Marine ship-oriented wave position planning method and device

Abstract

The invention provides a wave position planning method and device for a marine ship, and the method comprises the steps: obtaining a wave position number and corresponding first navigation time and second navigation time, and enabling the second navigation time to be obtained based on a pre-trained network model; obtaining third navigation time based on the first navigation time and the second navigation time; and uploading the first navigation time, the third navigation time and the beam position number to a satellite, so that the satellite serves the beam position corresponding to the beam position number in the time period from the first navigation time to the third navigation time. In the application, the time of the staring scene is divided into a plurality of time domains, each time domain can be added with a wave position number of staring according to a prediction result, finally, the staring scene of time division and domain division is formed, and compared with an existing satellite staring scheme, not only can ship communication be effectively guaranteed, but also the distribution and utilization efficiency of satellite resources can be optimized.

Description

Technical Field

[0001] The present application relates to the field of satellite communication technology, and in particular to a wave position planning method and device for ships at sea. Background Art

[0002] Slot planning is performed by the satellite network management system. The entire process involves the system pre-calculating a list of base slots that the satellite will need to cover in the future and then uploading it to the satellite. The satellite then uses this list to schedule beams to achieve coverage of specific slots. Slot planning supports both full coverage and staring scenarios. In the full coverage scenario, the satellite performs beam scanning across all slots within its coverage area, meeting the communication needs of all slots within it. In the staring scenario, the satellite performs beam coverage on a few specific slots within its coverage area, ensuring communication only for the slots covered by the beam.

[0003] For ocean-going users, staring is often used to ensure communication. However, using existing wave position staring methods, which constantly stare at the wave positions where ships may pass, can significantly waste satellite resources. Therefore, there is an urgent need for a wave position planning method specifically for ships at sea that can effectively ensure communication coverage for ships at sea while reducing the waste of satellite resources. Summary of the Invention

[0004] In view of this, the present application provides a wave position planning method and device for marine vessels to solve at least one of the above-mentioned problems.

[0005] In order to achieve the above objectives, this application adopts the following scheme: According to the first aspect of the present application, a wave position planning method for marine ships is provided, the method comprising: obtaining a wave position number and a corresponding first navigation time and a second navigation time, the second navigation time being obtained based on a pre-trained network model; obtaining a third navigation time based on the first navigation time and the second navigation time; annotating the first navigation time, the third navigation time and the wave position number to a satellite, so that the satellite serves the wave position corresponding to the wave position number within the time period from the first navigation time to the third navigation time.

[0006] As an embodiment of the present application, obtaining the wave position number in the above method includes: determining the wave position number based on the coordinates of the trajectory point.

[0007] As an embodiment of the present application, in the above method, obtaining the third flight time based on the first flight time and the second flight time includes: adding the first flight time and the second flight time to obtain the third flight time.

[0008] As an embodiment of the present application, obtaining the second navigation time in the above method includes: obtaining ship attribute data and environmental data; inputting a feature matrix containing the wave position number, the ship attribute data and the environmental data into a pre-trained network model to obtain the second navigation time.

[0009] As an embodiment of the present application, after obtaining the ship attribute data and the environmental data in the above method, the method further includes: converting the ship attribute data and the environmental data into digital codes.

[0010] As an embodiment of the present application, the ship attribute data in the above method includes the ship type and the ship load, and the environmental data includes wind direction and wave height. The conversion of the ship attribute data and the environmental data into digital codes includes: digitally numbering all ship types, and binary encoding the ship types according to the digital numbers; digitally taking values ​​for the ship load in tons; converting the wind direction into binary code according to the binary code correspondence table; and converting the wind force level and wave height level into corresponding level numbers based on preset standards.

[0011] As an embodiment of the present application, the above method also includes: obtaining the fourth navigation time and the fifth navigation time of the corresponding wave position according to the wave position number; obtaining the corresponding sixth navigation time according to the fourth navigation time and the fifth navigation time; and obtaining the feature matrix based on the wave position number, the sixth navigation time, the ship attribute data and the environmental data.

[0012] As an embodiment of the present application, the above method of obtaining the fourth navigation time and the fifth navigation time of the corresponding wave position according to the wave position number includes: determining the target wave position according to the wave position number; calculating the distance relationship between the trajectory point coordinates and the boundary of the target wave position; based on the distance relationship, determining the target trajectory point entering or exiting the target wave position; and determining the corresponding fourth navigation time and fifth navigation time according to the target trajectory point.

[0013] As an embodiment of the present application, the above method also includes: obtaining historical trajectory data, historical ship attribute data and historical environmental data; obtaining the historical stay time of the ship at each wave position based on the historical trajectory data; associating the wave position number of each wave position, the historical stay time, the historical ship attribute data, the historical environmental data and the known actual stay time of each wave position to form a training data set; using the training data set to perform model training to obtain the network model.

[0014] According to the second aspect of the present application, a wave position planning method for marine ships is provided, the method comprising: receiving a wave position number and a corresponding predicted entry time and predicted exit time; and providing service to the wave position corresponding to the wave position number based on the predicted entry time and the predicted exit time.

[0015] According to the third aspect of the present application, a wave position planning device for marine ships is provided, the device comprising: a first data acquisition unit, used to acquire a wave position number and a corresponding first navigation time and a second navigation time, the second navigation time being obtained based on a pre-trained network model; a second data acquisition unit, used to obtain a third navigation time based on the first navigation time and the second navigation time; a data annotation unit, used to annotation the first navigation time, the third navigation time and the wave position number to a satellite, so that the satellite serves the wave position corresponding to the wave position number within the time period from the first navigation time to the third navigation time.

[0016] As an embodiment of the present application, the first data acquisition unit includes a number acquisition module for determining the wave position number based on the coordinates of the trajectory point.

[0017] As an embodiment of the present application, the second data acquisition unit is specifically configured to add the first flight time and the second flight time to obtain a third flight time.

[0018] As an embodiment of the present application, the above-mentioned first data acquisition unit includes: an impact data acquisition module, used to obtain ship attribute data and environmental data; a wave position navigation time acquisition module, used to input the feature matrix containing the wave position number, the ship attribute data and the environmental data into a pre-trained network model to obtain a second navigation time.

[0019] As an embodiment of the present application, the first data acquisition unit further includes: a digital encoding module, configured to convert the ship attribute data and the environmental data into digital codes.

[0020] As an embodiment of the present application, the above-mentioned ship attribute data includes ship type and ship load, the environmental data includes wind direction and wave height, and the digital coding module includes: a ship type coding submodule, used to digitally number all ship types and binary encode the ship type according to the digital number; a ship load coding submodule, used to digitally value the ship load in tons; a wind direction coding submodule, used to binary code the wind direction according to the binary coding correspondence table; a wind force and wave height coding submodule, used to convert the wind force level and wave height level into corresponding level numbers based on preset standards.

[0021] As an embodiment of the present application, the above-mentioned first data acquisition unit also includes: a planned entry and exit time acquisition module, which is used to obtain the fourth navigation time and the fifth navigation time of the corresponding wave position according to the wave position number; a planned stay time acquisition module, which obtains the corresponding sixth navigation time according to the fourth navigation time and the fifth navigation time; a feature matrix acquisition module, which is used to obtain the feature matrix based on the wave position number, the sixth navigation time, the ship attribute data and the environmental data.

[0022] As an embodiment of the present application, the above-mentioned planned entry and exit time acquisition module includes: a target wave position determination submodule, used to determine the target wave position according to the wave position number; a distance calculation submodule, used to calculate the distance relationship between the trajectory point coordinates and the target wave position boundary; a target trajectory point determination submodule, used to determine the target trajectory point entering or exiting the target wave position based on the distance relationship; a planned entry and exit time determination submodule, used to determine the corresponding fourth navigation time and fifth navigation time according to the target trajectory point.

[0023] As an embodiment of the present application, the above-mentioned device also includes: a historical data acquisition unit, used to obtain historical trajectory data, historical ship attribute data and historical environmental data; a historical stay time acquisition unit, used to obtain the historical stay time of the ship at each wave position based on the historical trajectory data; a training data set composition unit, used to associate the wave position number of each wave position, the historical stay time, the historical ship attribute data, the historical environmental data and the known actual stay time of each wave position to form a training data set; a network training unit, used to use the training data set to perform model training to obtain the network model.

[0024] According to the fourth aspect of the present application, a wave position planning device for marine ships is provided, the device comprising: a data receiving unit for receiving a wave position number and a corresponding predicted entry time and predicted exit time; and a wave position service unit for providing service to the wave position corresponding to the wave position number based on the predicted entry time and the predicted exit time.

[0025] According to a fifth aspect of the present application, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0026] According to a sixth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0027] According to a seventh aspect of the present application, a computer program product is provided, comprising a computer program / instruction, which implements the steps of the above method when executed by a processor.

[0028] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 This is a flow chart of a wave position planning method for marine vessels provided in an embodiment of the present application; Figure 2 This is a schematic diagram of a process for determining a wave position number based on trajectory point coordinates provided in an embodiment of the present application; Figure 3 This is a schematic diagram of a wave position numbering sequence of a navigation track provided in an embodiment of the present application; Figure 4 is a schematic diagram of a process for obtaining a second flight time provided in an embodiment of the present application; Figure 5 This is a flow chart of converting ship attribute data and environmental data into digital codes according to an embodiment of the present application; Figure 6 Schematic diagram of a binary encoding correspondence table provided in an embodiment of the present application; Figure 7 Schematic diagram of the process of obtaining the feature matrix provided in the embodiment of the present application; Figure 8 This is a flow chart of obtaining the fourth navigation time and the fifth navigation time of a sea vessel passing through a wave position according to a wave position number provided in an embodiment of the present application; Figure 9 This is a schematic diagram of target entry and exit wave positions provided in an embodiment of the present application; Figure 10 This is a structural diagram of the improved BiGRU-Attention model provided in an embodiment of the present application; Figure 11 Schematic diagram of the training process of the network model provided in the embodiment of the present application; Figure 12This is a training flow chart for preprocessing the spatiotemporal sequence of historical trajectories provided in an embodiment of the present application; Figure 13 This is a simplified diagram of a network model training provided in an embodiment of the present application; Figure 14 This is a flow chart of a wave position planning method for marine vessels provided in another embodiment of the present application; Figure 15 This is a schematic structural diagram of a wave position planning device for marine vessels provided in an embodiment of the present application; Figure 16 is a structural diagram of a first data acquisition unit provided in an embodiment of the present application; Figure 17 is a structural diagram of a first data acquisition unit provided in another embodiment of the present application; Figure 18 is a structural diagram of a first data acquisition unit provided in another embodiment of the present application; Figure 19 Schematic diagram of the structure of the digital coding module provided in the embodiment of the present application; Figure 20 is a structural diagram of a first data acquisition unit provided in another embodiment of the present application; Figure 21 This is a schematic diagram of the structure of the planned entry and exit time acquisition module provided in an embodiment of the present application; Figure 22 This is a schematic structural diagram of a wave position planning device for marine vessels provided in an embodiment of the present application; Figure 23 This is a schematic block diagram of the system structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application are further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present application and their descriptions are used to explain the present application, but are not intended to limit the present application.

[0031] The information collected in the technical solution of this application is information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0032] The acquisition, transmission, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0033] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0034] like Figure 1 FIG. 1 is a flow chart of a method for wave position planning for a maritime vessel provided in an embodiment of the present application. This embodiment describes the present application from the perspective of a satellite network management system. The method includes the following steps: Step S101: Acquire a wave position number and a corresponding first flight time and second flight time, where the second flight time is obtained based on a pre-trained network model.

[0035] In this embodiment, the beam position number is a unique identifier for the target beam position, which is used to determine the specific geographical area that the satellite needs to cover. The first navigation time is the estimated starting time for the ship to enter the target beam position, which can be understood as the predicted moment when the ship reaches the boundary of the target beam position. The second navigation time is the estimated length of time the ship will sail within the target beam position, which can be understood as the predicted time the ship will stay in the target beam position, which can be obtained through a pre-trained network model. Therefore, in this embodiment, the first navigation time is a time point, such as Xh:Xm, Xm, Xm, Xm, 20XX; while the second navigation time is a time period, such as 5 hours.

[0036] Step S102: Obtain a third flight time based on the first flight time and the second flight time.

[0037] In this embodiment, the third sailing time is the estimated time for the ship to leave the target wave position, which is also a time point. After the first sailing time for entering the target wave position and the second sailing time for staying at the target wave position are obtained in step S101, the third sailing time of this step can be calculated by adding the first sailing time and the second sailing time.

[0038] Since adjacent wave positions share a common boundary, the third flight time for each wave position is the first flight time of the next adjacent wave position. Therefore, after obtaining the estimated flight time length (second flight time) for each wave position, the predicted wave position flight times are accumulated based on the departure time to obtain the first and third flight times for each wave position. For example, the first wave position is numbered 1, the first flight time is the departure time, and the second flight time is a specific number of predicted times. The third flight time is calculated as the departure time plus the second flight time. This third flight time can then be used as the first flight time for the next wave position numbered 2.

[0039] Assume that the wave position number sequence of the ship is {b1, b2, ..., b m}, m is the number of waves that the ship's planned route passes through; it can be calculated based on the ship's departure time. T 0 , wave number sequence {b1, b2, ..., b m} and the predicted sailing time of the ship at different wave positions {t1, t2, ..., t m}, we can get the ship's entry wave position b x The time is , sail out of wave position b x The time is , where x∈[1,m].

[0040] Step S103: the first flight time, the third flight time and the wave position number are added to the satellite, so that the satellite provides service for the wave position corresponding to the wave position number during the period from the first flight time to the third flight time.

[0041] The first navigation time (estimated entry time) and the third navigation time (estimated departure time) and the corresponding wave position number in this step are a kind of wave position planning information. The satellite network management system notifies the satellite of the estimated entry time, estimated departure time and corresponding wave position number of the ship that needs attention. After receiving this information, the satellite can perform subsequent beam scheduling and resource allocation based on this information, so as to provide effective communication coverage and guarantee when the ship sails to a specific wave position.

[0042] In this embodiment, the satellite network management system may make the following changes to adapt to the beam position planning method of this application: 1. The front-end page of the satellite network management system supports inputting the staring wave position number and the corresponding time interval.

[0043] 2. The gaze wave position number and time interval support dynamic maintenance and can be added or deleted on the front-end page.

[0044] 3. Add a configuration on the front end, which allows you to choose to add the gaze position or wave number to the satellite.

[0045] After obtaining the above-mentioned predicted entry time, predicted exit time and corresponding wave position number, the satellite network management system can select the appropriate satellite for annotation through the following five stages: Phase 1: For each gaze position, traverse all satellites, calculate the distance between the gaze position and each satellite, and determine the associated satellites and neighboring satellites of the gaze position based on the distance.

[0046] Phase 2: Traverse all gaze positions and execute the process of Phase 1 to obtain the associated satellites and neighboring satellites of all gaze positions.

[0047] Phase 3: Traverse all snapshot moments. If the snapshot moment belongs to the time interval of the staring wave position, execute the processes of phase 1 and phase 2 to obtain the associated wave positions of all satellites at all snapshot moments in the time interval.

[0048] Phase 4: For a specific satellite under a snapshot T, by comparing the associated wave positions of the specific satellite in snapshots T-1 and T+1, determine the entry and exit wave position identifiers of all associated wave positions in this snapshot, traverse all satellites under snapshot T, and traverse all snapshots.

[0049] Phase 5: Backfill operations are performed on all exit positions.

[0050] As can be seen from the above, the beam position planning method of this application divides the gaze scenario into multiple time domains. Each time domain can be assigned a gaze beam position number based on the prediction results, ultimately forming a time- and domain-divided gaze scenario. Compared with existing satellite gaze solutions, this method not only effectively ensures ship communications but also optimizes the allocation and utilization efficiency of satellite resources. This method is particularly suitable for providing high-priority, uninterrupted communication services to key maritime users (such as scientific research vessels, important transport fleets, and offshore operating platforms).

[0051] In one embodiment of the present application, obtaining the wave position number in step S101 includes determining the wave position number based on the track point coordinates. Maritime transport routes are typically planned in advance. Nearshore voyages are typically planned three to four days in advance, while ocean voyages are typically planned one to two weeks in advance. A planned route is the vessel's intended navigation path, typically consisting of a set of latitude and longitude coordinates (track point coordinates) or flight segments. Therefore, the wave position numbers of the wave positions passed through in this step can be obtained from the vessel's planned route.

[0052] In another embodiment of the present application, the above-mentioned determination of the wave position number based on the trajectory point coordinates may further include the following: Figure 2 Steps shown: Step S201: extracting the coordinates of the track points from the planned route of the ship at sea.

[0053] As previously discussed, a planned route typically consists of a sequence of ordered trackpoint coordinates, which, when connected, form the vessel's intended navigation path. By extracting the trackpoint coordinates from the planned route, we can construct a trackpoint coordinate sequence, where each element represents a key location in the vessel's planned voyage.

[0054] The planned route can also include time information, that is, each trajectory point coordinate can correspond to the expected time point information. Therefore, the trajectory point coordinates extracted from the planned route can also form a trajectory time-space sequence with the time information. Each element in the trajectory time-space sequence represents each key position of the ship's planned voyage and the corresponding planned time point.

[0055] The planned route may not directly include the coordinates of the trajectory points and the corresponding time information. In this case, the static planned route can be converted into a dynamic, time-stamped trajectory space-time sequence in the following way: First, the continuous planned route is divided into a series of sufficiently dense discrete trajectory points (for example, one point is taken every small distance or a small period of time), and each trajectory point has its latitude and longitude coordinates. Then, based on the departure time of the ship and an initial speed model (for example, the design speed of the ship, the average planned speed, or the constant speed assumption within a voyage), the estimated time for the ship to arrive at each discrete trajectory point is estimated. In this way, the trajectory space-time sequence of the ship to be served can be obtained, which contains the trajectory points and the corresponding timestamp information. For example, the trajectory space-time sequence can be [(t 1， lon 1， lat1),(t 2， lon 2， lat2),...,(t n， lon n， lat n )], where t1-t n is the timestamp, lon n , lat n are the longitude and latitude coordinates.

[0056] The planned route of a ship may exist in various formats, but this step can extract it uniformly into a standard trajectory point coordinate sequence.

[0057] Step S202: Obtain the corresponding wave position number based on the trajectory point coordinates.

[0058] In satellite internet, the entire Earth's surface is divided into several base stations. Each base station has a fixed location on the ground, so each track point coordinate must correspond to a station number. This step can be performed by traversing each track point in the track point coordinate sequence or track spatiotemporal sequence. For each track point's longitude and latitude coordinates, the base station definition data is searched to determine which predefined base station area the longitude and latitude coordinates fall within, and then the unique station number of the base station is recorded.

[0059] If a trajectory point happens to fall exactly on the boundary of two basic wave positions, the wave position number can be assigned according to preset rules, for example, the wave position corresponding to the expected service satellite with better signal quality is selected, or selected according to a specific priority.

[0060] This step converts geospatial information (coordinates) into position information directly related to the satellite system, allowing route data to directly serve satellite position planning.

[0061] Step S203: Deduplication operation is performed on the obtained wave position numbers.

[0062] Since a wave position usually includes multiple track points, it is necessary to remove duplicates from the wave position numbers obtained in step S202, that is, only one duplicate wave position number is retained, so that a track wave position number set with unique wave position numbers can be obtained. The track wave position number set is a wave position number sequence. Figure 3 The figure shows a schematic diagram of a wave position number sequence for a navigation track provided by an embodiment of the present application, where each hexagon represents a wave position, the arrow represents the navigation track, and the gray image represents the wave position passed by the ship. Through this step, a clear and non-redundant wave position number list can be provided.

[0063] It should be noted that the operations of steps S101 to S103 are performed for each wave position number in the wave position number list respectively, so as to ensure that there is always a satellite to provide communication service for the ship during its navigation.

[0064] In another embodiment of the present application, Figure 4 As shown, obtaining the second flight time in the above step S101 may include: Step S401: Acquire ship attribute data and environmental data.

[0065] Vessel attribute data refers to data related to the vessel's own characteristics that may affect its navigation speed and stability, including but not limited to: vessel type, ship load, ship size, ship design speed, ship age, etc. Environmental data refers to factors in the environment in which the vessel is sailing that may affect its navigation status, including but not limited to: meteorological data such as wind force, wind direction, visibility, etc.; hydrological data such as ocean current direction, wave height, flow rate, tide, etc.

[0066] In one embodiment of the present application, the ship attribute data selected are ship type and ship load, because the ship type directly determines the basic characteristics of the ship, such as the hydrodynamic shape, design speed range, and power system configuration. The ship load can significantly change the ship's displacement and wetted surface area, thereby directly affecting the water resistance and, in turn, the actual speed under a specific main engine power. Compared with other internal factors, these two factors have a higher weight on the sailing time and are relatively easy to obtain. This data can be directly obtained from the ship's Automatic Identification System (AIS).

[0067] Environmental data consists of meteorological and hydrological data. Meteorological data includes wind speed and direction data, while hydrological data includes wave height data. Wind speed and direction are standard meteorological forecast parameters, with mature forecast models and data services, making them relatively easy to obtain and quantify. Wave height is one of the most core and intuitive parameters describing sea conditions and a standard output in marine environmental forecasts. This environmental data can be obtained from third-party systems. The data obtained here represents forecast values, i.e., the corresponding meteorological and hydrological data forecasts within the time and space of the planned route.

[0068] Of course, the present application does not limit the selection of the above-mentioned ship attribute data and environmental data. In other embodiments of the present application, more internal and external influencing factors can also be added to obtain more accurate prediction results.

[0069] In another embodiment of the present application, after obtaining the ship attribute data and environmental data that affect the navigation status of the ship at sea, this step also includes: converting the ship attribute data and environmental data into digital codes.

[0070] This is to convert various types of ship attribute data and environmental data into a numerical format that can be processed by machine learning models and subsequently integrated with navigation information.

[0071] In another embodiment of the present application, Figure 5 In this step, converting the ship attribute data and environmental data into digital codes may further include: Step S501: digitally number all ship types, and perform binary coding on the ship types according to the digital numbers.

[0072] In this step, all ship types are first numerically numbered. For example, a predefined list of ship types can be used (e.g., container ship, bulk carrier, tanker, LNG carrier, passenger ferry, etc.). Each ship type in the list is then assigned a unique numerical number. For example, container ship: number 1, bulk carrier: number 2, tanker: number 3, and so on. The ship types are then binary-coded based on these numbers, converting the decimal numbers into binary form.

[0073] If there are not many types of ships, the numbers can be directly converted to their standard binary form (for example, the number 3 is directly converted to binary "11"). Alternatively, the numbers can be converted to binary form using a one-hot encoding method, such as converting the container ship mentioned above to [1, 0, 0], the bulk carrier to [0, 1, 0], and the tanker to [0, 0, 1]. This embodiment does not limit the specific conversion method.

[0074] Step S502: Taking a digital value of the ship's load in tons.

[0075] For the continuous numerical variable "ship load", this embodiment directly takes a digital value for it. Generally, the ship load is measured in tons, and this information is directly recorded in the AIS. Therefore, it can be directly obtained from the AIS and taken as a digital value.

[0076] Step S503: converting the wind direction into a binary code according to the binary code correspondence table.

[0077] In this embodiment, a binary code correspondence table is first compiled, in which the eight basic wind directions (east, south, west, north, southeast, southwest, northeast, northwest) are mapped to binary codes. Then, the corresponding wind direction can be converted into a binary code by querying the binary code correspondence table. A specific example of the binary code correspondence table can be found in Figure 6 Of course, the division of wind directions in this embodiment can be more detailed, such as dividing it into 16 basic wind directions, etc., and this application does not impose any limitation on this.

[0078] Step S504: Convert the wind force level and wave height level into corresponding level numbers based on a preset standard.

[0079] In this embodiment, the definition of wind force level can adopt the "Typhoon Business and Service Regulations" issued by the Meteorological Bureau, setting the wind force level to 18 levels from 0 to 17; and the wave height level also adopts a standard classification method, such as the internationally used sea state rating table.

[0080] Through the above steps S501-S504, the selected ship attribute data and environmental data can be converted from their original forms into numerical or binary encoding forms that are easier for the machine learning model to understand and process.

[0081] Step S402: inputting a feature matrix including the wave position number, the ship attribute data and the environmental data into a pre-trained network model to obtain a second navigation time.

[0082] The processed wave position number, ship attribute data and environmental data are combined into a structured data set. This data set is a feature matrix, which is usually a two-dimensional matrix. Each row represents a sample (the prediction of the sailing time for a specific wave position) and each column represents a feature.

[0083] The pre-trained network model here refers to a neural network model that has been learned and optimized using historical data. This network model can be a deep neural network (DNN), recurrent neural network (RNN), long short-term memory network (LSTM), gated recurrent unit (GRU), etc. The feature matrix constructed for the current ship and wave position to be predicted is input into this pre-trained network model. Based on the input features, the model uses its internally learned parameters and weights to perform calculations and inferences, ultimately outputting a predicted value, which is the estimated length of time the ship will sail in that specific wave position (for example, 3.5 hours).

[0084] In one embodiment of the present application, the above method further includes a process of obtaining a feature matrix, such as Figure 7 As shown, it includes the following steps: Step S701: Obtain the fourth flight time and the fifth flight time of the corresponding wave position according to the wave position number.

[0085] The fourth sailing time here is the planned entry time of the wave position, and the fifth sailing time is the planned exit time of the wave position, that is, the entry and exit time for a specific wave position in the route planning. The purpose of this step is to calculate the specific time points of the ship entering and leaving each wave position in the route planning. Figure 8 As shown, this step may further include the following sub-steps: Step S7011: Determine the target wave position according to the wave position number.

[0086] Step S7012: Calculate the distance relationship between the trajectory point coordinates and the target wave position boundary.

[0087] Step S7013: Based on the distance relationship, determine the target trajectory point entering or exiting the target wave position.

[0088] Step S7014: Determine the corresponding planned entry time and planned exit time according to the target trajectory point.

[0089] Specifically, after determining the target wave position according to the wave position number, the distance between the coordinates of each track point of the ship and the longitude and latitude coordinates of the six wave position boundary points of the target wave position can be calculated first.

[0090] Assume that the ship has n track points, and the wave positions it passes through are numbered 3-9. When the target wave position is number 3, calculate the longitude and latitude coordinates (lon i ,lat i ), i∈(0, n+1) and the six wave positions (lon j ,lat j ), i∈[1,6] The distance L between the longitude and latitude coordinates of the boundary point ij , the distance calculation formula uses the haversine equation, which is as follows: ; Then, the two boundary points with the shortest distance from the current trajectory point are selected to obtain the distance between the current trajectory point and the two boundary points. The distances between all trajectory points and the two boundary points with the shortest distance can form a first distance sequence. The first distance sequence can be expressed as {L im , L in}, i∈(0, n+1), m, n∈[1, 6], i is the trajectory point number, m, n are the two boundary points with the shortest distance to the trajectory point i.

[0091] Then, the distance between each trajectory point and the two corresponding boundary points with the shortest distance is added to obtain the second distance sequence. im +L in ), the second distance sequence obtained is L' i , i∈(0,n+1).

[0092] Finally, the two smallest values ​​in the second distance sequence are selected as the distance values ​​corresponding to the target trajectory points of the ship to be served entering or leaving the target wave position, and the time corresponding to the target trajectory point is the time when the ship to be served enters and leaves each wave position.

[0093] The methods of steps S7011-S7014 can be found in Figure 9 As shown, the sum of the distances from point i to the boundary points O1 and O2 is the shortest, and the sum of the distances from point j to the boundary points O3 and O4 is the shortest. Therefore, points i and j are the trajectory points entering or leaving the current wave position. Whether it is entering or leaving can be determined according to the heading or the timestamp of the trajectory point. Figure 9 For example, the wave position boundary determined by O1 and O2 is the boundary of the target entering the wave position, point i is the trajectory point of the target entering the wave position, and the time when the target enters the wave position is the time t corresponding to the trajectory point i. i The wave position boundary determined by O3 and O4 is the boundary of the target leaving the wave position, point j is the trajectory point of the target leaving the wave position, and the time when the target leaves the wave position is the time t corresponding to the trajectory point j. j .

[0094] Step S702: Obtaining a corresponding sixth flight time according to the fourth flight time and the fifth flight time.

[0095] In this embodiment, the sixth sailing time is the planned stay time for the port, which is obtained by subtracting the planned entry time from the planned departure time. For example, if the planned entry time for a port is Day 1 10:00 and the planned departure time is Day 1 15:00, the planned stay time is 5 hours.

[0096] Step S703: Obtaining the characteristic matrix based on the wave position number, the sixth navigation time, the ship attribute data and the environmental data.

[0097] Steps S701-S703 construct a more comprehensive and information-rich feature matrix by introducing and processing the “planned time” information and combining it with dynamic internal and external influencing data (ship attribute data and environmental data), thereby improving the accuracy and reliability of the subsequent network model prediction of the “actual wave position sailing time”.

[0098] In one embodiment of the present application, the above network model adopts an improved BiGRU-Attention network model. The improved BiGRU-Attention network model is introduced below. Figure 10 Shown is the structural diagram of the improved BiGRU-Attention model.

[0099] The Gated Recurrent Unit (GRU) is an efficient and simplified variant of the Long Short-Term Memory (LSTM) neural network. It simplifies the three gate structures of LSTM into reset gate and update gate structures, while retaining the characteristics of LSTM and improving the training efficiency of the network. During training, the update gate Determine the proportion of the state information of the previous moment to the current moment information; reset the gate Determines the degree of forgetting historical state information, thereby ensuring that important timing information is passed on. Its internal function relationship is as follows:

[0100] Where σ is the Sigmoid activation function, tanh is the hyperbolic tangent function; h t , h t-1 are the hidden states at time t and time t-1 respectively; is the weight matrix; ⊙ is the matrix of the Dahama. GRU controls the update gate z t and reset gate r t Decide on the information to be selected or not, when resetting the gate r t When closed to 0, historical information will be ignored and more useful current information will be captured; when the update gate z t If it is 1, the historical information will be passed on, realizing the function of "memorizing" long-term information.

[0101] BiGRU builds a bidirectional propagation structure based on the typical GRU structure, but the structure of the recurrent neural network can only remember information of relative time length, and the input sequence has an equal impact on the current output sequence. In order to emphasize the impact of trajectory attribute information at different times on the current trajectory association, this application adds a local attention mechanism based on the BiGRU network model. The hidden state vector output by the BiGRU model is (h1, h2, h3, ..., h n ), the hidden state vector at time t is , then the target sequence of the model's output is y1, y2, y3, ..., y n Each output in satisfies the following distribution conditions: ; where h' t is the hidden state vector that introduces attention, and the formula is as follows: ; h t is the hidden state vector at time t, W s , W c is the weight matrix, c t is the context vector, which is calculated by the context hidden vector of the current time. The calculation formula is as follows: ; The local attention mechanism uses a fixed window size method to reduce the computational cost. The window size is 2D+1, p t is the window center, and its calculation formula is as follows: ; Where σ is the sigmoid function, and Wp is the weight matrix; ; The improved BiGRU-Attention network model in this embodiment uses the mean squared error (MSE) loss function, which is the mean of the sum of squared differences between the model-predicted association probability f(x) and the target's true association value y: .

[0102] Correspondingly, the above-mentioned wave position planning method for marine ships also includes the training process of the network model, such as Figure 11 As shown, it includes the following steps: Step S111: Acquire historical trajectory data, historical ship attribute data, and historical environment data.

[0103] The historical trajectory data and historical ship attribute data in this step can be directly obtained from the Automatic Identification System (AIS). AIS data includes dynamic data (such as position, speed, and heading) of the ship during navigation, static information (such as ship name, MMSI, and ship type), and voyage-related information (such as load). Therefore, the ship type and ship load used for model training can be obtained from AIS data, as well as the corresponding historical trajectory data and timestamp information. This historical trajectory data and timestamp information can then constitute the historical trajectory spatiotemporal sequence.

[0104] Based on the timestamps of the historical trajectory data, associated historical environmental data, namely historical meteorological and hydrological data, can be obtained from a third-party system. This third-party system can be, for example, a commercial weather service provider or a historical database of a national meteorological and oceanographic agency. Specifically, for each point (or time period) in the historical trajectory data, the corresponding historical weather conditions, such as wind direction and wind speed, are obtained. Similarly, for each point or time period in the historical trajectory data, the corresponding historical hydrological data, such as wave height, is obtained. Then, as described in steps S501-S504 above, the historical environmental data is converted into historical digital codes.

[0105] In another embodiment of the present application, the acquired historical trajectory spatiotemporal sequence may be preprocessed, and the preprocessing process includes removing noise and abnormal data, and supplementing missing values. Figure 12 As shown, this step may further include: Step S1111: Use the Z-Score method to remove outliers in the historical trajectory data that do not conform to the standard normal distribution.

[0106] The Z-Score method uses the mean and standard deviation of the original data to standardize, eliminate outliers in the trajectory data that do not conform to the standard normal distribution, and reduce the impact of noise data on the trajectory prediction algorithm.

[0107] ; Where μ is the mean, σ is the standard deviation, and X i is the original data, Z i It's X i Usually, |Z|>3 is considered an outlier.

[0108] Step S1112: Obtain the first timestamp of the missing position in the historical trajectory data.

[0109] After removing outliers in step S1111, the historical trajectory data sequence is checked for missing data. Missing data typically manifests as discontinuities in the timestamp sequence (for example, the time interval between two consecutive AIS records is much greater than the normal reporting frequency) or the absence of corresponding location data at certain time points. When missing data is detected, the first timestamp at the missing location is determined. The first timestamp is simply used to indicate that it corresponds to the missing location and is not sequential.

[0110] Step S1113: Obtain the longitude and latitude coordinates of three trajectory data points before and after the first timestamp, calculate the average of the longitude and latitude coordinates, and fill them into the missing position corresponding to the first timestamp to obtain the historical trajectory spatiotemporal sequence.

[0111] For the missing point represented by the "first timestamp" located in S1112: obtain the latitude and longitude coordinates of the three valid historical trajectory data points immediately preceding the missing point, and obtain the latitude and longitude coordinates of the three valid historical trajectory data points immediately following the missing point. If the missing point is near the beginning or end of the trajectory, it may not be possible to obtain the three preceding and following points. In this case, you can abandon filling in the missing point.

[0112] Add the longitude and latitude values ​​of the six trajectory points and divide by the number of points to get the average longitude and latitude. Use the calculated average longitude and latitude as the coordinates of the missing position corresponding to the "first timestamp" to fill in the missing position. The formula is as follows:

[0113] Among them (lon i ,lat i ) is the latitude and longitude of the trajectory point with missing timestamp.

[0114] Step S112: obtaining the historical stay time of the ship at each wave position based on the historical trajectory data.

[0115] For details of this step, please refer to the description of S7011-S7014. The difference is that what is being processed now is "historical" data instead of "planned route", so it will not be further described.

[0116] Step S113: Correlate the wave position number of each wave position, the historical stay time, the historical ship attribute data, the historical environmental data and the known actual stay time of each wave position to form a training data set.

[0117] Specifically, this step extracts the wave position number for each wave position, then associates it with the vessel type code and tonnage value. This is also done by associating the historical meteorological and hydrological data codes (such as wind force level, wind direction code, and wave height level) corresponding to the period of time the wave position was historically occupied. This information is combined to form the model input data sequence. The known actual residence time at each wave position serves as the "target value or label" corresponding to the feature set of each model input data sequence—that is, the historical actual residence time of the vessel at that wave position. Together, these data constitute the model training dataset.

[0118] Each row of the relationship matrix in the training dataset represents a training sample (a historical wave position navigation event). The columns of the relationship matrix can be divided into two parts: Feature columns: correspond to the individual input features in the model input data sequence.

[0119] Label column: The actual stay time corresponding to this historical wave position voyage (from the known wave position voyage time series).

[0120] In this embodiment, in order to avoid the impact on gradient update and learning rate during model training, the constructed relationship matrix can be normalized by standard deviation. The normalization formula is as follows: matrix*=(matrix-μ) / σ; Where μ is the mean value set of each attribute, and σ is the standard deviation set of each attribute.

[0121] Step S114: Perform model training using the training data set to obtain the network model.

[0122] The training parameters of the network model here include but are not limited to learning rate, batch size, number of training rounds, BiGRU layer parameters, Attention mechanism parameters, optimizer, loss function, etc. This step specifically includes: Through forward computation, the BiGRU-Attention model calculates the predicted travel time for a target vessel at each wave position. Specifically, a batch of input features is extracted from the normalized training data and fed into the improved BiGRU-Attention network model. The model calculates through its internal BiGRU and Attention layers, outputting predicted travel times for each wave position within that batch of samples.

[0123] The mean square error loss function is used to calculate the error value between the predicted travel time and the true travel time label, and the current model parameters are compared with the historical model parameters, and the model parameters with smaller error values ​​are saved. For the samples of the current batch, the "predicted travel time" output by the model is compared with the corresponding "true travel time label", and the mean square error (MSE) formula is used to calculate the total error value (loss). At the end of each batch, the performance of the current model is evaluated on the validation set (a part separated from the training data and not involved in the gradient update) (for example, calculating the MSE on the validation set). If the error value of the current model on the validation set is less than the historical best error value, the parameters of the current model are saved. This ensures that after the training is completed, the model version that performs best on the validation data can be selected to prevent overfitting.

[0124] The results of BiGRU-Attention are back-propagated according to the error, and BiGRU-Attention performs forward and backward propagation of the input parameters along the sequence direction to extract the key information in the input relationship matrix.

[0125] The neuron parameters are optimized using the optimizer.

[0126] The training process is repeated until the set number of training rounds is reached.

[0127] After all the training is completed, the prediction result with the highest accuracy is selected as the target ship’s The final travel time of the wave position.

[0128] A simplified diagram of the above training process can be found in Figure 13 As shown in Figure 1, data preprocessing refers to the processing flow of steps S112-S113. It can be seen that through the above iterative optimization process, the model parameters are systematically adjusted, enabling it to learn the complex patterns of predicting wave position and sailing time from the input data, and ultimately obtain a model that performs optimally for this task.

[0129] like Figure 14 FIG2 is a flow chart of a wave position planning method for a maritime vessel provided in another embodiment of the present application. The method is applied to a satellite and includes the following steps: Step S141: Receive the wave position number and the corresponding predicted entry time and predicted exit time.

[0130] The wave position number and the corresponding predicted entry time and predicted exit time here are from the satellite network management system or similar management and control center on the ground side. The specific process of obtaining this information can be found in the corresponding description in the above embodiment and will not be repeated here.

[0131] Step S142: providing service to the wave position corresponding to the wave position number based on the predicted entry time and the predicted exit time.

[0132] Beam staring involves a satellite with a steerable beam continuously and centrally directing its communication beam to cover a specific geographic area identified by a "beam number" within a specific time period. "Staring" emphasizes the stability and focus of the beam within that time window, ensuring that target vessels within that area receive stable, high-quality communication services.

[0133] The satellite's internal task scheduling system arranges beam direction based on the received "predicted arrival time" and "predicted departure time." Before the arrival of the "predicted arrival time," the satellite begins adjusting its antenna or electronically directing the beam toward the target beam position. From the "predicted arrival time" to the "predicted departure time," the satellite maintains beam coverage of that beam position. If a ship navigates within that beam position, it can access the satellite-provided communications network. Once the "predicted departure time" is reached, the satellite releases the beam resource or adjusts it to the next beam position to be covered, according to subsequent planning.

[0134] As can be seen from the above, the beam position planning method for maritime vessels provided in this embodiment no longer blindly broadcasts signals or evenly distributes resources. Instead, it precisely deploys communication resources to the required location and time based on the ship's actual predicted path and time window. By preparing beam coverage before the ship arrives and providing continuous service during its passage, the stability and reliability of the communication connection are greatly improved. Moreover, because "staring" is time-sensitive and only occurs within the predicted time window, when the ship departs or does not arrive, the relevant resources can be used to serve other users or areas, avoiding the waste of precious satellite resources. This method is particularly suitable for providing high-priority, uninterrupted communication services to key maritime users (such as scientific research vessels, important transport fleets, offshore operation platforms, etc.).

[0135] like Figure 15 This is a schematic diagram of the structure of a wave position planning device for marine vessels provided in an embodiment of the present application, which is applied to a satellite network management system. The device includes: a first data acquisition unit 151, a second data acquisition unit 152, and a data injection unit 153, which are sequentially connected. Among them: The first data acquisition unit 151 is configured to acquire a wave position number and a corresponding first navigation time and a second navigation time, where the second navigation time is obtained based on a pre-trained network model.

[0136] The second data acquiring unit 152 is configured to obtain a third flight time based on the first flight time and the second flight time.

[0137] The data annotation unit 153 is used to annotate the first flight time, the third flight time and the wave position number to the satellite, so that the satellite can provide service for the wave position corresponding to the wave position number during the period from the first flight time to the third flight time.

[0138] In one embodiment of the present application, Figure 16 As shown, the first data acquisition unit 151 includes a number acquisition module 1511 for determining the wave position number based on the coordinates of the track point.

[0139] In another embodiment of the present application, the second data acquiring unit 152 is specifically configured to add the first flight time and the second flight time to obtain a third flight time.

[0140] In another embodiment of the present application, Figure 17 As shown, the first data acquisition unit 151 further includes: The impact data acquisition module 1512 is used to acquire ship attribute data and environmental data.

[0141] The wave position sailing time acquisition module 1513 is used to input the feature matrix including the wave position number, the ship attribute data and the environmental data into the pre-trained network model to obtain the second sailing time.

[0142] In another embodiment of the present application, Figure 18 As shown, the first data acquisition unit 151 further includes: a digital encoding module 1514, which is used to convert the ship attribute data and the environmental data into digital codes.

[0143] In another embodiment of the present application, the above-mentioned ship attribute data includes ship type and ship load, and the environmental data includes wind direction and wave height, such as Figure 19 As shown, the digital encoding module 1514 includes: The ship type coding submodule 15141 is used to digitally number all ship types and binary code the ship types according to the digital numbers.

[0144] The ship load coding submodule 15142 is used to digitally value the ship load in tons.

[0145] The wind direction coding submodule 15143 is used to convert the wind direction into a binary code according to the binary code corresponding table.

[0146] The wind force and wave height coding submodule 15144 is used to convert the wind force level and wave height level into corresponding level numbers based on preset standards.

[0147] In another embodiment of the present application, Figure 20 As shown, the first data acquisition unit 151 further includes: The planned entry and exit time acquisition module 1515 is used to obtain the planned entry time and planned exit time of the corresponding wave position according to the wave position number.

[0148] The planned stay time acquisition module 1516 obtains the corresponding planned stay time according to the planned entry time and the planned exit time.

[0149] The feature matrix acquisition module 1517 is configured to obtain the feature matrix based on the wave position number, the planned stay time, the ship attribute data, and the environmental data.

[0150] In another embodiment of the present application, Figure 21 As shown, the planned entry and exit time acquisition module 1515 includes: The target wave position determination submodule 15151 is used to determine the target wave position according to the wave position number.

[0151] The distance calculation submodule 15152 is used to calculate the distance relationship between the trajectory point coordinates and the target wave position boundary.

[0152] The target trajectory point determination submodule 15153 is used to determine the target trajectory point entering or exiting the target wave position based on the distance relationship.

[0153] The planned entry and exit time determination submodule 15154 is used to determine the corresponding planned entry time and planned exit time according to the target trajectory point.

[0154] In another embodiment of the present application, the above-mentioned device further includes: The historical data acquisition unit is used to acquire historical trajectory data, historical ship attribute data and historical environment data.

[0155] The historical stay time acquisition unit is used to obtain the historical stay time of the ship at each wave position based on the historical trajectory data.

[0156] The training data set forming unit is used to associate the wave position number of each wave position, the historical stay time, the historical ship attribute data, the historical environmental data and the known actual stay time of each wave position to form a training data set.

[0157] The network training unit is used to perform model training using the training data set to obtain the network model.

[0158] The detailed description of the above-mentioned units and modules can be found in the corresponding description in the aforementioned method embodiment, which will not be repeated here.

[0159] It can be seen from the above technical solution that the time of the staring scene in this application is divided into multiple time domains. Each time domain can add the staring wave position number according to the prediction results, and finally form a time-divided and domain-divided staring scene. Compared with the existing satellite staring solution, it can not only effectively ensure ship communication, but also optimize the allocation and utilization efficiency of satellite resources.

[0160] like Figure 22 FIG2 is a schematic structural diagram of a wave position planning device for marine vessels provided in another embodiment of the present application, which is applied to a satellite. The device includes: a data receiving unit 221 and a wave position service unit 222, wherein: The data receiving unit 221 is used to receive the wave position number and the corresponding predicted entry time and predicted exit time.

[0161] The wave position service unit 222 is used to provide service to the wave position corresponding to the wave position number based on the predicted entry time and the predicted exit time.

[0162] As can be seen from the above, the beam position planning device for maritime vessels provided in this embodiment no longer blindly broadcasts signals or evenly distributes resources. Instead, it precisely deploys communication resources to the required location and time based on the ship's actual predicted path and time window. By preparing beam coverage before the ship arrives and continuously providing service during its passage, the stability and reliability of the communication connection are greatly improved. Moreover, because "staring" is time-sensitive and only occurs within the predicted time window, when the ship leaves or does not arrive, the relevant resources can be used to serve other users or areas, avoiding the waste of precious satellite resources.

[0163] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the above method is implemented when the processor executes the program.

[0164] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0165] An embodiment of the present application also provides a computer program product, including a computer program / instruction, which implements the steps of the above method when the computer program / instruction is executed by a processor.

[0166] like Figure 23 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 23In addition, the electronic device 600 may also include all components shown in Figure 23 For components not shown, reference may be made to the prior art.

[0167] like Figure 23 As shown, the central processing unit 100 is sometimes also referred to as a controller or an operation control unit, and may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operations of various components of the electronic device 600 .

[0168] Memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information and may also store programs that execute the relevant information. The CPU 100 may execute the programs stored in memory 140 to implement information storage or processing.

[0169] The input unit 120 provides input to the CPU 100. The input unit 120 may be, for example, a keypad or touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0170] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and contains additional data. Examples of such memory are sometimes referred to as ERPOM. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application programs and function programs, or processes used by the central processing unit 100 to execute operations of the electronic device 600.

[0171] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, images, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0172] The communication module 110 is a transmitter / receiver that transmits and receives signals via the antenna 111. The communication module 110 (transmitter / receiver) is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0173] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless local area network modules. The communication module 110 (transmitter / receiver) is also coupled to a speaker 131 and a microphone 132 via an audio processor 130, providing audio output via the speaker 131 and receiving audio input from the microphone 132, thereby implementing common telecommunication functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 130 is coupled to the central processing unit 100, enabling local recording via the microphone 132 and playback of stored audio via the speaker 131.

[0174] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0175] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0176] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1The function specified in one or more boxes.

[0177] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0178] Specific embodiments are used in this application to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A wave position planning method for ships at sea, characterized in that: The method comprises: Obtaining a wave position number and a corresponding first navigation time and a second navigation time, where the second navigation time is obtained based on a pre-trained network model; Obtaining a third flight time based on the first flight time and the second flight time; The first flight time, the third flight time and the wave position number are added to the satellite, so that the satellite provides service for the wave position corresponding to the wave position number during a time period from the first flight time to the third flight time.

2. The wave position planning method for marine vessels according to claim 1, characterized in that: Obtaining the wave number includes: The wave position number is determined based on the track point coordinates.

3. The wave position planning method for marine vessels according to claim 1, characterized in that: The obtaining of a third navigation time based on the first navigation time and the second navigation time includes: The first flight time and the second flight time are added together to obtain a third flight time.

4. The wave position planning method for marine vessels according to claim 1, characterized in that: Obtaining the second flight time includes: Obtain ship attribute data and environmental data; A feature matrix including the wave position number, the ship attribute data and the environmental data is input into a pre-trained network model to obtain a second navigation time.

5. The wave position planning method for ships at sea according to claim 4, characterized in that: After obtaining the ship attribute data and the environmental data, the method further includes: The ship attribute data and the environmental data are converted into digital codes.

6. The wave position planning method for marine vessels according to claim 5, characterized in that: The ship attribute data includes ship type and ship load, and the environmental data includes wind direction and wave height. Converting the ship attribute data and the environmental data into digital codes includes: Digitally numbering the ship type, and performing binary coding on the ship type according to the digital number; Take digital value of ship load; Convert the wind direction into binary code according to the binary code corresponding table; Convert wind force levels and wave height levels into corresponding levels based on preset standards.

7. The method for wave position planning for ships at sea according to claim 4, characterized in that: The method further comprises: Obtaining the fourth navigation time and the fifth navigation time of the corresponding wave position according to the wave position number; Obtaining a corresponding sixth flight time according to the fourth flight time and the fifth flight time; The characteristic matrix is ​​obtained based on the wave position number, the sixth navigation time, the ship attribute data and the environmental data.

8. The method for wave position planning for ships at sea according to claim 7, characterized in that: The obtaining of the fourth navigation time and the fifth navigation time corresponding to the wave position according to the wave position number includes: Determine the target wave position according to the wave position number; Calculating the distance relationship between the trajectory point coordinates and the target wave position boundary; Determining a target trajectory point entering or exiting the target wave position based on the distance relationship; The corresponding fourth flight time and fifth flight time are determined according to the target trajectory point.

9. The wave position planning method for marine vessels according to claim 4, characterized in that: The method further comprises: Obtain historical trajectory data, historical ship attribute data and historical environmental data; Obtaining the historical stay time of the ship at each wave position based on the historical trajectory data; Associating the wave position number of each wave position, the historical stay time, the historical ship attribute data, the historical environmental data and the known actual stay time of each wave position to form a training data set; The training data set is used to perform model training to obtain the network model.

10. A wave position planning method for ships at sea, characterized in that: The method comprises: Receive the wave position number and the corresponding predicted entry time and predicted exit time; The wave position corresponding to the wave position number is served based on the predicted entry time and the predicted exit time.

11. A wave position planning device for ships at sea, characterized in that: The device comprises: A first data acquisition unit is configured to acquire a wave position number and a corresponding first navigation time and a second navigation time, wherein the second navigation time is obtained based on a pre-trained network model; a second data acquisition unit, configured to obtain a third flight time based on the first flight time and the second flight time; The data uploading unit is used to upload the first flight time, the third flight time and the wave position number to the satellite, so that the satellite can provide service for the wave position corresponding to the wave position number during the period from the first flight time to the third flight time.

12. A wave position planning device for ships at sea, characterized in that: The device comprises: A data receiving unit is used to receive the wave position number and the corresponding predicted entry time and predicted exit time; The wave position service unit is used to provide service to the wave position corresponding to the wave position number based on the predicted entry time and the predicted exit time.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 10 are implemented.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

15. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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