a ship's bridge crossing track, the time series of the ship's historical track from the time it enters the channel where the target bridge area is located to the current prediction time is converted into an RGB image. This RGB image is the ship's track RGB image. The ship's track RGB image is then used as input data for the trained ship bridge crossing track prediction model. The trained ship bridge crossing track prediction model is used to predict the ship's track RGB image, and the predicted track RGB image output is obtained. Step 5: The RGB image of the predicted track of the ship is reversely converted into a time series. The time series is the predicted result of the ship's bridge crossing track, completing the prediction of the ship's bridge crossing track.

2. A method for predicting a ship's bridge track according to claim 1, characterized in that In step 1, based on the AIS data of ships in the target bridge area, a time series of the tracks of several ships from entering the channel where the target bridge area is located to leaving the channel where the target bridge area is located is obtained. The specific process of using GAF to convert the time series of each ship's track into an RGB image is as follows: Step A1: Obtain AIS data of M ships in the target bridge area. M is determined based on actual forecast requirements. Theoretically, a larger value is better. The M ships are randomly numbered from 1 to M. The ship numbered i is called the i-th ship, where i = 1, 2, ..., M. The longitude, latitude, heading, speed and timestamp in the AIS data of each ship are used to describe the navigation characteristics of each track point of each ship. Specifically, each track point in the AIS data of each ship is called the original track point, and the i-th ship is called the j-th track point. i The navigation characteristics of the ship at the original track point are recorded as N i ji , where j i =1,2,...,n i , n i represents the number of original track points passed by the i-th ship, As shown in formula (1): in, Indicates that the i-th ship is at its j-th i The longitude of the original track point, Indicates that the i-th ship is at its j-th i The latitude of the original track point, Indicates that the i-th ship is at its j-th i The heading of the original track point, Indicates that the i-th ship is at its j-th i The speed of the original track point, Indicates that the i-th ship is at its j-th i Timestamp of each original track point; The original navigation behavior sequence of the i-th ship is recorded as S i , Step A2: Use Lagrange interpolation method to calculate the original navigation behavior sequence S of the i-th ship i Interpolation is performed to obtain the interpolated navigation behavior sequence of the i-th ship, which is recorded as C i Among them, m i Represents the original navigation behavior sequence S of the i-th ship i The number of track points that the i-th ship passes through after interpolation will affect the original navigation behavior sequence S of the i-th ship. i After interpolation, the track point that the i-th ship passes through is called the interpolation track point, that is, the i-th ship passes through m i interpolated track points, Indicates that the i-th ship is in its k-th i The ship navigation characteristics of k interpolation track points, i =1,2,...,m i , Indicates that the i-th ship is in its k-th i The longitude of the interpolated track points, Indicates that the i-th ship is in its k-th i The latitude of the interpolated track points, Indicates that the i-th ship is in its k-th i The heading of the interpolated track points, Indicates that the i-th ship is in its k-th i The speed of the interpolated track points, Indicates that the i-th ship is in its k-th i The timestamps of the interpolated track points; Step A3: Set the track point number threshold to 60, and determine whether the number of interpolated track points passed by each ship is less than 60. If so, delete the interpolated navigation behavior sequence of the ship; otherwise, retain it. The number of interpolated navigation behavior sequences of all ships retained at this time is counted and recorded as W, that is, the interpolated navigation behavior sequences of W ships are retained at this time, and these W ships are called retained ships, that is, W retained ships; The interpolated navigation behavior sequence of the wth retained ship is recorded as D w , s w represents the number of interpolation track points passed by the w-th retained ship, w=1,2,...,W, Indicates that Article w reserves the ship in its Article e w The ship navigation characteristics of the interpolation track points, e w =1,2,...,s w , s w represents the number of interpolated track points of the wth retained ship, Indicates that Article w reserves the ship in its Article e w The longitude of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The latitude of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The heading of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The speed of the interpolated track points, Indicates that Article w reserves the ship in its Article e w The timestamps of the interpolated track points; Step A4: Interpolated navigation behavior sequence D of the retained ship based on the wth item w Construct 5 subsequences for the wth reserved ship. The first subsequence is the longitude subsequence, the second subsequence is the latitude subsequence, the third subsequence is the heading subsequence, the fourth subsequence is the speed subsequence, and the fifth subsequence is the timestamp subsequence. The longitude subsequence of the wth reserved ship is recorded as The latitude subsequence is recorded as The heading subsequence is recorded as The speed subsequence is recorded as The timestamp subsequence is recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as for The e w data, recorded as Step A5: Use formula (2) to calculate the αth subsequence of the wth reserved ship. The e w data Scaling is performed, where α=1,2,...,5, to obtain the αth subsequence of the wth retained ship The e w data Scaling data in, express The maximum data in express The minimum data in ; Adopt the obtained to The αth scaled subsequence that constitutes the wth reserved ship is the αth scaling subsequence The e w Data: Step A6: Use formula (3) to calculate the αth scaled subsequence of the wth reserved ship The e w data Perform the transformation to obtain the αth scaled subsequence of the wth reserved ship The e w data The angle of the polar coordinates Among them, arccos is the inverse cosine symbol; Adopt the obtained to The αth scaled subsequence that constitutes the wth reserved ship The angle sequence of polar coordinates for The e w data, That is, the αth time series of the wth retained ship’s track; Step A7: Use the Gramian Angular Sum Fields (GASF) or Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) to convert the time series of each retained ship track into an RGB image, i.e., a track RGB image. In this case, W track RGB images are obtained.

3. A method for predicting a ship's bridge track according to claim 2, characterized in that In step A7, the specific process of converting the time series of the wth retained ship into the track RGB image using the Gramian Angular Sum Fields (GASF) of the Gramian Angular Field (GAF) is as follows: Step B1: Use formula (4) to calculate the RGB image pixel matrix of the αth time series of the wth retained ship track: Step B2: Use formula (5) to splice the RGB image pixel matrices of the five time series of the w-th retained ship track to obtain the RGB image pixel matrix of the w-th retained ship track: Step B3: Generate an RGB image of the wth retained ship track based on the RGB image pixel matrix of the wth retained ship track.

4. A method for predicting a ship's bridge track according to claim 2, characterized in that In step A7, the specific process of converting the time series of the wth retained ship into the track RGB image using the Gramian Angular Difference Fields (GADF) of the Gramian Angular Field (GAF) is as follows: Step C1: Use formula (6) to calculate the RGB image pixel matrix of the αth time series of the wth retained ship track: Step C2: Use formula (7) to splice the RGB image pixel matrices of the five time series of the w-th retained ship track to obtain the RGB image pixel matrix of the w-th retained ship track: Step C3: Generate an RGB image of the wth retained ship track based on the RGB image pixel matrix of the wth retained ship track.

5. A method for predicting a ship's bridge track according to claim 1, characterized in that The ship bridge track prediction model includes a generator and a discriminator. The generator includes a one-dimensional convolutional neural network layer, an attention layer and a fully connected layer. The one-dimensional convolutional neural network layer is called the first one-dimensional convolutional neural network layer, the attention layer is called the first attention layer, and the fully connected layer is called the first fully connected layer. The first one-dimensional convolutional neural network layer is used to access a pre-constructed random noise vector that satisfies the normal distribution, and perform preliminary feature extraction and dimensionality expansion on it, construct potential features containing the information required to generate the image, and output it to the first attention layer. The first attention layer is used to screen and weight the potential features output by the first one-dimensional convolutional neural network layer to focus on key features and integrate feature information, and obtain The image features after feature weighting are output to the first fully connected layer, and the first fully connected layer is used to integrate and map the image features after feature weighting outputted by the first attention layer to the image space to generate a final RGB image, i.e., a predicted track RGB image, and output it; the discriminator includes a one-dimensional convolutional neural network layer, an attention layer, and a fully connected layer, wherein the one-dimensional convolutional neural network layer is referred to as the second one-dimensional convolutional neural network layer, the attention layer is referred to as the second attention layer, and the fully connected layer is referred to as the second fully connected layer; the second one-dimensional convolutional neural network layer is used to access the predicted RGB image and the real RGB image outputted by the first fully connected layer, wherein the real RGB image is a Gramian angle field (Gramian angle field) Angular Field (GAF) converts the time series of the ship's track into an RGB image, and performs preliminary image feature extraction and feature difference comparison on the two to obtain preliminary image features that are output to the second attention layer. The second attention layer is used to further focus on the features that are more valuable for authenticity judgment in the preliminary image features output by the second one-dimensional convolutional neural network. The preliminary image features output by the second one-dimensional convolutional neural network are screened and weighted to obtain screened weighted image features that are output to the second fully connected layer. The second fully connected layer is used to perform a comprehensive analysis of the screened weighted image features output by the second attention layer to obtain a judgment result, which is a probability estimate of whether the two types of RGB images, the predicted RGB image and the real RGB image, connected to the second one-dimensional convolutional neural network layer, are real images or generated images.

6. A method for predicting a ship's bridge track according to claim 1, characterized in that When predicting a ship's bridge crossing track in step 4, the specific process of converting the time series of the ship's historical track from the time it enters the channel where the target bridge area is located to the current prediction time into an RGB image is as follows: first, obtain the AIS data of the ship from the time it enters the channel where the target bridge area is located to the current prediction time, process the AIS data of the ship according to steps A1 to A2 to obtain the interpolated navigation behavior sequence of the ship, and then directly treat the ship as a reserved ship and process it according to steps A4 to A7 to obtain the RGB image of the ship's track.

7. A method for predicting a ship's bridge track according to claim 6, characterized in that The specific process of step 5 inversely converting the predicted track RGB image of the ship into a time series is as follows: Step D1: The RGB pixel matrix corresponding to the RGB image of the predicted track of the ship is recorded as GAF. GAF is composed of the RGB image pixel matrices of the five time series of the predicted track of the ship. The RGB image pixel matrix of the αth time series of the predicted track of the ship is recorded as GAF. α , GAF α The number of rows and columns are both denoted as d, that is, GAF α is a d-order square matrix, and the number of interpolated track points of the predicted ship's track is d; GAF α The pixel value of the bth row and bth column in is recorded as (GAF α ) b,b , b=1,2,...,d; Step D2: Use formula (8) to convert the RGB image pixel matrix GAF of the predicted ship's track in the αth time series into α The pixel value of the bth row and the bth column in GAF α ) b,b Perform reverse transformation to obtain the corresponding time data Step D3: Perform denormalization and obtain || is the absolute value symbol; Step D4: Use to The αth time subsequence of the ship is predicted in, is the predicted time data of the αth time subsequence of the ship; Step D5: Sequentially convert the first time subsequence To the 5th time subsequence The time series is formed by splicing, which is the prediction result of the ship's bridge crossing track, completing the prediction of the ship's bridge crossing track.

Images