An intelligent ship navigation risk evaluation energy field modeling method fusing multi-ship interaction features

Abstract

The application provides a kind of intelligent ship navigation risk evaluation energy field modeling method of fusion multi-ship interaction characteristics, comprising: obtaining ship AIS data, extracting the longitude and latitude of ship and heading information in ship AIS data;Quantify ship traffic complexity, including density complexity, imminent complexity and dispersion complexity;According to density complexity, imminent complexity and dispersion complexity, quantitatively describe the traffic complexity between two ships, and extend it to reflect the traffic complexity of multi-ship interaction;According to traffic complexity, design navigation energy field model reflecting multi-ship interaction characteristics;Classify the navigation scene by combining ALARP criteria;Determine the quantitative basis for adaptive switching of intelligent ship operation mode.The application not only helps to reveal the risk evolution law of ship group behavior in complex traffic environment, but also provides theoretical support and algorithm basis for adaptive switching of intelligent ship operation mode, and promotes the practical application of intelligent ship in autonomous cognition and risk evaluation, etc.

Description

Technical Field

[0001] This invention relates to the field of maritime traffic management technology, and more particularly to an intelligent ship navigation risk assessment energy field modeling method that integrates multi-ship interaction characteristics. Background Technology

[0002] In actual maritime navigation, intelligent ships face interference from various dynamic environmental factors, especially in complex scenarios such as multi-ship convergence and high-density traffic flow. Effectively assessing the navigation environment and identifying potential risks has become a significant challenge to ensure their safe navigation. However, existing research still has significant gaps in navigation scenario assessment, lacking a quantitative assessment model that dynamically reflects the complexity of the current navigation environment for intelligent ships. In particular, in typical highly complex navigation scenarios such as multi-ship convergence and high-density traffic flow, traditional risk warning systems struggle to effectively capture the interaction relationships between ships and the spatiotemporal evolution characteristics of potential risk areas. This limitation not only restricts intelligent ships' understanding of environmental changes but also weakens their ability to adaptively switch between different operating modes. Summary of the Invention

[0003] To address the technical problem of the lack of an effective navigation risk assessment mechanism for intelligent ships in complex maritime traffic environments, as mentioned above, this invention provides an energy field modeling method for intelligent ship navigation risk assessment that integrates multi-ship interaction characteristics. This invention can dynamically quantify the complexity of traffic scenarios, identify potentially high-risk areas, and provide a quantitative basis for the adaptive switching of intelligent ship operation modes.

[0004] The technical means employed in this invention are as follows:

[0005] A method for modeling the energy field of intelligent ship navigation risk assessment that integrates multi-ship interaction features includes:

[0006] S1. Obtain ship AIS data and extract the ship's latitude, longitude, and heading information from the ship's AIS data;

[0007] S2. Quantify the complexity of ship traffic, including the density complexity, proximity complexity, and de-escalation complexity in the traffic environment;

[0008] S3. Based on the density complexity, approximation complexity and relief complexity, quantitatively describe the traffic complexity between two ships, and extend the traffic complexity between two ships to the traffic complexity that reflects the interaction characteristics of multiple ships.

[0009] S4. Based on the traffic complexity, design a navigation energy field model that reflects the interaction characteristics of multiple ships;

[0010] S5. Classify navigation scenarios based on the ALARP criterion;

[0011] S6. Determine the quantitative basis for the adaptive switching of intelligent ship operation modes.

[0012] Further, step S2 specifically includes:

[0013] S21. Calculate the density complexity using the following formula:

[0014]

[0015] In the above formula, Indicate density complexity, This indicates correction parameters that depend on the ship's navigation environment. The value depends on the ship type and navigation environment. express The relative distance between the two ships at any given time, , and For ships latitude and longitude and For ships latitude and longitude;

[0016] S22. Calculate the approximation complexity using the following formula:

[0017]

[0018] In the above formula, Indicates approximation complexity. Indicates the regulating factor. Represents the spatial convergence factor. Indicates the time convergence factor. Indicates the safe distance between ships, where:

[0019]

[0020]

[0021] In the above formula, and express Ships at all times and ships speed, and express Ships at all times and ships The course, , ;

[0022]

[0023] Safe distance between ships Calculated using the Potential Collision Risk Domain (PRSD) model, the shape of this ship's domain is an eccentric ellipse. The specific safety boundary is shown in the following formula:

[0024]

[0025] In the above formula, Indicates the forward boundary of SD. Indicates the backward boundary of SD. This represents the angle between point p and the bow direction. Indicates the length of the ship. This indicates the lateral influence parameter of SD. The parameter representing the longitudinal influence of SD. Indicates the potential collision risk index;

[0026] S23. Calculate the complexity of the scrambling process, using the following formula:

[0027]

[0028] In the above formula, This indicates the complexity of the solution.

[0029] Further, step S3 specifically includes:

[0030] S31. Based on the density complexity, approximation complexity, and unwinding complexity, the traffic complexity between the two ships is quantitatively described as follows:

[0031]

[0032] In the above formula, Indicates the traffic complexity between two ships. Indicate density complexity, Indicates approximation complexity. Indicates the complexity of the solution;

[0033] S32. Constructing the traffic complexity matrix in multi-ship interaction scenarios. As shown in the following formula:

[0034]

[0035] In the above formula, express The number of all vessels in the waterway at any given time;

[0036] S33, Computational Complexity Matrix The weight matrix is ​​shown in the following formula:

[0037]

[0038] In the above formula, Representing the complexity matrix The weight matrix;

[0039] S34. The complexity matrix A Performing the Hadamard product with the weight matrix yields... t The overall complexity of each ship's traffic situation at any given time is shown in the following formula:

[0040] .

[0041] Furthermore, in step S4, the risk function of the navigation energy field model is shown in the following equation:

[0042]

[0043] In the above formula, Indicates the distance from the center of the ship. This represents the calculated ship traffic complexity. n This indicates the number of ships perceived in the navigation scenario. This indicates the adjustment parameter.

[0044] Furthermore, in step S5, the risk value calculated from the navigation energy field is divided into four levels: negligible zone (0%–50%), ALARP lower limit zone (50%–70%), ALARP upper limit zone (70%–90%), and unacceptable zone (90%–100%).

[0045] Further, in step S6, the scenario levels classified by the ALARP criteria are mapped to intelligent ship operation modes, including:

[0046] The ALARP negligible zone corresponds to scenarios with low navigational safety risks, requiring no additional intervention; therefore, the recommended operating mode is autonomous. The ALARP lower limit zone corresponds to remote control mode. The ALARP upper limit zone corresponds to onboard personnel control mode. The ALARP unacceptable zone corresponds to emergency response mode.

[0047] Compared with the prior art, the present invention has the following advantages:

[0048] 1. The present invention provides an energy field modeling method for intelligent ship navigation risk assessment that integrates multi-ship interaction features. It is a traffic complexity measurement method based on the ship intrinsic properties and potential collision risk ship domain (PRSD) theory. It comprehensively quantifies the interaction risk between ships from three dimensions: density complexity, imminent complexity, and de-escalation complexity, effectively improving the risk perception capability of intelligent ships in complex scenarios such as multi-ship intersections and dense traffic.

[0049] 2. This invention provides an intelligent ship navigation risk assessment energy field modeling method that integrates multi-ship interaction characteristics. Addressing the shortcomings of traditional energy field models that neglect the dynamic coupling characteristics of multiple ships, this invention introduces a ship interaction mechanism to establish a navigation energy field model that reflects the mutual influence relationships between ships. This model can dynamically characterize the spatiotemporal evolution trend of risk areas, significantly enhancing the modeling accuracy and adaptability of the energy field method in complex traffic environments.

[0050] 3. The present invention provides an energy field modeling method for intelligent ship navigation risk assessment that integrates multi-ship interaction features. Combined with the ALARP risk tolerance criterion, a scenario classification mechanism based on risk level is established, and a quantitative basis for the adaptive switching of intelligent ship operation modes is proposed. This provides scientific support for the dynamic switching of intelligent ships between multiple operation modes and improves their safety assurance capability and operational stability in highly dynamic environments.

[0051] Based on the above reasons, this invention can be widely applied in fields such as maritime transportation. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a flowchart of the method of the present invention.

[0054] Figure 2 A distribution map of ships provided for embodiments of the present invention.

[0055] Figure 3 The calculation results of ship traffic complexity provided in the embodiments of the present invention.

[0056] Figure 4 A schematic diagram of a navigation energy field model provided for an embodiment of the present invention.

[0057] Figure 5 This is a simulated trajectory generated by the navigation energy field model provided in this embodiment of the invention.

[0058] Figure 6 The four risk value classification zones provided in the embodiments of the present invention. Detailed Implementation

[0059] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0060] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0061] like Figure 1 As shown, this invention provides an energy field modeling method for intelligent ship navigation risk assessment that integrates multi-ship interaction features, including:

[0062] S1. Obtain ship AIS data and extract the ship's latitude, longitude, and heading information from the ship AIS data; the ship distribution map is shown below. Figure 2 As shown;

[0063] S2. Quantify the complexity of ship traffic, including the density complexity, proximity complexity, and de-escalation complexity in the traffic environment;

[0064] S3. Based on the density complexity, approximation complexity and relief complexity, quantitatively describe the traffic complexity between two ships, and extend the traffic complexity between two ships to the traffic complexity that reflects the interaction characteristics of multiple ships.

[0065] S4. Based on the traffic complexity, design a navigation energy field model that reflects the interaction characteristics of multiple ships;

[0066] S5. Classify navigation scenarios based on the ALARP criterion;

[0067] S6. Determine the quantitative basis for the adaptive switching of intelligent ship operation modes.

[0068] In a specific implementation, as a preferred embodiment of the present invention, step S2 specifically includes:

[0069] S21. Calculate the density complexity using the following formula:

[0070]

[0071] In the above formula, Indicate density complexity, This indicates a correction parameter dependent on the ship's navigation environment, with a value of 1.81. The value depends on the ship type and navigation environment, and is typically 30. express The relative distance between the two ships at any given time, , and For ships latitude and longitude and For ships latitude and longitude;

[0072] S22. Calculate the approximation complexity using the following formula:

[0073]

[0074] In the above formula, Indicates approximation complexity. This represents the adjustment factor, with a value of 2. Represents the spatial convergence factor. Indicates the time convergence factor. Indicates the safe distance between ships, where:

[0075]

[0076]

[0077] In the above formula, and express Ships at all times and ships speed, and express Ships at all times and ships The course, , ;

[0078]

[0079] Safe distance between ships Calculated using the Potential Collision Risk Domain (PRSD) model, the shape of this ship's domain is an eccentric ellipse. The specific safety boundary is shown in the following formula:

[0080]

[0081] In the above formula, Indicates the forward boundary of SD. Indicates the backward boundary of SD. This represents the angle between point p and the bow direction. Indicates the length of the ship. This indicates the lateral influence parameter of SD. The parameter representing the longitudinal influence of SD. Indicates the potential collision risk index;

[0082] S23. Calculate the complexity of the scrambling process, using the following formula:

[0083]

[0084] In the above formula, This represents the decomposition complexity. The calculation results are as follows: Figure 3 As shown.

[0085] In a specific implementation, as a preferred embodiment of the present invention, step S3 specifically includes:

[0086] S31. Based on the density complexity, approximation complexity, and unwinding complexity, the traffic complexity between the two ships is quantitatively described as follows:

[0087]

[0088] In the above formula, Indicates the traffic complexity between two ships. Indicate density complexity, Indicates approximation complexity. Indicates the complexity of the solution;

[0089] S32. Constructing the traffic complexity matrix in multi-ship interaction scenarios. As shown in the following formula:

[0090]

[0091] In the above formula, express The number of all vessels in the waterway at any given time;

[0092] S33, Computational Complexity Matrix The weight matrix is ​​shown in the following formula:

[0093]

[0094] In the above formula, Representing the complexity matrix The weight matrix;

[0095] S34. The complexity matrix A Performing the Hadamard product with the weight matrix yields... t The overall complexity of each ship's traffic situation at any given time is shown in the following formula:

[0096] .

[0097] In a specific implementation, as a preferred embodiment of the present invention, in step S4, a navigation energy field model reflecting the interaction characteristics of multiple ships is designed based on traffic complexity, specifically as follows: Figure 4 As shown, a simulated trajectory is generated to calculate the risk, specifically as follows: Figure 5 As shown. The risk function of the navigation energy field model is shown in the following equation:

[0098]

[0099] In the above formula, Indicates the distance from the center of the ship. This represents the calculated ship traffic complexity. n This indicates the number of ships perceived in the navigation scenario. This represents the adjustment parameter, with a value of 0.1.

[0100] In a preferred embodiment of the present invention, in step S5, the risk value calculated from the navigation energy field is divided into four levels: the ALARP negligible zone (0%–50%), the ALARP lower limit zone (50%–70%), the ALARP upper limit zone (70%–90%), and the ALARP unacceptable zone (90%–100%). Specifically, as follows... Figure 6 As shown.

[0101] In a specific implementation, as a preferred embodiment of the present invention, step S6 involves mapping the scenario levels classified by the ALARP criteria to intelligent ship operation modes, including:

[0102] The ALARP negligible zone corresponds to scenarios with low navigational safety risks, requiring no additional intervention; therefore, the recommended operating mode is autonomous. The ALARP lower limit zone corresponds to remote control mode. The ALARP upper limit zone corresponds to onboard personnel control mode. The ALARP unacceptable zone corresponds to emergency response mode.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for energy field modeling of intelligent ship navigation risk assessment that integrates multi-ship interaction features, characterized in that, include: S1. Obtain ship AIS data and extract the ship's latitude, longitude, and heading information from the ship's AIS data; S2. Quantify the complexity of ship traffic, including the density complexity, proximity complexity, and de-escalation complexity in the traffic environment; S3. Based on the density complexity, approximation complexity and relief complexity, quantitatively describe the traffic complexity between two ships, and extend the traffic complexity between two ships to the traffic complexity that reflects the interaction characteristics of multiple ships. S4. Based on the traffic complexity, design a navigation energy field model that reflects the interaction characteristics of multiple ships. The risk function of the navigation energy field model is shown in the following formula: In the above formula, Indicates the distance from the center of the ship. This represents the calculated ship traffic complexity. n This indicates the number of ships perceived in the navigation scenario. Indicates the adjustment parameter; S5. Based on the ALARP criterion, the navigation scenario is classified into four levels, and the risk value calculated from the navigation energy field is divided into four levels: ALARP negligible zone, ALARP lower limit zone, ALARP upper limit zone, and ALARP unacceptable zone. S6. Determine the quantitative basis for adaptive switching of intelligent ship operation modes, and map the scenario levels classified by the ALARP criterion to intelligent ship operation modes, including: The ALARP neglect zone corresponds to scenarios with low navigational safety risks, requiring no additional intervention; therefore, the recommended operating mode is autonomous. The ALARP lower limit corresponds to the remote control mode; the ALARP upper limit corresponds to the onboard personnel control mode; and the ALARP unacceptable zone corresponds to the emergency response mode.

2. The method for energy field modeling of intelligent ship navigation risk assessment integrating multi-ship interaction features according to claim 1, characterized in that, Step S2 specifically includes: S21. Calculate the density complexity using the following formula: In the above formula, Indicate density complexity, This indicates correction parameters that depend on the ship's navigation environment. The value depends on the ship type and navigation environment. express The relative distance between the two ships at any given time, , and For ships latitude and longitude and For ships latitude and longitude; S22. Calculate the approximation complexity using the following formula: In the above formula, Indicates approximation complexity. Indicates the regulating factor. Represents the spatial convergence factor. Indicates the time convergence factor. Indicates the safe distance between ships, where: In the above formula, and express Ships at all times and ships speed, and express Ships at all times and ships The course, , ; Safe distance between ships Based on the potential collision risk domain model, the shape of this ship domain is an eccentric ellipse, and the ship... The specific safety boundary is shown in the following formula: In the above formula, Indicates the forward boundary of SD. Indicates the backward boundary of SD. This represents the angle between point p and the bow direction. Indicates the length of the ship. This indicates the lateral influence parameter of SD. The parameter representing the longitudinal influence of SD. Indicates the potential collision risk index; S23. Calculate the complexity of the scrambling process, using the following formula: In the above formula, This indicates the complexity of the solution.

3. The method for energy field modeling of intelligent ship navigation risk assessment integrating multi-ship interaction features according to claim 1, characterized in that, Step S3 specifically includes: S31. Based on the density complexity, approximation complexity, and unwinding complexity, the traffic complexity between the two ships is quantitatively described as follows: In the above formula, Indicates the traffic complexity between two ships. Indicate density complexity, Indicates approximation complexity. Indicates the complexity of the solution; S32. Constructing the traffic complexity matrix in multi-ship interaction scenarios. As shown in the following formula: In the above formula, express The number of all vessels in the waterway at any given time; S33, Computational Complexity Matrix The weight matrix is ​​shown in the following formula: In the above formula, Representing the complexity matrix The weight matrix; S34. The complexity matrix A Performing the Hadamard product with the weight matrix yields... t The overall complexity of each ship's traffic situation at any given time is shown in the following formula: 。

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