Virtual electronic channel design method and system based on AIS technology

Abstract

The invention discloses a virtual electronic channel design method and system based on an AIS (automatic identification system) technology, and the method comprises the steps: carrying out the precise correction of the position of a ship through multi-source data fusion in combination with AIS and GNSS (global navigation satellite system) data, generating a continuous and smooth virtual channel, and achieving the real-time dynamic correction of the channel through an environment adaptation correction term and a Kalman filtering algorithm. The influence of ocean current, wind speed, tide and other factors on the channel is dealt with. The system transmits updated virtual navigation channel data to a ship terminal and a port management center in real time in a low-delay manner through a VHF data link and a 5G edge computing technology, so that safe navigation of the ship under the condition of no physical navigation mark is ensured. In addition, the system can dynamically adjust the channel path, improves the emergency response capability of the port, and keeps the efficient operation of the port. The method is suitable for ship navigation and safety guarantee in extreme environments such as freezing and typhoon, and has a wide application prospect in sea areas which are susceptible to typhoon, such as frozen ports in northern China, East China Sea and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of ship navigation and maritime management, and particularly to a method and system for designing a virtual electronic waterway based on AIS technology. Background Art

[0002] Traditionally, in order to ensure safe navigation at sea, port management parties often have to take measures such as closing or restricting navigation, resulting in a significant decline in shipping efficiency and difficulty in conducting maritime rescue in a timely manner in case of emergencies.

[0003] At the same time, although the existing virtual Automatic Identification System (AIS) beacon technology can display virtual identifiers on electronic nautical charts, most of them are limited to displaying discrete single-point information and cannot form a continuous virtual waterway. The existing technology does not match the actual sea conditions in terms of data processing, path generation, and dynamic correction, and there are problems such as insufficient navigation accuracy and high update latency. Therefore, the existing technology cannot meet the requirements of ships for continuous navigation paths in harsh environments, resulting in a serious threat to the safety of ships under extreme climate conditions such as freezing and typhoons. Summary of the Invention

[0004] Technical Objective: Aiming at the deficiencies of existing virtual AIS beacons, the present invention discloses a method and system for designing a virtual electronic waterway based on AIS technology. Through multi-source data collection and fusion, an advanced data processing and optimization algorithm is used to generate a continuous, smooth, and dynamically correctable virtual electronic waterway to replace the functions of physical beacons in harsh environments such as freezing and typhoons, thereby improving the accuracy and safety of ship navigation and providing effective guarantee for maritime emergency rescue.

[0005] Technical Solution: To achieve the above technical objective, the present invention adopts the following technical solution:

[0006] A method for designing a virtual electronic waterway based on AIS technology specifically includes the following steps:

[0007] Obtain the AIS information of ships through AIS base stations. The AIS information includes the longitude, latitude, speed, heading, and timestamp data of ships. At the same time, combine the data of the Global Navigation Satellite System (GNSS) to correct the errors of AIS data, and use radar and marine environment sensors to collect sea current, tide, and wind speed environment data;

[0008] Based on the collected data, set the reference points of the virtual waterway, and use the Bezier curve fitting method to generate a continuous and smooth virtual waterway, and introduce an environmental adaptation correction term to dynamically adjust the path;

[0009] By calculating the AIS signal drift and using the Kalman filtering algorithm to optimize and correct the ship state in real time, the shipping route is optimized.

[0010] The Bayesian estimation method is used to fuse multi-source data to optimize the calculation accuracy of the virtual shipping lane.

[0011] Through the Very High Frequency (VHF) data link and 5G edge computing technology, the updated virtual shipping lane data is transmitted to the ship terminal and the port management center in real time.

[0012] The ship terminal receives the updated virtual shipping lane data and displays it in real time, guides the ship navigation through the electronic chart system, and at the same time transmits the ship state data back to the port management center for monitoring and adjustment.

[0013] Preferably, the following error correction formula is used to correct the AIS data:

[0014]

[0015] where P GNSS is the ship position obtained by the GNSS system, and P AIS is the ship position obtained by the AIS system. T GNSS and T AIS are the timestamps corresponding to the GNSS system and the AIS system respectively.

[0016] Preferably, the Bezier curve fitting method is used to generate the virtual shipping lane, and its expression is:

[0017] P(t) = (1 - t) 2 P0 + 2(1 - t)tP1 + t 2 P2 + W i

[0018] where P0, P1, and P2 are the control points of the Bezier curve, t is a parameter between 0 and 1, and W i is the environmental adaptation correction term, and its calculation method is:

[0019] W i = αW i-1 + (1 - α)D i

[0020] where D i is the environmental correction parameter, α is the adaptive coefficient, and 0 < α < 1.

[0021] Preferably, the formula for calculating the AIS signal drift is:

[0022]

[0023] Among them, x n and y n are respectively the longitude and latitude coordinates of the nth waterway point, λ is the unadjusted coefficient, and F current is the ocean current force influence factor, and its calculation formula is:

[0024] F current =ρAC d V 2

[0025] Among them, ρ is the seawater density, A is the ship's flow-facing area, C d is the drag coefficient, and V is the ocean current velocity.

[0026] Preferably, the update formula for real-time optimization and correction of the waterway state using the Kalman filter algorithm is:

[0027] X k =DX k-1 +FU k +W k

[0028] Among them, X k is the waterway state at the kth moment, that is, the longitude and latitude coordinates of the waterway point, D is the state transition matrix, F is the control input matrix, U k is the control input, and W k is the process noise.

[0029] Preferably, the transmission delay correction formula using 5G edge computing is:

[0030]

[0031] Among them, S is the data packet size, B is the communication bandwidth, L edge is the delay correction factor optimized by edge computing, and μ is the delay adjustment coefficient.

[0032] The present invention also provides a virtual electronic waterway design system based on AIS technology for implementing a virtual electronic waterway design method as described above, including:

[0033] A data acquisition module for obtaining ship data provided by an AIS base station, correcting the AIS data in combination with GNSS data, and simultaneously collecting ocean current, tide, and wind speed environment data;

[0034] A virtual waterway generation module for setting virtual waterway reference points based on the collected data, generating a continuous and smooth virtual waterway through the Bezier curve fitting method, and introducing an environmental adaptation correction term to dynamically adjust the path;

[0035] The ship state optimization module is used to calculate the AIS signal drift amount and perform real-time optimization and correction on the ship state through the Kalman filtering algorithm, so as to further optimize the virtual waterway;

[0036] The data fusion module is used to fuse multi-source data by using the Bayesian estimation method to optimize the calculation accuracy of the virtual waterway;

[0037] The display module is used to display the generated virtual waterway in real time on the electronic nautical chart;

[0038] The data transmission module is used to transmit the optimized virtual waterway data to the ship terminal and the port management center through the VHF data link and 5G edge computing technology;

[0039] The monitoring and feedback module is used for the ship terminal to receive the updated virtual waterway data and display it in real time, and at the same time transmit the ship state back to the port management center for monitoring, analysis and adjustment.

[0040] Preferably, the formula for the data fusion module to optimize the virtual waterway by using the Bayesian estimation method is:

[0041]

[0042] where X k is the ship state at the k-th moment, Z k is the observation data from different data sources, including AIS, GNSS, radar, and marine environment sensors, P(X k |Z k ) is the posterior distribution of the ship state given the observation data Z k , that is, the final ship state estimate, P(Z k |X k ) is the likelihood function, indicating the probability of the observation data Z k under the ship state X k , P(X k ) is the prior distribution, indicating the initial assumption of the ship state, and P(Z k ) is the marginal likelihood, indicating the total probability of the observation data.

[0043] Preferably, the data transmission module further includes:

[0044] The VHF communication unit is used to broadcast the optimized virtual waterway data to the ship terminal through the VHF data link;

[0045] The 5G communication unit is used to transmit the updated virtual waterway data to the port management center and the ship terminal in real time through the 5G network.

[0046] Preferably, the ship state optimization module uses the Kalman filter algorithm for ship state estimation, and its Kalman filter state update formula is:

[0047]

[0048] Wherein, is the predicted ship state, Kk is the Kalman gain, and y k is the innovation value, that is, the difference between the actual measurement value and the predicted value.

[0049] Advantages: The virtual electronic waterway design method and system based on AIS technology provided by the present invention have the following advantages:

[0050] 1. Through multi-source data fusion, the present invention combines AIS and GNSS data for error correction, effectively eliminates the influence of environmental factors on navigation data, and generates a continuous and smooth virtual waterway. In extreme environments such as freezing or typhoons, traditional navigation aids may fail, while the present invention can provide accurate virtual waterway information to ensure the safe navigation of ships. Through real-time dynamic correction, the system can adjust the virtual waterway path according to changes in sea current, wind speed, etc., ensuring that ships can still maintain precise navigation in complex sea conditions.

[0051] 2. During the process of generating the virtual waterway, the present invention introduces an environmental adaptation correction term and realizes the real-time dynamic optimization of the waterway through the Kalman filter algorithm. This enables the virtual waterway to adapt to environmental changes such as sea current, tide, and wind speed, ensuring that the generated virtual waterway path is smooth and stable, and further improving the navigation accuracy and the system's adaptability.

[0052] 3. Through the low-latency data transmission technology, the present invention can update the virtual waterway in real time and transmit the information to ships and the port management center. This greatly improves the emergency response ability of the port under extreme weather conditions, avoids the operation interruption caused by traditional navigation bans or restrictions, ensures the continuous normal operation of the port, and at the same time improves the response speed of ships to waterway changes and enhances the efficiency of maritime emergency rescue. Description of the Drawings

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art.

[0054] Figure 1 It is the flowchart of the virtual electronic waterway design method based on AIS technology of the present invention;

[0055] Figure 2 It is the overall block diagram of the virtual electronic waterway design system based on AIS technology of the present invention;

[0056] Figure 3 Schematic diagram of the Bessel curve fitting of the present invention;

[0057] Figure 4 Schematic diagram of the Kalman filter optimization of the present invention;

[0058] Figure 5 Schematic diagram of the data fusion and transmission of the present invention. Detailed implementation manners

[0059] The present invention will be more clearly and completely described below by way of a preferred embodiment in conjunction with the accompanying drawings, but the present invention is not limited to the scope of the embodiments described herein.

[0060] As Figure 1 shown, a virtual electronic waterway design method based on AIS technology specifically includes the following steps:

[0061] S1. Obtain the AIS information of ships through AIS base stations. The AIS information includes the longitude, latitude, speed, heading, and timestamp data of the ships. At the same time, correct the errors of the AIS data by combining GNSS data, and collect the sea current, tide, and wind speed environment data by using radar and marine environment sensors. Specifically, it includes the following steps:

[0062] S11. Install or use existing AIS base stations in the target area to receive the AIS information sent by ships in real time. The AIS information includes the longitude, latitude, speed, heading, and timestamp of the ships; the AIS base stations receive and record this information at regular intervals (such as every second or every minute) and store it as basic navigation data;

[0063] S12. Obtain the real-time longitude and latitude information of the ships by installing GNSS receivers on the ships or near the AIS base stations. The GNSS system provides high-precision positioning information. Compare the AIS data and the GNSS data, calculate the deviation between the two, and use the following error correction formula to correct the AIS data:

[0064]

[0065] Wherein, P GNSS is the ship position obtained by the GNSS system, that is, the longitude and latitude of the ship, P AIS is the ship position obtained by the AIS system, T GNSS and T AIS are the timestamps corresponding to the GNSS system and the AIS system respectively, and δ AIS represents the displacement error per unit time and can be used to correct the AIS data.

[0066] S13. Install or utilize existing radar systems and marine environment sensors to collect environmental data such as ocean currents, wind speeds, and tides. Specifically, it includes: an ocean current sensor for real-time measurement of the speed and direction of ocean currents; an anemometer for real-time measurement of wind speed and direction; a tide sensor for measuring tidal changes; regularly collect the above environmental data and record their corresponding timestamps. These data will be used together with AIS and GNSS data for the generation and dynamic correction of virtual waterways.

[0067] S2. Based on the collected data, set the virtual waterway reference points and use the Bezier curve fitting method to generate a continuous and smooth virtual waterway. At the same time, introduce an environmental adaptation correction term to dynamically adjust the path, specifically including the following steps:

[0068] S21. Collect the position (latitude, longitude, speed, and heading) of the ship and environmental data (ocean currents, wind speeds, tides, etc.) through AIS and GNSS data, determine the preliminary reference points of the virtual waterway, and select several key points as the initial reference points of the virtual waterway. Usually, these reference points should be several fixed or moving positions on the path passed by the ship. The position of the reference point can be calculated by the following formula:

[0069] x n = Lat n + ΔLat, y n = Lon n + ΔLon

[0070] Where Lat n and Lon n are the latitude and longitude of the nth data point, and ΔLat and ΔLon are the offsets obtained through historical data or real-time correction algorithms for error correction;

[0071] S22. Select multiple set reference points (such as 3 or more control points) to generate the virtual waterway; the Bezier curve is a mathematical method that defines a smooth curve through control points and can ensure the continuity and smoothness of the waterway path in waterway fitting;

[0072] Suppose 3 control points P0, P1, and P2 are selected, where P0 and P2 are the starting and ending points of the waterway, and P1 is the control point. Calculate the virtual waterway path according to the standard formula of the Bezier curve:

[0073] P(t) = (1 - t) 2 P0 + 2(1 - t)tP1 + t 2 P2

[0074] Where P0, P1, and P2 are the Bezier curve control points, representing the key positions of the virtual waterway, t is a parameter between 0 and 1, which is a parameter controlling the curve curvature and controls the smoothness of the curve;

[0075] According to multiple selected reference points, a smooth virtual navigation path is generated by fitting with a Bezier curve. This path will pass through all the reference points and ensure the continuity and smoothness of the waterway.

[0076] S23. To ensure that the virtual waterway can adapt to the influence of dynamic environmental factors such as ocean currents, wind speeds, and tides, the system will correct the fitted Bezier curve according to environmental data. According to the real-time collected environmental data (such as wind speed, ocean current, tide, etc.), the environmental correction term W is calculated. i The introduction of the correction term is to dynamically adjust the navigation path to compensate for the path error caused by environmental changes (such as the deviation of the ship or changes in tides and wind speeds). The calculation formula is as follows:

[0077] W i = αW i-1 + (1 - α)D i

[0078] where W i-1 is the correction value at the previous moment, D i is the environmental correction parameter, usually calculated based on the real-time collected data of wind speed, ocean current, tide, etc. α is the adaptive coefficient, 0 < α < 1, which is used to balance the influence of historical correction and current data.

[0079] Adding the correction term to the Bezier curve fitting formula, the corrected path formula is:

[0080] P(t) = (1 - t) 2 P0 + 2(1 - t)tP1 + t 2 P2 + W i

[0081] By introducing the correction term, the path of the virtual waterway will be able to adapt to environmental changes and adjust the waterway position in real time.

[0082] S24. Real-time monitor the changes in environmental data (such as ocean current, wind speed, etc.) during the ship's navigation process, and regularly update the environmental correction term to reflect the current environmental influence; according to the new environmental data, continuously adjust the control points of the Bezier curve so that the waterway can always adapt to the current environmental conditions. By continuously optimizing the control points and correcting the path, ensure the smoothness, continuity, and accuracy of the virtual waterway.

[0083] As Figure 3 shown, it demonstrates how to generate a smooth waterway using a Bezier curve. The figure shows three control points P0, P1, and P2, which are determined by reference points and historical data. The control polygon connects the control points with dashed lines, indicating that the generation of the Bezier curve depends on these control points.

[0084] S3. Calculate the AIS signal drift volume based on the ship's operating status and environmental data, and use the Kalman filter algorithm to perform real-time optimization and correction of the ship's status, thereby further optimizing the navigation path. The specific steps are as follows:

[0085] S31. Calculate the AIS signal drift volume based on the comparison between AIS data and GNSS data. Since the AIS signal may be affected by environmental factors (such as ocean currents and wind speeds) and system errors, there is a deviation between the actual position of the ship and the position displayed by AIS. Calculate the drift volume through the following formula:

[0086]

[0087] where x n and y n are the longitude and latitude coordinates of the nth navigation point respectively, λ is the unadjusted coefficient, and F current is the ocean current force influence factor, and its calculation formula is:

[0088] F current =ρAC d V 2

[0089] where ρ is the seawater density, A is the ship's cross-sectional area facing the current, C d is the drag coefficient, and V is the ocean current speed.

[0090] If the drift volume exceeds the predetermined threshold, the AIS signal needs to be corrected;

[0091] S32. Define the system state variable X k , representing the ship's position and speed at the current moment; define the state transition matrix D to describe the state change of the system from one moment to another. The state transition equation is usually:

[0092] X k =DX k-1 +FU k +W k

[0093] where X k is the ship's state at the kth moment, D is the state transition matrix describing the state change of the ship from one moment to another, F is the control input matrix used to describe the ship's speed and heading, U k is the control input representing the ship's motion state, and W k is the process noise used to describe the system uncertainty caused by external interference;

[0094] S33. Predict the ship's state at the current moment based on the ship's state and control input at the previous moment:

[0095]

[0096] Among them, is the predicted ship state;

[0097] Calculate the error covariance matrix of the predicted ship state:

[0098]

[0099] Among them, is the prediction error covariance, Q k is the process noise covariance;

[0100] S34. Obtain the real-time ship position measurement value Z provided by AIS, GNSS or other sensors k , and calculate the measurement innovation, that is, the difference between the actual measurement value and the predicted value:

[0101]

[0102] Among them, y k is the innovation value, H k is the measurement matrix, usually the identity matrix, indicating that the measurement value is directly related to the position of the ship;

[0103] Calculate the Kalman gain K k , which determines the weight of the measurement value when updating the state:

[0104]

[0105] Among them, R k is the measurement noise covariance;

[0106] Update the state estimate of the ship using the Kalman gain:

[0107]

[0108] Through the updated ship state Optimize the actual position, speed and heading of the ship;

[0109] Update the error covariance matrix:

[0110] P k =(I-K k H k )P k -

[0111] Among them, I is the identity matrix.

[0112] S35. Update the path of the virtual waterway according to the optimized ship status (position, speed, and heading) to ensure that the ship sails along the latest waterway. Continuously update the ship status and make dynamic corrections based on the current environmental data (such as sea current, wind speed, tide, etc.) to adjust the virtual waterway path in real time to maintain the smoothness and accuracy of the waterway.

[0113] S36. The system continuously collects AIS, GNSS, and environmental data, monitors the ship status and environmental conditions in real time to ensure that the ship's navigation path always remains accurate. Regularly execute the Kalman filter algorithm to optimize the ship status in real time, ensure that the ship can adapt to the dynamically changing environment, and adjust the navigation path in real time.

[0114] As Figure 4 shown, the figure illustrates the application process of the Kalman filter in ship status optimization. The predicted ship status is the blue dots and lines in the figure, representing the ship status predicted based on historical data. The real-time ship position measurement value Z k is the red dot, which comes from the real-time data of GNSS or other sensors. The updated ship status is the green dot, which is obtained by combining the predicted value and the measurement value. The arrow shows the process of state prediction, measurement innovation, and state update, demonstrating how the Kalman filter corrects the ship status in real time to improve the accuracy of the navigation path.

[0115] S4. Adopt the Bayesian estimation method to fuse data from multiple sources to optimize the calculation accuracy of the virtual waterway. The specific implementation steps are as follows:

[0116] S41. The Bayesian estimation method is based on Bayes' theorem, which allows estimating the system state (such as the virtual waterway path) by combining new observation data under the condition of known certain prior information, that is, preliminary assumptions. Set the state variables X k of the ship, such as the longitude and latitude, speed, and heading of the ship. Set the observation data Z k as the input data, which comes from different data sources such as AIS, GNSS, radar, environmental sensors, etc.

[0117] The prior distribution represents our preliminary assumption about the ship status without any data. Assume that the initial position and speed of the ship are determined by historical data or system design:

[0118] P(X k ) = Prior(X k )

[0119] The prior distribution is usually inferred based on past statistical data or estimated through models.

[0120] The likelihood function represents the probability of observing data given a certain state. Since each data source has different measurement noises and errors, different likelihood functions need to be set for the data of each source:

[0121] P(Z k |X k ) = Likelihood(Z k |X k )

[0122] For each data source (AIS, GNSS, radar, sensor), the corresponding likelihood function is established according to its noise model and measurement error.

[0123] S42. Bayes' theorem is used to update the prior distribution and perform posterior inference by combining new observation data. The formula is as follows:

[0124]

[0125] Among them, X k is the ship state at the k-th moment, Z k is the observation data from different data sources, including AIS, GNSS, radar, and marine environment sensors. P(X k |Z k ) is the posterior distribution of the ship state given the observation data Z k , that is, the final ship state estimate. P(Z k |X k ) is the likelihood function, which represents the probability of the observation data Z k under the ship state X k . P(X k ) is the prior distribution, which represents the initial assumption of the ship state. P(Z k ) is the marginal likelihood, which represents the total probability of the observation data;

[0126] By combining the prior distributions and likelihood functions of different data sources, the posterior distribution of the ship state is calculated. The posterior distribution will combine all source data (AIS, GNSS, radar, sensor) to give the most likely ship state estimate:

[0127]

[0128] Among them, Z1, Z2,..., Z i represent the observation data from different data sources.

[0129] S5. Through the VHF data link and 5G edge computing technology, the updated virtual channel data is transmitted to the ship terminal and the port management center in real time.

[0130] S51. Generate optimized virtual channel data based on real-time ship status and environmental data (such as AIS, GNSS, ocean currents, wind speed, etc.), including the ship's current latitude and longitude, speed, heading, expected route, channel curve parameters and other information; convert the generated virtual channel data into a format suitable for transmission (such as JSON, XML, CSV, etc.), and each data should contain the ship ID, timestamp, channel path point information, ship status (position, speed, heading, etc.), and environmental correction information.

[0131] S52. VHF communication system is a traditional ship communication method, usually used for short-distance, low-latency data transmission. Ensure that VHF base stations have been installed at multiple key points in the port or sea area, and the ship is equipped with VHF communication equipment;

[0132] Compress the virtual channel data (e.g., use ZIP or a specific algorithm to reduce the data volume) to adapt to the bandwidth limitation of the VHF data link, and modulate the data according to the VHF communication standard to ensure that it can be effectively transmitted through the VHF frequency band;

[0133] The port management center or maritime data center broadcasts the formatted virtual channel data to the ship through the VHF data link. The broadcast content includes: updated virtual channel path, environmental correction parameters, and the real-time position, speed, and heading of the ship on the channel;

[0134] The ship terminal receives the signal from the VHF base station through the VHF receiver, decompresses and decodes the data to ensure data integrity and accuracy, and the decoded data is directly transmitted to the ship's Electronic Chart Distributing System (ECDIS) for display and further processing.

[0135] S53. Deploy 5G base stations and edge computing nodes in ports and sea areas to ensure that the communication network has wide coverage and is efficient. Edge computing nodes can process data close to communication terminals, reducing data transmission delays and bandwidth requirements.

[0136] The port management center uploads the virtual channel data to the edge computing node through the high-speed 5G network. The edge computing node is responsible for real-time processing and analysis of the data, and makes corrections and optimizations based on real-time environmental data (such as wind speed, tide, and current). If a new ship enters a specific area, the edge computing node can dynamically adjust and update the virtual channel data. The specific optimization correction formula is:

[0137]

[0138] Among them, S is the packet size, B is the communication bandwidth, and L is edge is the delay correction factor after edge computing optimization, μ is the delay adjustment coefficient;

[0139] The optimized virtual waterway data is transmitted to the communication equipment of the ship through the 5G network. After the ship terminal receives the updated virtual waterway data, it can be displayed in real time on the Electronic Chart Display and Information System (ECDIS) to assist the ship's driver in precise navigation.

[0140] S6. The ship terminal receives the updated virtual waterway data and displays it in real time, while transmitting the ship's status back to the port management center for monitoring, analysis, and adjustment.

[0141] S61. After the ship terminal receives data from VHF or 5G, it performs data verification to ensure that the received virtual waterway data is not lost or damaged. If the received data is incorrect, the ship system will request retransmission;

[0142] Once the data verification is passed, the ship's Electronic Chart Display and Information System (ECDIS) will display the updated virtual waterway in real time, and the driver can adjust the ship's navigation plan according to the new virtual waterway path to ensure navigation safety;

[0143] The ship terminal transmits data such as its current position, speed, and heading back to the port management center to synchronize the ship's navigation status in real time. The port management center can monitor based on the real-time data and provide guidance for the ship's navigation.

[0144] S62. The port management center monitors the position of the ship on the virtual waterway in real time by receiving the data transmitted back from the ship terminal to ensure that the ship always sails along the predetermined waterway;

[0145] If the system detects that the ship fails to sail along the predetermined waterway (for example, deviates from the virtual waterway), the system will automatically notify the ship and provide adjustment suggestions. The port management center can send new waterway data to the ship to ensure safe navigation.

[0146] S63. All virtual waterway data and ship status data will be regularly backed up to the cloud server to ensure data security and traceability. The backed-up data can be used for future analysis and optimization;

[0147] Using the data stored in the cloud, the port management center can analyze the historical data of ship navigation, discover potential problems, and further optimize the virtual waterway.

[0148] As Figure 2 shown, the present invention also provides a virtual electronic waterway design system based on AIS technology for implementing a virtual electronic waterway design method based on AIS technology as described above, including:

[0149] The data acquisition module is used to obtain ship data provided by AIS base stations, correct the errors of AIS data in combination with GNSS data, and collect environmental data such as sea current, tide, and wind speed.

[0150] This module is responsible for obtaining ship data from AIS base stations in real time, including information such as longitude, latitude, speed, course, and timestamp, and correcting the errors of AIS data in combination with GNSS data. In addition, the module also needs to collect environmental data such as sea current, tide, and wind speed, which are the basis for generating virtual channels and optimizing ship routes.

[0151] The virtual channel generation module is used to set the reference points of the virtual channel based on the collected data, generate a continuous and smooth virtual channel through the Bezier curve fitting method, and introduce an environmental adaptation correction term to dynamically adjust the route.

[0152] Based on the collected ship data and environmental data, this module sets the reference points of the virtual channel and uses the Bezier curve fitting method to generate a continuous and smooth virtual channel. The generation of the virtual channel path is a process of multiple iterations and corrections, which not only considers the current state of the ship but also introduces an environmental adaptation correction term (such as sea current, wind speed, etc.) to enable the path to dynamically adapt to changing environmental conditions. The main steps are: data acquisition and preprocessing; reference point setting and path fitting; environmental adaptation correction and path dynamic adjustment.

[0153] The ship state optimization module is used to calculate the drift of the AIS signal and perform real-time optimization and correction of the ship state through the Kalman filtering algorithm, thereby further optimizing the virtual channel.

[0154] This module calculates the drift of the AIS signal and combines the Kalman filtering algorithm to optimize and correct the real-time state of the ship. The optimization targets include the longitude, latitude, speed, course, etc. of the ship. By performing real-time correction, the accuracy of the ship state is improved, thereby enhancing the accuracy of the virtual channel.

[0155] The Kalman filtering algorithm dynamically updates the current ship state based on the historical data and environmental conditions of the ship, and corrects the errors caused by AIS signal drift and external environmental factors (such as sea current, wind speed, etc.).

[0156] The data fusion module is used to fuse multi-source data using the Bayesian estimation method to optimize the calculation accuracy of the virtual channel.

[0157] This module is responsible for fusing data from different sources using Bayesian estimation method. By fusing AIS data, GNSS data, environmental data, etc., it optimizes the calculation accuracy of the virtual waterway, reduces the impact of errors from a single data source on waterway generation. The Bayesian estimation method can dynamically adjust the virtual waterway path according to real-time environmental data and changes in ship status. The main steps are: multi-source data access and synchronization; application of Bayesian estimation method to data fusion; waterway optimization and accuracy improvement.

[0158] Display module, used to display the generated virtual waterway in real time on the electronic nautical chart;

[0159] This module is responsible for displaying the generated virtual waterway in real time on the ship's electronic nautical chart system. The display module can receive updated data transmitted from the data transmission module and present it on the electronic nautical chart, providing intuitive navigation guidance for the crew. The display content includes: the generated virtual waterway path, the current position of the ship, speed, course, etc.

[0160] Data transmission module, used to transmit the optimized virtual waterway data to the ship terminal and the port management center through the VHF data link and 5G edge computing technology;

[0161] This module is responsible for transmitting the optimized virtual waterway data to the ship terminal and the port management center in real time through the VHF data link and 5G edge computing technology. The VHF data link is mainly used for short-distance and low-bandwidth communication, while the 5G edge computing technology is used for low-latency and high-bandwidth data transmission to ensure that the ship and the port management center can receive virtual waterway updates in real time. The transmission process is: data is compressed and modulated after generation; data is transmitted through the VHF and 5G edge computing networks; ensure real-time updates of the ship terminal and the port management center.

[0162] The said data transmission module further includes:

[0163] VHF communication unit, used to broadcast the optimized virtual waterway data to the ship terminal through the VHF data link;

[0164] 5G communication unit, used to transmit the updated virtual waterway data to the port management center and the ship terminal in real time through the 5G network.

[0165] Monitoring and feedback module, used for the ship terminal to receive the updated virtual waterway data and display it in real time, and at the same time transmit the ship status back to the port management center for monitoring, analysis and adjustment.

[0166] This module is responsible for receiving the updated virtual waterway data at the ship terminal and displaying it in real time. At the same time, it monitors the navigation status of the ship. The ship status (such as position, speed, and heading) will be transmitted back to the port management center to facilitate the port to monitor, analyze, and make necessary adjustments to the ship's navigation. The feedback process is as follows: The ship terminal receives the virtual waterway data and updates the waterway display; the ship position and status information are transmitted back to the port management center in real time; the port management center dynamically adjusts and optimizes the virtual waterway according to the feedback data.

[0167] The monitoring and feedback module further includes:

[0168] A real-time data reception and verification module, which is used to receive data from the ship terminal and perform verification to ensure the integrity of the transmitted data;

[0169] A waterway path update module, which is used to optimize and update the virtual waterway path according to the real-time monitored data to ensure the safe navigation of the ship.

[0170] As Figure 5 shown, this figure shows the fusion and transmission process of multi-source data. The multiple data source modules on the left respectively represent AIS data, GNSS data, radar data, and environmental data;

[0171] The arrow points to the central data fusion module, which uses the Bayesian estimation method to fuse multi-source data to obtain more accurate ship status and virtual waterway information;

[0172] After the data fusion module, the data is transmitted through the arrow to the data transmission module. This module uses the VHF data link and 5G edge computing technology to achieve low-latency real-time data transmission;

[0173] Finally, the data is transmitted to the display module and the monitoring and feedback module on the right respectively. The former displays the virtual waterway in real time on the ship terminal, and the latter transmits the data back to the port management center for monitoring and adjustment.

[0174] The above is only the preferred embodiment of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A virtual electronic waterway design method based on AIS technology, characterized in that Specifically, it includes the following steps: Obtain the AIS information of the ship through the AIS base station. The AIS information includes the longitude, latitude, speed, course, and timestamp data of the ship. At the same time, combine the GNSS data to correct the errors of the AIS data, and use radar and marine environment sensors to collect sea current, tide, and wind speed environment data; Based on the collected data, set the virtual channel reference point, and use the Bezier curve fitting method to generate a continuous and smooth virtual channel. At the same time, introduce an environmental adaptation correction term to dynamically adjust the path; Calculate the AIS signal drift amount, and use the Kalman filter algorithm to perform real-time optimization and correction on the ship's state, so as to optimize the navigation path; Adopt the Bayesian estimation method to fuse the multi-source data to optimize the calculation accuracy of the virtual channel; Through the VHF data link and 5G edge computing technology, transmit the optimized virtual channel data to the ship terminal and the port management center in real time; The ship terminal receives the updated virtual channel data and displays it in real time, guides the ship's navigation through the electronic chart system, and at the same time transmits the ship's state data back to the port management center for monitoring, analysis, and adjustment.

2. The virtual electronic waterway design method based on AIS technology according to claim 1, wherein, Use the following error correction formula to correct the AIS data: Among them, P GNSS is the ship position obtained by the GNSS system, and P AIS is the ship position obtained by the AIS system. T GNSS and T AIS are the timestamps corresponding to the GNSS system and the AIS system respectively.

3. A virtual electronic waterway design method based on AIS technology according to claim 1, characterized in that The expression for generating the virtual channel using the Bezier curve fitting method is: P(t) = (1 - t) 2 P0 + 2(1 - t)tP1 + t 2 P2 + W i where P0, P1, P2 are the control points of the Bézier curve, t is a parameter between 0 and 1, and W i is an environmental adaptation correction term, and its calculation method is as follows: W i = αW i-1 + (1 - α)D i Among them, D i is the environmental correction parameter, α is the adaptive coefficient, and 0 < α < 1.

4. A virtual electronic waterway design method based on AIS technology according to claim 1, characterized in that The formula for calculating the AIS signal drift amount is: where x n and y n are respectively the longitude and latitude coordinates of the nth waterway point, λ is the unadjusted coefficient, and F current is the ocean current force influence factor, and its calculation formula is: F current = ρAC d V 2 where ρ is the seawater density, A is the flow-facing area of the ship, C d is the drag coefficient, and V is the sea current velocity.

5. A virtual electronic waterway design method based on AIS technology according to claim 1, characterized in that The state transition equation of the Kalman filter algorithm is: X k = DX k-1 + FU k + W k Among them, X k is the ship state at the k-th moment, D is the state transition matrix, F is the control input matrix, U k is the control input, and W k is the process noise.

6. The virtual electronic waterway design method based on AIS technology according to claim 1, wherein The transmission delay correction formula for 5G edge computing is: Among them, S is the data packet size, B is the communication bandwidth, L edge is the delay correction factor optimized for edge computing, and μ is the delay adjustment coefficient.

7. A virtual electronic waterway design system based on AIS technology, characterized in that, Used to implement a virtual electronic channel design method based on AIS technology as described in any one of claims 1-6, including: A data acquisition module, used to obtain ship data provided by the AIS base station, correct the errors of the AIS data in combination with the GNSS data, and collect sea current, tide, and wind speed environment data at the same time; A virtual channel generation module, used to set the virtual channel reference point based on the collected data, generate a continuous and smooth virtual channel through the Bezier curve fitting method, and introduce an environmental adaptation correction term to dynamically adjust the path; A ship state optimization module, used to calculate the AIS signal drift amount, and perform real-time optimization and correction on the ship's state through the Kalman filter algorithm, so as to further optimize the virtual channel; A data fusion module, used to fuse multi-source data using the Bayesian estimation method to optimize the calculation accuracy of the virtual channel; A display module, used to display the generated virtual channel on the electronic chart in real time; A data transmission module, used to transmit the optimized virtual channel data to the ship terminal and the port management center through the VHF data link and 5G edge computing technology; A monitoring and feedback module, used for the ship terminal to receive the updated virtual channel data and display it in real time, and at the same time transmit the ship's state back to the port management center for monitoring, analysis, and adjustment.

8. An AIS technology-based virtual electronic waterway design system according to claim 7, characterized in that The formula for the data fusion module to optimize the virtual channel using the Bayesian estimation method is: where X k is the ship state at the k-th moment, Z k is the observation data from different data sources, including AIS, GNSS, radar, and marine environment sensors. P(X k |Z k ) is the posterior distribution of the ship state given the observation data Z k , that is, the final ship state estimate. P(Z k |X k ) is the likelihood function, which represents the probability of the observation data Z k under the ship state X k . P(X k ) is the prior distribution, which represents the initial hypothesis of the ship state. P(Z k ) is the marginal likelihood, which represents the total probability of the observation data.

9. The virtual electronic waterway design system based on AIS technology according to claim 7, characterized in that, The data transmission module further includes: A VHF communication unit, used to broadcast the optimized virtual channel data to the ship terminal through the VHF data link; A 5G communication unit, used to transmit the updated virtual channel data to the port management center and the ship terminal in real time through the 5G network.

10. The virtual electronic waterway design system based on AIS technology according to claim 7, characterized in that, The ship state optimization module uses the Kalman filter algorithm for ship state estimation, and its Kalman filter state update formula is as follows: Among them, is the predicted ship state, and K k is the Kalman gain, and y k is the innovation value, that is, the difference between the actual measurement value and the predicted value.

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