Ship intelligent navigation analysis method and system based on situation awareness

Abstract

The invention relates to the technical field of intelligent navigation analysis, and particularly discloses a ship intelligent navigation analysis method and system based on situation awareness, and the method employs a neural network model based on deep learning to extract the multi-level features of navigation environment monitoring information. And performing context correlation analysis on the multi-level features of the navigation environment monitoring information to realize significance distribution modeling of the features of different levels, thereby identifying key features in the navigation environment monitoring information, filtering redundant information, and improving the accuracy of the navigation environment monitoring information. Therefore, the situation awareness of the navigation environment and the intelligent evaluation of the channel passing state can be carried out on the basis. In this way, the processing efficiency and accuracy of the navigation environment monitoring information can be effectively improved, and then the reliability and safety of ship intelligent navigation analysis are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent navigation analysis, and more specifically, to a method and system for ship intelligent navigation analysis based on situational awareness. Background Art

[0002] With the growth of global trade and the increasing busyness of maritime transport, the safety and efficiency of ship navigation have become the focus of attention. In order to improve the autonomous navigation capabilities of ships in complex and changing environments, intelligent navigation analysis methods and technologies based on situational awareness have been rapidly developed. For example, the invention patent with publication number CN118245756B proposes a method and system for intelligent ship navigation analysis based on situational awareness, which perceives the navigation environment based on navigation environment monitoring information, performs land and water segmentation on the navigation environment monitoring image, and marks non-driving ships and obstructions, thereby segmenting the navigable area, and using digital twin technology to build a digital model of the waterway, realizing real-time monitoring of the waterway situation and evaluation of the traffic conditions, thereby providing intelligent navigation decision support for ships.

[0003] In the prior art, the processing of navigation environment monitoring information is mainly based on the extraction and fusion of low-level to high-level features of navigation environment monitoring information to achieve the perception of the navigation environment. However, this processing method ignores the control of feature quality. Specifically, in a complex ship navigation environment, the sources of monitoring information are wide and complex, containing a large amount of noise and redundant information. For example, affected by factors such as weather changes (such as heavy rain, dense fog), lighting conditions (night, strong light reflection), and water environment (turbid water, complex underwater terrain interference), the monitoring information may be mixed with background information and error signals that are not related to the ship's navigation decision. Directly fusing these unscreened features will cause the fused feature map to contain a large amount of interference information, reducing the recognition of key features. This not only increases the computational burden of subsequent analysis, but may also mislead the judgment of the navigation environment, reducing the accuracy and reliability of subsequent environmental situation awareness and waterway traffic status assessment.

[0004] Therefore, an optimized situational awareness-based ship intelligent navigation analysis method and system are expected. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method and system for intelligent navigation analysis of ships based on situational awareness, which uses a neural network model based on deep learning to extract multi-level features of navigation environment monitoring information, and performs contextual association analysis on the multi-level features of the navigation environment monitoring information to achieve significance distribution modeling of features at different levels, thereby identifying key features in the navigation environment monitoring information and filtering out redundant information, so as to facilitate situational awareness of the navigation environment and intelligent assessment of the channel traffic status on this basis. In this way, the processing efficiency and accuracy of the navigation environment monitoring information can be effectively improved, thereby improving the reliability and safety of the ship's intelligent navigation analysis.

[0006] According to one aspect of the present application, a method for analyzing ship intelligent navigation based on situational awareness is provided, which includes: Obtain navigation environment monitoring information; Extracting low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features; Performing feature filtering processing based on significance distribution modeling on the set of navigation environment image monitoring features to obtain a filtered set of navigation environment image monitoring features; Calculating the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and building a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image; Performing target detection and land-water segmentation on the multi-level fusion feature map of the navigation environment image to obtain environmental perception information; The channel situation is evaluated based on the environmental perception information to obtain channel situation evaluation information.

[0007] According to another aspect of the present application, a ship intelligent navigation analysis system based on situation awareness is provided, which includes: Environmental monitoring information acquisition module, used to obtain navigation environment monitoring information; An environment monitoring feature extraction module, used to extract low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features; A feature filtering processing module, used for performing feature filtering processing on the set of navigation environment image monitoring features based on significance distribution modeling to obtain a set of filtered navigation environment image monitoring features; A feature fusion module is used to calculate the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and to build a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image; An environment perception module, used for performing target detection and land-water segmentation on the multi-level fusion feature map of the navigation environment image to obtain environment perception information; A channel situation assessment module is used to assess the channel situation based on the environmental perception information to obtain channel situation assessment information.

[0008] Compared with the prior art, the method and system for intelligent ship navigation analysis based on situational awareness provided by the present application uses a neural network model based on deep learning to extract multi-level features of navigation environment monitoring information, and performs contextual association analysis on the multi-level features of navigation environment monitoring information to achieve significant distribution modeling of features at different levels, thereby identifying key features in navigation environment monitoring information and filtering out redundant information, so as to facilitate situational awareness of the navigation environment and intelligent assessment of the passage status of the waterway on this basis. In this way, the processing efficiency and accuracy of navigation environment monitoring information can be effectively improved, thereby improving the reliability and safety of intelligent ship navigation analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0010] Figure 1 The present invention is a flowchart of a method for analyzing ship intelligent navigation based on situational awareness according to an embodiment of the present application.

[0011] Figure 2 This is a data flow diagram of a ship intelligent navigation analysis method based on situational awareness according to an embodiment of the present application.

[0012] Figure 3 This is a flowchart of sub-step S3 of the ship intelligent navigation analysis method based on situation awareness according to an embodiment of the present application.

[0013] Figure 4 This is a flowchart of sub-step S31 of the ship intelligent navigation analysis method based on situation awareness according to an embodiment of the present application.

[0014] Figure 5 This is a flowchart of sub-step S32 of the ship intelligent navigation analysis method based on situation awareness according to an embodiment of the present application.

[0015] Figure 6 It is a block diagram of a ship intelligent navigation analysis system based on situation awareness according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0018] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0020] It is worth noting that in this application, all actions to obtain data are carried out in compliance with the relevant data protection laws and policies of the country where the data is located, and with the authorization given by the owner of the corresponding device.

[0021] As mentioned in the background technology above, patent CN118245756B proposes a method and system for intelligent navigation analysis of ships based on situational awareness. It perceives the navigation environment according to the navigation environment monitoring information, segments the navigation environment monitoring image into land and water, and marks non-driving ships and obstacles. It then segments the navigable area, and uses digital twin technology to build a digital model of the waterway, realizing real-time monitoring of the waterway situation and assessment of traffic conditions, thereby providing intelligent navigation decision support for ships.

[0022] In the prior art, the processing of navigation environment monitoring information mainly relies on feature extraction and fusion from low-level to high-level to achieve navigation environment perception. However, this approach fails to pay sufficient attention to the control of feature quality. Specifically, in a complex ship navigation environment, the sources of monitoring information are diverse and complex, containing a large amount of noise and redundant data. For example, affected by factors such as weather changes (such as heavy rain, dense fog), lighting conditions (such as night, strong light reflection), and water environment (such as turbid water, complex underwater terrain interference), the monitoring information may be mixed with background information or erroneous signals that are not related to the ship's navigation decision. Directly fusing these unscreened features will cause the fused feature map to contain a large amount of interference information, thereby reducing the recognition of key features. This not only increases the computational burden of subsequent analysis, but may also mislead the judgment of the navigation environment, thereby reducing the accuracy and reliability of environmental situation awareness and waterway traffic status assessment. In response to the above technical problems, this application proposes an optimized method for intelligent ship navigation analysis based on situational awareness, which uses a neural network model based on deep learning to extract multi-level features of navigation environment monitoring information, and performs contextual association analysis on the multi-level features of navigation environment monitoring information to achieve significant distribution modeling of features at different levels, thereby identifying key features in navigation environment monitoring information and filtering out redundant information, so as to facilitate situational awareness of the navigation environment and intelligent assessment of the passage status on this basis. In this way, the processing efficiency and accuracy of navigation environment monitoring information can be effectively improved, thereby improving the reliability and safety of ship intelligent navigation analysis.

[0023] Figure 1 The present invention is a flowchart of a method for analyzing ship intelligent navigation based on situational awareness according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the ship intelligent navigation analysis method based on situation awareness according to an embodiment of the present application. Figure 1 and Figure 2 As shown, the ship intelligent navigation analysis method based on situational awareness includes the steps of: S1, obtaining navigation environment monitoring information; S2, extracting low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features; S3, performing feature filtering processing based on significance distribution modeling on the set of navigation environment image monitoring features to obtain a set of filtered navigation environment image monitoring features; S4, calculating the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and building a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image; S5, performing target detection and land and water segmentation on the multi-level fusion feature map of the navigation environment image to obtain environmental perception information; S6, evaluating the waterway situation based on the environmental perception information to obtain waterway situation evaluation information.

[0024] In the above-mentioned intelligent navigation analysis method for ships based on situational awareness, the step S1 is to obtain navigation environment monitoring information. It should be understood that the navigation environment of ships is complex and changeable, and is affected by many factors, such as weather conditions, water terrain, activities of other ships, etc. By accurately and comprehensively obtaining these navigation environment information, a reliable data source can be provided for subsequent intelligent navigation decisions. In actual operation, image data of the navigation area can be obtained with the help of various technical means such as high-resolution cameras, thermal imaging cameras and satellite remote sensing. Among them, high-resolution cameras and thermal imaging cameras can intuitively capture the water surface conditions around the ship, and provide visual information such as the position, posture, number of other ships and floating objects on the water surface; satellite remote sensing can monitor the environmental conditions of large areas of water from a macro perspective, such as weather changes, water flow trends, etc.

[0025] Specifically, with the help of high-resolution cameras, the situation on the water surface around the ship can be captured intuitively, including key visual information such as the position, posture and number of other ships. These cameras are usually installed in key positions on the ship to ensure that the largest possible field of view can be covered. High-resolution cameras can not only clearly capture the scene during the day, but also provide sufficient detail support for situations with poor lighting conditions, such as dawn or dusk. Through their advanced optical systems and sensors, these cameras can maintain stable performance under various lighting conditions. Even in low-light environments, they can capture enough information to identify possible obstacles or the presence of other ships. This allows the crew to grasp the changes in the surrounding environment at any time and make corresponding operational adjustments in time. In addition, these cameras can also identify floating objects on the water surface, which is crucial for avoiding collisions and ensuring navigation safety. By analyzing this visual information, potential risks can be discovered in time and appropriate measures can be taken to avoid them. For example, on busy waterways, real-time monitoring of surrounding traffic conditions can effectively prevent collisions with other ships.

[0026] At the same time, the application of thermal imaging cameras further enhances the ability to perceive the navigation environment at night or in other low-light conditions. Since thermal imaging cameras rely on detecting the heat emitted by objects to generate images, they can effectively identify targets in the surrounding environment even in complete darkness or foggy weather. This plays an irreplaceable role in maintaining navigation safety in extremely low visibility conditions. For example, when sailing at night, thermal imaging cameras can help crews find small boats or other obstacles that may not have turned on lighting equipment, so that they can react in advance and reduce the risk of collision. Similarly, in dense fog, thermal imaging cameras can penetrate the fog and show the true conditions of the waters ahead, greatly improving navigation safety. This technology based on thermal difference imaging not only improves the accuracy of detection, but also provides an additional layer of safety for navigation. Especially in some complex water environments, such as near the coastline or the confluence of rivers, there may be a large number of small fishing boats or leisure boats, which may be potential sources of danger. Thermal imaging cameras can provide valuable visual assistance in this situation, helping crews better understand the surrounding environment and make more informed decisions.

[0027] Satellite remote sensing technology provides the ability to monitor the environmental conditions of large areas of water from a macro perspective. By receiving data from satellites, important information such as weather trends, water flow direction and speed can be obtained in real time. This helps the captain and crew prepare for bad weather in advance, such as adjusting the route to avoid the upcoming storm, or optimizing the navigation route according to the direction of the water flow to save fuel consumption. Satellite remote sensing can also provide valuable data on marine ecosystems, help monitor pollution, track red tide phenomena, etc., so as to better protect the marine environment. For large cargo ships sailing long distances, the information obtained by satellite remote sensing is particularly critical because it is related to the safety and efficiency of the entire voyage. Satellite remote sensing is not only limited to providing meteorological information, but can also be used to monitor changes in sea level height, glacier movement, and terrain changes in coastal areas, all of which are important factors affecting the safety of ship navigation. By comprehensively using this information, dynamic planning of navigation paths can be achieved to ensure that ships always follow the safest and most economical routes.

[0028] High-resolution cameras are responsible for close-range, localized, and detailed observations; thermal imaging cameras focus on improving detection capabilities in low-light or extreme weather conditions; and satellite remote sensing provides a broad perspective, covering a wide range of sea areas and the weather dynamics above them. The three complement each other and together provide a solid basic data layer for ship navigation, making intelligent navigation analysis based on situational awareness possible. For example, in certain specific navigation scenarios, if you need to cross a sea area where dense fog often occurs, high-resolution cameras and thermal imaging cameras can work together, with the former providing detailed surface structure information and the latter responsible for penetrating the dense fog to ensure navigation safety. At the same time, the weather forecast information provided by satellite remote sensing can help formulate detour plans in advance to avoid entering dangerous areas directly. Such a combined application not only increases the safety factor of navigation, but also improves navigation efficiency and reduces unnecessary fuel consumption and time waste.

[0029] In the above-mentioned situational awareness-based ship intelligent navigation analysis method, the step S2 extracts low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features. Specifically, since the navigation environment image usually contains a variety of complex information, such as ships, water surface fluctuations, water terrain, etc., these information have different scales and feature expressions in the image. Therefore, in order to comprehensively capture various features in the navigation environment image, the present application adopts a multi-scale feature extraction technology to extract low-level to high-level features of the navigation environment monitoring information. In a specific example of the present application, a CNN model is used to perform multi-level feature extraction on the navigation environment monitoring information to obtain a set of navigation environment image monitoring features. Those of ordinary skill in the art should know that the CNN model has a powerful feature extraction capability, and it can automatically learn features of different levels from image data through a combination of multi-layer convolutional layers and pooling layers. Specifically, the CNN model uses convolution kernels of different scales to perform sliding convolution on the navigation environment image to capture image features of different scales, starting from basic simple features such as edges and textures, and gradually extracting higher-level semantic features, such as the navigation status of the ship, details of water surface fluctuations, and complex changes in water terrain, thereby converting the original navigation environment monitoring information into more representative and analyzable feature data, forming a collection of navigation environment image monitoring features, and realizing a comprehensive description and characterization of the navigation environment monitoring information.

[0030] In the above-mentioned intelligent navigation analysis method for ships based on situational awareness, the step S3 performs feature filtering processing based on significant distribution modeling on the set of navigation environment image monitoring features to obtain a set of filtered navigation environment image monitoring features. It should be understood that, considering that in a complex navigation environment, navigation environment image monitoring features at different levels have different importance, for example, some of the information may have a key impact on the judgment of the navigation environment, while other information may be relatively redundant, and even interfere with the correct judgment, such as the characteristics generated by background noise, the characteristics of irrelevant small objects that appear occasionally, etc. Therefore, in order to ensure data quality, it is necessary to further perform feature filtering processing on the set of navigation environment image monitoring features. In particular, considering that in different navigation scenarios, the features that play a key role in ship navigation decisions are dynamically changing. For example, when sailing in a narrow channel, the location features of the channel boundary and obstacles are crucial; in a multi-ship intersection scenario, the speed, heading and relative position features of other ships are more critical. Therefore, in order to achieve targeted screening of features at different levels, this application proposes a saliency distribution modeling method based on context feature guidance, which uses the context information of the set of navigation environment image monitoring features to model the saliency description of each navigation environment image monitoring feature, thereby filtering the set of navigation environment image monitoring features, thereby retaining key features that have an important impact on navigation environment judgment, filtering out redundant and interfering features, improving the quality and effectiveness of features, and thus improving the reliability of subsequent channel situation analysis. Among them, Figure 3 FIG. 1 is a flowchart of sub-step S3 of the method for analyzing ship intelligent navigation based on situation awareness according to an embodiment of the present application. Figure 3 As shown, the step S3 includes the steps of: S31, re-arranging the set of navigation environment image monitoring features based on a sequence roughly described by significance to obtain a sequence of rearranged navigation environment image monitoring features; S32, evaluating the importance of each rearranged navigation environment image monitoring feature in the sequence of rearranged navigation environment image monitoring features based on the guidance of the preceding context features to obtain a sequence of closed-loop strength factors of navigation environment image monitoring feature clues; S33, based on the sequence of closed-loop strength factors of navigation environment image monitoring feature clues, feature filtering the set of navigation environment image monitoring features to obtain the set of filtered navigation environment image monitoring features.

[0031] Figure 4 FIG. 1 is a flowchart of sub-step S31 of the method for analyzing ship intelligent navigation based on situation awareness according to an embodiment of the present application. Figure 4As shown, the step S31 includes the steps of: S311, extracting the maximum eigenvalue of each navigation environment image monitoring feature in the set of navigation environment image monitoring features as a significance rough description factor to obtain a sequence of significance rough description factors of navigation environment image monitoring features; S312, based on the ascending arrangement of the sequence of significance rough description factors of navigation environment image monitoring features, rearranging the set of navigation environment image monitoring features to obtain the sequence of rearranged navigation environment image monitoring features.

[0032] More specifically, the step S311 is expressed as follows:

[0033] in, represents a set of navigation environment image monitoring features, , , and respectively represent the first, second, and third features in the set of navigation environment image monitoring features and Navigation environment image monitoring features, represents the sequence of rough descriptive factors of the significance of navigation environment image monitoring features, Represents the number of rough descriptive factors of the significance of navigation environment image monitoring features, represents the maximum value function, express The corresponding rough description factor of the significance of the navigation environment image monitoring features.

[0034] That is, in the image feature space, by extracting the maximum eigenvalue of each navigation environment image monitoring feature, it is used as a rough description factor of the significance of the navigation environment image monitoring feature, so as to project the high-dimensional image features into the low-dimensional space, complete the initial dimensionality reduction of feature expression, and significantly reduce the complexity of subsequent data processing. Here, the extraction of the maximum eigenvalue provides a highly discriminative sorting criterion for subsequent feature processing, which can effectively characterize the relative significance of each navigation environment image monitoring feature in the overall data distribution, and then assist in completing the preliminary feature screening based on importance, providing a solid data preprocessing foundation for subsequent in-depth analysis and decision-making.

[0035] More specifically, the step S312 is expressed as follows:

[0036] in, represents the sorting function, , , and respectively represent the first, second, and third in the sequence of the rearranged navigation environment image monitoring features and Rearrange navigation environment image monitoring features, A sequence representing the rearranged navigation environment image monitoring features.

[0037] That is, the sequence of the rough description factors of the significance of the navigation environment image monitoring features is arranged in ascending order. After the sorting is completed, the sequence of the navigation environment image monitoring features is synchronously rearranged according to this sorting rule. Here, the ascending order can make each image monitoring feature be arranged in order from low to high according to the significance, which naturally introduces a good information flow structure for the subsequent feature filtering operation.

[0038] Figure 5 FIG. 1 is a flowchart of sub-step S32 of the method for analyzing ship intelligent navigation based on situation awareness according to an embodiment of the present application. Figure 5 As shown, the step S32 includes the steps of: S321, extracting the i-th rearranged navigation environment image monitoring feature from the sequence of the rearranged navigation environment image monitoring features as the navigation environment image monitoring feature to be analyzed; S322, inputting the first i-1 rearranged navigation environment image monitoring features in the sequence of the rearranged navigation environment image monitoring features into a key clue capture network to obtain key clue guidance features of the preceding distribution of navigation environment image monitoring features; S323, inputting the key clue guidance features of the preceding distribution of the navigation environment image monitoring features and the navigation environment image monitoring features to be analyzed into a clue weaving gating network to obtain a navigation environment image monitoring feature clue weaving closed-loop strength factor corresponding to the navigation environment image monitoring features to be analyzed.

[0039] More specifically, the step S322 includes: calculating the characteristic distribution significance factor based on the statistical characteristic value of each rearranged navigation environment image monitoring feature in the first i-1 rearranged navigation environment image monitoring features to obtain a sequence of navigation environment image monitoring feature distribution significance factors, wherein the statistical characteristic value includes the characteristic variance, characteristic mean and maximum eigenvalue of the rearranged navigation environment image monitoring feature; performing normalization processing on the sequence of navigation environment image monitoring feature distribution significance factors based on the Sigmoid function to obtain a sequence of navigation environment image monitoring feature distribution significance weights; based on the sequence of navigation environment image monitoring feature distribution significance weights, performing weighted aggregation on the first i-1 rearranged navigation environment image monitoring features to obtain the key clue guidance feature of the preceding distribution of the navigation environment image monitoring feature.

[0040] The above step S322 is expressed by the formula:

[0041] in, represents the feature variance of the rearranged navigation environment image monitoring feature, represents the feature mean of the rearranged navigation environment image monitoring feature, The first one in the sequence of the rearranged navigation environment image monitoring features is represented Rearrange navigation environment image monitoring features, express The corresponding navigation environment image monitoring feature distribution significance factor, represents an exponential function with a natural constant as base, express The corresponding navigation environment image monitoring feature distribution significance weight, represents the key clue capture network, It represents the key clue guidance feature of the preceding distribution of the navigation environment image monitoring feature.

[0042] That is, after the rearrangement is completed, the first The first rearranged navigation environment image monitoring features are used as the navigation environment image monitoring features to be analyzed. The rearrangement of the navigation environment image monitoring feature is only an exemplary method and does not refer to a specific feature. The rearranged navigation environment image monitoring features are used to carry out feature extraction and analysis of contextual patterns, and the key information hidden therein is excavated, thereby obtaining the key clue guiding features of the preceding distribution of the navigation environment image monitoring features, so as to provide sufficient contextual information support for the navigation environment image monitoring features to be analyzed, making the analysis process of the rearranged navigation environment image monitoring features more comprehensive and accurate, thereby improving the accuracy of feature filtering.

[0043] In particular, in a preferred example of the present application, based on the foregoing The statistical characteristic value of each rearranged navigation environment image monitoring feature in the rearranged navigation environment image monitoring features is used to calculate its characteristic distribution significance factor to obtain a sequence of navigation environment image monitoring feature distribution significance factors, including: on the basis of the statistical characteristic value, an edge correlation factor is also introduced, and the edge correlation factor is used to enhance the edge-global characteristic distribution synergy of the rearranged navigation environment image monitoring feature. Specifically, in this preferred example, the calculation process of the navigation environment image monitoring feature distribution significance factor can be expressed by the formula:

[0044] in, is the marginal correlation factor.

[0045] Specifically, the key clue capture network, based on the statistical characteristics of the rearranged navigation environment image monitoring features, uses the edge significance factor of the extreme value of the feature relative to the overall self-consistent trend, the edge-global consistency factor of the volatility of the feature and the overall self-consistent level, and the edge correlation factor of the fluctuation range of the feature based on the overall self-consistent relationship to measure the global significance self-consistent edge coordination of the rearranged navigation environment image monitoring features, that is, to coordinate the global self-consistent relationship of the rearranged navigation environment image monitoring features based on the edge significance, so as to model the consistency and coordination of the edge distribution in the global range under the significance distribution mode of the rearranged navigation environment image monitoring features. In this way, while ensuring that the extreme edge data distribution is consistent with the overall global self-consistent trend, it is possible to ensure that the data fluctuation based on the edge distribution is also self-consistent with the global whole. Therefore, the key clue guidance features of the preceding distribution of the navigation environment image monitoring features can essentially improve the contribution of the rearranged navigation environment image monitoring features with contextual significance and certain edge fluctuation characteristics to the overall self-consistent model by re-inferring the edge-global feature distribution synergy, thereby providing a reference indicator for global self-consistent fusion based on edge significance synergy for the potential model of the rearranged navigation environment image monitoring feature distribution.

[0046] More specifically, the step S323 is expressed by the formula:

[0047] in, represents the inverse matrix, express and The covariance matrix between represents the transpose of a matrix, express The corresponding navigation environment image monitoring feature clue weaving closed-loop strength factor.

[0048] That is, by using the clue weaving gated network, the key clue guiding features of the preceding distribution of the navigation environment image monitoring features and the navigation environment image monitoring features to be analyzed are compared and analyzed, so as to accurately measure the similarity and difference between the two at the feature level, and finally obtain the navigation environment image monitoring feature clue weaving closed-loop strength factor. The navigation environment image monitoring feature clue weaving closed-loop strength factor can intuitively reflect the close correlation between the navigation environment image monitoring features to be analyzed and the key clue guiding features of the preceding distribution of the navigation environment image monitoring features. The higher the degree of correlation, the higher the degree of fit between the navigation environment image monitoring features to be analyzed and the distribution law of the preceding image monitoring features. Based on this, it is possible to further screen and optimize each navigation environment image monitoring feature, eliminate those features that have a low correlation with the key clue guiding features of the preceding distribution and do not conform to the overall self-consistent trend, thereby retaining those key and significant features to enhance the inherent logic between the features.

[0049] Specifically, in a specific example of the present application, the step S33 includes: based on the comparison between the navigation environment image monitoring feature clue weaving closed-loop strength factor and the preset threshold, determining whether to filter the navigation environment image monitoring feature corresponding to the navigation environment image monitoring feature clue weaving closed-loop strength factor, which is expressed by the formula:

[0050] in, Indicates the preset threshold value, Represents a selection function.

[0051] That is, a preset threshold is set as a benchmark, and according to the size of the closed-loop strength factor of the navigation environment image monitoring feature clue weaving, it is judged whether the navigation environment image monitoring feature to be analyzed should be retained or filtered. If the closed-loop strength factor of the navigation environment image monitoring feature clue weaving is higher than the preset threshold, it indicates that the navigation environment image monitoring feature to be analyzed has a high correlation with the key clue guidance feature of the preceding distribution of the navigation environment image monitoring feature, which conforms to the overall self-consistent trend, so it is retained; on the contrary, if the closed-loop strength factor of the navigation environment image monitoring feature clue weaving is lower than the preset threshold, it indicates that the navigation environment image monitoring feature to be analyzed has a weak correlation with the key clue guidance feature of the preceding distribution of the navigation environment image monitoring feature, which may not conform to the distribution law of the overall data, so it is filtered out. In this way, the navigation environment image monitoring feature set can be effectively streamlined, the quality of the navigation environment image monitoring feature set can be improved, the processing speed can be accelerated, and the calculation amount and complexity of subsequent data processing can be reduced.

[0052] In the above-mentioned intelligent navigation analysis method for ships based on situational awareness, the step S4 calculates the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and builds a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image. It should be understood that since the navigation environment image monitoring features of different levels carry different amounts of information, in order to more accurately understand the navigation environment, it is necessary to further aggregate and analyze the set of filtered navigation environment image monitoring features. In this regard, in order to achieve effective focus on key features and make full use of the complementarity between feature maps of different levels, the present application adopts an attention mechanism and a feature pyramid network for feature fusion. Specifically, the attention mechanism can dynamically emphasize the key features that have an important impact on the judgment of the navigation environment by calculating the attention scores of each feature, while relatively weakening the influence of redundant and interfering features, which helps to improve the model's sensitivity and understanding of the navigation environment. The feature pyramid network achieves cross-scale feature fusion by building connections between feature maps at different levels, thereby capturing richer contextual information and finer feature details, and generating multi-level fused feature maps of the navigation environment image. This not only enhances the expressiveness of the feature maps, but also provides a more comprehensive and reliable information basis for subsequent channel situation analysis and intelligent navigation decisions.

[0053] In the above-mentioned situational awareness-based ship intelligent navigation analysis method, the step S5 performs target detection and water-land segmentation on the multi-level fusion feature map of the navigation environment image to obtain environmental perception information. Here, target detection is intended to identify various target objects in the navigation environment, such as other ships on the waterway, obstacles, etc.; water-land segmentation is intended to distinguish between water and land and determine the navigable area of ​​the ship. It should be understood that target detection and water-land segmentation for navigation environment image features are prior art. For example, it can adopt the principles disclosed in Chinese patent CN118245756B to perform target detection and water-land segmentation for navigation environment image features. Of course, it can also adopt other principles to perform target detection and water-land segmentation, which is not limited to this application.

[0054] In the above-mentioned intelligent navigation analysis method for ships based on situational awareness, the step S6 evaluates the channel situation based on the environmental perception information to obtain the channel situation evaluation information. Specifically, the environmental perception information includes the location, type, state, and distribution of water and land of various target objects in the navigation environment. Through the comprehensive analysis and processing of these information, for example, by using digital twin technology to build a digital model of the navigation environment, and to simulate and predict the changes in the channel situation in real time, the situation information such as the degree of congestion of the channel, the distribution of obstacles, and the difficulty of driving in the current channel can be evaluated, thereby providing a key basis for intelligent navigation decision-making. For example, in the case of congested channels, ships can choose to avoid or adjust their speed to avoid potential collision risks; in the case of obstacles in the channel, ships can plan detour routes in advance to ensure smooth navigation. It is worth mentioning that the method for evaluating the channel situation using environmental perception information involved in this application can adopt the method disclosed in Chinese patent CN118245756B, and of course other equivalent evaluation methods can also be adopted, which is not limited to this application.

[0055] Specifically, digital twin technology achieves accurate simulation of the real world by mapping entities and behaviors of the physical world into virtual space. For ship navigation, this means being able to create a digital model that is highly consistent with the actual navigation environment. This model not only includes basic geographical information of waters and land, but also covers all dynamic and static target objects in the navigation area. For example, data obtained through high-resolution cameras, thermal imaging cameras and satellite remote sensing are input into the system, and after a series of processing and conversion, a basic data set is formed for calculation and analysis. In this process, advanced algorithms are used to analyze the images, extract key information such as the location, type, speed of other ships, and the presence or absence of floating objects on the water surface, and integrate them into the digital model. This real-time updated digital model can reflect the actual situation of the current channel, allowing the crew to have the most accurate information at any point in time.

[0056] The specific content of the waterway situation assessment includes the assessment of the degree of congestion in the waterway, the distribution of obstacles, and the difficulty of driving in the current waterway. The assessment of the degree of congestion in the waterway is mainly based on the real-time tracking and data analysis of all ships in the navigation area. By comparing the relative distances and speeds between different ships, it can be determined which areas have a higher risk of collision or the possibility of traffic congestion. In addition, considering the impact of weather conditions and water flow changes on ship navigation, the system will also comprehensively consider these factors and give a more comprehensive risk assessment report. When faced with congested waterways, ships can choose appropriate avoidance measures or adjust their speed based on these assessment results to avoid potential collision risks. For example, when it is found that there is a peak traffic period in the waterway ahead, the probability of encountering other ships can be reduced by reducing speed or changing routes to ensure navigation safety.

[0057] As for the assessment of navigation difficulty, it is mainly based on the analysis of the conditions of the waterway itself, such as the influence of natural factors such as water depth, water flow speed, and tidal changes. At the same time, the complexity of the surrounding environment needs to be considered, such as whether there are a large number of ships coming and going, whether it is close to areas with frequent fishing activities, and other human factors. By comprehensively considering these variables, detailed navigation suggestions can be provided to ships, including the selection of the best route, the appropriate sailing speed, and the arrangement of necessary stops. Such an evaluation mechanism can not only help crew members better understand the current navigation environment, but also provide an important reference for formulating long-term navigation plans.

[0058] In summary, the situational awareness-based intelligent ship navigation analysis method according to the embodiment of the present application is explained, which uses a deep learning-based neural network model to extract multi-level features of navigation environment monitoring information, and performs context association analysis on the multi-level features of the navigation environment monitoring information to achieve significant distribution modeling of features at different levels, thereby identifying key features in the navigation environment monitoring information and filtering out redundant information, so as to facilitate situational awareness of the navigation environment and intelligent assessment of the channel traffic status on this basis. In this way, the processing efficiency and accuracy of the navigation environment monitoring information can be effectively improved, thereby improving the reliability and safety of the ship's intelligent navigation analysis.

[0059] Furthermore, a ship intelligent navigation analysis system based on situation awareness is also provided.

[0060] Figure 6 FIG. 1 is a block diagram of a ship intelligent navigation analysis system based on situational awareness according to an embodiment of the present application. Figure 6As shown, according to the embodiment of the present application, the ship intelligent navigation analysis system 100 based on situational awareness includes: an environmental monitoring information acquisition module 110, which is used to acquire navigation environment monitoring information; an environmental monitoring feature extraction module 120, which is used to extract low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features; a feature filtering processing module 130, which is used to perform feature filtering processing based on significance distribution modeling on the set of navigation environment image monitoring features to obtain a set of filtered navigation environment image monitoring features; a feature fusion module 140, which is used to calculate the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and build a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image; an environmental perception module 150, which is used to perform target detection and land and water segmentation on the multi-level fusion feature map of the navigation environment image to obtain environmental perception information; a channel situation assessment module 160, which is used to assess the channel situation based on the environmental perception information to obtain channel situation assessment information.

[0061] Here, those skilled in the art can understand that the specific operations of each module in the above-mentioned ship intelligent navigation analysis system based on situation awareness have been referred to above. Figures 1 to 5 The description of the ship intelligent navigation analysis method based on situation awareness has been introduced in detail, and therefore, its repeated description will be omitted.

[0062] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0063] In the above embodiments, the description of each embodiment has its own emphasis. For the parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0064] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0065] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0066] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for analyzing ship intelligent navigation based on situational awareness, characterized in that: include: Obtain navigation environment monitoring information; Extracting low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features; Performing feature filtering processing based on significance distribution modeling on the set of navigation environment image monitoring features to obtain a filtered set of navigation environment image monitoring features; Calculating the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and building a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image; Performing target detection and land-water segmentation on the multi-level fusion feature map of the navigation environment image to obtain environmental perception information; The channel situation is evaluated based on the environmental perception information to obtain channel situation evaluation information.

2. The method for analyzing ship intelligent navigation based on situational awareness according to claim 1 is characterized in that: Extracting low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features, including: A CNN model is used to perform multi-level feature extraction on the navigation environment monitoring information to obtain a set of navigation environment image monitoring features.

3. The method for analyzing ship intelligent navigation based on situational awareness according to claim 2 is characterized in that: Performing feature filtering processing based on significance distribution modeling on the set of navigation environment image monitoring features to obtain a filtered set of navigation environment image monitoring features, including: Rearranging the set of navigation environment image monitoring features based on the significance rough description to obtain a sequence of rearranged navigation environment image monitoring features; Performing importance evaluation based on the guidance of the preceding context feature on each rearranged navigation environment image monitoring feature in the sequence of rearranged navigation environment image monitoring features to obtain a sequence of navigation environment image monitoring feature clue weaving closed-loop strength factors; Based on the sequence of closed-loop strength factors woven from the navigation environment image monitoring feature clues, feature filtering is performed on the set of navigation environment image monitoring features to obtain the set of filtered navigation environment image monitoring features.

4. The method for analyzing ship intelligent navigation based on situational awareness according to claim 3 is characterized in that: The set of navigation environment image monitoring features is sequence-rearranged based on the significance rough description to obtain a sequence of rearranged navigation environment image monitoring features, including: Extracting the maximum eigenvalue of each navigation environment image monitoring feature in the set of navigation environment image monitoring features as a significant rough description factor to obtain a sequence of significant rough description factors of the navigation environment image monitoring features; Based on the ascending arrangement of the sequence of the significance rough description factors of the navigation environment image monitoring features, the set of the navigation environment image monitoring features is rearranged to obtain the sequence of the rearranged navigation environment image monitoring features.

5. The method for analyzing ship intelligent navigation based on situational awareness according to claim 4 is characterized in that: The importance of each rearranged navigation environment image monitoring feature in the sequence of rearranged navigation environment image monitoring features is evaluated based on the guidance of the preceding context feature to obtain a sequence of navigation environment image monitoring feature clue weaving closed-loop strength factors, including: Extracting the i-th rearranged navigation environment image monitoring feature from the sequence of rearranged navigation environment image monitoring features as the navigation environment image monitoring feature to be analyzed; Inputting the first i-1 rearranged navigation environment image monitoring features in the sequence of the rearranged navigation environment image monitoring features into a key clue capture network to obtain a navigation environment image monitoring feature front-order distribution key clue guidance feature; The key clue guidance features of the preceding distribution of the navigation environment image monitoring features and the navigation environment image monitoring features to be analyzed are input into the clue weaving gating network to obtain the navigation environment image monitoring feature clue weaving closed-loop strength factor corresponding to the navigation environment image monitoring features to be analyzed.

6. The method for analyzing ship intelligent navigation based on situational awareness according to claim 5 is characterized in that: The first i-1 rearranged navigation environment image monitoring features in the sequence of the rearranged navigation environment image monitoring features are input into the key clue capture network to obtain the navigation environment image monitoring feature front-order distribution key clue guidance features, including: Calculating the characteristic distribution significance factor of each rearranged navigation environment image monitoring feature based on the statistical characteristic value of each rearranged navigation environment image monitoring feature in the first i-1 rearranged navigation environment image monitoring features to obtain a sequence of navigation environment image monitoring feature distribution significance factors, wherein the statistical characteristic value includes the characteristic variance, characteristic mean and maximum characteristic value of the rearranged navigation environment image monitoring feature; Performing a normalization process based on a Sigmoid function on the sequence of navigation environment image monitoring feature distribution significance factors to obtain a sequence of navigation environment image monitoring feature distribution significance weights; Based on the sequence of the navigation environment image monitoring feature distribution significance weights, the first i-1 rearranged navigation environment image monitoring features are weighted aggregated to obtain the navigation environment image monitoring feature preceding distribution key clue guidance features.

7. The method for analyzing ship intelligent navigation based on situational awareness according to claim 6 is characterized in that: The method further comprises calculating the feature distribution significance factor of each rearranged navigation environment image monitoring feature in the first i-1 rearranged navigation environment image monitoring features based on the statistical feature value of each rearranged navigation environment image monitoring feature to obtain a sequence of navigation environment image monitoring feature distribution significance factors, including: On the basis of the statistical characteristic value, an edge correlation factor is further introduced, and the edge correlation factor is used to enhance the edge-global feature distribution synergy of the rearranged navigation environment image monitoring feature.

8. The method for analyzing ship intelligent navigation based on situational awareness according to claim 7 is characterized in that: Based on the sequence of closed-loop strength factors of the navigation environment image monitoring feature clues, feature filtering is performed on the set of navigation environment image monitoring features to obtain the set of filtered navigation environment image monitoring features, including: Based on the comparison between the navigation environment image monitoring feature clue weaving closed-loop strength factor and a preset threshold, it is determined whether to filter the navigation environment image monitoring feature corresponding to the navigation environment image monitoring feature clue weaving closed-loop strength factor.

9. A ship intelligent navigation analysis system based on situational awareness, characterized in that: include: Environmental monitoring information acquisition module, used to obtain navigation environment monitoring information; An environment monitoring feature extraction module, used to extract low-level to high-level features of the navigation environment monitoring information to obtain a set of navigation environment image monitoring features; A feature filtering processing module, used for performing feature filtering processing on the set of navigation environment image monitoring features based on significance distribution modeling to obtain a set of filtered navigation environment image monitoring features; A feature fusion module is used to calculate the attention score of each feature in the set of filtered navigation environment image monitoring features to generate an attention feature map, and to build a feature pyramid network to fuse feature maps of different levels to obtain a multi-level fusion feature map of the navigation environment image; An environment perception module, used for performing target detection and land-water segmentation on the multi-level fusion feature map of the navigation environment image to obtain environment perception information; A channel situation assessment module is used to assess the channel situation based on the environmental perception information to obtain channel situation assessment information.

Images