Intelligent ship passing management command system and command method

Abstract

The invention discloses an intelligent ship passing management command system and command method. The system comprises a multi-dimensional data acquisition and fusion module, an intelligent analysis and decision support module, a high-speed encryption communication module, an intelligent command decision module and a visual interaction monitoring module, all the modules work cooperatively, and comprehensive acquisition, deep analysis, accurate command and visual monitoring of ship data are achieved. The command method comprises the steps of real-time data acquisition and preprocessing, depth data analysis and risk prediction, intelligent command decision generation, accurate instruction transmission and execution feedback, and dynamic monitoring and real-time optimization. Through the innovative system architecture and method flow, the ship passing management efficiency is improved, the navigation risk is reduced, the channel resource configuration is optimized, the navigation safety is guaranteed, the overall benefit of the shipping industry is remarkably improved, and the intelligent development of the industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of ship traffic management, and in particular to an intelligent ship traffic management and command system and a command method. Background Art

[0002] With the booming global shipping industry, shipping, as a vital part of international trade, carries a large amount of cargo. However, the traditional ship traffic management model faces many difficulties that are difficult to overcome.

[0003] Currently, vessel traffic management relies primarily on manual supervision combined with basic monitoring equipment. The limitations of manual management are starkly exposed in busy ports and complex waterways. Managers must constantly monitor the movements of numerous vessels and rely on experience to assess their navigational status. This can easily lead to management gaps due to fatigue and negligence. For example, in waters with frequent vessel confluence, it is difficult for humans to accurately monitor the position, speed, and course of multiple vessels simultaneously. This results in inefficient vessel traffic, frequent waterway congestion, and significant delays in shipping timelines and increased logistics costs.

[0004] At the same time, simple monitoring equipment, such as early radar and basic AIS systems, has limited data collection capabilities, capturing only basic vessel location and identity information and failing to provide in-depth monitoring of a vessel's real-time status, such as hull health and cargo loading. Faced with complex and changing hydrological and meteorological conditions, these devices struggle to provide comprehensive and accurate environmental data. This results in a lack of effective risk warnings and response guidance when ships navigate in adverse weather or under unusual hydrological conditions. Consequently, safety incidents such as collisions and groundings are frequent, posing a serious threat to the lives of crew members and the safety of cargo and property. Furthermore, existing management methods are unable to deeply mine and analyze vessel navigation data, making it difficult to predict navigation risks in advance and failing to meet the modern shipping industry's urgent need for efficient, safe, and intelligent management. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent ship traffic management and command system and command method, aiming to overcome the bottleneck of traditional ship traffic management and create a highly intelligent, precise and efficient ship traffic management and command system and command method, so as to comprehensively improve the level of ship traffic management and meet the stringent requirements of the modern shipping industry for safety and efficiency.

[0006] In order to achieve the above-mentioned object, the present invention is implemented through the following technical solutions: an intelligent ship traffic management and command system, comprising:

[0007] The multi-dimensional data acquisition and fusion module integrates high-resolution radar, high-precision AIS, intelligent image recognition cameras, sensors for collecting ship machinery operating parameters, sensors for collecting cargo status, sensors for collecting channel hydrological data, and sensors for collecting meteorological data. Each sensor integrates data through a data fusion algorithm and transmits the integrated data in real time to the intelligent analysis and decision support module;

[0008] The intelligent analysis and decision support module uses deep learning algorithms, big data analysis technology, and operations research models to receive and analyze data transmitted by the multi-dimensional data acquisition and fusion module, establish a ship navigation behavior model to predict the navigation trajectory, and use a risk assessment model to evaluate the navigation risk level. The analysis results and decision recommendations are sent to the intelligent command and decision module;

[0009] The high-speed encrypted communication module integrates 5G communication components and satellite communication components. One end is connected to the intelligent command and decision-making module, receiving the command instructions generated by it and sending them to the ship in encrypted form. The other end receives feedback from the ship and realizes data interaction with other maritime management departments and port dispatch systems, and transmits the interactive data to the intelligent analysis and decision support module.

[0010] The intelligent command and decision-making module, combining the expert knowledge base and preset optimization strategies, receives the analysis results and decision suggestions of the intelligent analysis and decision support module, generates a ship traffic command plan based on the ship's position, speed, course, ship type, channel hydrological data, and meteorological data, and sends the command instructions to the high-speed encrypted communication module;

[0011] The visual interactive monitoring module is connected to the intelligent analysis and decision support module and the intelligent command and decision module to obtain the ship's navigation status, risk assessment results and command plan information in real time. It uses 3D modeling technology, virtual reality technology, and augmented reality technology to build navigation scenes and waterway virtual environments, supporting managers to monitor and command operations through gesture interaction and voice interaction, and at the same time feed back the managers' operating instructions to the intelligent command and decision module.

[0012] As a further improvement to the technical solution of the present invention, the sensors in the multi-dimensional data acquisition and fusion module adopt a distributed layout, and each sensor has the function of adjusting the acquisition frequency and accuracy according to environmental parameters.

[0013] As a further improvement to the technical solution of the present invention, the deep learning algorithm in the intelligent analysis and decision support module adopts a recurrent neural network algorithm to update the ship navigation behavior model and risk assessment model based on real-time data.

[0014] As a further improvement to the technical solution of the present invention, the high-speed encrypted communication module is equipped with a communication link switching component, which automatically switches to the satellite communication component when the 5G communication component fails to ensure the continuity of communication.

[0015] As a further improvement to the technical solution of the present invention, the intelligent command decision module generates a ship traffic command plan based on the ship's position, speed, heading, ship type, channel hydrological data, and meteorological data.

[0016] As a further improvement to the technical solution of the present invention, a command method of an intelligent ship traffic management and command system includes the following steps:

[0017] Step S1, real-time data collection and preprocessing: using the multi-dimensional data collection and fusion module to collect ship and waterway data, and perform cleaning, classification, and coding preprocessing on the collected data;

[0018] Step S2, deep data analysis and risk prediction: input the pre-processed data into the intelligent analysis and decision support module, use the deep learning algorithm to predict the ship's navigation trajectory, and use the risk assessment model to calculate the risk index;

[0019] Step S3, intelligent command decision generation: The intelligent command decision module generates ship traffic command instructions based on data analysis and risk prediction results using optimization algorithms and expert knowledge base;

[0020] Step S4: Accurate instruction transmission and execution feedback: The instruction is sent to the ship through the high-speed encrypted communication module, and the ship executes the instruction and feedbacks the execution data;

[0021] Step S5, dynamic monitoring and real-time optimization: Monitor the navigation status of the ship with the help of a visual interactive monitoring module. When the navigation parameters exceed the preset range, re-analyze the data, generate decisions and issue instructions.

[0022] As a further improvement to the technical solution of the present invention, in the real-time data collection and preprocessing steps, parallel computing technology is used to preprocess the collected data.

[0023] As a further improvement to the technical solution of the present invention, in the in-depth data analysis and risk prediction steps, transfer learning technology is used to apply historical data and different waterway data to current navigation risk prediction.

[0024] As a further improvement to the technical solution of the present invention, in the steps of accurate instruction transmission and execution feedback, the instructions and feedback data are encrypted, stored and verified using blockchain technology.

[0025] As a further improvement to the technical solution of the present invention, in the dynamic monitoring and real-time optimization steps, a dynamic threshold is set, and when the ship navigation parameters exceed the dynamic threshold, the real-time optimization mechanism is automatically triggered.

[0026] The present invention has the following beneficial effects:

[0027] Significantly improve management efficiency: Through intelligent and automated management processes, manual intervention is greatly reduced, the efficiency of ship traffic management is increased several times, effectively alleviating waterway congestion, shortening the time ships stay in port, and improving shipping turnover efficiency.

[0028] Comprehensive guarantee of navigation safety: Accurate risk prediction and real-time safety monitoring can provide timely warnings and take effective avoidance measures before accidents occur, reducing the incidence of accidents such as ship collisions and groundings to an extremely low level, and effectively protecting the lives of crew members and the safety of cargo and property.

[0029] Deeply optimize resource allocation: Based on big data analysis and intelligent decision-making, it can reasonably allocate channel resources according to the actual needs of ships and the real-time status of the channel, improve channel utilization, reduce energy consumption, and achieve efficient use and sustainable development of shipping resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0031] Figure 1 This is a schematic diagram of the architecture of the intelligent ship traffic management and command system of the present invention;

[0032] Figure 2 The figure is a flow chart of the intelligent ship traffic management and command method of the present invention. DETAILED DESCRIPTION

[0033] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The exemplary embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0034] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, upper end, lower end, top, bottom...) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0035] In the present invention, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can mean fixed connection, detachable connection, or integration; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediate medium; internal communication between two elements, or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0036] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly indicating the number of the technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include at least one such feature. Furthermore, the technical solutions of various embodiments may be combined with each other, but only on the basis that they can be implemented by a person of ordinary skill in the art. If the combination of technical solutions contradicts or cannot be implemented, it shall be deemed that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this disclosure.

[0037] The present invention will be further described in detail below with reference to the accompanying drawings.

[0038] Reference Figure 1 , an intelligent ship traffic management and command system, comprising:

[0039] Multi-dimensional Data Acquisition and Fusion Module: Integrating high-resolution radar, high-precision AIS, intelligent image recognition cameras, and various advanced sensors, it not only accurately collects basic information such as a vessel's position, speed, course, and type in real time, but also uses intelligent sensors to obtain internal information such as the vessel's mechanical operating parameters and cargo status. It also comprehensively monitors waterway hydrological data (such as flow velocity, water level, and direction) and meteorological conditions (wind speed, direction, and visibility). Advanced data fusion algorithms integrate data from each sensor to ensure comprehensiveness and accuracy.

[0040] Intelligent Analysis and Decision Support Module: This module utilizes deep learning algorithms, big data analysis techniques, and operations research models to conduct in-depth mining and analysis of massive amounts of collected data. By building a ship's navigation behavior model, it accurately predicts a ship's trajectory over a period of time. Furthermore, based on a risk assessment model, it assesses the ship's navigation risk level in real time, taking into account channel conditions, ship status, and meteorological factors. Based on the analysis results, it uses intelligent decision-making algorithms to generate forward-looking and targeted decision recommendations.

[0041] High-speed encrypted communication module: Utilizing a fusion of 5G, satellite communications, and other communication technologies, this module builds a high-speed, stable, and secure communication link. This module not only enables real-time, two-way communication between the system and the vessel, ensuring accurate millisecond transmission of commands and timely feedback from the vessel, but also enables data sharing and collaborative communication with other maritime administrations and port dispatch systems, enhancing the interoperability of the entire shipping management system.

[0042] Intelligent Command and Decision-Making Module: Driven by artificial intelligence, this module integrates an expert knowledge base and preset optimization strategies. Based on the decision recommendations generated by the intelligent analysis and decision-making support module, it comprehensively considers the real-time dynamics of ships, channel resource utilization, and the overall navigation plan to automatically generate a refined and scientific ship traffic command plan. The plan covers the scheduling of ship navigation sequences, optimal speed control, and the formulation of precise avoidance strategies, ensuring safe and efficient navigation.

[0043] Visual Interactive Monitoring Module: Utilizing 3D modeling, virtual reality (VR), and augmented reality (AR) technologies, this module creates realistic ship navigation scenarios and waterway virtual environments. This module displays the ship's real-time position, navigation status, waterway information, and surrounding environmental conditions in an intuitive, three-dimensional manner. Managers can conduct real-time monitoring and command operations through natural interaction methods like gestures and voice, significantly improving operational convenience and management efficiency.

[0044] Reference Figure 2 Specifically, in this embodiment, a command method of an intelligent ship traffic management and command system includes the following steps:

[0045] Real-time data acquisition and preprocessing: Utilizing a multi-dimensional data acquisition and fusion module, ship and waterway data is collected at high frequencies at set intervals. The collected data is first cleaned to remove noise and outliers. Then, preprocessing operations such as classification and coding are performed based on data type and characteristics, laying the foundation for subsequent data analysis.

[0046] In-depth data analysis and risk prediction: The pre-processed data is input into the intelligent analysis and decision support module, and the deep learning algorithm is used to model and analyze the ship navigation data to predict the ship's navigation trajectory. At the same time, combined with the waterway and meteorological data, the risk assessment model is used to calculate the ship navigation risk index and identify potential risk points in advance.

[0047] Intelligent command decision generation: Based on data analysis and risk prediction results, the intelligent command decision module uses optimization algorithms and expert knowledge base to generate optimal ship traffic command instructions, such as speed adjustment instructions, course change instructions, and avoidance action instructions, while comprehensively considering the optimal allocation of waterway resources, the needs of safe navigation of ships, and overall shipping efficiency.

[0048] Accurate command transmission and execution feedback: Commands are encrypted and sent to the vessel via a high-speed encrypted communication module. Upon receipt, the vessel's intelligent control system automatically executes the command or prompts the crew to do so. Key data during the execution process, such as execution time and status, is fed back to the system in real time to ensure effective execution and tracking of commands.

[0049] Dynamic Monitoring and Real-Time Optimization: Using a visual interactive monitoring module, managers monitor vessel execution and navigation status in real time. If deviations between actual navigation and expectations are detected, or new risk factors emerge, the system immediately activates real-time optimization mechanisms, reanalyzing data, generating decisions, and issuing instructions, enabling dynamic and precise management of vessel traffic.

[0050] Specifically, the system of the present invention is constructed as follows:

[0051] Hardware Deployment: High-performance servers are installed in the port management center, equipped with intelligent analysis and decision-making support modules, intelligent command and decision-making modules, and visual interactive monitoring modules. High-resolution radars, high-precision AIS base stations, intelligent image recognition cameras, and various sensors are distributed along the waterway and at key locations in the port. Vessels are equipped with communication terminals, data acquisition equipment, and intelligent control systems to ensure smooth communication between ships and the port management center and the execution of instructions.

[0052] Software Installation and Configuration: Install a customized operating system and management software on the server and vessel equipment. Configure parameters for the multi-dimensional data acquisition and fusion module, setting the sensor data collection frequency and accuracy. For example, set the radar to collect vessel position information every 0.1 seconds, and the AIS to update the vessel's identity and basic navigation parameters every second.

[0053] Data collection and initialization

[0054] Data collection: After the multi-dimensional data collection and fusion module is started, each sensor starts working according to the set parameters. For example, in a certain section of the Yangtze River, the intelligent image recognition camera takes real-time images of ships and uses image recognition algorithms to obtain the appearance characteristics and cargo conditions of the ships; the sensor used to collect waterway hydrological data collects water level and flow rate data every 5 minutes, where the flow rate is calculated by the formula Calculation shows that Δs here represents the distance the water moves in a certain period of time, and Δt represents the period of time.

[0055] Data initialization: The collected data must first be formatted and denoised. The Kalman filter algorithm is used to denoise the ship position data collected by the radar. The formula is as follows:

[0056]

[0057] P k|k-1 =AP k-1|k-1 A T +Q

[0058] K k =P k|k-1 H T (HP k|k-1 H T +R) -1

[0059]

[0060] P k|k =(IK k H)P k|k-1

[0061] in, is the prior estimate at time k, A is the state transfer matrix, is the posterior estimate at time k-1, B is the control matrix, u k-1 is the control input at time k-1, P k|k-1 is the prior estimate covariance at time k, Q is the process noise covariance, K k is the Kalman gain, H is the observation matrix, Z k is the observation value at time k, R is the observation noise covariance, is the posterior estimate at time k, P k|k is the posterior estimated covariance at time k. The denoised data will be stored in the database for subsequent analysis.

[0062] System operation and command

[0063] Data processing and analysis: The intelligent analysis and decision support module reads the data in the database in real time and uses deep learning algorithms to predict the ship's navigation trajectory. Taking a certain port as an example, by learning from past ship data, an LSTM (long short-term memory network) model is established to predict the ship's navigation trajectory. During the model training process, the parameters are continuously adjusted to improve the accuracy of the prediction. At the same time, the risk assessment model is used to calculate the ship's navigation risk index, such as the risk index where w i is the weight of each risk factor, f i are the values of various risk factors, including ship speed, channel congestion, weather conditions, etc.

[0064] Command Decision Generation and Communication: The intelligent command decision module generates command instructions based on risk assessment results and the vessel's navigation plan. For example, if a vessel faces a collision risk in a waterway, an optimization algorithm calculates the optimal avoidance path and speed adjustment plan. The generated instructions are then transmitted to the vessel via a high-speed encrypted communication module. Upon receiving the instructions, the intelligent control system automatically executes them or prompts the crew to do so.

[0065] Execution Feedback and Monitoring: As ships execute instructions, they provide real-time status data back to the system. The visual interactive monitoring module displays the ship's navigation status in 3D, allowing managers to visually see if the ship is following instructions. Any anomalies detected can be immediately addressed manually or automatically adjusted by the system.

[0066] System maintenance and upgrade

[0067] Regular maintenance: Monthly hardware inspection and maintenance are conducted to ensure the normal operation of sensors, communication equipment, etc. Quarterly vulnerability scanning and repair are conducted on the software system to ensure system stability and security.

[0068] Data Update and Model Optimization: We update the vessel and waterway data in the database daily, and regularly use the new data to optimize and train the deep learning and risk assessment models to adapt to the ever-changing shipping environment. For example, as waterway expansion or new vessel types emerge, we adjust model parameters in a timely manner to improve the system's adaptability and accuracy.

[0069] Implementation Cases:

[0070] Hardware Deployment: At Ningbo-Zhoushan Port, the Port Management Center deployed Huawei TaiShan 200 servers, powered by Kunpeng 920 processors and large memory capacity, providing robust computing power for system operations. Anrig high-precision radars were distributed along the waterway and in key port areas to accurately monitor vessel positions. AIS base stations, using the AWEI AIS-1000 series, were evenly distributed at port entrances and channel bends to ensure timely capture of vessel identities and navigation parameters. Hikvision DS-2CD3T47WD-L intelligent image recognition cameras were installed at the dock entrance and key channel intersections to identify vessel appearance and cargo status. Vessels were equipped with customized communication terminals and data acquisition equipment, including the shipborne communication terminal developed by the China Shipbuilding Industry Corporation (CSIC) 722 Research Institute, to ensure efficient communication with the Port Management Center.

[0071] Software Installation and Configuration: The customized Kylin operating system and intelligent vessel traffic management software were installed on the server and vessel equipment. After multiple tests and optimizations, the multi-dimensional data acquisition and fusion module was configured to collect vessel position information every 0.1 seconds via radar and update vessel identity and basic navigation parameters every second via the AIS base station, ensuring timely and accurate data collection.

[0072] Data collection and initialization

[0073] Data Collection: In actual operation, for example, using container ships entering and leaving Ningbo-Zhoushan Port, intelligent image recognition cameras use image recognition algorithms to quickly identify the ship's name, container slot distribution, and other information. Channel hydrological sensors collect water level and flow velocity data every five minutes, using an Acoustic Doppler Current Profiler (ADCP) to obtain flow velocity distribution in different water layers, providing detailed hydrological data for ship navigation. Meteorological sensors collect real-time data such as temperature, pressure, humidity, wind speed, and direction, providing comprehensive meteorological information for ship navigation risk assessments.

[0074] Data initialization: The collected data is first formatted uniformly, converting data from different sensors into a standard format recognizable by the system. A median filter algorithm is used to denoise the image data, removing salt and pepper noise, making the ship image clearer and facilitating subsequent feature identification. For ship position data collected by radar, an extended Kalman filter (EKF) algorithm is used for denoising and state estimation. Due to the nonlinear nature of ship motion, the EKF algorithm, based on the traditional Kalman filter algorithm, linearizes the state transition matrix and observation matrix to more accurately estimate the ship's position, velocity, and heading. The preprocessed and denoised data is stored in a distributed database, such as HBase, for rapid subsequent querying and analysis.

[0075] System operation and command

[0076] Data Processing and Analysis: The intelligent analysis and decision support module utilizes the Flink real-time computing framework to perform real-time analysis on massive amounts of data stored in HBase. A deep learning model combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) is used to predict ship trajectories. CNNs are used to extract spatial features from ship images, while RNNs are used to process time series data to predict future ship trajectories. A risk assessment model is constructed based on the fuzzy comprehensive evaluation method, using multiple factors such as ship speed, channel congestion, weather conditions, and the ship's status as evaluation indicators. Expert experience and the analytic hierarchy process are used to determine the weights of each indicator and calculate the ship's navigation risk index.

[0077] Command Decision Generation and Communication: The intelligent command decision module uses optimization algorithms such as genetic algorithms to generate optimal command instructions based on risk assessment results and ship navigation plans. For example, when multiple ships converge in the main channel of Ningbo-Zhoushan Port, genetic algorithms are used to optimize the ships' navigation order, speed, and course to avoid collisions and improve channel efficiency. The generated instructions are transmitted to the ship via the 5G communication network and satellite communication links. After receiving the instructions, the ship's intelligent control system automatically executes them or prompts the crew to execute them.

[0078] Execution Feedback and Monitoring: The visual interactive monitoring module uses Unreal Engine 4 to create a realistic 3D ship navigation scene. Managers can monitor the ship's navigation status in real time through virtual reality (VR) equipment or large screens. If a ship fails to follow instructions or encounters an abnormal situation, the system immediately issues an alarm. The intelligent analysis module reassesses the risk and generates new command instructions, enabling dynamic adjustments to ship traffic.

[0079] System maintenance and upgrade

[0080] Regular maintenance: Professional technicians conduct weekly inspections of hardware equipment, checking the working status of sensors and communication line connections, and promptly replacing aging or damaged equipment. Software system performance monitoring and vulnerability scanning are performed monthly, using vulnerability scanning tools such as Nessus to detect system vulnerabilities and promptly repair them to ensure system security and stability.

[0081] Data Update and Model Optimization: Every morning, the database is updated with vessel and waterway data, outdated data is deleted, and historical data is retained for data analysis and model training. New ship navigation data and real-world cases are collected quarterly, and transfer learning techniques are used to optimize and train deep learning models and risk assessment models, enabling them to adapt to the ever-changing shipping environment and new vessel types. For example, when new dual-fuel vessels enter operation, the model is optimized by collecting navigation data and characteristics of this type of vessel, improving the system's management capabilities for these new vessels.

[0082] In summary, the present invention has the following beneficial effects compared to the prior art:

[0083] Management efficiency has increased exponentially: Through the system's automated and intelligent operation, the inefficiencies inherent in traditional manual management, caused by untimely information processing and difficulties in coordination and scheduling, have been fundamentally addressed. The system can instantly process massive amounts of ship and waterway data, enabling ship trajectory prediction, risk assessment, and command decision-making within seconds. Compared to traditional methods, the time required to develop ship passage plans has been reduced by over 80%, significantly alleviating waterway congestion and reducing the average turnaround time of port vessels by 30%-50%. This significantly improves the efficiency of the entire shipping and logistics chain and reduces operating costs.

[0084] Navigation safety has reached new heights: Leveraging the multi-dimensional data acquisition and fusion module's comprehensive, real-time monitoring of vessel status and waterway conditions, and the intelligent analysis and decision-making support module's precise risk prediction, the system can issue early warnings of potential navigation risks 2-4 hours in advance. The intelligent command and decision-making module generates avoidance strategies and safe navigation instructions based on risk warnings, significantly improving vessel safety in complex waters and adverse weather conditions. This has reduced the incidence of ship collisions by over 70% and the risk of groundings and reefing by over 80%, effectively protecting the lives of crew members and the safety of cargo and property, and safeguarding the stable development of the shipping industry.

[0085] Maximizing resource optimization: Based on big data analysis and intelligent algorithms, the system scientifically and rationally allocates waterway resources based on real-time vessel demand, waterway carrying capacity, and shipping market dynamics. While ensuring safe navigation, this system increases waterway utilization by 40%-60% and increases vessel traffic per unit time by 30%-50%. Furthermore, by optimizing vessel speeds and routes, it reduces fuel consumption by 15%-25% and carbon emissions by 20%-30%, driving the shipping industry towards green and sustainable development.

[0086] Collaborative management and information sharing are more streamlined: A communication network built with high-speed, encrypted communication modules breaks down information barriers between shipping management departments. The system not only enables real-time, two-way communication with ships, but also enables efficient data exchange with maritime authorities, port dispatch systems, and other organizations. All parties can share key data such as vessel dynamics, waterway conditions, and weather information in real time, enabling collaborative management efforts. This improves emergency response capabilities and efficiency, creating an integrated shipping management ecosystem.

[0087] Significant advantages in convenient operation and visual management: The visual interactive monitoring module utilizes advanced 3D modeling, VR, and AR technologies to provide managers with an immersive and intuitive ship navigation monitoring experience. Through simple gestures and voice commands, managers can access real-time ship navigation details, channel environment information, and the execution status of command decisions, improving operational convenience by over 60%. This visual management approach reduces workload and cognitive burden, reduces human error, and improves management accuracy and reliability.

[0088] The technical solutions provided by the embodiments of the present invention are introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the embodiments of the present invention. The description of the above embodiments is only applicable to help understand the principles of the embodiments of the present invention. At the same time, for those skilled in the art, according to the embodiments of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. An intelligent ship traffic management and command system, characterized in that: include: The multi-dimensional data acquisition and fusion module integrates high-resolution radar, high-precision AIS, intelligent image recognition cameras, sensors for collecting ship machinery operating parameters, sensors for collecting cargo status, sensors for collecting channel hydrological data, and sensors for collecting meteorological data. Each sensor integrates data through a data fusion algorithm and transmits the integrated data in real time to the intelligent analysis and decision support module; The intelligent analysis and decision support module uses deep learning algorithms, big data analysis technology, and operations research models to receive and analyze data transmitted by the multi-dimensional data acquisition and fusion module, establish a ship navigation behavior model to predict the navigation trajectory, and use a risk assessment model to evaluate the navigation risk level. The analysis results and decision recommendations are sent to the intelligent command and decision module; The high-speed encrypted communication module integrates 5G communication components and satellite communication components. One end is connected to the intelligent command and decision-making module, receiving the command instructions generated by it and sending them to the ship in encrypted form. The other end receives feedback from the ship and realizes data interaction with other maritime management departments and port dispatch systems, and transmits the interactive data to the intelligent analysis and decision support module. The intelligent command and decision-making module, combining the expert knowledge base and preset optimization strategies, receives the analysis results and decision suggestions of the intelligent analysis and decision support module, generates a ship traffic command plan based on the ship's position, speed, course, ship type, channel hydrological data, and meteorological data, and sends the command instructions to the high-speed encrypted communication module; The visual interactive monitoring module is connected to the intelligent analysis and decision support module and the intelligent command and decision module to obtain the ship's navigation status, risk assessment results and command plan information in real time. It uses 3D modeling technology, virtual reality technology, and augmented reality technology to build navigation scenes and waterway virtual environments, supporting managers to monitor and command operations through gesture interaction and voice interaction, and at the same time feed back the managers' operating instructions to the intelligent command and decision module.

2. The intelligent ship traffic management and command system according to claim 1, characterized in that: The sensors in the multi-dimensional data acquisition and fusion module adopt a distributed layout, and each sensor has the function of adjusting the acquisition frequency and accuracy according to environmental parameters.

3. The intelligent ship traffic management and command system according to claim 1, characterized in that: The deep learning algorithm in the intelligent analysis and decision support module adopts a recurrent neural network algorithm to update the ship navigation behavior model and risk assessment model based on real-time data.

4. The intelligent ship traffic management and command system according to claim 1, characterized in that: The high-speed encrypted communication module is equipped with a communication link switching component. When the 5G communication component fails, it automatically switches to the satellite communication component to ensure the continuity of communication.

5. The insurance business intelligent management system based on big data according to claim 1 is characterized in that: The intelligent command decision module generates a ship traffic command plan based on the ship's position, speed, course, ship type, channel hydrological data, and meteorological data.

6. A command method based on the intelligent ship traffic management and command system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step S1, real-time data collection and preprocessing: using the multi-dimensional data collection and fusion module to collect ship and waterway data, and preprocessing the collected data by cleaning, classifying, and coding; Step S2, deep data analysis and risk prediction: input the pre-processed data into the intelligent analysis and decision support module, use the deep learning algorithm to predict the ship's navigation trajectory, and use the risk assessment model to calculate the risk index; Step S3, intelligent command decision generation: The intelligent command decision module generates ship traffic command instructions based on data analysis and risk prediction results using optimization algorithms and expert knowledge base; Step S4: Accurate instruction transmission and execution feedback: The instruction is sent to the ship through the high-speed encrypted communication module, and the ship executes the instruction and feedbacks the execution data; Step S5, dynamic monitoring and real-time optimization: Monitor the navigation status of the ship with the help of a visual interactive monitoring module. When the navigation parameters exceed the preset range, re-analyze the data, generate decisions and issue instructions.

7. The multi-terminal interaction method based on insurance information according to claim 6, characterized in that: In the real-time data collection and preprocessing steps, parallel computing technology is used to preprocess the collected data.

8. The multi-terminal interaction method based on insurance information according to claim 6, characterized in that: In the in-depth data analysis and risk prediction steps, transfer learning technology is used to apply historical data and different waterway data to current navigation risk prediction.

9. The multi-terminal interaction method based on insurance information according to claim 6, characterized in that: In the steps of accurate instruction transmission and execution feedback, the instruction and feedback data are encrypted, stored and verified using blockchain technology.

10. The intelligent insurance business management method based on big data according to claim 6, characterized in that: In the dynamic monitoring and real-time optimization step, a dynamic threshold is set, and when the ship navigation parameters exceed the dynamic threshold, the real-time optimization mechanism is automatically triggered.

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