Intelligent underwater power oil containment boom traction system based on multi-source cooperation and use method

Abstract

The invention discloses an intelligent underwater power oil containment boom traction system based on multi-source cooperation, which comprises a main controller, an environment sensing unit, a navigation and positioning unit, a self-adaptive adjustment unit, a dynamic modeling and intelligent decision-making unit, a redundant system unit, an intelligent control unit, a power management unit and a plurality of underwater power devices, the main controller is responsible for overall task allocation and coordination; the environment sensing unit is used for identifying and tracking the oil containment boom; the navigation and positioning unit realizes multi-source acousto-optic fusion positioning; the redundant system unit is used for function takeover when part of devices fail; the intelligent control unit monitors the state of each underwater power device in real time; and the power management unit monitors the electric quantity state of each underwater power device in real time. Through cooperative work of a plurality of underwater power devices, strong traction force is provided, the laying and dragging requirements of the heavy oil containment boom in a heavy storm environment can be effectively met, and the spilled oil recovery efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of marine engineering technology, and in particular to an intelligent underwater power oil boom traction system based on multi-source collaboration and a use method thereof. Background Art

[0002] Oil spills at sea have a serious impact on the environment and economy. Therefore, the rapid and effective recovery of oil spills has become an important research topic. Traditional oil spill recovery methods mainly rely on mechanical recovery, spraying of oil spill dispersants and controlled combustion. However, these methods have many technical defects and limitations when facing large-scale oil spills and complex sea conditions.

[0003] In addition, there are unmanned automatic towing devices, but the traction force is insufficient: although the existing unmanned automatic towing equipment (such as the automatic navigation and automatic recovery electric oil boom and its operation method disclosed in application number CN201910740094.9) can realize unmanned operation, it still has limitations in traction force and is difficult to meet the deployment and towing requirements of heavy oil booms.

[0004] There are also the following problems:

[0005] The traction of a single unmanned device is insufficient, and multiple devices need to work together. However, the existing technology has technical difficulties in multi-device collaborative control and error correction, making it difficult to achieve efficient collaborative work.

[0006] The data fusion accuracy is low. The existing multi-source data fusion method has low accuracy in complex sea conditions and is difficult to provide high-precision state estimation and error correction, which affects the overall performance of the system.

[0007] Poor real-time performance. Traditional data fusion methods are computationally complex and have poor real-time performance, making it difficult to meet the real-time control requirements of offshore oil spill recovery operations.

[0008] In summary, the existing technologies in offshore oil spill recovery operations have technical defects such as insufficient traction, complex operation, poor environmental adaptability, environmental pollution, high safety risks, difficulties in collaborative work and low data fusion accuracy. There is an urgent need for a new type of intelligent underwater powered oil boom traction system that can effectively solve the above problems and improve the efficiency and safety of oil spill recovery. Summary of the invention

[0009] The technical problem to be solved by the present invention is that the existing offshore oil spill recovery operations have technical defects such as insufficient traction, complex operation, poor environmental adaptability, environmental pollution, high safety risks, difficulties in collaborative work and low data fusion accuracy.

[0010] In order to solve the above technical problems, the technical solution of the present invention is to provide an intelligent underwater power boom traction system based on multi-source collaboration, including a main controller, an environment perception unit, a navigation and positioning unit, an adaptive adjustment unit, a dynamic modeling and intelligent decision-making unit, a redundant system unit, an intelligent control unit, a power management unit and multiple underwater power devices, wherein:

[0011] The underwater power unit is connected to the power management unit, including a propeller, a control module, a power module and a communication module, and each underwater power unit is equipped with a local controller with autonomous decision-making capabilities;

[0012] The main controller is connected to the environmental perception unit and is responsible for overall task allocation and coordination to ensure that multiple underwater power units work in coordination according to the predetermined strategy;

[0013] The environmental perception unit is connected to the adaptive adjustment unit and the navigation and positioning unit, and is integrated with sonar, camera and current meter to identify and track the oil boom;

[0014] The navigation and positioning unit includes an acoustic sensor and an optical sensor to realize multi-source acoustic and optical fusion positioning. The acoustic sensor is used for long-distance positioning, and the optical sensor is used for short-distance precise positioning.

[0015] The adaptive adjustment unit is connected with the dynamic modeling and intelligent decision-making unit, including a decision-making system based on fuzzy logic or neural network, and a data fusion system based on Kalman filtering or multi-sensor data fusion algorithm;

[0016] The dynamic modeling and intelligent decision-making unit is connected with the redundant system unit and the intelligent control unit, including the deep learning model and the reinforcement learning agent, and uses the system recognition model to update the model parameters in real time;

[0017] Redundant system units, used to take over functions when some devices fail;

[0018] The intelligent control unit is connected to the power management unit to monitor the status of each underwater power device in real time, and adjust the working status according to the actual situation to achieve coordinated control and error correction of multi-source underwater power devices;

[0019] The power management unit monitors the power status of each underwater power device in real time.

[0020] Optionally, the main controller includes a collaborative control strategy module, which adopts a consistency algorithm to achieve information exchange and collaborative action between multiple underwater power devices, and the real-time data provided by the environmental perception module serves as input to the consistency algorithm.

[0021] Optionally, a backup controller is provided in the redundant system unit, and the backup controller is connected to the main controller via a high-speed communication link.

[0022] Optionally, the control module in the underwater power device is provided with a microprocessor and equipped with sensors for obtaining the status of the underwater power device, the power module uses a replaceable lithium battery, and the communication module is used to communicate with other devices within the system and an external control center.

[0023] Optionally, also include:

[0024] A data storage module, used for storing system operation data and sensor data;

[0025] Fault diagnosis module, which detects system faults and provides suggested solutions;

[0026] Simulation training module, used to simulate different operating environments and fault conditions to train system response:

[0027] Remote control module, which allows operators to remotely monitor and control the operation of the system;

[0028] Modular design and standardized interfaces for rapid assembly, maintenance and upgrade of the system;

[0029] Software configured system using communication and control interfaces based on international standards.

[0030] The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration includes the following steps:

[0031] S1, system initialization, the main controller assigns specific tasks to each underwater power device according to the preset mission plan, including initial position, path planning and working mode;

[0032] S2. During the inflation and deployment of the oil boom, the operator or automatic equipment installs the underwater power device on the oil boom at predetermined intervals;

[0033] S3. Each underwater power device adjusts the thrust and direction of the propeller according to the command of the main controller, and cooperates to provide sufficient traction to enable the oil boom to operate within the predetermined area;

[0034] S4. The power management unit monitors the power state and charge state of each underwater power device in real time through voltage, current and temperature sensors;

[0035] S5, the redundant system unit monitors the status of each underwater power device in real time, and when a device failure is detected, the redundant system is immediately activated to take over its function;

[0036] S6, dynamic modeling and intelligent decision-making unit uses deep learning and reinforcement learning technology to update model parameters in real time and optimize system performance;

[0037] S7. After the task is completed, the main controller issues a recovery command, and each underwater power device stops working and returns to the designated position;

[0038] Optionally, in step S4, when the power level is lower than a set threshold, the power management unit starts the charging process and controls the charging equipment to perform constant current charging, constant voltage charging or pulse charging to ensure that the battery is charged within a safe range. When the battery state of charge is lower than the set threshold, the power management unit prompts that the battery needs to be replaced and coordinates the battery replacement process.

[0039] Optionally, step S5 includes:

[0040] S51, the intelligent control unit analyzes the sensor data on the underwater power device and identifies abnormal conditions. When an abnormal condition is detected, the system issues a fault alarm and records fault information;

[0041] S52, real-time synchronization of data between the main controller and the backup controller to ensure that the backup controller can take over at any time. When the main controller fails, the backup controller automatically takes over its functions;

[0042] S53, detect system failures in real time through sensors and monitoring systems, and isolate failed components to prevent the spread of failures;

[0043] S54, the redundant system unit takes over the function of the failed component to ensure the reliability and continuity of the system. The system records the fault information and prompts the operator to repair the fault;

[0044] S55. After the repair is completed, the system is reinitialized and resumes normal operation. The redundant system unit synchronizes the data during the failure to the main controller.

[0045] Optionally, step S6 includes:

[0046] S61, collecting sensor data during the operation of the system, and collecting control signals during the operation of the system;

[0047] S62, cleaning, normalizing and segmenting the collected data to prepare training and test data sets;

[0048] S63. Design the structure of a deep neural network, including an input layer, multiple hidden layers, and an output layer. Use a training data set and adjust the network weights and biases through a back-propagation algorithm so that the model can accurately predict the state and behavior of the system.

[0049] S64. Use the test data set to verify the performance of the model, adjust the hyperparameters to optimize the model, deploy the trained model to the system, and predict and adjust the system status in real time;

[0050] S65. Define the system's state space, action space, and reward function, build a simulation environment, and design a reinforcement learning agent, including a policy network and a value network;

[0051] S66, enables the agent to explore and learn in the simulation environment, and continuously adjusts the strategy through trial and error to maximize the cumulative reward;

[0052] S67. Use optimization algorithms such as gradient descent to adjust the parameters of the policy network, improve the performance of the policy, deploy the trained policy to the system, make real-time decisions and control the system behavior;

[0053] S68. Collect the input and output data of the system, including sensor data and control signals. Select the appropriate model structure, estimate the model parameters, so that the model can accurately describe the dynamic behavior of the system, use independent data sets to verify the performance of the model, adjust the model structure and parameters to optimize the model, and update the model parameters in real time during the operation of the system to adapt to the dynamic changes of the system.

[0054] Optionally, step S7 includes:

[0055] S71, the main controller sends a recovery command to each underwater power device according to the task completion status, and each underwater power device stops the current work and prepares to return to the designated position after receiving the recovery command;

[0056] S72, the navigation and positioning unit plans the optimal recovery path according to the current environment and the position of each underwater power device, and the adaptive adjustment unit dynamically adjusts the recovery path of each underwater power device according to the real-time environmental data and mission requirements;

[0057] S73. Each underwater power device coordinates and returns through a consistency algorithm to avoid conflicts and duplication of work, and the intelligent control unit monitors the status of each underwater power device in real time;

[0058] S74. After each underwater power unit returns to the designated location, personnel or automatic equipment will recover and maintain the underwater power unit. The power management unit will check the power status of each underwater power unit and perform necessary charging or battery replacement. The system will record the data during the recovery process, analyze and optimize it, and prepare for the next use.

[0059] In summary, the present invention has at least one of the following beneficial effects:

[0060] 1. The present invention provides powerful traction through the coordinated work of multiple underwater power devices, which can effectively cope with the deployment and towing requirements of heavy oil booms in strong winds and waves, and significantly improve the efficiency of oil spill recovery.

[0061] 2. The present invention adopts an improved SH adaptive federated filtering method to achieve coordinated control and error correction of multi-source power units, ensure the coordination and consistency of each power unit when performing tasks, avoid conflicts and duplication of work, and improve the overall performance of the system.

[0062] 3. The present invention integrates sonar, camera and current meter through the environmental perception module, combined with machine vision technology, can identify and track the oil boom in real time, provide high-precision environmental data, and ensure the stable operation of the system under complex sea conditions.

[0063] 4. The adaptive adjustment unit of the present invention includes a decision-making system based on fuzzy logic or neural network, and a data fusion system based on Kalman filtering or multi-sensor data fusion algorithm, which can dynamically adjust the working state of each power device according to real-time environmental data and task requirements, thereby improving the flexibility and adaptability of the system.

[0064] 5. The dynamic modeling and intelligent decision-making unit of the present invention utilizes deep learning and reinforcement learning techniques to update model parameters in real time, optimize system performance, and ensure optimal operating conditions under different environments.

[0065] 6. The redundant system unit of the present invention is used to take over the functions when some devices fail, ensuring the reliability and continuity of the system, preventing system shutdown or performance degradation, improving the safety of the system, and significantly improving the efficiency, safety and reliability of offshore oil spill recovery operations. It has broad application prospects and significant economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0067] The following combination Figure 1 The present invention is described in further detail.

[0068] The present invention discloses an intelligent underwater power oil boom traction system based on multi-source collaboration, referring to Figure 1 , including a main controller, an environmental perception unit, a navigation and positioning unit, an adaptive adjustment unit, a dynamic modeling and intelligent decision-making unit, a redundant system unit, an intelligent control unit, a power management unit and multiple underwater power devices, among which,

[0069] The underwater power unit is connected to the power management unit, including a propeller, a control module, a power module and a communication module, and each underwater power unit is equipped with a local controller with autonomous decision-making capabilities;

[0070] Specifically, the underwater power unit is fixed to the oil boom through magnetic or mechanical connection, and the controller is a high-efficiency and low-noise electric thruster; the control module is equipped with a microprocessor and sensors to obtain the status of the underwater power unit, including speed, position and temperature; the power module uses a replaceable high-energy density lithium battery, and the communication module is used to communicate with other devices inside the system and an external control center, and is responsible for sending and receiving data, including status information, control instructions, environmental data, etc. At the same time, the information transmission between underwater power units adopts an anti-interference communication protocol to ensure the communication quality in complex underwater environments.

[0071] The main controller is connected to the environmental perception unit and is responsible for overall task allocation and coordination to ensure that multiple underwater power units work in coordination according to the predetermined strategy;

[0072] Specifically, the main controller includes a collaborative control strategy module, which adopts a consistency algorithm, such as the Leader-Follower algorithm, the Flocking algorithm, etc., to realize information exchange and collaborative actions among multiple underwater power devices. Through iteration, the state of each underwater power device gradually tends to be consistent, thereby realizing coordinated and consistent task execution. The real-time data provided by the environmental perception module is used as the input of the consistency algorithm;

[0073] Its working principle is:

[0074] Information exchange: Each underwater power device exchanges status information, such as position, speed, direction, etc., with other adjacent devices through the communication module;

[0075] State update: Each underwater power device uses a consensus algorithm to update its own state based on the neighbor information received. This process is usually implemented through first-order differential equations or difference equations;

[0076] Iterative convergence: Through multiple iterations, the states of all underwater power devices gradually converge to a common value or reach a predetermined coordination state;

[0077] The environmental perception unit is connected to the adaptive adjustment unit and the navigation and positioning unit, and is integrated with sonar, camera and flow meter, combined with machine vision technology, to identify and track the oil boom;

[0078] The navigation and positioning unit includes an acoustic sensor and an optical sensor to realize multi-source acoustic and optical fusion positioning. The acoustic sensor is used for long-distance positioning, and the optical sensor is used for short-distance precise positioning.

[0079] The adaptive adjustment unit is connected with the dynamic modeling and intelligent decision-making unit, including a decision-making system based on fuzzy logic or neural network, and a data fusion system based on Kalman filtering or multi-sensor data fusion algorithm;

[0080] The decision system based on fuzzy logic maps the input value to the fuzzy set, performs fuzzy operation and reasoning, and finally obtains the output value. The steps of implementing the decision system based on fuzzy logic in the present invention are as follows:

[0081] 1. Input variables: speed, position;

[0082] 2. Membership function: defines fuzzy sets of speed and position, such as "low", "medium", and "high";

[0083] 3. Rule base: define If-Then rules, such as "if the speed is high and the position is deviated, then slow down";

[0084] 4. Reasoning and defuzzification: Perform fuzzy reasoning based on the rule base to obtain the control signal and defuzzify it into a specific value;

[0085] In the decision-making system based on neural network, the neural network simulates the working principle of biological neurons, performs complex nonlinear mapping and pattern recognition, has adaptive learning ability, and can adjust its own parameters according to input data. The steps implemented in the present invention are:

[0086] 1. Network structure: input layer (speed, position), hidden layer (several neurons), output layer (control signal);

[0087] 2. Training data: collect speed, position and corresponding control signals;

[0088] 3. Training process: Use the back-propagation algorithm to adjust the network weights and biases to make the output as close to the desired control signal as possible;

[0089] 4. Application: Output corresponding control signals according to new speed and position data.

[0090] In the data fusion system based on the Kalman filter algorithm, the Kalman filter is a recursive algorithm used to estimate the state of a dynamic system. By combining the dynamic model of the system and the sensor measurement data, an optimal estimate is provided. The steps implemented in the present invention are:

[0091] 1. State prediction: predict the speed and position at the next moment based on the dynamic model of the system;

[0092] 2. Covariance prediction: predict the covariance matrix at the next moment;

[0093] 3. Measurement update: Combine sensor measurement data to update estimates of speed and position;

[0094] 4. Iteration: Repeat the above steps to continuously update the estimates of speed and position

[0095] In a data fusion system based on a multi-sensor data fusion algorithm, multi-sensor data fusion improves the perception accuracy and reliability of the system by combining data from multiple sensors. The steps implemented in the present invention are:

[0096] 1. Data preprocessing: denoising and calibrating the data from sonar, camera and current meter;

[0097] 2. Data fusion: Use Kalman filtering to fuse the data from sonar, camera and flow meter to obtain the optimal speed and position estimate;

[0098] 3. State update: Update the system’s speed and position estimates based on the fused data;

[0099] 4. Feedback adjustment: adjust the operating parameters of the sensor according to the estimated results of speed and position;

[0100] The dynamic modeling and intelligent decision-making unit is connected with the redundant system unit and the intelligent control unit, including the deep learning model and the reinforcement learning agent, and uses the system recognition model to update the model parameters in real time;

[0101] Specifically, in the dynamic modeling and intelligent decision-making unit, the deep learning model extracts high-level features from the system operation data and provides them to the reinforcement learning agent and the system identification model. The reinforcement learning agent learns the optimal strategy based on the features extracted by the deep learning model, makes real-time decisions and controls the system behavior. The system identification model builds and updates the dynamic model of the system based on the input and output data of the system and provides it to the deep learning model and the reinforcement learning agent. The adaptive adjustment unit combines deep learning, reinforcement learning and system identification technologies to adjust the working state of the system in real time and improve the flexibility and adaptability of the system, including:

[0102] 1. Deep learning model training:

[0103] Collect sensor data and control signals during system operation, design a convolutional neural network (CNN) to extract spatial features from the data, train the CNN using the collected data, adjust the network weights through the back-propagation algorithm, deploy the trained CNN into the system, and predict the system status in real time;

[0104] 2. Reinforcement Learning Agent Strategy Optimization:

[0105] Define the system's state space (such as position, speed), action space (such as acceleration, deceleration) and reward function (such as minimizing energy consumption), design a deep Q network (DQN) to learn the optimal strategy, in the simulation environment, the agent continuously adjusts the strategy through trial and error to maximize the cumulative reward, uses the gradient descent algorithm to optimize the parameters of DQN, and deploys the trained DQN strategy to the system to make real-time decisions and control system behavior;

[0106] 3. System identification model update:

[0107] Collect the input and output data of the system, select a state space model to describe the dynamic behavior of the system, use the Kalman filter algorithm to estimate the model parameters, use an independent data set to verify the model performance, and update the model parameters in real time during the operation of the system to adapt to the dynamic changes of the system;

[0108] Redundant system units, which are divided into two types: hardware redundancy and software redundancy, are used to take over functions when some devices fail. When some devices fail, the backup components take over the functions of the failed components to avoid system downtime or performance degradation, and ensure system continuity and reliability;

[0109] Specifically, two controllers are configured in the system, one as the main controller, and a backup controller is set in the redundant system unit. The backup controller and the main controller are connected through a high-speed communication link. The main controller is responsible for normal operation, and the backup controller is in standby state. The main controller and the backup controller synchronize data in real time through a high-speed communication link to ensure that the backup controller can take over at any time; a master-slave switching algorithm is set in the control system. When the main controller fails, the backup controller automatically takes over its function, detects system failures in real time through sensors and monitoring systems, and isolates failed components to prevent the spread of failures;

[0110] The system is equipped with a fault detection module to monitor the status of the main controller in real time. When a fault is detected in the main controller, it will immediately switch to the backup controller. Multiple sensors are configured at key locations to ensure that the system can still obtain accurate data even if one sensor fails. Multiple power modules are configured to ensure that the system can still supply power normally even if one power module fails. Multiple communication paths are configured to ensure that data can still be transmitted through other paths even if one path fails.

[0111] The intelligent control unit is connected to the power management unit and adopts the improved SH adaptive federated filtering method to monitor the status of each underwater power device in real time and adjust the working status according to the actual situation to achieve coordinated control and error correction of multi-source underwater power devices.

[0112] Specifically, in the intelligent control unit, the SH adaptive federated filtering method is an algorithm for multi-source data fusion and error correction, and the implementation steps are:

[0113] 1. Model the underwater multi-source integrated navigation system and obtain each navigation sensor and its error model;

[0114] 2. Based on the error model, an improved SH adaptive filtering method is proposed;

[0115] 3. In the federated filter, an improved SH adaptive filtering method is used for time update and measurement update;

[0116] 4. Through the federated filter, the data of multiple sensors are fused to obtain the optimal state estimation;

[0117] The implementation steps of the intelligent control unit are:

[0118] 1. Each underwater power device is equipped with a sensor to collect data such as position, speed, and power in real time. The sensor data is transmitted to the intelligent control unit through the communication module. The intelligent control unit monitors the status of each underwater power device in real time, analyzes the data, and identifies abnormal situations;

[0119] 2. The intelligent control unit sends control instructions to each underwater power device to adjust the operating status of the thruster. Each underwater power device adjusts its own status according to the received control instructions and feeds back the new status information to the intelligent control unit to form a closed-loop control;

[0120] The specific process is:

[0121] 1. System modeling: Model the underwater multi-source combined navigation system to obtain each navigation sensor and its error model. The error model of the position sensor can be expressed as:

[0122] x k+1 =Ax k +Bu k +w k

[0123] In the formula, x k is the state vector, u k is the control input, w k is the process noise;

[0124] 2. Error correction: Use the improved SH adaptive filtering method to correct the sensor data errors, and use Kalman filtering for state estimation and error correction:

[0125]

[0126] In the formula, k k is the Kalman gain, z k is the measurement value, H is the measurement matrix;

[0127] 3. Collaborative control: Use a consensus algorithm to achieve collaborative control of multiple underwater power devices, using the Leader-Follower algorithm:

[0128]

[0129] In the formula, u i is the control input of the ith device, N i is the neighbor set of the ith device, a ij is the adjacency matrix element;

[0130] 4. Real-time monitoring and status adjustment: The intelligent control unit monitors the status of each underwater power device in real time, analyzes data, and identifies abnormal situations. Based on the calculation results, the intelligent control unit sends control instructions to each underwater power device to adjust the operating status of the thruster. Each underwater power device adjusts its own status according to the received control instructions and feeds back the new status information to the intelligent control unit to form a closed-loop control;

[0131] The power management unit monitors the power status of each underwater power device in real time.

[0132] In a further embodiment, the system of the present invention further comprises:

[0133] A data storage module, used for storing system operation data and sensor data;

[0134] Fault diagnosis module, which detects system faults and provides suggested solutions;

[0135] Simulation training module, used to simulate different operating environments and fault conditions to train system response:

[0136] Remote control module, which allows operators to remotely monitor and control the operation of the system;

[0137] Modular design and standardized interfaces for rapid assembly, maintenance and upgrade of the system;

[0138] XML or JSON-based software configuration system, using communication and control interfaces based on international standards, such as the ROS (Robot Operating System) framework.

[0139] The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration includes the following steps:

[0140] S1, system initialization, the main controller assigns specific tasks to each underwater power device according to the preset mission plan, including initial position, path planning and working mode;

[0141] Specifically, when the system is started, each underwater power unit establishes a connection with the main controller through the wireless communication module to ensure the stability and real-time performance of data transmission. The main controller assigns specific tasks to each power unit according to the preset task plan, including initial position, path planning and working mode. The environmental perception module is started to collect surrounding environmental data (such as water flow speed, direction, temperature, etc.) in real time, and transmit the data to the navigation and positioning unit and the adaptive adjustment unit.

[0142] S2, deployment phase: during the deployment of the oil boom, operators or automatic equipment install underwater power units on the oil boom at predetermined intervals to ensure that the power units are evenly distributed. Each power unit exchanges information with the main controller and other power units through the communication module to ensure that the status and position of each device are synchronized. The environmental perception module monitors the position and status of the oil boom in real time and provides high-precision data to support the decision-making of the navigation and positioning unit and the adaptive adjustment unit.

[0143] S3, in the towing phase, each underwater power unit adjusts the thrust and direction of the propeller according to the instructions of the main controller, and cooperates to provide sufficient traction to ensure that the oil boom operates within the predetermined area. At the same time, the navigation and positioning unit combines the data of the acoustic sensor and the optical sensor to provide high-precision positioning information to ensure the accurate deployment and towing of the oil boom. The adaptive adjustment unit dynamically adjusts the working state of each power unit according to the real-time environmental data (such as water flow speed, direction, etc.) and task requirements to ensure the flexibility and adaptability of the system. The intelligent control unit adopts the improved SH adaptive federal filtering method to perform error correction on the state of each power unit to ensure the coordination and consistency of each device;

[0144] S4, charging and battery replacement, the power management unit monitors the power status and charge state of each underwater power device in real time through voltage, current and temperature sensors. When the power is lower than the set threshold, the power management unit starts the charging process and controls the charging equipment to perform constant current charging, constant voltage charging or pulse charging to ensure that the battery is charged within a safe range. When the battery charge state is lower than the set threshold, the power management unit prompts that the battery needs to be replaced and coordinates the battery replacement process, including battery removal, installation and initialization;

[0145] S5, fault handling and redundancy management, the redundant system unit monitors the status of each underwater power device in real time, and when a device failure is detected, the redundant system is immediately activated to take over its function;

[0146] S51, the intelligent control unit analyzes the sensor data on the underwater power device, such as voltage, current, temperature, position, speed, etc., and identifies abnormal conditions, such as low voltage, high temperature, or position deviation, etc. When an abnormal condition is detected, the system issues a fault alarm and records the fault information;

[0147] S52, real-time data synchronization between the main controller and the backup controller to ensure that the backup controller can take over at any time. When the main controller fails, the backup controller automatically takes over its functions to ensure the continuous operation of the system, realize redundant control algorithms and data synchronization, and ensure that when the main controller fails, the backup controller can take over seamlessly. The redundant control algorithm includes the main-backup switching algorithm, fault detection and isolation algorithm, etc.;

[0148] S53, detect system failures in real time through sensors and monitoring systems, and isolate failed components to prevent the spread of failures;

[0149] S54, the redundant system unit takes over the function of the failed component to ensure the reliability and continuity of the system. The system records the fault information and prompts the operator to repair the fault;

[0150] S55. After the repair is completed, the system is reinitialized and resumes normal operation. The redundant system unit synchronizes the data during the failure to the main controller to ensure the consistency and integrity of the data.

[0151] S6, dynamic modeling and intelligent decision-making, the dynamic modeling and intelligent decision-making unit uses deep learning and reinforcement learning technology to update model parameters in real time and optimize system performance;

[0152] S61, collecting sensor data during the operation of the system, including position, speed, power, etc., and collecting control signals during the operation of the system, including control instructions of the thruster, etc.;

[0153] S62, cleaning, normalizing and segmenting the collected data to prepare training and test data sets;

[0154] S63. Design the structure of a deep neural network. Common network structures include convolutional neural networks (CNN) and recurrent neural networks (RNN), which include an input layer, multiple hidden layers, and an output layer. Use a training data set and adjust the network weights and biases through a back-propagation algorithm so that the model can accurately predict the state and behavior of the system.

[0155] S64. Use the test data set to verify the performance of the model, adjust the hyperparameters to optimize the model, deploy the trained model to the system, and predict and adjust the system status in real time;

[0156] S65. Define the state space, action space and reward function of the system, build a simulation environment, and design reinforcement learning agents, including policy networks and value networks. Commonly used algorithms include deep Q networks (DQN), policy gradients (PG) and proximal policy optimization (PPO);

[0157] S66, enables the agent to explore and learn in the simulation environment, and continuously adjusts the strategy through trial and error to maximize the cumulative reward;

[0158] S67. Use optimization algorithms such as gradient descent to adjust the parameters of the policy network, improve the performance of the policy, deploy the trained policy to the system, make real-time decisions and control the system behavior;

[0159] S68. Collect the input and output data of the system, including sensor data and control signals. Select a suitable model structure, such as a linear model, a nonlinear model or a neural network model, use algorithms such as the least squares method and Kalman filtering to estimate the model parameters so that the model can accurately describe the dynamic behavior of the system, use independent data sets to verify the performance of the model, adjust the model structure and parameters to optimize the model, and update the model parameters in real time during the operation of the system to adapt to the dynamic changes of the system;

[0160] S7, system recovery. After the task is completed, the main controller issues a recovery command, and each underwater power device stops working and returns to the designated position;

[0161] S71, the main controller sends a recovery command to each underwater power device according to the task completion status, and each underwater power device stops the current work and prepares to return to the designated position after receiving the recovery command;

[0162] S72, the navigation and positioning unit plans the optimal recovery path according to the current environment and the position of each underwater power device, and the adaptive adjustment unit dynamically adjusts the recovery path of each underwater power device according to the real-time environmental data and mission requirements;

[0163] S73. Each underwater power unit coordinates its return through a consistency algorithm to avoid conflicts and duplication of work. The intelligent control unit monitors the status of each underwater power unit in real time to ensure the safety and efficiency of the recovery process.

[0164] S74. After each underwater power unit returns to the designated location, personnel or automatic equipment will recover and maintain the underwater power unit. The power management unit will check the power status of each underwater power unit and perform necessary charging or battery replacement. The system will record the data during the recovery process, analyze and optimize it, and prepare for the next use.

[0165] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. The intelligent underwater power boom traction system based on multi-source collaboration is characterized by: It includes a main controller, an environmental perception unit, a navigation and positioning unit, an adaptive adjustment unit, a dynamic modeling and intelligent decision-making unit, a redundant system unit, an intelligent control unit, a power management unit and multiple underwater power devices, among which: The underwater power unit is connected to the power management unit, including a propeller, a control module, a power module and a communication module, and each underwater power unit is equipped with a local controller with autonomous decision-making capabilities; The main controller is connected to the environmental perception unit and is responsible for overall task allocation and coordination to ensure that multiple underwater power units work in coordination according to the predetermined strategy; The environmental perception unit is connected to the adaptive adjustment unit and the navigation and positioning unit, and is integrated with sonar, camera and current meter to identify and track the oil boom; The navigation and positioning unit includes an acoustic sensor and an optical sensor to realize multi-source acoustic and optical fusion positioning. The acoustic sensor is used for long-distance positioning, and the optical sensor is used for short-distance precise positioning. The adaptive adjustment unit is connected with the dynamic modeling and intelligent decision-making unit, including a decision-making system based on fuzzy logic or neural network, and a data fusion system based on Kalman filtering or multi-sensor data fusion algorithm; The dynamic modeling and intelligent decision-making unit is connected with the redundant system unit and the intelligent control unit, including the deep learning model and the reinforcement learning agent, and uses the system recognition model to update the model parameters in real time; Redundant system units, used to take over functions when some devices fail; The intelligent control unit is connected to the power management unit to monitor the status of each underwater power device in real time, and adjust the working status according to the actual situation to achieve coordinated control and error correction of multi-source underwater power devices; The power management unit monitors the power status of each underwater power device in real time.

2. The intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 1 is characterized in that: The main controller includes a collaborative control strategy module, which adopts a consistency algorithm to achieve information exchange and collaborative actions among multiple underwater power devices. The real-time data provided by the environmental perception module serves as input to the consistency algorithm.

3. The intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 1 is characterized in that: A backup controller is arranged in the redundant system unit, and the backup controller is connected to the main controller via a high-speed communication link.

4. The intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 1 is characterized in that: The control module in the underwater power device is provided with a microprocessor and equipped with a sensor for obtaining the status of the underwater power device. The power module uses a replaceable lithium battery, and the communication module is used to communicate with other devices inside the system and an external control center.

5. The intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 1 is characterized in that: Also includes: A data storage module, used for storing system operation data and sensor data; Fault diagnosis module, which detects system faults and provides suggested solutions; Simulation training module, used to simulate different operating environments and fault conditions to train system response: Remote control module, which allows operators to remotely monitor and control the operation of the system; Modular design and standardized interfaces for rapid assembly, maintenance and upgrade of the system; Software configured system using communication and control interfaces based on international standards.

6. The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration is characterized in that: The following steps are involved: S1, system initialization, the main controller assigns specific tasks to each underwater power device according to the preset mission plan, including initial position, path planning and working mode; S2. During the inflation and deployment of the oil boom, the operator or automatic equipment installs the underwater power device on the oil boom at predetermined intervals; S3. Each underwater power device adjusts the thrust and direction of the propeller according to the command of the main controller, and cooperates to provide sufficient traction to enable the oil boom to operate within the predetermined area; S4. The power management unit monitors the power state and charge state of each underwater power device in real time through voltage, current and temperature sensors; S5, the redundant system unit monitors the status of each underwater power device in real time, and when a device failure is detected, the redundant system is immediately activated to take over its function; S6, dynamic modeling and intelligent decision-making unit uses deep learning and reinforcement learning technology to update model parameters in real time and optimize system performance; S7. After the mission is completed, the main controller issues a recovery command, and each underwater power device stops working and returns to the designated position.

7. The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 6 is characterized in that: In step S4, when the power level is lower than the set threshold, the power management unit starts the charging process and controls the charging equipment to perform constant current charging, constant voltage charging or pulse charging to ensure that the battery is charged within a safe range. When the battery's state of charge is lower than the set threshold, the power management unit prompts that the battery needs to be replaced and coordinates the battery replacement process.

8. The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 6 is characterized in that: Step S5 includes: S51, the intelligent control unit analyzes the sensor data on the underwater power device and identifies abnormal conditions. When an abnormal condition is detected, the system issues a fault alarm and records fault information; S52, real-time synchronization of data between the main controller and the backup controller to ensure that the backup controller can take over at any time. When the main controller fails, the backup controller automatically takes over its functions; S53, detect system failures in real time through sensors and monitoring systems, and isolate failed components to prevent the spread of failures; S54, the redundant system unit takes over the function of the failed component to ensure the reliability and continuity of the system. The system records the fault information and prompts the operator to repair the fault; S55. After the repair is completed, the system is reinitialized and resumes normal operation. The redundant system unit synchronizes the data during the failure to the main controller.

9. The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 6 is characterized in that: Step S6 includes: S61, collecting sensor data during the operation of the system, and collecting control signals during the operation of the system; S62, cleaning, normalizing and segmenting the collected data to prepare training and test data sets; S63. Design the structure of a deep neural network, including an input layer, multiple hidden layers, and an output layer. Use a training data set and adjust the network weights and biases through a back-propagation algorithm so that the model can accurately predict the state and behavior of the system. S64. Use the test data set to verify the performance of the model, adjust the hyperparameters to optimize the model, deploy the trained model to the system, and predict and adjust the system status in real time; S65. Define the system's state space, action space, and reward function, build a simulation environment, and design a reinforcement learning agent, including a policy network and a value network; S66, enables the agent to explore and learn in the simulation environment, and continuously adjusts the strategy through trial and error to maximize the cumulative reward; S67. Use optimization algorithms such as gradient descent to adjust the parameters of the policy network, improve the performance of the policy, deploy the trained policy to the system, make real-time decisions and control the system behavior; S68. Collect the input and output data of the system, including sensor data and control signals. Select the appropriate model structure, estimate the model parameters, so that the model can accurately describe the dynamic behavior of the system, use independent data sets to verify the performance of the model, adjust the model structure and parameters to optimize the model, and update the model parameters in real time during the operation of the system to adapt to the dynamic changes of the system.

10. The method for using the intelligent underwater power oil boom traction system based on multi-source collaboration according to claim 6 is characterized in that: Step S7 includes: S71, the main controller sends a recovery command to each underwater power device according to the task completion status, and each underwater power device stops the current work and prepares to return to the designated position after receiving the recovery command; S72, the navigation and positioning unit plans the optimal recovery path according to the current environment and the position of each underwater power device, and the adaptive adjustment unit dynamically adjusts the recovery path of each underwater power device according to the real-time environmental data and mission requirements; S73. Each underwater power device coordinates and returns through a consistency algorithm to avoid conflicts and duplication of work, and the intelligent control unit monitors the status of each underwater power device in real time; S74. After each underwater power unit returns to the designated location, personnel or automatic equipment will recover and maintain the underwater power unit. The power management unit will check the power status of each underwater power unit and perform necessary charging or battery replacement. The system will record the data during the recovery process, analyze and optimize it, and prepare for the next use.

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