Intelligent fish blocking and ship passing system based on multi-mode AI fusion and self-adaptive control

Abstract

The invention relates to an intelligent fish blocking and ship passing system based on multi-mode AI fusion and self-adaptive control, and belongs to the field of water conservancy projects and intelligent control. Aiming at the problems that a traditional electric fish blocking device is low in ship communication efficiency, poor in environmental adaptability and high in maintenance cost, a multi-mode sensor (laser radar, sonar, infrared thermal imaging and the like) fusion technology is adopted, and a reinforcement learning optimization proportion-integral-differential (PID) control parameter and a graph neural network (GNN) path planning algorithm are combined. All-weather high-precision sensing and dynamic gate regulation and control are realized; and equipment faults are predicted through a long short-term memory (LSTM) model, so that the shutdown risk is reduced. According to the system, the ship dredging efficiency is improved by 25%, the accident rate is reduced by 70%, the maintenance cost is reduced by 40%, the system adapts to extreme environments, and intelligent water conservancy construction is supported.

Description

Technical Field

[0001] The present invention belongs to the fields of water conservancy engineering and intelligent control, and relates to an intelligent fish-blocking and ship-passing system based on multimodal AI fusion and adaptive control. Background Art

[0002] As a key facility for water area ecological protection and ship navigation management, the intelligent level of the electric fish-blocking device directly affects fishery production and shipping safety. Traditional devices mostly rely on fixed metal pipes or manually operated gates, which have problems such as low ship-passing efficiency, poor environmental adaptability, and high maintenance costs. Although existing technologies have initially realized the automation of the gate through image recognition and Proportional-Integral-Derivative Controller (PID) control, in complex scenarios such as extreme weather and multi-ship parallel navigation, they still face bottlenecks such as insufficient perception robustness, limited control dynamic optimization ability, and lack of equipment health management. For example, a single vision sensor is easily interfered by fog, fixed PID parameters are difficult to adapt to dynamic water flow resistance, and there is a lack of prediction ability for faults such as propeller wear. Therefore, it is urgent to break through the limitations of existing technologies and improve the comprehensive performance of the system through the collaborative innovation of multimodal perception fusion, adaptive control algorithms, and predictive maintenance technologies. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide an intelligent fish-blocking and ship-passing system based on multimodal AI fusion and adaptive control, deeply integrating cutting-edge artificial intelligence and Internet of Things technologies to build an intelligent closed-loop of the entire "perception - decision - control - maintenance" link. First, by integrating multi-source sensors such as lidar, sonar, and infrared thermal imaging, combined with a lightweight MobileNet V3 model and an attention mechanism-driven data fusion network (MM-DFN), the limitations of single vision perception are broken through, and all-weather high-precision target detection is achieved. Secondly, the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is introduced to dynamically optimize the PID control parameters, combined with a hydrodynamic simulation model to compensate for the countercurrent resistance, significantly improving the gate deflection response speed and energy efficiency. In addition, the system innovatively deploys an LSTM time series prediction model and vibration sensors to early warn of faults such as propeller wear and circuit anomalies, build a predictive maintenance system, and reduce the risk of sudden shutdown. To further improve the collaborative efficiency of multi-ship passing, the system generates an optimal path planning strategy based on a Graph Neural Network (GNN) and AIS data, and embeds an abnormal behavior detection module (YOLOv7 + Transformer) to link the law enforcement platform to standardize the water area order.

[0004] To achieve the above purpose, the present invention provides the following technical solutions:

[0005] An intelligent fish-blocking and ship-passing system based on multimodal AI fusion and adaptive control, comprising:

[0006] A multi-modal perception unit, which is used to collect environmental data through multi-source sensors, including lidar, sonar, infrared thermal imaging cameras, and visible light cameras;

[0007] An edge computing node, which is used to run a multi-modal data fusion network (Multi-Modal Data Fusion Network, MM-DFN), a target detection model, and a path planning algorithm, and output control instructions;

[0008] An adaptive control module, including a proportional-integral-derivative controller (PID) based on reinforcement learning and a countercurrent resistance compensation unit, which is used to dynamically adjust the gate deflection parameters;

[0009] A predictive maintenance unit, including a vibration sensor and a long short-term memory time series prediction model (Long Short-Term Memory, LSTM), which is used to monitor the health status of the device and generate warning signals;

[0010] An intelligent collaborative management terminal, which is used to receive Automatic Identification System (AIS) data and generate a multi-ship collaborative passing strategy;

[0011] A deformable garbage interception device, which adopts a shape memory alloy mesh claw structure and is used to adaptively unfold or fold to intercept floating objects;

[0012] The multi-modal perception unit, the edge computing node, the adaptive control module, the predictive maintenance unit, and the intelligent collaborative management terminal are connected through a communication link to form a closed-loop control.

[0013] Furthermore, the MM-DFN includes:

[0014] A hybrid attention mechanism module, which is used to dynamically weight and fuse lidar point clouds, sonar echoes, and infrared thermal imaging feature maps;

[0015] Residual blocks and cross-modal interaction layers, which are used to generate an environmental semantic map;

[0016] The output of the MM-DFN is connected to the target detection model of the edge computing node. The target detection model is a lightweight Mobile Net V3 model, which supports real-time processing of 4K resolution video streams.

[0017] Furthermore, the reinforcement learning-based PID controller adopts the Deep Deterministic Policy Gradient (DDPG) algorithm, whose state space includes gate angle error, water flow speed and ship weight, and action space is PID parameters (K p ,K i ,K d ) incremental adjustment;

[0018] The countercurrent resistance compensation unit generates a propeller power distribution strategy based on a fluid mechanics simulation model, wherein the countercurrent propeller power is 800W and the downstream propeller power is 500W.

[0019] Furthermore, the input of the LSTM time series prediction model includes the frequency domain characteristics and the time domain root mean square value of the vibration spectrum, and the output is the wear probability of the equipment in the next 7 days;

[0020] The predictive maintenance unit is configured with a three-level warning mechanism. When the frequency deviation exceeds 5%, a first-level warning is triggered, and the cloud platform is linked to push a maintenance notification.

[0021] Furthermore, the intelligent collaborative management terminal constructs a spatiotemporal graph model through a graph neural network (GNN), where node attributes include ship location, speed, and hull size, and edge weights are dynamically calculated based on the difference between ship spacing and estimated arrival time.

[0022] The terminal also integrates an abnormal behavior detection module, which uses a YOLOv7-Transformer hybrid architecture to identify unregistered vessels and speeding behaviors.

[0023] Furthermore, the YOLOv7 model of the abnormal behavior detection module is used for ship detection, and the mean average precision (mAP@0.5) reaches 96.8%;

[0024] The Transformer encoder fuses the time-series AIS data, marks the offending vessel after comparing it with the maritime database, and issues a warning through a directional acoustic alarm device.

[0025] Furthermore, the deformable garbage interception device automatically deploys when the sonar detects that the density of floating objects exceeds a threshold value, and activates the boost mode;

[0026] The system is also equipped with an unmanned cleaning boat as an optional accessory for collaborative removal of high-density garbage.

[0027] Furthermore, the system deploys solar power supply modules, including photovoltaic panels and lithium battery packs, to support off-grid operation;

[0028] The edge computing node adopts NVIDIA Jetson AGX Orin, with an inference latency of ≤50 ms, and realizes edge-cloud collaborative computing through the Kubernetes framework.

[0029] Furthermore, under extreme weather conditions, the multimodal perception unit switches to the dominant detection mode of lidar and infrared thermal imaging;

[0030] The adaptive control module activates the anti-turbulence mode, increases the power of the countercurrent propeller by 20%, and simultaneously adjusts the brightness of the fog lights to the maximum.

[0031] Furthermore, the system stores operation records through blockchain technology to ensure the immutability of data;

[0032] The system complies with the intelligent ship safety standards of the International Maritime Organization (IMO) and supports integration with 5G and the Beidou Navigation System.

[0033] The beneficial effects of the present invention are as follows: Through the multimodal sensor fusion (lidar, sonar, infrared thermal imaging, etc.) and the data fusion network (MM-DFN) driven by the hybrid attention mechanism, the present invention breaks through the limitations of single visual perception and significantly improves the detection accuracy and robustness in complex environments (such as heavy fog and heavy rain); by using the Deep Deterministic Policy Gradient (DDPG) algorithm to dynamically optimize the proportional-integral-derivative (PID) control parameters and combining the countercurrent resistance compensation technology, the gate deflection response time is shortened to 1.5 seconds, and the energy efficiency is increased by 30%; the multi-ship cooperative path planning and abnormal behavior detection module (YOLOv7-Transformer) based on the Graph Neural Network (GNN) reduces the risk of navigation conflicts and improves the ship passing efficiency by 25%; the Long Short-Term Memory (LSTM) predictive maintenance model can predict equipment failures 7 days in advance, reducing the maintenance cost and the probability of sudden shutdown by 40%; the deformable garbage interception device and the solar power supply module enhance the environmental adaptability of the system, and the accident rate is reduced by 70%. The overall solution meets the requirements of intelligent water conservancy, and has high reliability, low operation and maintenance costs, and wide applicability.

[0034] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0036] Figure 1 It is a structural diagram of the multi-modal perception unit layout and the adaptive control module;

[0037] Figure 2 It is an architecture diagram of the MM-DFN network;

[0038] Figure 3 It is a flow chart of the edge-cloud collaborative computing framework and maintenance management;

[0039] Figure 4 It is a schematic diagram of the intelligent collaborative passing algorithm logic. Specific implementation manners

[0040] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0041] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation to the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0042] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the drawings are only for illustrative purposes and should not be construed as a limitation to the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0043] The intelligent fish-blocking and ship-passing system designed by the present invention has the following characteristics:

[0044] (1) In terms of principle: Traditional devices rely on single image recognition and fixed PID parameter control, which are vulnerable to environmental interference, leading to misjudgment or response delay. The improved system adopts multi-modal AI fusion technology, integrating lidar, sonar, infrared thermal imaging, and visible light cameras. Through the attention mechanism, it dynamically weights multi-source data, breaking through the perception blind spots of a single sensor. At the same time, based on the reinforcement learning-based adaptive PID control algorithm, it can dynamically adjust parameters in real-time according to water flow speed, vessel weight, and deflection error, achieving precise angle control and significantly improving the response efficiency under complex working conditions.

[0045] (2) In terms of algorithms: The system uses the lightweight Mobile Net V3 model, and through mixed-precision training and TensorRT acceleration, it realizes real-time processing (frame rate ≥ 30 FPS) of 4K resolution (3840×2160) video streams. The training dataset is extended to cover 12 types of vessels (including large cargo ships, unmanned boats, and trawlers). The data sources include AIS historical trajectories, satellite remote sensing, and simulation engines (Unity3D), and the generalization ability is enhanced through random fogging and motion blur.

[0046] The multi-vessel collaborative navigation algorithm constructs a spatio-temporal graph model based on the graph neural network (GNN):

[0047] Node attributes: Vessel position (GPS coordinates), speed (m / s), hull size (length × width), and destination;

[0048] Edge weights: Dynamically calculated from the vessel spacing and the estimated time difference of arrival (ETA), and the formula is:

[0049]

[0050] where Δt ij is the time difference between two vessels arriving at the conflict point;

[0051] Conflict prediction: Adopt the spatio-temporal collision detection formula

[0052]

[0053] If the distance between multiple vessels D 安全 < 50m, then hierarchical staggering instructions (interval 30 - 60 seconds) are generated through the GAT (Graph Attention Network).

[0054] The abnormal behavior detection module adopts the YOLOv7-Transformer hybrid architecture:

[0055] The front end uses YOLOv7 to detect vessels (mAP@0.5 = 96.8%), and the back end fuses temporal AIS data through the Transformer encoder to identify unregistered vessels (compared with the maritime database) and speeding behaviors (threshold: ±10% of the channel speed limit);

[0056] The model was trained with 100,000 frames of labeled data, with an inference delay of ≤50ms on NVIDIA Jetson AGX Orin and an overall accuracy of 99.2% (4.7% higher than traditional YOLOv5+Kalman filtering).

[0057] (3) Reliability: The system deploys a LSTM-based predictive maintenance module, which analyzes the health status of the equipment in real time through vibration sensors and current monitoring. It can warn of propeller wear or sensor failure 7 days in advance, reducing maintenance costs by 40%. In addition, the edge-cloud collaborative architecture supports remote diagnosis and OTA updates to ensure long-term stable operation of the system.

[0058] (4) Safety: The system adds a two-way abnormal behavior detection function and links the law enforcement platform to automatically alarm illegal ships. The multimodal perception network can identify underwater obstacles and the risk of people falling into the water, triggering power-off protection and garbage interception device folding, reducing the accident rate by 70%. The system complies with the IMO smart ship safety standards and uses blockchain technology to ensure that operation records cannot be tampered with.

[0059] (5) Convenience: The system adopts a single-sided fixed design and combines with a solar power module to achieve off-grid deployment. The edge node (Jetson AGX Orin) works in conjunction with the cloud-based digital twin platform to support remote monitoring and strategy optimization. Operation and maintenance personnel can switch control modes with one click through the visual interface, improving operational efficiency by 50%.

[0060] (6) Adaptability: This device is designed for extreme weather and complex water scenes. Through multimodal data fusion and adaptive control algorithms, it can achieve stable operation in a temperature range of -30℃ to 60℃ and in foggy and rainy environments. The garbage interception device is upgraded to a deformable claw structure to adapt to high floating object density watersheds; the countercurrent resistance compensation module optimizes propeller power distribution based on fluid mechanics simulation to ensure accurate deflection under turbulent conditions.

[0061] 1. Device technical design

[0062] The device structure is as follows Figure 1 shown.

[0063] 1. Multimodal Sensing Unit: Integrates a lidar (detection range of 200m), sonar (underwater obstacle detection), an infrared thermal imaging camera (resolution of 640×480), and a 4K rotating camera (supporting HDR), and realizes data fusion through the MM-DFN network. MM-DFN adopts a hybrid attention mechanism (parallel channel attention and spatial attention), and dynamically weights the confidence of different modalities by fusing lidar point clouds, sonar echoes, and infrared thermal imaging feature maps at the feature level. The network includes 3 residual blocks and a cross-modal interaction layer, and outputs a fused environmental semantic map. As Figure 2 shown.

[0064] 2. Edge Computing Node: Adopts NVIDIA Jetson AGX Orin, with a built-in GPU acceleration module, and supports real-time operation of Mobile Net V3, GNN, and LSTM models.

[0065] 3. Adaptive Control Module: Includes a PID controller driven by reinforcement learning, a countercurrent resistance compensation unit, and a dual-propeller power distributor (the power of the downstream propeller is 500W, and the power of the upstream propeller is 800W). The state space of the DDPG algorithm includes the current gate angle error, water flow velocity, and vessel weight; the action space is the incremental adjustment of the PID parameters (K p , K i , K d ); the reward function is designed as: R = -(αe 2 + βP 功耗 ), where α = 0.7 and β = 0.3. The training adopts the OU noise exploration strategy, and the learning rate is set to 1e -4 , and the discount factor γ = 0.99.

[0066] 4. Predictive Maintenance Unit: Deploys vibration sensors (frequency range 0 - 10kHz) and current monitoring modules, and synchronizes data with the cloud digital twin platform in real time.

[0067] 5. Intelligent Cooperative Management Terminal: Integrates an AIS receiver, a speaker (directional acoustic alarm), and a visual interaction interface, and supports the issuance of multi-vessel path planning instructions.

[0068] 6. Deformable Garbage Interception Device: Adopts a shape memory alloy mesh claw structure, which can be adaptively folded or unfolded, and the interception efficiency is increased to 95%.

[0069] 7. Solar Power Supply System: Optionally equipped with photovoltaic panels (output power of 200W) and lithium battery packs, and supports off-grid deployment.

[0070] Technical Preparation:

[0071] Data collection and training: Build a multi-source database covering large ships, unmanned boats, extreme weather scenarios, and equipment failure samples, and use transfer learning to optimize the Mobile Net V3 and GNN models.

[0072] Hydrodynamics simulation: Pretrain the propeller power distribution model through ANSYS Fluent to generate a countercurrent resistance compensation parameter library.

[0073] Edge-cloud collaborative deployment: Build a distributed computing framework based on Kubernetes to achieve model lightweighting and OTA updates.

[0074] Device classification:

[0075] This system consists of three core modules: the perception and decision-making layer, the control and execution layer, and the maintenance and management layer (as Figure 3 shown):

[0076] Perception and decision-making layer: The multi-modal perception unit collects environmental data, and the edge node runs the AI algorithm to output the passing instruction and risk warning.

[0077] Control and execution layer: The adaptive PID controller adjusts the propeller power to drive the electric fish barrier device to deflect precisely (error ±0.5°).

[0078] Maintenance and management layer: The predictive maintenance unit analyzes the equipment health status, and the cloud platform remotely monitors and generates maintenance work orders. The input of the LSTM network is the frequency domain characteristics of the vibration spectrum (energy distribution from 1 - 5 kHz extracted by FFT) and the time domain root mean square value. The network structure is 2 layers with 128 neurons, and it outputs the wear probability in the next 7 days. The warning threshold is set based on the statistics of historical fault data, and a first-level warning is triggered when the frequency deviation exceeds 5%.

[0079] II. Device working process

[0080] Figure 4 It is a schematic diagram of the intelligent collaborative passing algorithm logic.

[0081] Non-passing ship status:

[0082] The multi-modal perception unit periodically scans the river surface, and the lidar and sonar cooperate to detect underwater obstacles. After the edge node analyzes the image data through the Mobile Net V3 model and confirms no target, it sends a command to maintain a 90° angle to the control module. The adaptive PID controller dynamically adjusts the propeller power according to the feedback of the angle sensor to ensure the stability of the device.

[0083] Single ship passing status:

[0084] Infrared thermal imaging identifies the ship's outline, sonar measures the ship's draft, and the AIS receiver obtains the ship's registration information. The GNN model calculates the optimal gate opening angle (60°), and the reinforcement learning PID controller dynamically adjusts the propeller power with a response time ≤ 1.5 seconds. After passing, the system automatically resets and activates the self-cleaning mode of the garbage interception device.

[0085] Multi-ship collaborative passing status: AIS data and camera images are input into the GNN model to predict the conflict points of the ship routes. The edge node generates a peak-shifting passing instruction (with an interval of 30 seconds) and broadcasts navigation prompts through the speaker. The adaptive controller allocates additional power to the countercurrent propeller to offset the turbulence interference caused by multi-ship parallel navigation.

[0086] III. Response to Special Situations

[0087] Extreme weather (heavy fog / heavy rain):

[0088] Perception strategy: LiDAR and infrared thermal imaging dominate target detection, and sonar assists in positioning the ship's location.

[0089] Control strategy: The PID controller switches to the "anti-turbulence mode", and the power of the countercurrent propeller is increased by 20%.

[0090] Safety strategy: The brightness of the fog lights is automatically adjusted to the maximum, and the garbage interception device is folded to prevent jamming.

[0091] Equipment aging warning:

[0092] The LSTM model detects abnormal vibration spectra of the propeller (such as frequency deviation > 5%), triggering a three-level warning:

[0093] Level 1: The cloud platform pushes a maintenance notice.

[0094] Level 2: Automatically switch to the standby propeller and limit the gate opening angle to a safe range (≤ 75°).

[0095] Level 3: Force shutdown and activate the audible and visual alarm.

[0096] Unregistered ship intrusion:

[0097] 1. The YOLOv7 model identifies the hull features and marks them as "unregistered" after comparing with the AIS database.

[0098] 2. The system links with the law enforcement platform to upload the violation record, the speaker plays a warning voice, and at the same time limits the gate opening to 50%.

[0099] High-density garbage accumulation:

[0100] 1. Sonar detects that the density of floating objects exceeds the threshold, triggering the garbage interception device to expand and start the pressurization mode.

[0101] 2. The edge node schedules the collaborative operation of the unmanned cleaning ship (optional accessory) to ensure that the propeller is unblocked.

[0102] The intelligent fish-blocking and ship-passing system designed in this paper realizes multi-dimensional innovations in principles, algorithms, reliability, etc. through the deep integration of multi-modal AI fusion, adaptive control, and predictive maintenance technologies. The system exhibits high robustness in environments from -30°C to 60°C and complex water area scenarios, with a 25% increase in passing efficiency, a 70% reduction in accident rate, and a 40% reduction in maintenance cost. Its edge-cloud collaborative architecture and modular design can quickly adapt to fishery protection areas and water conservancy hubs of different scales, providing a replicable and easily popularizable solution for the construction of "smart water conservancy". In the future, it will further explore the integrated application with 5G and Beidou navigation to promote the comprehensive intelligentization of aquaculture and shipping management.

[0103] Example 1: Application of Multi-modal Sensing and Adaptive Control in Extreme Weather

[0104] Workflow:

[0105] Multi-modal data collection: When the system detects foggy weather, the lidar (detection range 200m) and the infrared thermal imaging camera (resolution 640×480) are activated to replace the visible light camera to dominate target detection, and the sonar synchronously scans for underwater obstacles.

[0106] Data fusion and target recognition: The edge computing node runs the multi-modal data fusion network (MM-DFN), dynamically weights the lidar point cloud and the infrared feature map through a hybrid attention mechanism to generate an environmental semantic map; the lightweight MobileNetV3 model identifies the ship's contour (frame rate ≥ 30FPS).

[0107] Adaptive control response: The DDPG algorithm dynamically adjusts the PID parameters (K p Increase by 10%) according to the current water flow speed (5m / s) and the gate angle error (±0.5°); the power of the countercurrent propeller is increased to 960W (original 800W) to offset the turbulent interference.

[0108] Execution and feedback: After the gate deflects to 60°, the angle sensor real-time feeds back data to the PID controller. If the error exceeds the threshold, secondary parameter optimization is triggered to ensure that the response time ≤ 1.5 seconds.

[0109] Effect: The detection accuracy remains above 95% in foggy environments, the gate response efficiency is increased by 25%, and the energy consumption is reduced by 15%.

[0110] Example 2: Predictive Maintenance and Equipment Fault Warning

[0111] Workflow:

[0112] Vibration data acquisition: A vibration sensor (frequency range 0 - 10 kHz) monitors the vibration spectrum of the propeller in real time, and a current sensor records the motor load data.

[0113] Feature extraction and model prediction: The edge node performs a fast Fourier transform (FFT) on the vibration signal, extracts the energy distribution in the 1 - 5 kHz frequency band and the root mean square value in the time domain (RMS = 0.8g), and inputs them into an LSTM model (2 layers with 128 neurons).

[0114] Fault warning and maintenance linkage: The LSTM predicts that the probability of propeller wear in the next 7 days is 82% (threshold 70%), triggering a secondary warning: automatically switch to a spare propeller, limit the gate opening to 75°; the cloud platform pushes a maintenance work order and synchronizes it to the operation and maintenance personnel's terminal.

[0115] Historical data update: After maintenance is completed, new fault samples are added to the training set to optimize the model's generalization ability.

[0116] Effect: The maintenance cost is reduced by 40%, the sudden shutdown rate drops by 60%, and the equipment life is extended by 30%.

[0117] Example 3: Multi - ship collaborative path planning and abnormal behavior detection

[0118] Workflow:

[0119] Data integration: The intelligent collaborative management terminal receives AIS data (ship position, speed) and camera images, and constructs a spatio - temporal graph model (node attributes include hull size, destination).

[0120] Conflict prediction and path planning: The graph neural network (GNN) calculates the edge weights Predicts the conflict point between two ships (safe distance D 安全 <50m), generates a staggering instruction (interval 30 seconds).

[0121] Abnormal behavior handling: The YOLOv7 model detects unregistered ships (mAP@0.5 = 96.8%), and the Transformer encoder marks them as illegal targets after comparing with the AIS database; the system limits the gate opening to 50% and links to the law enforcement platform to upload alarm records.

[0122] Instruction execution: The adaptive control module allocates additional power to the counter - current propeller (800W → 1000W) to offset the turbulence caused by multi - ship parallel operation and ensure the smooth deflection of the gate.

[0123] Effect: The ship - passing efficiency is increased by 25%, the recognition accuracy of illegal behaviors is 99.2%, and the accident rate is reduced by 70%.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the present technical solution, and they should all be covered within the scope of the claims of the present invention.

Claims

1. Intelligent fish interception and ship passing system based on multi-modal AI fusion and adaptive control, characterized by: include: A multimodal perception unit, which is used to collect environmental data through multi-source sensors, including lidar, sonar, infrared thermal imaging camera, and visible light camera; Edge computing nodes are used to run the Multi-Modal Data Fusion Network (MM-DFN), target detection model and path planning algorithm, and output control instructions; An adaptive control module, including a proportional-integral-derivative controller (PID) based on reinforcement learning and a reverse flow resistance compensation unit, is used to dynamically adjust the gate deflection parameters; Predictive maintenance unit, including vibration sensor and long short-term memory (LSTM) time series prediction model, used to monitor equipment health status and generate early warning signals; Intelligent collaborative management terminal, used to receive ship automatic identification system (Automatic Identification System, AIS) data and generate multi-ship collaborative passage strategy; A deformable garbage interception device, which uses a shape memory alloy mesh claw structure and is used to adaptively expand or fold to intercept floating objects; The multimodal sensing unit, edge computing node, adaptive control module, predictive maintenance unit and intelligent collaborative management terminal are connected via a communication link to form a closed-loop control.

2. The intelligent fish intercepting ship passing system based on multimodal AI fusion and adaptive control according to claim 1 is characterized by: The MM-DFN comprises: A hybrid attention mechanism module for dynamic weighted fusion of lidar point clouds, sonar echoes, and infrared thermal imaging feature maps; Residual blocks and cross-modal interaction layers are used to generate semantic maps of the environment; The output of the MM-DFN is connected to the target detection model of the edge computing node, and the target detection model is a lightweight Mobile Net V3 model that supports real-time processing of 4K resolution video streams.

3. The intelligent fish intercepting ship passing system based on multimodal AI fusion and adaptive control according to claim 1 is characterized by: The reinforcement learning-based PID controller adopts the Deep Deterministic Policy Gradient (DDPG) algorithm, whose state space includes gate angle error, water flow speed and ship weight, and action space is PID parameters (K p ,K i ,K d ) incremental adjustment; The countercurrent resistance compensation unit generates a propeller power distribution strategy based on a fluid mechanics simulation model, wherein the countercurrent propeller power is 800W and the downstream propeller power is 500W.

4. The intelligent fish intercepting ship passing system based on multimodal AI fusion and adaptive control according to claim 1 is characterized by: The input of the LSTM time series prediction model includes the frequency domain characteristics and time domain root mean square value of the vibration spectrum, and the output is the wear probability of the equipment in the next 7 days; The predictive maintenance unit is configured with a three-level warning mechanism. When the frequency deviation exceeds 5%, a first-level warning is triggered, and the cloud platform is linked to push a maintenance notification.

5. The intelligent fish intercepting ship passing system based on multimodal AI fusion and adaptive control according to claim 1 is characterized by: The intelligent collaborative management terminal constructs a spatiotemporal graph model through a graph neural network (GNN), where node attributes include ship location, speed and hull size, and edge weights are dynamically calculated based on the difference between ship spacing and estimated arrival time. The terminal also integrates an abnormal behavior detection module, which uses a YOLOv7-Transformer hybrid architecture to identify unregistered vessels and speeding behaviors.

6. The intelligent fish intercepting ship passing system based on multi-modal AI fusion and adaptive control according to claim 5 is characterized by: The YOLOv7 model of the abnormal behavior detection module is used for ship detection, and the mean average precision (mAP@0.5) reaches 96.8%; The Transformer encoder fuses the time-series AIS data, marks the offending vessel after comparing it with the maritime database, and issues a warning through a directional acoustic alarm device.

7. The intelligent fish intercepting ship passing system based on multi-modal AI fusion and adaptive control according to claim 1 is characterized by: The deformable garbage interception device automatically deploys when the sonar detects that the density of floating objects exceeds a threshold value, and activates the boost mode; The system is also equipped with an unmanned cleaning boat as an optional accessory for collaborative removal of high-density garbage.

8. The intelligent fish intercepting ship passing system based on multimodal AI fusion and adaptive control according to claim 1 is characterized by: The system deploys solar power supply modules, including photovoltaic panels and lithium battery packs, to support off-grid operation; The edge computing node uses NVIDIA Jetson AGX Orin, with an inference delay of ≤50ms, and implements edge-cloud collaborative computing through the Kubernetes framework.

9. The intelligent fish intercepting ship passing system based on multimodal AI fusion and adaptive control according to claim 1 is characterized by: Under extreme weather conditions, the multimodal perception unit switches to the laser radar and infrared thermal imaging dominant detection mode; The adaptive control module starts the anti-turbulence mode, the counter-flow propeller power is increased by 20%, and the brightness of the fog lights is adjusted to the maximum.

10. The intelligent fish intercepting ship passing system based on multi-modal AI fusion and adaptive control according to claim 1 is characterized by: The system stores operation records through blockchain technology to ensure that the data cannot be tampered with; The system complies with the International Maritime Organization (IMO) smart ship safety standards and supports integration with 5G and Beidou navigation systems.

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