Ship collision monitoring and early warning method, device and system based on buoy array wave perception

Abstract

The present invention discloses a method, device and system for ship collision prevention monitoring and early warning based on buoy array wave perception, which relates to the technical fields of ship navigation safety and bridge protection. Raw data of the wake wave motion caused by ship navigation is collected; the raw data of the wave motion is preprocessed to obtain a multi-dimensional physical feature parameter set for characterizing the ship wake; the feature parameter set is compared with a pre-stored ship wake feature library to inversely identify the ship type and speed; based on the feature parameter set, through a fusion algorithm of time difference of arrival positioning method and wave direction line intersection positioning method, the real-time position, heading and real-time distance relative to the bridge of the ship are calculated; according to the time sequence in which the ship wake is sensed by different rows of buoy arrays, combined with the calculated heading information and the change trend of the feature parameters, the early warning stage in which the ship is located is judged, and differential early warning or protection actions corresponding to this stage are executed. The present invention can implement hierarchical early warning and full-element linkage protection.

Description

Technical Field

[0001] This invention relates to the field of ship navigation safety and bridge protection technology, and more specifically to a method, equipment and system for ship collision prevention monitoring and early warning based on buoy array wave sensing. Background Technology

[0002] As key nodes in national transportation infrastructure, bridges spanning rivers and seas directly impact national economic development and public safety. With the rapid development of the shipping industry and the significant trend towards larger and faster ships, collisions between ships and bridges caused by ship yaw are frequent, becoming one of the major risks facing bridge engineering. Therefore, developing highly reliable, all-weather bridge collision monitoring technology is of significant practical importance.

[0003] Existing bridge collision avoidance monitoring technologies are mainly developing along three technical paths: First, the technical path based on autonomous ship reporting, represented by the Automatic Identification System (AIS), which achieves monitoring by having ships actively transmit their identity and navigation information; second, the technical path based on active electromagnetic wave detection, with radar monitoring systems as the core, which achieves ship detection and tracking by transmitting electromagnetic waves and receiving echoes; and third, the technical path based on optical perception, with video surveillance systems as the main body, which achieves target recognition and tracking by acquiring ship images through cameras and combining them with computer vision algorithms.

[0004] While the aforementioned technologies have achieved some success in their respective applicable scenarios, they all share common limitations that are difficult to overcome: AIS technology relies entirely on the active activation and accurate information transmission of ship equipment, rendering it completely ineffective for ships without AIS or with malfunctioning equipment; radar technology relies on actively emitting electromagnetic waves, making it susceptible to wave clutter interference; video surveillance technology is limited by ambient lighting conditions, with its perception capabilities significantly reduced in low-visibility environments such as nighttime and foggy weather. Essentially, existing technologies have not escaped their dependence on electromagnetic waves or optical signals, resulting in perception blind spots in typical scenarios such as ship equipment malfunctions or harsh environments, making it difficult to achieve truly reliable all-weather monitoring. Therefore, developing a new monitoring technology path that does not rely on active ship cooperation, does not emit detection signals, and is unaffected by ambient lighting, to achieve passive, all-weather ship perception, has become an urgent technical problem to be solved in this field. Summary of the Invention

[0005] In view of this, the present invention provides a method, device, and system for ship collision prevention monitoring and early warning based on wave sensing using a buoy array. It uses the wake wave—a physical phenomenon inevitably generated during ship navigation—as the sensing object, and inverses the ship's motion state by analyzing the physical characteristics of the wave. This breaks through the traditional technical framework that relies on electromagnetic waves or optical signals, realizing a paradigm shift from "active transmission-reception" to "passive sensing-inversion." Specifically, the present invention uses a multi-row buoy array deployed in navigable waters to simultaneously collect ship wake wave data; then, it extracts multi-dimensional physical characteristics of the wake wave (wave height, period, energy spectral density, waveform distortion index, etc.); compares these characteristics with a pre-stored ship wake wave feature database to inversely identify the ship type and speed; based on the feature parameters, it calculates the ship's real-time position and heading using a wave arrival time difference and wave direction line intersection fusion algorithm; finally, based on the timing of wake wave sensing by different numbers of buoys, combined with the heading deviation and the trend of feature parameter changes, it implements graded early warning and all-element linkage protection.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A collision avoidance monitoring and early warning method based on buoy array wave sensing includes the following steps: Raw data on wake wave motion caused by ship navigation are collected using a buoy array. The raw wave motion data is preprocessed and features are extracted to obtain a set of multidimensional physical feature parameters for characterizing ship wake waves; The feature parameter set is compared with the pre-stored ship wake feature library to invert and identify the ship type and speed; Based on the feature parameter set, a fusion algorithm combining the time difference of arrival (TDOA) positioning method and the wave direction line intersection positioning method is used to calculate the real-time position, heading, and real-time distance of the ship relative to the bridge. Based on the timing of the ship's wake being sensed by buoy arrays of different numbers, combined with the calculated heading information and the changing trends of characteristic parameters, the ship's warning stage is determined, and differentiated warning or protective actions corresponding to that stage are executed.

[0007] Optionally, raw data on wake wave motion caused by ship navigation can be collected using buoy arrays. Specifically, at least two buoy arrays are deployed along the waterway in the bridge navigation channel. The buoy arrays consist of multiple rows of wave monitoring buoys, forming a tiered defense monitoring network.

[0008] Optionally, the multidimensional physical parameter set includes effective wave height, spectral peak period, specific frequency band energy spectral density, and waveform distortion index.

[0009] Optionally, the waveform distortion index is obtained by quantifying the steepness and asymmetry of the waveform and is used to characterize the degree of turbulence of the wake wave relative to the regular wave; the specific frequency band energy spectral density is the integrated power spectral density within the characteristic frequency band of the ship wake wave and is used to characterize the energy intensity of the ship wake wave.

[0010] Optionally, the ship wake feature library is established by pre-collecting wake samples of ships of different tonnages, speeds, and types, and training them through machine learning. It stores the mapping relationship between ship type, speed, and multi-dimensional physical feature parameter set.

[0011] Optionally, the warning phase may include at least: Phase 1: When the ship's wake is detected by the first row of buoys, the system establishes a ship trajectory file and continuously monitors the ship without issuing an active warning. Second stage: When the ship's wake is detected by the second row of buoys and the calculation results show that its course deviates from the preset channel, the ship is determined to be in a slight deviation warning state and the primary warning is activated. Third stage: When the ship's wake is detected by the third row of buoys and the course continues to deviate, and the waveform distortion index increases by more than the preset percentage threshold compared to the previous stage, the ship is determined to be in a moderate deviation warning state and a medium warning is activated. Phase 4: When the ship's wake is detected by the fourth row of buoys and the course continues to deviate, or the predicted trajectory will enter the high-risk collision avoidance zone of the bridge, and the energy spectral density is significantly enhanced compared to the previous phase, the ship is determined to be in a high-risk state, and advanced early warning and physical protection are activated.

[0012] Optionally, when determining the early warning stage of a vessel, the dynamic changes in the waveform distortion index and energy spectral density are also considered to assist in identifying the vessel type and speed, thereby correcting the early warning level; when the waveform distortion index continues to rise and the energy spectral density is significantly enhanced, it is determined to be a large-tonnage, high-speed vessel, and the early warning level is raised accordingly. The primary warning includes activating the bridge area video surveillance device for AI identification and tracking; the intermediate warning includes selectively activating at least one of VHF radio communication equipment, high-powered laser alarm, and directional acoustic warning device based on weather conditions, and linking the shore-based warning lighthouse to switch to warning status; the advanced warning and protection includes linking the activation of at least one of the active interception device at the bridge pier, anti-collision airbag, steel casing or flexible anti-collision device, switching the shore-based warning lighthouse to emergency status, and sending an emergency alarm to the maritime authorities containing the ship's precise location, course, and predicted collision time.

[0013] A collision prevention monitoring and early warning device based on buoy array wave sensing, utilizing any one of the aforementioned collision prevention monitoring and early warning methods based on buoy array wave sensing, includes: A buoy array, deployed in a bridge channel, the array comprising at least two columns arranged along the channel direction, each column comprising at least four rows of wave monitoring buoys; The data processing center, which is connected to the buoy array, is configured to perform ship wake feature extraction, feature library comparison, ship type inversion, ship positioning calculation, and early warning stage judgment. The early warning and protection module is connected to the data processing center and is configured to execute corresponding early warning and protection commands according to different early warning stages.

[0014] Optionally, each buoy in the buoy array includes an inertial measurement unit, a satellite positioning module, a communication antenna, a solar panel, and a battery; the buoy array achieves high-precision synchronization by receiving timing signals from the satellite navigation system; the sampling frequency of the inertial measurement unit can be dynamically adjusted according to the early warning stage, using a basic sampling frequency in normal mode and a higher sampling frequency in alert mode; The data processing center is configured to: firstly, filter the original wave signal to remove environmental noise such as wind-induced waves and tides; then extract the multi-dimensional physical feature parameter set; and finally, based on the feature parameter set, calculate the ship's position and heading by fusing the time-of-arrival positioning method and the wave direction line intersection positioning method.

[0015] A collision avoidance monitoring and early warning system based on buoy array wave perception is provided. The system utilizes any one of the methods for collision avoidance monitoring and early warning based on buoy array wave perception. The system is further configured to perform data fusion and cross-validation between the calculated ship information and the ship information received by the AIS system. When the AIS data is missing or abnormal, the system's calculation result is used as the primary basis for early warning judgment.

[0016] As can be seen from the above technical solution, compared with the prior art, the present invention provides a method, device and system for ship collision prevention monitoring and early warning based on buoy array wave sensing, which has the following beneficial effects: 1. Adopting a passive physical inversion sensing paradigm that differs from existing technologies. This invention utilizes the physical characteristics of wake waves inevitably generated during ship navigation as the basis for perception and positioning, belonging to the "passive perception-physical inversion" technical paradigm. Unlike existing mainstream technologies based on active ship reporting (such as AIS), active electromagnetic wave detection (such as radar), or optical image recognition (such as video surveillance), the perception principle of this invention does not rely on the active cooperation of the ship, does not emit any detection signals, and is not constrained by ambient lighting conditions. It differs fundamentally in its perception object, information carrier, and data processing flow. This technical approach provides a solution for bridge collision avoidance monitoring independent of existing technological systems, effectively filling the perception blind spots of existing methods in scenarios such as nighttime, severe weather, and ship equipment malfunctions.

[0017] 2. A multi-dimensional feature-driven precision perception system was established.

[0018] This invention constructs a "waveform fingerprint" of ship wakes by extracting multi-dimensional physical features such as wave height, period, energy spectral density, and waveform distortion index, which can effectively distinguish ship wakes from natural waves. In particular, the introduction of the waveform distortion index provides a key basis for the identification of large-tonnage, high-speed ships, significantly improving the accuracy of perception.

[0019] 3. A ship type inversion method based on a wake wave feature library was established.

[0020] This invention pre-constructs a ship wake feature library and compares the feature parameters extracted in real time with the samples in the library, thereby enabling auxiliary identification of ship type and speed. This provides data support for dynamically correcting the warning level and avoids false alarms or missed alarms caused by "one-size-fits-all" warnings.

[0021] 4. A hierarchical early warning logic integrating "spatiotemporal-course-feature" was constructed.

[0022] The early warning triggering logic of this invention not only considers the timing (spatial position) and course deviation of the perceived wake, but also innovatively introduces the dynamic change trend of waveform distortion index and energy spectral density, realizing earlier and more accurate identification of dangerous ship conditions, which is significantly better than the early warning mechanism based on a single distance or a single course in the prior art.

[0023] 5. Achieved a closed-loop, coordinated protection system involving all elements.

[0024] This invention achieves full-element coordination of "buoy-shore-shipborne-protection devices," dynamically scheduling resources such as video surveillance, audible and visual alarms, radio communication, and physical protection based on the early warning level, forming a closed-loop control from information early warning to physical protection. In particular, the adaptive adjustment of the buoy sampling frequency demonstrates the system's level of intelligence.

[0025] 6. Low deployment and maintenance costs, strong compatibility

[0026] The buoy is powered by solar energy, which significantly reduces operating costs; the system supports data fusion with existing monitoring systems such as AIS, and can be seamlessly integrated into the existing waterway management system, making it valuable for widespread application.

[0027] In summary, this invention, through its innovative technical solution, effectively addresses the shortcomings of existing bridge collision monitoring technologies, providing a novel technical means for bridge safety protection, and possesses significant theoretical and engineering application value. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0029] Figure 1 Workflow diagram of a buoy array wave sensing method for monitoring ship channel deviation.

[0030] Figure 2 System deployment diagram; Figure 3 Front sectional view of the buoy structure; Figure 4 Top view of the buoy structure.

[0031] In the picture: 1. First-stage buoy array; 2. Second-stage buoy array; 3. Third-stage buoy array; 4. Fourth-stage buoy array; 5. First wake wave; 6. Second wake wave; 7. Third wake wave; 8. Ship; 9. Warning zone and channel; 10. Enlarged view of buoys; 11. Shore monitoring center; 12. Warning equipment; 13. Bridge; 14. Bridge pier; 15. Danger zone; 16. Antenna; 17. Antenna module; 18. Inertial measurement unit; 19. Rubber airbag; 20. Sensor mounting plate; 21. Solar panel; 22. Circuit board; 23. GPS module; 24. Data acquisition unit; 25. Battery module; 26. Ballast weight; 27. Towing rope; 28. Fixing weight. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] This embodiment discloses a collision avoidance monitoring and early warning method based on buoy array wave sensing, including the following steps: Raw data on wake wave motion caused by ship navigation are collected using a buoy array. The raw wave motion data is preprocessed and features are extracted to obtain a set of multidimensional physical feature parameters for characterizing ship wake waves; The feature parameter set is compared with the pre-stored ship wake feature library to invert and identify the ship type and speed; Based on the feature parameter set, a fusion algorithm combining the time difference of arrival (TDOA) positioning method and the wave direction line intersection positioning method is used to calculate the real-time position, heading, and real-time distance of the ship relative to the bridge. Based on the timing of the ship's wake being sensed by buoy arrays of different numbers, combined with the calculated heading information and the changing trends of characteristic parameters, the ship's warning stage is determined, and differentiated warning or protective actions corresponding to that stage are executed.

[0034] like Figure 1 As shown, the anti-ship collision monitoring equipment and early warning system of the present invention is deployed in the navigable waters of bridge navigation channels. The core of the system includes a four-stage buoy array deployment, forming a tiered defense monitoring network: The first-stage buoy array 1 is positioned upstream of the bridge 13 at its furthest point (e.g., several hundred to several thousand meters from the bridge pier, adjustable according to channel conditions), serving as the system's "forward-facing sensing node." When the first wake 5 generated by the ship 8 reaches the buoys in this stage, the inertial measurement units 18 (IMUs) built into each buoy immediately capture the changes in acceleration and angular velocity caused by the wave at the basic sampling frequency, and synchronize this recording via high-precision satellite timing. At this point, the system begins to extract the multi-dimensional physical characteristic parameters of the wake (effective wave height). Spectral peak period Energy spectral density and waveform distortion index The system compares the data with the background wave field model to preliminarily confirm the ship's wake signal. The system establishes a ship trajectory file, but the ship remains under safe navigation monitoring and does not trigger an active warning.

[0035] The second-stage buoy array 2 is positioned downstream of the first-stage buoy array at an appropriate location (the spacing can be adjusted according to channel conditions). When the tail wave 6 of the second wave reaches the buoys in this stage, the system automatically increases the buoy sampling frequency to alert mode and merges the data from the preceding and following rows of buoys. The ship's position is initially calculated using the time difference of arrival method, and the course is determined by combining it with the wave direction line intersection method. Simultaneously, the characteristic parameters extracted from the preceding and following rows of buoys are compared: if... and It is still in the characteristic frequency band of ship wake waves, and the waveform distortion index is... If the ship begins to ascend and at the same time the calculated course deviates from the preset channel by more than the preset angle threshold, the system determines that the ship has entered the boundary area between the warning zone and channel 9 and initiates a primary warning.

[0036] The third-stage buoy array 3 is positioned closer to the bridge (e.g., within several hundred meters of the bridge piers). When the third wave wake 7 reaches the buoys in this stage, the system uses Kalman filtering to predict the trajectory based on data from all three rows of buoys, resulting in a more accurate heading. and speed At this point, the focus should be on the waveform distortion index. Changes: If Comparison An increase exceeding a preset threshold indicates intensified wake turbulence, potentially affecting large vessels or those with abnormal speeds. Combined with a continued deviation from the intended course, the system determines the vessel has entered a moderately dangerous state and activates a medium-level warning.

[0037] The fourth stage buoy array 4 is positioned closest to the bridge (e.g., within 100 meters of the bridge piers). When the ship's wake reaches the buoys in this stage, the system comprehensively analyzes the energy spectral density. Changes: If The waveform distortion index is significantly stronger than the previous buoy, indicating a significant increase in the ship's energy output (possibly due to emergency acceleration or a large tonnage vessel); at the same time, the waveform distortion index... The system reaches its peak value. Based on the trajectory prediction model, it is determined that the ship will enter danger zone 15 (i.e., the high-risk collision avoidance zone of the bridge) within a predetermined time. The system immediately activates the advanced warning and activates the physical protection device of the bridge pier 14.

[0038] The shore-based monitoring center 11 maintains communication with each buoy array via antenna 16, receiving monitoring data in real time. Warning equipment 12 includes high-intensity laser alarms, directional acoustic warning devices, etc., which are activated according to weather conditions and warning levels to issue differentiated warnings.

[0039] like Figure 3 and Figure 4 As shown, a single monitoring buoy ( Figure 2 The detailed structure of the buoy (enlarged figure 10) includes: Top structure: Solar panel 21 is installed on top of the buoy to provide a continuous power supply for the system; Antenna module 17 realizes hybrid transmission of mobile network and satellite communication through antenna 16 to ensure real-time data transmission; Rubber airbag 19 provides the main buoyancy to ensure the stability of the buoy in the water.

[0040] Internal Electronics: Circuit board 22 integrates the system's core processing functions, including data acquisition, edge computing, and communication control. The inertial measurement unit 18 (integrating a three-axis accelerometer and a three-axis gyroscope) is mounted on the sensor mounting plate 20, supporting high-frequency sampling and accurately capturing subtle vibrations caused by waves. The GPS module 23 provides positioning and timing services, ensuring time synchronization for all buoys within the array. The data acquisition unit 24 is responsible for packaging and timestamping the raw IMU data, uploading it in real time via the communication module. The battery module 25 is an energy storage unit, forming a hybrid power supply system with the solar panel, with power consumption dynamically adjusted by an intelligent power management chip based on system load.

[0041] Underwater section: Ballast lead blocks 26 are installed at the bottom of the buoy to lower the center of gravity and improve the anti-overturning ability; towing rope 27 connects the buoy and the fixed lead blocks 28 to prevent the buoy from drifting and ensure the stability of the array's geometric position.

[0042] Adaptive sampling mechanism: In this embodiment, the sampling frequency of the inertial measurement unit 18 can be dynamically adjusted according to the warning stage. In normal mode (no ship or only the first stage), the basic sampling frequency is used to reduce power consumption; when the ship enters the second stage (primary warning), the system automatically increases the sampling frequency to the alert mode; after entering the third stage (intermediate warning), the sampling frequency is further increased; in the fourth stage (advanced warning), the buoys located near the danger zone use the highest sampling frequency and enter the "impact monitoring mode" to retain high-precision data for post-event analysis.

[0043] Hardware support for feature extraction: The high precision and variable sampling rate of the inertial measurement unit 18 are the foundation for extracting multidimensional features. The effective wave height can be calculated by analyzing the time-domain waveform of the triaxial acceleration. Waveform distortion index By performing spectral analysis on the angular velocity data, the spectral peak period can be obtained. and energy spectral density The synergy between these hardware and algorithms enables buoys to accurately separate ship wake signals from complex wave fields.

[0044] Combination Figure 1 and Figure 2 The system's multi-level early warning mechanism is implemented as follows. This embodiment particularly emphasizes the dynamic triggering logic based on multi-dimensional wave characteristics and the linkage of all elements.

[0045] Phase 1 (Safety Monitoring): When the first wake 5 generated by the ship 8 is detected by the Phase 1 buoy array 1, the system automatically extracts the wake feature parameter set. Since only a single row of buoys is triggered at this time, the positioning accuracy is limited. The system only marks the vessel as a "potential target," establishes a trajectory file, and eliminates natural wind and wave interference through a background wave field model. The data is transmitted to the shore monitoring center 11 via antenna 16 and stored in the time-series database. No audible or visual warnings are issued during this stage, but the system continues to track the data in the background.

[0046] Phase Two (Primary Warning): When the tail wave 6 of the second wave is detected by the Phase Two buoy array 2, the system automatically increases the buoy sampling frequency to alert mode. By fusing data from both rows of buoys, the initial position of the vessel is calculated using the time difference of arrival (TDOA) method. and heading angle At the same time, compare the changes in feature parameters extracted from the two rows of buoys: if and If it is still within the characteristic frequency band of the ship's wake and the course deviates from the preset waterway by more than the preset angle threshold, the system determines that the ship is in a mild deviation state and activates the primary warning. The specific associated actions include: The monitoring screen of the shore monitoring center 11 automatically calls out the video image of the area, and the high-definition pan-tilt camera locks on the target and performs AI recognition; Send a warning message to the ship through the VHF radio communication device; The system automatically records the ship's trajectory, characteristic parameters and warning time, and generates a log for future reference.

[0047] The third stage (intermediate warning): When the third wave of wake 7 is sensed by the third-stage buoy array 3, the system further increases the sampling frequency of the relevant buoys, and uses the data of the three rows of buoys to perform Kalman filter prediction on the trajectory to obtain a more accurate course and speed . At this time, the system focuses on analyzing the change of the waveform distortion index: If is more than the preset ratio threshold, it indicates that the degree of ship wake disorder has increased significantly, which may be a large-tonnage ship or an abnormal speed. At the same time, the course and the deviation continues (that is, the course is not corrected), then it is determined that the ship enters the moderate danger state and activates the intermediate warning. The associated actions include: Continue to send warning messages through the VHF radio, activate the strong laser alarm, and increase the warning frequency; The video monitoring system continuously tracks and records, and at the same time cross-verifies the target information with the AIS data. If the AIS is not turned on, it is marked as a "dark ship"; The shore-based warning beacon switches to the warning state to remind other nearby ships to pay attention; The system compares the current characteristic parameters with the pre-stored ship wake characteristic library, initially identifies the ship type, and marks it on the monitoring interface.

[0048] The fourth stage (advanced warning and protection): When the ship's wake is sensed by the fourth-stage buoy array 4, the system raises the sampling frequency of the relevant buoys to the highest mode. According to the significant enhancement of the energy spectral density and the waveform distortion index reaching the peak, combined with the trajectory prediction model to judge that the ship will enter the danger zone 15 within a predetermined time, and immediately activate the advanced warning and physical protection. The associated actions include: Associated activation of the physical protection device of the bridge pier: Trigger the active interception device at the bridge pier 14 to quickly deploy and form a buffer area around the bridge pier; at the same time, the steel caisson or flexible anti-collision equipment enters the standby state; Highest level of audible and visual warning: All warning devices 12 operate continuously at maximum power; shore-based warning lighthouses switch to emergency status; Emergency Notification: The system automatically sends an emergency alert to maritime authorities and bridge management units, containing information such as the vessel's precise location, course, predicted collision time, and identified vessel type. Buoy self-adjustment: Buoys located near danger zones maintain the highest sampling frequency and enter "impact monitoring mode" to retain high-precision data for post-event analysis.

[0049] Once the vessel has safely departed or the risk has been confirmed to have passed, the system will determine the wake energy. Attenuation to background level, waveform distortion index Once normal operation is restored, the warnings will be automatically deactivated step by step, and all devices will return to their normal state (sampling frequency will return to basic mode, and warning beacons will return to normal state).

[0050] The system data processing and communication flow of this invention is as follows: Data Acquisition and Synchronization: Each buoy acquires triaxial acceleration and angular velocity data at a variable frequency via inertial measurement unit 18, and obtains precise time and position via GPS module 23. All buoys achieve high-precision synchronization using timing signals to ensure the accuracy of wave arrival time difference calculation. The acquired data is packaged locally and transmitted in real time to shore monitoring center 11 via antenna module 17 using a hybrid mode of primary mobile network and backup satellite communication.

[0051] Data processing center workflow: Data preprocessing: First, the raw signal is filtered to remove environmental noise such as wind, waves, and tides; then, all buoy data are unified to the same coordinate system; finally, the data is aligned by timestamps to form a spatiotemporally synchronized dataset.

[0052] Feature extraction: Sliding window analysis is performed on the time series data of each buoy (the window length and step size can be dynamically adjusted according to the warning stage) to extract the following multidimensional features: Significant wave height Displacement is obtained by double integration of acceleration, and the eigenvalue of wave height is calculated. Spectral peak period : Perform spectral analysis on acceleration data to obtain power spectral density and identify the period corresponding to the spectral peak; Energy spectral density of a specific frequency band The integrated power spectrum within the characteristic frequency band of a ship's wake represents the energy of the wake. Waveform distortion index : Calculate waveform steepness and asymmetry, and quantify the degree of deviation from regular waves using a machine learning model.

[0053] Feature library comparison and type inversion: The extracted feature parameter set PP is input into the pre-stored ship wake feature library, and a pattern recognition algorithm is used for comparison. The most matching ship type and estimated speed range are output to provide a basis for the correction of the warning level.

[0054] Location calculation: A strategy combining Time Difference of Arrival (TDOA) and Direction of Arrival (DOA) methods is employed. TDOA: Using the time difference of the same wake arriving at different buoys, a system of equations is established to solve for the ship's position; DOA: Based on the wave main direction measured by each buoy, draw direction lines and find the intersection point as the position estimate; Fusion Algorithm: Kalman filtering is used to weight and fuse the TDOA and DOA results to dynamically correct the ship's position and heading.

[0055] Early warning judgment: Based on the currently sensed number of buoys, the calculated course deviation, the changing trend of characteristic parameters, and the ship type information obtained by comparing with the feature library, the warning level and linkage command are output according to the preset warning logic.

[0056] Communication and Command Issuance: The shore-based monitoring center distributes early warning commands to each execution terminal via mobile network or dedicated data link. Critical commands employ a confirmation mechanism, while retaining an interface for manual intervention.

[0057] The power supply and maintenance scheme of the system of this invention is as follows: Energy Management: Each buoy converts solar energy into electrical energy via solar panels 21 to charge battery modules 25. The intelligent power management chip automatically adjusts power consumption modes based on system load and warning stages. Normal mode: basic sampling frequency, normal communication interval, and low daily power consumption; Alert mode (second stage): Higher sampling frequency, shorter communication interval, and correspondingly increased power consumption; Advanced Alert Mode (Phase 3 and 4): Highest sampling frequency, real-time communication, significantly increased power consumption, short-term battery support; Low power mode: When there is no ship activity, it automatically reduces to the lowest sampling frequency, extends the communication interval, and ensures long-term battery life.

[0058] Self-diagnosis: Each buoy has a built-in self-test program that regularly monitors parameters such as battery status, sensor status, and communication status. In case of an anomaly, the program reports to the shore monitoring center via the communication module, prompting maintenance personnel to handle the issue promptly.

[0059] Ease of maintenance: The buoy features a modular design, and the electronics compartment can be removed entirely from the top for quick on-site replacement. The underwater components are connected by a tow rope, reducing the difficulty of maintenance operations.

[0060] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0061] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for ship collision prevention monitoring and early warning based on buoy array wave sensing, characterized in that, Includes the following steps: Raw data on wake wave motion caused by ship navigation are collected using a buoy array. The raw wave motion data is preprocessed and features are extracted to obtain a set of multidimensional physical feature parameters for characterizing ship wake waves; The feature parameter set is compared with the pre-stored ship wake feature library to invert and identify the ship type and speed; Based on the feature parameter set, a fusion algorithm combining the time difference of arrival (TDOA) positioning method and the wave direction line intersection positioning method is used to calculate the real-time position, heading, and real-time distance of the ship relative to the bridge. Based on the timing of the ship's wake being sensed by buoy arrays of different numbers, combined with the calculated heading information and the changing trends of characteristic parameters, the ship's warning stage is determined, and differentiated warning or protective actions corresponding to that stage are executed.

2. The method for ship collision prevention monitoring and early warning based on buoy array wave sensing according to claim 1, characterized in that, The raw data of wake wave motion caused by ship navigation is collected by using buoy arrays. Specifically, at least two buoy arrays are deployed along the waterway in the bridge navigation channel. The buoy arrays consist of multiple rows of wave monitoring buoys, forming a tiered defense monitoring network.

3. The method for ship collision prevention monitoring and early warning based on buoy array wave sensing according to claim 1, characterized in that, The set of multidimensional physical characteristic parameters includes effective wave height, spectral peak period, energy spectral density of a specific frequency band, and waveform distortion index.

4. The collision avoidance monitoring and early warning method based on buoy array wave sensing according to claim 3, characterized in that, The waveform distortion index is obtained by quantifying the steepness and asymmetry of the waveform and is used to characterize the degree of turbulence of the wake wave relative to the regular wave; the specific frequency band energy spectral density is the integrated power spectral density within the characteristic frequency band of the ship wake wave and is used to characterize the energy intensity of the ship wake wave.

5. The method for ship collision prevention monitoring and early warning based on buoy array wave sensing according to claim 1, characterized in that, The ship wake feature library is established by pre-collecting wake samples of ships of different tonnages, speeds, and types, and training them through machine learning. It stores the mapping relationship between ship type, speed, and a set of multi-dimensional physical feature parameters.

6. The method for ship collision prevention monitoring and early warning based on buoy array wave sensing according to claim 1, characterized in that, The early warning phase includes at least: Phase 1: When the ship's wake is detected by the first row of buoys, the system establishes a ship trajectory file and continuously monitors the ship without issuing an active warning. Second stage: When the ship's wake is detected by the second row of buoys and the calculation results show that its course deviates from the preset channel, the ship is determined to be in a slight deviation warning state and the primary warning is activated. Third stage: When the ship's wake is detected by the third row of buoys and the course continues to deviate, and the waveform distortion index increases by more than the preset percentage threshold compared to the previous stage, the ship is determined to be in a moderate deviation warning state and a medium warning is activated. Phase 4: When the ship's wake is detected by the fourth row of buoys and the course continues to deviate, or the predicted trajectory will enter the high-risk collision avoidance zone of the bridge, and the energy spectral density is significantly enhanced compared to the previous phase, the ship is determined to be in a high-risk state, and advanced early warning and physical protection are activated.

7. The method for ship collision prevention monitoring and early warning based on buoy array wave sensing according to claim 6, characterized in that, When determining the early warning stage of a vessel, the dynamic changes in the waveform distortion index and energy spectral density are also combined to assist in identifying the vessel type and speed, so as to correct the early warning level. When the waveform distortion index continues to rise and the energy spectral density is significantly enhanced, it is determined to be a large-tonnage high-speed vessel, and the early warning level is raised accordingly. The primary warning includes activating the bridge area video surveillance device for AI identification and tracking; the intermediate warning includes selectively activating at least one of VHF radio communication equipment, high-powered laser alarm, and directional acoustic warning device based on weather conditions, and linking the shore-based warning lighthouse to switch to warning status; the advanced warning and protection includes linking the activation of at least one of the active interception device at the bridge pier, anti-collision airbag, steel casing or flexible anti-collision device, switching the shore-based warning lighthouse to emergency status, and sending an emergency alarm to the maritime authorities containing the ship's precise location, course, and predicted collision time.

8. A collision prevention monitoring and early warning device based on buoy array wave sensing, utilizing the collision prevention monitoring and early warning method based on buoy array wave sensing as described in any one of claims 1-7, comprising: A buoy array, deployed in a bridge channel, the array comprising at least two columns arranged along the channel direction, each column comprising at least four rows of wave monitoring buoys; The data processing center, which is connected to the buoy array, is configured to perform ship wake feature extraction, feature library comparison, ship type inversion, ship positioning calculation, and early warning stage judgment. The early warning and protection module is connected to the data processing center and is configured to execute corresponding early warning and protection commands according to different early warning stages.

9. A collision avoidance monitoring and early warning device based on buoy array wave sensing as described in claim 8, characterized in that, Each buoy in the buoy array includes an inertial measurement unit, a satellite positioning module, a communication antenna, a solar panel, and a battery; the buoy array achieves high-precision synchronization by receiving timing signals from the satellite navigation system; the sampling frequency of the inertial measurement unit can be dynamically adjusted according to the early warning stage, using a basic sampling frequency in normal mode and a higher sampling frequency in alert mode; The data processing center is configured to: filter the raw wave signal to remove environmental noise from wind-induced waves and tides; and extract the multidimensional physical feature parameter set. Based on the feature parameter set, the ship's position and heading are calculated by combining the time difference of arrival (TDOA) positioning method and the wave direction line intersection positioning method.

10. A collision prevention monitoring and early warning system based on buoy array wave sensing, utilizing the collision prevention monitoring and early warning method based on buoy array wave sensing as described in any one of claims 1-7, characterized in that, The system is also configured to perform data fusion and cross-validation between the calculated ship information and the ship information received by the AIS system. When AIS data is missing or abnormal, the system's calculation results are used as the primary basis for early warning judgment.

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