Ship tracking method and device, electronic equipment and storage medium

Abstract

The invention discloses a ship tracking method and device, electronic equipment and a storage medium. According to the specific scheme, at least one to-be-tracked ship of a target ship in a preset sea area range is determined, and to-be-processed data of each to-be-tracked ship at the current moment is acquired; performing feature extraction processing on the to-be-processed data of each to-be-tracked ship to obtain a first feature vector and a second feature vector; for at least one to-be-tracked ship, inputting the first feature vector and the second feature vector corresponding to each to-be-tracked ship into the feature tracking model corresponding to the current moment to obtain a predicted feature vector corresponding to each to-be-tracked ship; and determining predicted ship tracking information corresponding to the predicted feature vector, so as to determine a navigation planning route of the target ship based on the predicted ship tracking information. According to the invention, the accuracy and efficiency of ship tracking information analysis are improved, and the effect of obtaining and predicting ship tracking information in real time is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a ship tracking method, device, electronic equipment and storage medium. Background Art

[0002] In order to ensure the smooth and safe navigation of the current ship, the navigation information of other ships in the surrounding waters is usually analyzed to plan the navigation route of the current ship based on the analysis results.

[0003] At present, the analysis method of the navigation information of surrounding ships is mainly based on manual analysis of one of the acquired ship automatic identification system (Automatic Identification System, AIS) data or radar data to obtain the analysis results. And determine the navigation route of the current ship based on the analysis results. However, due to the certain lag of AIS data, it is impossible to obtain the navigation information of surrounding ships in real time. In addition, when radar data is obtained through radar equipment, it will be affected by the clutter in the sea, and there will be omissions and misdetections of navigation information. Based on this, only one kind of data is used for analysis, which will result in the analysis results not being real-time and having low accuracy. Moreover, manual analysis not only has low processing efficiency, but also has certain subjectivity. Summary of the invention

[0004] The present invention provides a ship tracking method, device, electronic equipment and storage medium, which improve the accuracy and efficiency of ship tracking information analysis and achieve the effect of real-time acquisition and prediction of ship tracking information.

[0005] According to one aspect of the present invention, a ship tracking method is provided, the method comprising:

[0006] Determine at least one ship to be tracked within a preset sea area of ​​the target ship, and obtain the data to be processed of each ship to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment;

[0007] Performing feature extraction processing on the first data and the second data of each ship to be tracked respectively to obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data;

[0008] For at least one ship to be tracked, a first feature vector and a second feature vector corresponding to each ship to be tracked are input into a feature tracking model corresponding to the current moment, to obtain a predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment;

[0009] Based on the predicted feature vector, the predicted ship tracking information corresponding to the predicted feature vector is determined to determine the navigation planning route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographic location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

[0010] According to another aspect of the present invention, there is provided a ship tracking device, the device comprising:

[0011] A data acquisition module, used to determine at least one ship to be tracked within a preset sea area of ​​the target ship, and to acquire the data to be processed of each ship to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment;

[0012] A feature extraction module, used to perform feature extraction processing on the first data and the second data of each ship to be tracked, respectively, to obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data;

[0013] A model prediction module, for at least one ship to be tracked, inputting a first feature vector and a second feature vector corresponding to each ship to be tracked into a feature tracking model corresponding to a current moment, to obtain a predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to a previous moment based on an observed feature vector at a previous moment, and the observed feature vector at a previous moment is determined based on historical ship data at a previous moment;

[0014] The tracking information determination module is used to determine the predicted ship tracking information corresponding to the predicted feature vector based on the predicted feature vector, so as to determine the navigation planning route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographic location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

[0015] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by at least one processor so that the at least one processor can execute the ship tracking method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the ship tracking method of any embodiment of the present invention when executed.

[0020] According to another aspect of the present invention, there is provided a computer program product, comprising a computer program, wherein the computer program, when executed by a processor, implements the ship tracking method according to any embodiment of the present invention.

[0021] The technical solution of the embodiment of the present invention provides data support for the subsequent prediction of ship tracking information by determining at least one ship to be tracked within the preset sea area of ​​the target ship and obtaining the data to be processed of each ship to be tracked at the current moment. The first data and the second data of each ship to be tracked are subjected to feature extraction processing respectively to obtain the first feature vector corresponding to the first data and the second feature vector corresponding to the second data. For at least one ship to be tracked, the first feature vector and the second feature vector corresponding to each ship to be tracked are input into the feature tracking model corresponding to the current moment to obtain the predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment. Based on this, accurate tracking of at least one ship to be tracked is achieved. Based on the predicted feature vector, the predicted ship tracking information corresponding to the predicted feature vector is determined to determine the navigation planning route of the target ship based on the predicted ship tracking information. The present invention solves the problems in the prior art that the analysis results are not real-time and the accuracy and efficiency of data processing are low, improves the accuracy and efficiency of ship tracking information analysis, and achieves the effect of obtaining predicted ship tracking information in real time.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a ship tracking method provided by an embodiment of the present invention;

[0025] Figure 2 is a flow chart of a method for determining a feature tracking model provided by an embodiment of the present invention;

[0026] Figure 3 is a flowchart of a method for determining ship tracking information provided by an embodiment of the present invention;

[0027] Figure 4 is an example diagram of a feature tracking model updating method provided by an embodiment of the present invention;

[0028] Figure 5 is a structural schematic diagram of a ship tracking device provided by an embodiment of the present invention;

[0029] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the ship tracking method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Embodiment 1

[0033] Figure 1 This is a flow chart of a ship tracking method provided by the first embodiment of the present invention. This embodiment can be applied to the case where the ship information of one or more ships to be tracked at the next moment is predicted based on the first data collected by the ship automatic identification system and the second data collected by the ship radar equipment. The method can be executed by a ship tracking device, which can be implemented in the form of hardware and / or software, and can be configured in electronic devices such as mobile phones, computers or servers. Figure 1 As shown, the method includes:

[0034] S110, determining at least one ship to be tracked within a preset sea area of ​​the target ship, and obtaining the data to be processed of each ship to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment.

[0035] The target ship may be a currently sailing ship. During the navigation of the target ship, in order to ensure the safe and smooth navigation of the target ship, the data of the surrounding ships may be detected. The surrounding ships may be at least one ship to be tracked within a preset sea area. The preset sea area may be a sea area with the target ship as the center and a preset distance as the radius. For example, the preset sea area may be a sea area with the target ship as the center and a radius of 20 nautical miles. The ship to be tracked is a ship within the preset sea area. Since the preset sea area may include one or more ships to be tracked, there may be at least one ship to be tracked.

[0036] Since the data to be processed of the ship to be tracked changes in real time, the data to be processed at the current moment can be obtained. The data to be processed includes first data and second data. The first data is the position, speed, heading angle and other data of the ship to be tracked collected by the ship automatic identification system (Automatic Identification System, AIS) of the target ship. The second data is the position, speed and heading angle and other data of the ship to be tracked collected by the ship radar equipment of the target ship. The ship automatic identification system is a technical system for automatic identification of ships and maritime communications. The ship to be tracked equipped with the ship automatic identification system can automatically send its position, speed, heading angle and other data to the automatic identification system of the target ship. The ship radar equipment can be understood as the radar equipment of the target ship. The position, speed, heading angle and other information of the ship to be tracked around the target ship can be detected by the ship radar equipment.

[0037] Specifically, at least one ship to be tracked within a preset sea area of ​​the target ship at the current moment is determined. First data of each ship to be tracked at the current moment is obtained according to the ship automatic identification system of the target ship. Second data of each ship to be tracked at the current moment is obtained according to the ship radar equipment detection of the target ship. The first data and the second data are used as data to be processed.

[0038] Exemplarily, the preset sea area is 20 nautical miles around the target ship. Determine at least one ship to be tracked within 20 nautical miles around the target ship. At the current moment, the ship automatic identification system of the target ship receives the position, heading angle, speed and other information sent by the ship automatic identification system of each ship to be tracked, so as to obtain the first data of each ship to be tracked. The position, heading angle, speed and other information of each ship to be tracked are detected by the ship radar equipment of the target ship, so as to obtain the second data of each ship to be tracked. Based on this, data support is provided for subsequent ship tracking information analysis.

[0039] S120, performing feature extraction processing on the first data and the second data of each ship to be tracked, respectively, to obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data.

[0040] The first feature vector may be a feature vector extracted from the first data feature. The second feature vector may be a feature vector extracted from the second data feature. Optionally, the first feature vector and the second feature vector may be bird's eye view (BEV) feature vectors.

[0041] Specifically, for at least one ship to be tracked, the feature extractor corresponding to the ship automatic identification system performs feature extraction processing on the first data of each ship to be tracked, and converts the position, speed, heading angle and other information in the first data into a first feature vector. The feature extractor corresponding to the ship radar device performs feature extraction processing on the second data of each ship to be tracked, and converts the position, speed, heading angle and other information in the second data into a second feature vector. It should be noted that for each first data of the ship to be tracked, there is a corresponding first feature vector. For each second data of the ship to be tracked, there is a corresponding second feature vector.

[0042] Exemplarily, the feature extractor corresponding to the ship automatic identification system is an AIS feature extractor, the feature extractor corresponding to the ship radar device is a radar feature extractor, and the feature vector is a BEV feature vector. For each ship to be tracked, the AIS feature extractor is used to convert the position, speed, heading angle and other data in the first data of the current ship to be tracked into a BEV feature vector corresponding to the first data. The radar feature extractor is used to convert the position, speed, heading angle and other data in the second data of the current ship to be tracked into a BEV feature vector corresponding to the second data.

[0043] In an embodiment of the present invention, the first data includes geographic location information, navigation speed information, and heading angle information of the current ship to be tracked. The first feature vector may be determined by: for each ship to be tracked, determining the relative distance information between the current ship to be tracked and the target ship according to the geographic location information in the first data of the current ship to be tracked and the geographic location information of the target ship; determining the feature parameters corresponding to the current ship to be tracked in the preset coordinate system based on the relative distance information, and the navigation speed information and heading angle information of the current ship to be tracked; and determining the first feature vector corresponding to the first data based on the feature parameters.

[0044] Among them, the geographic location information can be understood as the longitude and latitude information corresponding to the ship to be tracked. The heading speed information is the speed of the ship to be tracked. The heading angle information can be a parameter of the angle between the navigation direction of the ship to be tracked and the true north direction. The relative distance information can be understood as the distance between the current ship to be tracked and the target ship. Optionally, the relative distance information may include the lateral distance and longitudinal distance between the current ship to be tracked and the target ship. The preset coordinate system may be a coordinate system set according to actual needs. Optionally, the preset coordinate system may be a BEV coordinate system with the target ship as the origin. The characteristic parameters may be composed of relative distance information, navigation speed information and heading angle information. For example, the characteristic parameters may include (x, y, v, r), where x represents the lateral distance between the current ship to be tracked and the target ship, y represents the longitudinal distance between the current ship to be tracked and the target ship, v represents the navigation speed information of the current ship to be tracked, and r represents the heading angle information of the current ship to be tracked.

[0045] Specifically, for each ship to be tracked, the lateral distance information and longitudinal distance information of the current ship to be tracked relative to the target ship are determined based on the geographic location information in the first data of the current ship to be tracked and the geographic location information of the target ship. The characteristic parameters corresponding to the current ship to be tracked in the preset coordinate system are determined based on the lateral distance information, the longitudinal distance information, and the navigation speed information and the heading angle information of the current ship to be tracked. Based on the characteristic parameters, a characteristic vector corresponding to the first data is determined.

[0046] Exemplarily, the preset coordinate system is taken as the BEV coordinate system. For each ship to be tracked, the lateral distance information and longitudinal distance information of the current ship to be tracked relative to the target ship are determined based on the geographic location information in the first data of the current ship to be tracked and the geographic location information of the target ship. The lateral distance information is used as the first characteristic parameter corresponding to the BEV coordinate system, and the longitudinal distance information is used as the second characteristic parameter corresponding to the BEV coordinate system. The navigation speed information of the current ship to be tracked is used as the third characteristic parameter corresponding to the BEV coordinate system, and the heading angle information of the current ship to be tracked is used as the fourth characteristic parameter corresponding to the BEV coordinate system. Based on these four characteristic parameters, the first characteristic vector corresponding to the first data is determined.

[0047] Correspondingly, the second data includes the geographic location information, navigation speed information and heading angle information of the current ship to be tracked. The second feature vector can be determined in the following manner: for each ship to be tracked, the relative distance information between the current ship to be tracked and the target ship is determined according to the geographic location information in the second data of the current ship to be tracked and the geographic location information of the target ship; based on the relative distance information, and the navigation speed information and heading angle information of the current ship to be tracked, the feature parameters corresponding to the current ship to be tracked in the preset coordinate system are determined; based on the feature parameters, the second feature vector corresponding to the second data is determined.

[0048] In an embodiment of the present invention, before inputting the first feature vector and the second feature vector corresponding to each ship to be tracked into the feature tracking model corresponding to the current moment, a preset filter corresponding to the feature vector may be determined first. The specific determination method may be: determining the first feature category of the first feature vector and the second feature category of the second feature vector based on the environmental information corresponding to the current moment; determining the first preset filter corresponding to the first feature category and the second preset filter corresponding to the second feature category in the feature tracking model, so as to process the first feature vector and the second feature vector based on the first preset filter and the second preset filter in the feature tracking model.

[0049] Optionally, the feature tracking model includes at least three types of preset filters, namely Kalman filter, extended Kalman filter and error Kalman filter. Among them, the feature tracking model can be a model for predicting the feature vector of the ship to be tracked at the next moment. The Kalman filter (KF) is used to process the input feature vector using the linear system state equation to achieve the optimal estimate of the feature vector or state at the next moment. The extended Kalman filter (EKF) is used to process the input feature vector using the nonlinear system state equation to achieve the optimal estimate of the feature vector or state at the next moment. The error Kalman filter (ESKF) is used to process the input feature vector using the nonlinear system state equation and the observation equation to achieve the optimal estimate of the feature vector or state at the next moment.

[0050] The environmental information may be environmental information within a preset sea area collected by sensors in the target ship. For example, the environmental information may include weather information and wave information. The feature category may be a category obtained by pre-classifying feature vectors corresponding to the data to be processed collected under different environmental information according to actual needs. Optionally, the feature vectors may be divided into: a first feature vector under simple environmental information, a first feature vector under complex environmental information, a second feature vector under simple environmental information, and a second feature vector under complex environmental information according to the feature category.

[0051] At least three types of preset filters are included in the feature tracking model. The first preset filter can be a filter selected from at least three types of preset filters and matched with the first feature category. For example, if the first feature vector corresponding to the first feature category is the first feature vector under simple environmental information, the first preset filter can be a Kalman filter; if the first feature vector corresponding to the first feature category is the first feature vector under a complex environment, the first preset filter can be an extended Kalman filter. The second preset filter can be a filter selected from at least three types of preset filters and matched with the second feature category. For example, if the second feature vector corresponding to the second feature category is the second feature vector under simple environmental information, the second preset filter can be an extended Kalman filter. If the second feature vector corresponding to the second feature category is the second feature vector under complex environmental information, the second preset filter can be an error Kalman filter.

[0052] Specifically, according to the environmental information corresponding to the current moment, a first feature category corresponding to the first feature vector and a second feature category corresponding to the second feature vector are determined. According to the first feature category, a first preset filter corresponding to the first feature category and a second preset filter corresponding to the second feature category are selected from the feature tracking model to process the first feature vector based on the first preset filter and to process the second feature vector based on the second preset filter.

[0053] Exemplarily, in combination with the above example, the first feature vector is a BEV feature vector obtained by processing the first data collected by the AIS system, and the first feature vector can be referred to as the AIS feature vector. Correspondingly, the second feature vector is a BEV feature vector obtained by processing the second data collected by the ship radar device, and the second feature vector can be referred to as the radar feature vector.

[0054] According to the environmental information at the current moment, it is determined whether the environment at the current moment is a complex environment or a simple environment. If the environmental information at the current moment is simple environmental information, the first feature category corresponding to the AIS feature vector is determined, and the first preset filter corresponding to the first feature category is determined to be a Kalman filter. Accordingly, the second feature category corresponding to the radar feature vector is determined, and the second preset filter corresponding to the second feature category is determined to be an extended Kalman filter.

[0055] If the current environmental information is complex environmental information, the first feature category corresponding to the AIS feature vector is determined, and the first preset filter corresponding to the first feature category is determined to be an extended Kalman filter. Correspondingly, the second feature category corresponding to the radar feature vector is determined, and the second preset filter corresponding to the second feature category is determined to be an error Kalman filter.

[0056] In an embodiment of the present invention, a specific method for determining the feature category of a feature vector based on environmental information may be: determining the current sea condition type corresponding to the environmental information based on the environmental information corresponding to the current moment and a preset environmental assessment standard; determining the first feature category of the first feature vector and the second feature category of the second feature vector based on the current sea condition type and a preset mapping relationship, wherein the preset mapping relationship is a mapping relationship between the sea condition type and the feature category.

[0057] Among them, the preset environmental assessment standard can be a standard pre-set according to actual needs, which is used to assess whether the environmental information is simple environmental information or complex environmental information. The current sea condition type can be the type of sea area conditions corresponding to the environmental information at the current moment. For example, if the environmental information at the current moment is determined to be simple environmental information according to the preset environmental assessment standard, the current sea condition type is a simple sea condition. Correspondingly, if the environmental information at the current moment is complex environmental information, the current sea condition type is a complex sea condition.

[0058] Specifically, the weather information, wave information and other environmental information at the current moment are analyzed according to the preset environmental assessment standard to determine the current sea condition type corresponding to the environmental information. According to the preset mapping relationship between the sea condition type and the feature category and the current sea condition type, the first feature category corresponding to the first feature vector is determined. Correspondingly, according to the preset mapping relationship and the current sea condition type, the second feature category corresponding to the second feature vector is determined. Based on this, it is convenient to subsequently determine the preset filter corresponding to the feature category according to the feature category, so as to process the feature vector based on the preset filter.

[0059] S130. For at least one ship to be tracked, a first feature vector and a second feature vector corresponding to each ship to be tracked are input into a feature tracking model corresponding to the current moment to obtain a predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment.

[0060] Among them, the feature tracking model can be a model for predicting the feature vector of the ship to be tracked at the next moment. The predicted feature vector can be the feature vector of the ship to be tracked at the next moment predicted by the feature tracking model. The feature tracking model is a model that is constantly being optimized and updated. The observed feature vector at the previous moment is a feature vector obtained after feature extraction of the historical ship data at the previous moment. The historical ship data includes the historical first data collected by the ship automatic identification system at the previous moment and / or the historical second data collected by the ship radar device at the previous moment. The historical first data may include the geographical location information, navigation speed information and heading angle information of the ship to be tracked collected by the ship automatic identification system at the previous moment. The historical second data may include the geographical location information, navigation speed information and heading angle information of the ship to be tracked collected by the ship radar device at the previous moment.

[0061] Specifically, for at least one ship to be tracked, the first feature vector and the second feature vector corresponding to each ship to be tracked are input into the feature tracking model corresponding to the current moment to obtain the predicted feature vector corresponding to each ship to be tracked. Based on this, the predicted feature vector corresponding to each ship to be tracked is obtained. It should be noted that the feature tracking model at the current moment is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment.

[0062] Exemplarily, the feature tracking model can be a model for processing feature vectors of multiple ships to be tracked. If there are 10 ships to be tracked, the first feature vectors and the second feature vectors of the 10 ships to be tracked can be input into the feature tracking model at the current moment, and the input feature vectors are processed by the kinematic matrix in the feature tracking model to obtain the predicted feature vectors of the 10 ships to be tracked at the next moment, so as to determine the geographical location information, navigation speed information and heading angle information of the 10 ships to be tracked at the next moment based on the predicted feature vectors.

[0063] S140. Based on the predicted feature vector, determine the predicted ship tracking information corresponding to the predicted feature vector, so as to determine the navigation planning route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographic location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

[0064] The predicted geographic location information can be understood as the longitude and latitude information of the ship to be tracked at the next moment. The predicted navigation speed information can be understood as the navigation speed information of the ship to be tracked at the next moment. The predicted heading angle information can be understood as the parameter information of the angle between the navigation direction of the ship to be tracked at the next moment and the true north direction. The navigation planning route can be the navigation route of the target ship determined based on the predicted ship tracking information of at least one ship to be tracked.

[0065] Specifically, the predicted geographic location information, predicted sailing speed information and predicted heading angle information of each ship to be tracked at the next moment are determined by predicting the feature vector, and the predicted geographic location information, predicted sailing speed information and predicted heading angle information are used as the predicted ship tracking information of the ship to be tracked. According to the predicted ship tracking information of each ship to be tracked, the navigation planning route of the target ship is determined.

[0066] Exemplarily, in combination with the above example, the predicted geographic location information, predicted sailing speed information and predicted heading angle information of each ship to be tracked at the next moment are determined according to the predicted feature vectors of the 10 ships to be tracked at the next moment. According to the predicted geographic location information, predicted sailing speed information and predicted heading angle information of the 10 ships to be tracked, the downstream regulatory control tasks of the target ship are guided to determine the safe navigation planning route of the target ship.

[0067] The technical solution of this embodiment provides data support for the subsequent prediction of ship tracking information by determining at least one ship to be tracked within the preset sea area of ​​the target ship and obtaining the data to be processed for each ship to be tracked at the current moment. The first data and the second data of each ship to be tracked are subjected to feature extraction processing respectively to obtain the first feature vector corresponding to the first data and the second feature vector corresponding to the second data. For at least one ship to be tracked, the first feature vector and the second feature vector corresponding to each ship to be tracked are input into the feature tracking model corresponding to the current moment to obtain the predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment. Based on this, accurate tracking of at least one ship to be tracked is achieved. Based on the predicted feature vector, the predicted ship tracking information corresponding to the predicted feature vector is determined to determine the navigation planning route of the target ship based on the predicted ship tracking information. The present invention solves the problems in the prior art that the analysis results are not real-time and the accuracy and efficiency of data processing are low, improves the accuracy and efficiency of ship tracking information analysis, and achieves the effect of real-time acquisition of predicted ship tracking information.

[0068] Embodiment 2

[0069] Figure 2 This is a flow chart of a feature tracking model determination method provided by the second embodiment of the present invention. This embodiment is based on the above embodiment. Before using the feature tracking model corresponding to the current moment to process the feature vector, it is necessary to first determine the feature tracking model corresponding to the current moment. The specific implementation method can refer to the technical solution of this embodiment. Among them, the technical terms that are the same or corresponding to the above embodiment are not repeated here. Figure 2 As shown, the method includes:

[0070] S210. Acquire historical ship data of at least one ship to be tracked at a previous moment, wherein the historical ship data includes historical first data collected by a ship automatic identification system and / or historical second data collected by a ship radar device.

[0071] The historical first data may include the geographical location information, navigation speed information and heading angle information of the ship to be tracked collected by the ship automatic identification system of the target ship at the previous moment before the current moment. The historical second data may include the geographical location information, navigation speed information and heading angle information of the ship to be tracked collected by the ship radar equipment of the target ship at the previous moment.

[0072] Specifically, at least one ship to be tracked that was within a preset sea area of ​​the target ship at the last moment is determined. The historical first data of the at least one ship to be tracked at the last moment is obtained according to the ship automatic identification system of the target ship. The historical second data of the at least one ship to be tracked at the last moment is obtained according to the ship radar equipment of the target ship. The historical first data and / or the historical second data are used as historical ship data, so as to update the model parameters in the feature tracking model based on the historical ship data.

[0073] It should be noted that when optimizing the feature tracking model, the historical ship data obtained may be the historical first data, the historical second data, or the historical first data and the historical second data. The corresponding preset filter in the feature tracking model may be optimized and updated through different historical ship data.

[0074] For example, in conjunction with the above examples, see Figure 3 When optimizing and updating the feature tracking model at the last moment, at least one ship to be tracked within 20 nautical miles around the target ship at the last moment can be determined first, and the historical first data and / or historical second data of each ship to be tracked can be obtained. The historical first data corresponds to Figure 3 The historical second data corresponds to Figure 3 Radar data in .

[0075] S220: Perform feature extraction processing on historical ship data to determine an observation feature vector corresponding to the ship to be tracked.

[0076] The observed feature vector may be a feature vector obtained by extracting features from historical ship data at the previous moment.

[0077] Specifically, if the historical ship data is the historical first data collected by the ship automatic identification system, the feature extractor corresponding to the ship automatic identification system is used to perform feature extraction processing on the historical first data to obtain the observed feature vector corresponding to the historical first data. If the historical ship data is the historical second data collected by the ship radar equipment, the feature extractor corresponding to the ship radar equipment is used to perform feature extraction on the historical second data to obtain the observed feature vector corresponding to the historical second data. Correspondingly, if the historical ship data includes the historical first data collected by the ship automatic identification system and the historical second data collected by the ship radar equipment, the observed feature vector corresponding to the historical first data and the observed feature vector corresponding to the historical second data are obtained using the corresponding feature extractors, respectively.

[0078] For example, see Figure 3In combination with the above examples, the AIS feature extractor corresponds to the feature extractor corresponding to the ship automatic identification system mentioned in the above embodiment. The radar feature extractor corresponds to the feature extractor corresponding to the ship radar device mentioned in the above embodiment. The AIS feature extractor is used to perform feature extraction processing on the AIS data to obtain the BEV feature vector corresponding to the AIS data. The radar feature extractor is used to perform feature extraction processing on the radar data to obtain the BEV feature vector corresponding to the radar data. The BEV feature vector corresponding to the AIS data and the BEV feature vector corresponding to the radar data are used as the observation feature vector of the ship to be tracked.

[0079] S230. Based on the ship information corresponding to the observed feature vector, a preset matching algorithm is used to match the observed feature vector with at least one historical prediction feature vector corresponding to the previous moment, and a target historical prediction feature vector matching the observed feature vector is determined.

[0080] The ship information can be understood as the information of the ship to be tracked corresponding to the observed feature vector. The specific ship to be tracked corresponding to the observed feature vector can be determined through the ship information.

[0081] The preset matching algorithm may be a pre-set algorithm for matching the observed feature vector and the historical predicted feature vector. Optionally, the preset matching algorithm may be a LAPJV algorithm (Linear Assignment Problem Jonker-Volgenant). The historical predicted feature vector may be a feature vector obtained by predicting the historical ship data of the previous moment through a feature tracking model. That is, the moment corresponding to the historical predicted feature vector is the same as the moment corresponding to the observed feature vector. For example, the observed feature vector may be the actual observed feature vector corresponding to the historical ship data at moment A, and the historical predicted feature vector may be the predicted feature vector corresponding to the ship to be tracked at moment A.

[0082] Since the feature tracking model is a multi-input multi-output model, the previous moment may include one or more historical prediction feature vectors corresponding to the ship to be tracked. The target historical prediction feature vector may be a historical prediction feature vector that matches the observed feature vector.

[0083] Specifically, according to the ship information of the observed feature vector, the ship to be tracked corresponding to the observed feature vector is determined. According to the ship to be tracked corresponding to the observed feature vector, the feature vector is matched using a preset matching algorithm, that is, from at least one historical prediction feature vector, a target historical prediction feature vector corresponding to the same ship to be tracked is determined.

[0084] For example, in conjunction with the above examples, see Figure 3, according to the ship information of the observed feature vector, determine the ship to be tracked corresponding to the observed feature vector. If the feature tracking model at the previous moment corresponds to 10 historical prediction feature vectors of ships to be tracked, then the ship to be tracked that is consistent with the ship to be tracked corresponding to the observed feature vector can be determined from the 10 ships to be tracked, and the target historical prediction feature vector corresponding to the ship to be tracked can be determined.

[0085] S240: Based on the observed feature vector and the target historical predicted feature vector, the model parameters of the feature tracking model are optimized to obtain the feature tracking model corresponding to the current moment.

[0086] Specifically, the difference information between the observed feature vector and the target predicted feature vector is determined according to the observed feature vector and the target historical predicted feature vector. The parameter information of the corresponding preset filter in the feature tracking model is adjusted according to the difference value to obtain the optimized corresponding preset filter and the optimized feature tracking model.

[0087] In an embodiment of the present invention, a specific method for optimizing the feature tracking model may be: determining the difference information between the observed feature vector and the target historical prediction feature vector; determining the historical sea condition type corresponding to the previous moment, and determining the target filter in the feature tracking model based on the historical sea condition type and the data acquisition dimension corresponding to the observed feature vector; based on the difference information, optimizing the state parameters of the target filter in the feature tracking model to obtain the feature tracking model corresponding to the current moment.

[0088] Among them, the difference information can be used to characterize the difference between the observed feature vector and the target historical prediction feature vector. The historical sea condition type can be used to characterize the sea condition information or environmental information corresponding to the previous moment. The historical sea condition type can be a simple sea condition or a complex sea condition. There are two data collection dimensions, one is to collect historical ship data through the ship automatic identification system, and the other is to collect historical ship data through ship radar equipment. The data collection dimension can determine whether the collection device corresponding to the observed feature vector is a ship automatic identification system or a ship radar device. The target filter can be a preset filter corresponding to the historical sea condition type. The state parameter can be the parameter data used to process the feature vector in the target filter.

[0089] Specifically, the difference information between the observed feature vector and the target historical predicted feature vector is determined. According to the historical sea condition type at the previous moment and the data acquisition dimension corresponding to the observed feature vector, the target filter is determined from at least three preset filters of the feature tracking model. According to the difference information, the state parameters of the target filter in the feature tracking model are optimized and adjusted to obtain the optimized target filter and feature tracking model, that is, the feature tracking model corresponding to the current moment is obtained. Based on this, the feature vector of the data to be used at the current moment can be processed according to the feature tracking model corresponding to the current moment to obtain the predicted feature vector.

[0090] Exemplarily, in combination with the above example, if the historical sea condition type is a simple sea condition and the observed feature vector is obtained based on historical ship data collected by the ship automatic identification system, the target filter is a Kalman filter. Accordingly, the Kalman filter is updated using the difference information between the observed feature vector and the target historical prediction feature vector. If the historical sea condition type is a simple sea condition and the observed feature vector is obtained based on historical ship data collected by ship radar equipment, the target filter is an extended Kalman filter. Accordingly, the extended Kalman filter is updated using the difference information.

[0091] If the historical sea condition type is complex sea condition and the observed feature vector is obtained based on the historical ship data collected by the ship automatic identification system, the target filter is the extended Kalman filter. Accordingly, the extended Kalman filter is updated using the difference information. If the historical sea condition type is complex sea condition and the observed feature vector is obtained based on the historical ship data collected by the ship radar equipment, the target filter is the error Kalman filter. Accordingly, the error Kalman filter is updated using the difference information.

[0092] For example, see Figure 3 and Figure 4, combined with the above examples, the BEV feature vector corresponding to the AIS data is referred to as the AIS feature vector, and the BEV feature vector corresponding to the radar data is referred to as the radar feature vector. When the historical sea condition type is a simple sea condition, the AIS feature vector is a linear feature vector, and the difference information between the AIS feature vector and the target historical prediction feature vector is used to update the state parameters in the Kalman filter. When the historical sea condition type is a simple sea condition, the radar feature vector is a weak nonlinear feature vector, and the difference information between the radar feature vector and the target historical prediction feature vector is used to update the extended Kalman filter. When the historical sea condition type is a complex sea condition, the AIS feature vector is a weak nonlinear feature vector, and the difference information between the AIS feature vector and the target historical prediction feature vector is used to update the extended Kalman filter. When the historical sea condition type is a complex sea condition, the radar feature vector is a strong nonlinear feature vector, and the difference information between the radar feature vector and the target historical prediction feature vector is used to update the error Kalman filter. Based on this, the optimization update processing of the preset filter in the feature tracking model is realized to obtain the optimized feature tracker, so as to obtain the predicted BEV feature vector at the next moment based on the feature tracker at the current moment, so as to guide the downstream regulation and control tasks of the target ship according to the predicted BEV feature vector.

[0093] The technical solution of this embodiment obtains historical ship data of at least one ship to be tracked at the previous moment, and performs feature extraction processing on the historical ship data to determine the observed feature vector corresponding to the ship to be tracked, so as to provide data support for the optimization and update of the subsequent feature tracking model. Based on the ship information corresponding to the observed feature vector, the observed feature vector is matched with at least one historical prediction feature vector corresponding to the previous moment using a preset matching algorithm to determine the target historical prediction feature vector that matches the observed feature vector. The model parameters of the feature tracking model are optimized through the observed feature vector and the target historical prediction feature vector to obtain the feature tracking model corresponding to the current moment. In the above manner, the real-time dynamic adjustment of the preset filter in the feature tracking model is realized, so that the optimized feature tracking model can run efficiently under various environmental information, and the reliability and adaptability of the feature tracking model are enhanced. Based on this, it is convenient to obtain the predicted ship tracking information of the next moment according to the optimized feature tracking model, thereby improving the accuracy and efficiency of the ship tracking information analysis and achieving the effect of obtaining the predicted ship tracking information in real time.

[0094] Embodiment 3

[0095] Figure 5 FIG. 1 is a schematic diagram of the structure of a ship tracking device provided by Embodiment 3 of the present invention. Figure 5As shown, the device includes: a data acquisition module 310, a feature extraction module 320, a model prediction module 330 and a tracking information determination module 340.

[0096] The data acquisition module 310 is used to determine at least one ship to be tracked within the preset sea area of ​​the target ship, and obtain the data to be processed of each ship to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment; the feature extraction module 320 is used to perform feature extraction processing on the first data and the second data of each ship to be tracked, respectively, to obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data; the model prediction module 330 is used to input the first feature vector and the second feature vector corresponding to each ship to be tracked into the model prediction module 330 for at least one ship to be tracked. In the feature tracking model corresponding to the current moment, a predicted feature vector corresponding to each ship to be tracked is obtained, wherein the feature tracking model is determined after optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment; a tracking information determination module 340 is used to determine the predicted ship tracking information corresponding to the predicted feature vector based on the predicted feature vector, so as to determine the navigation planning route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographic location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

[0097] The technical solution of this embodiment provides data support for the subsequent prediction of ship tracking information by determining at least one ship to be tracked within the preset sea area of ​​the target ship and obtaining the data to be processed for each ship to be tracked at the current moment. The first data and the second data of each ship to be tracked are subjected to feature extraction processing respectively to obtain the first feature vector corresponding to the first data and the second feature vector corresponding to the second data. For at least one ship to be tracked, the first feature vector and the second feature vector corresponding to each ship to be tracked are input into the feature tracking model corresponding to the current moment to obtain the predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment. Based on this, accurate tracking of at least one ship to be tracked is achieved. Based on the predicted feature vector, the predicted ship tracking information corresponding to the predicted feature vector is determined to determine the navigation planning route of the target ship based on the predicted ship tracking information. The present invention solves the problems in the prior art that the analysis results are not real-time and the accuracy and efficiency of data processing are low, improves the accuracy and efficiency of ship tracking information analysis, and achieves the effect of real-time acquisition of predicted ship tracking information.

[0098] On the basis of the above embodiment, optionally, the first data includes geographic location information, navigation speed information and heading angle information of the current ship to be tracked, and the feature extraction module includes: a distance information determination unit, which is used to determine the relative distance information between the current ship to be tracked and the target ship for each ship to be tracked according to the geographic location information in the first data of the current ship to be tracked and the geographic location information of the target ship; a feature parameter determination unit, which is used to determine the feature parameters corresponding to the current ship to be tracked in a preset coordinate system based on the relative distance information, and the navigation speed information and heading angle information of the current ship to be tracked; and a feature vector determination unit, which is used to determine a first feature vector corresponding to the first data based on the feature parameters.

[0099] Optionally, the device also includes: a filter type determination module, including a feature vector classification unit, used to determine a first feature category of the first feature vector and a second feature category of the second feature vector based on environmental information corresponding to the current moment; a preset filter determination unit, used to determine a first preset filter corresponding to the first feature category and a second preset filter corresponding to the second feature category in the feature tracking model, so as to process the first feature vector and the second feature vector based on the first preset filter and the second preset filter in the feature tracking model.

[0100] Optionally, a feature vector classification unit is used to determine the current sea condition type corresponding to the environmental information based on the environmental information corresponding to the current moment and the preset environmental assessment standard; determine the first feature category of the first feature vector and the second feature category of the second feature vector based on the current sea condition type and the preset mapping relationship, wherein the preset mapping relationship is the mapping relationship between the sea condition type and the feature category.

[0101] Optionally, the device also includes: a model optimization module, which includes: a historical data acquisition unit, used to acquire historical ship data of at least one ship to be tracked at the previous moment, wherein the historical ship data includes historical first data collected by a ship automatic identification system and / or historical second data collected by a ship radar device; an observation feature vector determination unit, used to perform feature extraction processing on the historical ship data, and determine the observation feature vector corresponding to the ship to be tracked; a vector matching unit, used to match the observation feature vector with at least one historical prediction feature vector corresponding to the previous moment based on the ship information corresponding to the observation feature vector using a preset matching algorithm, and determine a target historical prediction feature vector that matches the observation feature vector; a model optimization unit, used to optimize the model parameters of the feature tracking model based on the observation feature vector and the target historical prediction feature vector, and obtain the feature tracking model corresponding to the current moment.

[0102] Optionally, a model optimization unit is used to determine the difference information between the observed feature vector and the target historical prediction feature vector; determine the historical sea condition type corresponding to the previous moment, and determine the target filter in the feature tracking model based on the historical sea condition type and the data collection dimension corresponding to the observed feature vector; based on the difference information, optimize the state parameters of the target filter in the feature tracking model to obtain the feature tracking model corresponding to the current moment.

[0103] Optionally, the feature tracking model includes at least three types of preset filters, which are Kalman filter, extended Kalman filter and error Kalman filter.

[0104] The ship tracking device provided in the embodiment of the present invention can execute the ship tracking method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0105] Embodiment 4

[0106] Figure 61 is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0107] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0108] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0109] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a ship tracking method.

[0110] In some embodiments, the ship tracking method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the ship tracking method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the ship tracking method in any other appropriate manner (e.g., by means of firmware).

[0111] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0112] The computer programs for implementing the ship tracking method of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0113] Embodiment 5

[0114] Embodiment 5 of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute a ship tracking method, the method comprising:

[0115] Determine at least one ship to be tracked within a preset sea area of ​​the target ship, and obtain the data to be processed of each ship to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment; perform feature extraction processing on the first data and the second data of each ship to be tracked respectively, and obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data; for at least one ship to be tracked, input the first feature vector and the second feature vector corresponding to each ship to be tracked into the feature tracking model corresponding to the current moment, and obtain a predicted feature vector corresponding to each ship to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to the previous moment based on the observed feature vector at the previous moment, and the observed feature vector at the previous moment is determined based on the historical ship data at the previous moment; based on the predicted feature vector, determine the predicted ship tracking information corresponding to the predicted feature vector, so as to determine the navigation planning route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographical location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

[0116] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0117] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0118] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0119] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0121] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A ship tracking method, characterized in that: include: Determine at least one ship to be tracked within a preset sea area of ​​the target ship, and obtain the data to be processed of each of the ships to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment; Performing feature extraction processing on the first data and the second data of each ship to be tracked respectively to obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data; For at least one of the ships to be tracked, a first feature vector and a second feature vector corresponding to each of the ships to be tracked are input into a feature tracking model corresponding to a current moment, to obtain a predicted feature vector corresponding to each of the ships to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to a previous moment based on an observed feature vector at a previous moment, and the observed feature vector at a previous moment is determined based on historical ship data at a previous moment; Based on the predicted feature vector, the predicted ship tracking information corresponding to the predicted feature vector is determined, so as to determine the planned navigation route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographic location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

2. The method according to claim 1, characterized in that The first data includes geographical location information, navigation speed information, and heading angle information of the current ship to be tracked, and feature extraction processing is performed on the first data of each ship to be tracked to determine a first feature vector corresponding to the first data, including: For each of the ships to be tracked, determining the relative distance information between the current ship to be tracked and the target ship according to the geographical location information in the first data of the current ship to be tracked and the geographical location information of the target ship; Based on the relative distance information, and the navigation speed information and heading angle information of the current ship to be tracked, determining the characteristic parameters corresponding to the current ship to be tracked in the preset coordinate system; Based on the characteristic parameters, a first characteristic vector corresponding to the first data is determined.

3. The method according to claim 1, characterized in that: Before inputting the first feature vector and the second feature vector corresponding to each of the ships to be tracked into the feature tracking model corresponding to the current moment, the method further includes: Determining a first feature category of the first feature vector and a second feature category of the second feature vector according to the environment information corresponding to the current moment; Determine a first preset filter corresponding to the first feature category and a second preset filter corresponding to the second feature category in the feature tracking model, so as to process the first feature vector and the second feature vector according to the first preset filter and the second preset filter in the feature tracking model.

4. The method according to claim 3, characterized in that: The determining, based on the environment information corresponding to the current moment, a first feature category of the first feature vector and a second feature category of the second feature vector includes: Determine the current sea condition type corresponding to the environmental information according to the environmental information corresponding to the current moment and the preset environmental assessment standard; According to the current sea condition type and a preset mapping relationship, a first feature category of the first feature vector and a second feature category of the second feature vector are determined, wherein the preset mapping relationship is a mapping relationship between the sea condition type and the feature category.

5. The method according to claim 1, characterized in that The method further comprises: Acquire historical ship data of at least one of the ships to be tracked at a previous moment, wherein the historical ship data includes historical first data collected by the ship automatic identification system and / or historical second data collected by the ship radar equipment; Performing feature extraction processing on the historical ship data to determine an observation feature vector corresponding to the ship to be tracked; Based on the ship information corresponding to the observed feature vector, a preset matching algorithm is used to match the observed feature vector with at least one historical prediction feature vector corresponding to the previous moment, to determine a target historical prediction feature vector that matches the observed feature vector; Based on the observed feature vector and the target historical prediction feature vector, the model parameters of the feature tracking model are optimized to obtain the feature tracking model corresponding to the current moment.

6. The method according to claim 5, characterized in that The optimizing the model parameters of the feature tracking model based on the observed feature vector and the target historical prediction feature vector to obtain the feature tracking model corresponding to the current moment includes: Determining difference information between the observed feature vector and the target historical prediction feature vector; Determining the historical sea condition type corresponding to the previous moment, and determining the target filter in the feature tracking model according to the historical sea condition type and the data acquisition dimension corresponding to the observation feature vector; Based on the difference information, the state parameters of the target filter in the feature tracking model are optimized to obtain the feature tracking model corresponding to the current moment.

7. The method according to claim 1, characterized in that The feature tracking model includes at least three types of preset filters, which are Kalman filter, extended Kalman filter and error Kalman filter.

8. A ship tracking device, characterized in that: include: A data acquisition module, used to determine at least one ship to be tracked within a preset sea area of ​​the target ship, and to acquire the data to be processed of each of the ships to be tracked at the current moment, wherein the data to be processed includes the first data collected by the ship automatic identification system of the target ship and the second data collected by the ship radar equipment; A feature extraction module, used for performing feature extraction processing on the first data and the second data of each ship to be tracked, respectively, to obtain a first feature vector corresponding to the first data and a second feature vector corresponding to the second data; A model prediction module, for at least one of the ships to be tracked, inputting a first feature vector and a second feature vector corresponding to each of the ships to be tracked into a feature tracking model corresponding to a current moment, to obtain a predicted feature vector corresponding to each of the ships to be tracked, wherein the feature tracking model is determined by optimizing the feature tracking model corresponding to a previous moment based on an observed feature vector at a previous moment, and the observed feature vector at a previous moment is determined based on historical ship data at a previous moment; A tracking information determination module is used to determine the predicted ship tracking information corresponding to the predicted feature vector based on the predicted feature vector, so as to determine the navigation planning route of the target ship based on the predicted ship tracking information, wherein the predicted ship tracking information includes the predicted geographic location information, predicted navigation speed information and predicted heading angle information of the ship to be tracked at the next moment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the ship tracking method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the ship tracking method according to any one of claims 1 to 7 when executed.

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