Water flow estimation and ship waterline monitoring combined design method and system

Abstract

The invention provides a joint design method and system for water flow estimation and ship waterline monitoring, and belongs to the technical field of ship monitoring, and the method comprises the following steps: firstly, a shore-based center deploys an underwater robot and plans a motion path according to the needs of a monitoring task, and builds a water gauge data set for model training; when the underwater robot arrives at the nearby area of the ship, the underwater robot measures the water flow speed and direction by using a Doppler velocimeter, and predicts a future water flow velocity field in combination with the trajectory information of the underwater robot; meanwhile, the underwater robot collects a waterline image through an airborne camera, pre-processes the waterline image, and calculates the credibility of the waterline image; and further, the underwater robot corrects the attitude in real time according to water flow velocity field prediction and waterline image credibility, and joint estimation of the water flow and the ship waterline is realized. According to the invention, water flow estimation and ship waterline monitoring are combined, and stable and efficient omnibearing ship waterline monitoring is realized through the underwater robot.

Description

Technical Field

[0001] The invention belongs to the technical field of ship monitoring, and specifically relates to a joint design method and system for water flow estimation and ship waterline monitoring. Background Art

[0002] The safety of ships has increasingly become a focus of attention in the shipping industry. As one of the indicators for evaluating the floating stability and structural safety of ships, the waterline is currently mainly monitored by manual visual inspection. Although this method has played a role in ensuring the safety of ships, it is costly, inefficient, and cannot achieve all-weather, all-round real-time monitoring of the waterline. In order to meet the growing demand for intelligent ports and promote the development of intelligent ports, it is urgent to use underwater robots to monitor the waterline of ships.

[0003] In the prior art, the publication number is CN118366016A, and the name is "A method for intelligent monitoring of ships based on cross-domain perception of underwater robots", which discloses a method for intelligent monitoring of ships based on cross-domain perception of underwater robots. This method first sets the ship markers and establishes a marker data set, then deploys the underwater robot to the monitoring waters to perform the marker monitoring task, uses the onboard camera to collect the hull marker image, and then performs image segmentation on the collected surface marker image, extracts the feature information of the image, and detects the marker through the model. However, this method does not take into account the impact of water flow on the underwater robot. The water flow will cause the underwater robot's posture to change in real time, reducing reliability and failing to monitor the waterline in real time.

[0004] Furthermore, the publication number is CN110749890A, and the name is “A method for collaborative ocean current estimation among multiple underwater robots”, which discloses a method for collaborative ocean current estimation among multiple underwater robots. The method is based on the position and distance information of the surface mother ship and the position estimation information, ocean current estimation information, and distance information of other underwater robots; the ocean current estimation result is integrated with the Doppler velocity measurement information to compensate for the position error caused by the Doppler velocity measurement of the current, thereby realizing the position estimation of the underwater robot. This method estimates the water flow based on the state information and velocity measurement information among multiple underwater robots, but does not jointly design the water flow estimation with the actual monitoring task, resulting in competition for communication and computing resources. In order to realize the stable and efficient monitoring of the ship's waterline by underwater robots, it is urgent to design a joint design method for water flow estimation and ship waterline monitoring. Summary of the invention

[0005] In order to solve the problem that the existing technology has low reliability and cannot monitor the ship's waterline in real time, and at the same time to solve the problem that the existing technology does not jointly design water flow estimation and actual monitoring tasks, resulting in competition for communication and computing resources. The present invention provides a joint design method and system for water flow estimation and ship waterline monitoring, which achieves the purpose of stable and efficient monitoring of the ship's waterline by an underwater robot.

[0006] The technical solution of the combined design method and system for water flow estimation and ship waterline monitoring of the present invention is:

[0007] A joint design method for water flow estimation and ship waterline monitoring, characterized in that it includes the following steps:

[0008] S1. Initialize the underwater robot, select the ship to be monitored, match the corresponding number of underwater robots according to the number of water gauges to be monitored, and the shore-based control center divides each underwater robot into respective monitoring areas according to the monitoring task requirements; specifically, set the monitoring target as the ship's waterline, the underwater robot collects images of the ship's waterline, establishes a water gauge data set, trains the water gauge data set through a neural network to obtain a deep learning model and deploys the underwater robot to the mission waters, and the underwater robot performs the monitoring task according to the path and monitoring area planned by the shore-based control center;

[0009] S2. After the underwater robot enters the monitoring area, it measures the water flow through a Doppler velocimeter, collects the water flow velocity and flow direction data at the same time within a predetermined time interval, compares the actual running trajectory of the underwater robot with the path planned by the shore-based control center, establishes the deviation, and obtains the position, speed and attitude of the underwater robot through an inertial navigation locator;

[0010] S3, each underwater robot communicates through broadcasting, sends and receives other local water flow velocity data, and integrates the current trajectory deviation and flow velocity data through Kalman filtering to predict the future flow velocity field; and uses the underwater robot to collect waterline images in real time, and pre-processes the collected waterline images, and then pre-processes the waterline images and obtains credibility;

[0011] S4. The underwater robot collects its own position, speed and attitude data through the inertial navigation positioning device to obtain its current attitude, and corrects its attitude through the waterline image credibility and the water flow velocity field prediction as feedback signals; when the waterline image credibility reaches the threshold, the underwater robot adjusts its attitude through the water flow velocity field prediction to minimize the attitude optimization function and achieve stable monitoring; if the waterline image credibility does not reach the threshold, the underwater robot continues to search and adjust its attitude along the path planned by the shore-based control center until the waterline image credibility reaches the threshold;

[0012] S5. Use an edge detection algorithm to identify the edges in the preprocessed waterline image, and use double threshold segmentation to separate the waterline in the image; specifically, the underwater robot uses a deep learning algorithm to identify the water gauge number and combines it with the separated waterline to obtain the position of the waterline on the water gauge, and then obtains the draft value of the ship through the water gauge reading algorithm and uploads it to the shore-based control center;

[0013] S6. According to the monitoring task requirements, the underwater robot (4) determines whether the monitoring task is completed. If not, it continues to perform monitoring. If the task is completed, the shore-based control center (1) issues a command to control the underwater robot (4) to return.

[0014] A further improvement of the technical solution of the present invention is that: in step S1, a water gauge data set is established for training the deep learning model, and the monitored ship water gauge is a water gauge drawn on both sides of a large ship, distributed at the bow, midship and stern, which is specifically expressed as follows:

[0015]

[0016] Where C is the ship's water gauge reading, H d is the height of the water gauge character spacing, F is the character reading closest to the waterline, H w It is the height from the waterline to the nearest character.

[0017] A further improvement of the technical solution of the present invention is that: in step S2, the water flow is measured by a Doppler velocimeter, and the water flow velocity and flow direction data are collected simultaneously within a predetermined time interval, specifically,

[0018] At preset time intervals, the Doppler velocimeter emits sound wave pulses and receives sound waves reflected from the seabed or particles in the water. The speed and direction of the water flow are determined by calculating the changes in the sound wave frequency. The formula is:

[0019]

[0020] Where v is the velocity of the water flow, Δf is the Doppler frequency shift, c is the speed of sound waves in water, and f 0 is the frequency of the sound waves emitted by the Doppler velocimeter, and θ is the angle between the direction of the sound wave emission and the direction of the water flow.

[0021] A further improvement of the technical solution of the present invention is that in step S2, the actual running trajectory of the underwater robot is compared with the path planned by the shore-based control center to establish a deviation, specifically,

[0022] After collecting the water flow velocity and direction data, the underwater robot will compare these data with its actual motion trajectory and establish the trajectory deviation Δd:

[0023]

[0024] Where Δd is the trajectory deviation, (x a ,x p ) is the actual position coordinate of the underwater robot, (y a ,y p ) are the predetermined position coordinates of the underwater robot.

[0025] A further improvement of the technical solution of the present invention is that in step S3, each underwater robot communicates by broadcasting, sends and receives other local water flow velocity data, and integrates the current trajectory deviation and flow velocity data through Kalman filtering to predict the future flow velocity field, specifically,

[0026] Assume that the flow velocity data collected by each underwater robot is v i (t), where i represents the i-th underwater robot and t represents time. Set the initial state estimate and the covariance matrix P 0 , predict the state and covariance of the next moment according to the underwater robot dynamics model:

[0027]

[0028] Where k represents the time step, is the predicted state matrix at time step k, F k is the state transition matrix, is the optimal estimated state matrix at time step k-1, B k is the control matrix, u k is the control input matrix at time step k, P k|k-1 is the prediction covariance matrix at time step k, P k-1 is the covariance at time step k-1, is the transpose of the state transfer matrix, Q k is the process noise covariance matrix;

[0029] When there is new measurement data z k When , update the state estimate:

[0030]

[0031] P k =(IK k H k ) k|k-1

[0032] Among them, K k is the Kalman gain matrix, P k|k-1 is the prediction covariance matrix at time step k, Hk is the observation matrix, is the transpose of the observation matrix, R k is the observation noise covariance matrix, is the updated state estimate matrix at time step k, is the predicted state matrix at time step k, I is the identity matrix, P k The updated covariance matrix at time step k, P k|k-1 is the prediction covariance matrix at time step k.

[0033] A further improvement of the technical solution of the present invention is that: in step S3, the collected waterline image is preprocessed, and then the credibility of the waterline image is calculated based on the preprocessed image, including the following steps:

[0034] S3.1. Denoising the image: Images often contain noise. To reduce random noise and improve image clarity, the image is convolved using a Gaussian kernel function to remove the Gaussian noise.

[0035] S3.2, enhance contrast, improve the visual effect of the image, make the image details clearer, use histogram equalization, as follows:

[0036]

[0037] Where r(k) is the gray level of the input image, T(r(k)) is the cumulative probability of gray level r(k), s(k) is the gray level of the output image, k is the gray level of the pixel in the image, and n j is the pixel value of gray level j, n is the total pixel value;

[0038] S3.3. After image preprocessing, the waterline image credibility C is calculated:

[0039]

[0040] Among them, N e is the number of edge pixels detected, N t is the total number of pixels in the image, is the variance of the image noise.

[0041] A further improvement of the technical solution of the present invention is that in step S4, the underwater robot collects position, speed and posture data, as well as the credibility of the waterline image, and performs joint posture correction so that the credibility of the waterline image reaches a threshold and the posture is stabilized. The optimization function is expressed as follows:

[0042]

[0043] in, is the minimization objective function of the underwater robot, n is the total number of time steps, v i (t) is the actual speed of the i-th underwater robot at time t, is the predicted velocity of the ith underwater robot at time t, q is the current posture quaternion, q d is the desired attitude quaternion, λ is the weight coefficient, and C is the waterline image credibility.

[0044] A further improvement of the technical solution of the present invention is that: in step S5, the edge detection algorithm is used to identify the edge in the preprocessed waterline image, and double threshold segmentation is used to separate the waterline in the image, which specifically includes the following steps:

[0045] S5.1, calculating the gradient magnitude and direction of each pixel in the waterline image;

[0046] S5.2, apply non-maximum suppression to refine the edges;

[0047] S5.3, apply double threshold detection to determine real and potential edges;

[0048] S5.4. Determine edges through edge tracking and hysteresis threshold.

[0049] A system for water flow estimation and ship waterline monitoring, characterized by: using the joint design method described in any one of claims 1-8, including a perception module, a thruster module, a control module and a wireless communication module located on an underwater robot.

[0050] A further improvement of the above technical solution of the present invention is that: the perception module includes an airborne camera for collecting the waterline of the ship on the water surface and a Doppler velocimeter for obtaining the flow velocity of the water flow; the thruster module includes an underwater thruster and a drive module thereof for providing power; the control module includes a processing unit for data processing and model deployment and an inertial navigation positioning device for obtaining the position and posture of the underwater robot; the wireless communication module includes a wireless radio frequency communicator for communication between underwater robots and an electromagnetic communicator for communication between the shore-based control center and the underwater robot.

[0051] Due to the adoption of the above technical solution, the technical advances achieved by the present invention include:

[0052] Traditional ship waterline monitoring methods can usually only monitor the waterline at a single fixed position, which is often limited by the water environment and the ship's berthing position, and cannot monitor the waterline in all directions. The maneuverability of underwater robots can monitor the ship's waterline from all angles and can also be deployed quickly.

[0053] The prior art does not jointly design the underwater robot's ship waterline monitoring and water flow estimation, resulting in competition for communication computing resources. This scheme proposes a joint design method for water flow estimation and ship waterline monitoring. According to steps S2 and S3 in the claims, the underwater robot collects and fuses data, and then according to step S4 in the claims, the posture of the underwater robot is adjusted in real time through water flow estimation to improve resource utilization. According to step S5 in the claim specification, reliable and accurate identification of the ship's waterline can be guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 It is a work flow chart of a joint design method for water flow estimation and ship waterline monitoring of the present invention;

[0055] Figure 2 It is a working schematic diagram of a single underwater robot of a combined design method and system for water flow estimation and ship waterline monitoring of the present invention;

[0056] Figure 3 It is a working schematic diagram of multiple underwater robots of a joint design method and system for water flow estimation and ship waterline monitoring of the present invention.

[0057] In the attached figure: 1. Shore-based control center; 2. Underwater robot communication and power supply composite cable car; 3. Monitoring area; 4. Underwater robot; 5. Ship. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in combination with specific implementation methods and with reference to the accompanying drawings. In the following description, the description of the public structure and technology is omitted to avoid unnecessary confusion of the concept of the present invention.

[0059] The present invention provides a system for estimating water flow and monitoring the waterline of a ship 5 , the system mainly comprising a sensing module, a propeller module, a control module and a wireless communication module arranged on an underwater robot 4 .

[0060] Specifically, the above-mentioned perception module includes an onboard camera for collecting the waterline of the ship 5 on the water surface and a Doppler velocimeter for obtaining the water flow velocity.

[0061] The thruster module includes an underwater thruster and a driving module thereof for providing power.

[0062] The control module includes a microcomputer for processing data and an inertial navigation positioning device for obtaining the 4-position posture of the underwater robot.

[0063] The wireless communication module includes a wireless radio frequency communicator for communication between the underwater robots 4 and an electromagnetic communicator for communication between the shore-based control center 1 and the underwater robot 4 .

[0064] At the same time, the present invention also provides a joint design method for water flow estimation and ship waterline monitoring, which refers to Figure 1 , Figure 2 and Figure 3 It can be seen that the following steps are included:

[0065] S1. The shore-based control center 1 initializes the underwater robot 4, selects the ship 5 to be monitored, matches the corresponding number of underwater robots 4 according to the number of water gauges to be monitored, and at the same time, the shore-based control center divides each underwater robot 4 into respective monitoring areas 3 according to the monitoring task requirements; specifically, the monitoring target is set to the waterline of the ship 5, the underwater robot 4 collects the image of the waterline of the ship 5, establishes a water gauge data set, trains the water gauge data set through a neural network to obtain a deep learning model and deploys the underwater robot 4 to the mission waters, and the underwater robot 4 performs the monitoring task according to the path and monitoring area 3 planned by the shore-based control center 1.

[0066] The water gauge dataset established above is used for training the deep learning model. The water gauge of the monitored ship 5 is the water gauge drawn on both sides of the large ship 5, distributed at the bow, midship and stern, which is specifically expressed as follows:

[0067]

[0068] Where C is the ship's water gauge reading, H d is the height of the water gauge character spacing, F is the character reading closest to the waterline, H w It is the height from the waterline to the nearest character.

[0069] S2. After the underwater robot 4 enters the monitoring area 3, the water flow is measured by a Doppler velocimeter, and the water flow velocity and flow direction data are collected simultaneously within a predetermined time interval. The actual motion trajectory of the underwater robot 4 is compared with the path planned by the shore-based control center, and a deviation is established. The position, speed and posture of the underwater robot 4 are obtained through an inertial navigation locator.

[0070] Specifically, after the underwater robot 4 enters the monitoring area, it measures the water flow through the Doppler velocimeter, and collects the water flow velocity and flow direction data at the same time within a predetermined time interval. Within the preset time interval, the Doppler velocimeter will emit sound wave pulses and receive sound waves reflected from the seabed or particles in the water, and determine the speed and flow direction of the water flow by calculating the change in the sound wave frequency. The formula is expressed as follows:

[0071]

[0072] Where v is the velocity of the water flow, Δf is the Doppler frequency shift, c is the speed of sound waves in water, and f 0 is the frequency of the sound waves emitted by the Doppler velocimeter, and θ is the angle between the direction of the sound wave emission and the direction of the water flow.

[0073] After collecting the water flow velocity and direction data, the underwater robot 4 will compare these data with its actual motion trajectory and establish the trajectory deviation Δd:

[0074]

[0075] Where Δd is the trajectory deviation, (x a ,x p ) is the actual position coordinate of the underwater robot, (y a ,y p ) are the predetermined position coordinates of the underwater robot.

[0076] S3. Each underwater robot 4 communicates through broadcasting, sends and receives other local water flow velocity data, and integrates the current trajectory deviation and flow velocity data through Kalman filtering to predict the future flow velocity field; and uses the underwater robot 4 to collect waterline images in real time, and pre-processes the collected waterline images, and then calculates the credibility of the waterline images based on the pre-processed images.

[0077] Specifically, when performing the monitoring task, each underwater robot 4 will exchange the water flow velocity data measured locally by itself through the wireless communication module in a broadcasting manner, and use Kalman filtering to integrate the current flow velocity data and trajectory deviation. Assume that the flow velocity data collected by each underwater robot 4 is v i (t), where i represents the i-th underwater robot 4 and t represents time. Set the initial state estimate and the covariance matrix P 0 , predict the state and covariance of the next moment according to the underwater robot 4 dynamic model:

[0078]

[0079] Where k represents the time step, is the predicted state matrix at time step k, F k is the state transition matrix, is the optimal estimated state matrix at time step k-1, B k is the control matrix, u k is the control input matrix at time step k, P k|k-1 is the prediction covariance matrix at time step k, P k-1 is the covariance at time step k-1, is the transpose of the state transfer matrix, Q kis the process noise covariance matrix.

[0080] When there is new measurement data z k When , update the state estimate:

[0081]

[0082] P k =(IK k H k ) k|k-1

[0083] Among them, K k is the Kalman gain matrix, P k|k-1 is the prediction covariance matrix at time step k, H k is the observation matrix, is the transpose of the observation matrix, R k is the observation noise covariance matrix, is the updated state estimate matrix at time step k, is the predicted state matrix at time step k, I is the identity matrix, P k The updated covariance matrix at time step k, P k|k-1 is the prediction covariance matrix at time step k.

[0084] For the collected waterline images, preprocessing is first performed to remove noise from the image and enhance the contrast to improve the image quality. The specific process is as follows:

[0085] S3.1. Denoising the image: Images often contain noise. In order to reduce random noise and improve image clarity, the Gaussian kernel function is used to convolve the image and remove the Gaussian noise.

[0086] S3.2, enhance contrast, improve the visual effect of the image, make the image details clearer, use histogram equalization, as follows:

[0087]

[0088] Where r(k) is the gray level of the input image, T(r(k)) is the cumulative probability of gray level r(k), s(k) is the gray level of the output image, k is the gray level of the pixel in the image, and n j is the pixel value at gray level j, and n is the total pixel value.

[0089] S3.3. After image preprocessing, the waterline image credibility C is calculated:

[0090]

[0091] Among them, N eis the number of edge pixels detected, N t is the total number of pixels in the image, is the variance of the image noise.

[0092] S4. The underwater robot 4 collects its own position, speed and attitude data through the inertial navigation positioning device to estimate its current attitude, and corrects its own attitude through the waterline image credibility and the water flow velocity field prediction as feedback signals; when the waterline image credibility reaches the threshold, the underwater robot 4 adjusts its attitude through the water flow velocity field prediction to minimize the attitude optimization function and achieve stable monitoring; if the waterline image credibility does not reach the threshold, the underwater robot 4 continues to search and adjust its attitude along the path planned by the shore-based control center until the waterline image credibility reaches the threshold.

[0093] Specifically, the underwater robot 4 collects position, speed and posture data, as well as the credibility of the waterline image, and performs joint posture correction so that the credibility of the waterline image reaches a threshold and the posture is stabilized. The optimization function is expressed as follows:

[0094]

[0095] in, is the minimization objective function of the underwater robot 4, n is the total number of time steps, v i (t) is the actual speed of the i-th underwater robot 4 at time t, is the predicted speed of the ith underwater robot 4 at time t, q is the current posture quaternion, q d is the desired attitude quaternion, λ is the weight coefficient, and C is the waterline image credibility.

[0096] S5. Use an edge detection algorithm to identify the edges in the preprocessed waterline image, and use double threshold segmentation to separate the waterline in the image; specifically, the underwater robot 4 uses a deep learning algorithm to identify the water gauge numbers and combines them with the separated waterline to obtain the position of the waterline on the water gauge, and then uses the water gauge reading algorithm to obtain the draft depth of the ship and upload it to the shore-based control center 1.

[0097] The specific steps include:

[0098] S5.1, calculating the gradient magnitude and direction of each pixel in the waterline image;

[0099] S5.2, apply non-maximum suppression to refine the edges;

[0100] S5.3, apply double threshold detection to determine real and potential edges;

[0101] S5.4. Determine edges through edge tracking and hysteresis threshold.

[0102] The YOLOv5 algorithm is used for draft gauge character recognition. The recognized draft gauge characters and the separated ship waterline are combined with the draft gauge reading algorithm to obtain the ship's draft value.

[0103] S6. According to the monitoring task requirements, the underwater robot (4) determines whether the monitoring task is completed. If not, it continues to perform monitoring. If the task is completed, the shore-based control center (1) issues a command to control the underwater robot (4) to return.

[0104] In the above embodiments, the present invention provides a joint design method and system for water flow estimation and ship waterline monitoring. The maneuverability of the underwater robot in the present invention can monitor the ship's waterline at various angles and can also be rapidly deployed. At the same time, the present invention adjusts the posture of the underwater robot in real time through water flow estimation, thereby improving resource utilization and ensuring reliable and accurate identification of the ship's waterline.

[0105] The above-described embodiments are merely descriptions of preferred implementations of the present invention, and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, various modifications and improvements made by ordinary persons in the art to the technical solution of the present invention should fall within the protection scope of the present invention, and the technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A joint design method for water flow estimation and ship waterline monitoring, characterized by: The following steps are included: S1, initializing the underwater robot (4), selecting the ship (5) to be monitored, matching the corresponding number of underwater robots (4) according to the number of water gauges to be monitored, and the shore-based control center divides each underwater robot (4) into respective monitoring areas (3) according to the monitoring task requirements; specifically, setting the monitoring target as the waterline of the ship (5), the underwater robot (4) collects images of the waterline of the ship (5), establishes a water gauge data set, trains the water gauge data set through a neural network to obtain a deep learning model and deploys the underwater robot (4) to the mission water area, and the underwater robot (4) performs the monitoring task according to the path and monitoring area (3) planned by the shore-based control center (1); S2, after the underwater robot (4) enters the monitoring area (3), the water flow is measured by a Doppler velocimeter, and the water flow velocity and flow direction data are collected at the same time within a predetermined time interval, and the actual motion trajectory of the underwater robot (4) is compared with the path planned by the shore-based control center to establish a deviation, and the position, speed and posture of the underwater robot (4) are obtained by an inertial navigation positioning device; S3, each underwater robot (4) communicates through broadcasting, sends and receives other local water flow velocity data, and integrates the current trajectory deviation and flow velocity data through Kalman filtering to predict the future flow velocity field; and uses the underwater robot (4) to collect waterline images in real time, and pre-processes the collected waterline images, and then calculates the credibility of the waterline images based on the pre-processed images; S4, the underwater robot (4) acquires its own position, speed and attitude data through an inertial navigation positioning device to obtain its current attitude, and corrects its own attitude through the waterline image credibility and the water flow velocity field prediction as feedback signals; when the waterline image credibility reaches a threshold, the underwater robot (4) adjusts its attitude through the water flow velocity field prediction to minimize the attitude optimization function and achieve stable monitoring; if the waterline image credibility does not reach the threshold, the underwater robot (4) continues to search and adjust its attitude along the path planned by the shore-based control center until the waterline image credibility reaches the threshold; S5, using an edge detection algorithm to identify the edges in the preprocessed waterline image, and using double threshold segmentation to separate the waterline in the image; specifically, the underwater robot (4) uses a deep learning algorithm to identify the water gauge number and combines it with the separated waterline to obtain the position of the waterline on the water gauge to obtain the draft depth of the ship (5), and uploads it to the shore-based control center (1); S6. According to the monitoring task requirements, the underwater robot (4) determines whether the monitoring task is completed. If not, it continues to perform monitoring. If the task is completed, the shore-based control center (1) issues a command to control the underwater robot (4) to return.

2. A joint design method for water flow estimation and ship waterline monitoring according to claim 1, characterized in that: In step S1, a water gauge data set is established for training the deep learning model. The water gauge of the monitored ship (5) is a water gauge drawn on both sides of the large ship (5), distributed at the bow, the middle and the stern, which is specifically expressed as follows: Where C is the ship's water gauge reading, H d is the height of the water gauge character spacing, F is the character reading closest to the waterline, H w It is the height from the waterline to the nearest character.

3. A joint design method for water flow estimation and ship waterline monitoring according to claim 1, characterized in that: In step S2, the water flow is measured by a Doppler velocimeter, and the water flow velocity and flow direction data are collected simultaneously within a predetermined time interval, specifically, At preset time intervals, the Doppler velocimeter emits sound wave pulses and receives sound waves reflected from the seabed or particles in the water. The speed and direction of the water flow are determined by calculating the changes in the sound wave frequency. The formula is: Among them, v is the water flow velocity, Δf is the Doppler frequency shift, c is the speed of sound waves propagating in water, f0 is the frequency of sound waves emitted by the Doppler velocimeter, and θ is the angle between the sound wave emission direction and the water flow direction.

4. The combined design method for water flow estimation and ship waterline monitoring according to claim 1 is characterized by: In step S2, the actual running trajectory of the underwater robot (4) is compared with the path planned by the shore-based control center to establish a deviation, specifically, After collecting the water flow velocity and direction data, the underwater robot (4) will compare these data with its actual motion trajectory and establish the trajectory deviation Δd: Where Δd is the trajectory deviation, (x a ,x p ) is the actual position coordinate of the underwater robot (4), (y a ,y p ) are the predetermined position coordinates of the underwater robot (4).

5. The combined design method for water flow estimation and ship waterline monitoring according to claim 1 is characterized by: In step S3, each underwater robot (4) communicates by broadcasting, sends and receives other local water flow velocity data, and integrates the current trajectory deviation and flow velocity data through Kalman filtering to predict the future flow velocity field, specifically, Assume that the flow velocity data collected by each underwater robot (4) is v i (t), where i represents the i-th underwater robot (4) and t represents time; Setting the initial state estimate And covariance matrix P0, predict the state and covariance of the next moment according to the underwater robot dynamics model: Where k represents the time step, is the predicted state matrix at time step k, F k is the state transition matrix, is the optimal estimated state matrix at time step k-1, B k is the control matrix, u k is the control input matrix at time step k, P k|k-1 is the prediction covariance matrix at time step k, P k-1 is the covariance at time step k-1, is the transpose of the state transfer matrix, Q k is the process noise covariance matrix; When there is new measurement data z k When , update the state estimate: P k =(I-K k H k )P k|k-1 Among them, K k is the Kalman gain matrix, P k|k-1 is the prediction covariance matrix at time step k, H k is the observation matrix, is the transpose of the observation matrix, R k is the observation noise covariance matrix, is the updated state estimate matrix at time step k, is the predicted state matrix at time step k, I is the identity matrix, P k The updated covariance matrix at time step k, P k|k-1 is the prediction covariance matrix at time step k.

6. A joint design method for water flow estimation and ship waterline monitoring according to claim 1, characterized in that: In step S3, the collected waterline image is preprocessed, and then the credibility of the waterline image is calculated based on the preprocessed image, including the following steps: S3.

1. Denoising the image: Images often contain noise. To reduce random noise and improve image clarity, the image is convolved using a Gaussian kernel function to remove the Gaussian noise. S3.2, enhance contrast, improve the visual effect of the image, make the image details clearer, use histogram equalization, as follows: Where r(k) is the gray level of the input image, T(r(k)) is the cumulative probability of gray level r(k), s(k) is the gray level of the output image, k is the gray level of the pixel in the image, and n j is the pixel value of gray level j, n is the total pixel value; S3.

3. After image preprocessing, the waterline image credibility C is calculated: Among them, N e is the number of edge pixels detected, N t is the total number of pixels in the image, is the variance of the image noise.

7. A joint design method for water flow estimation and ship waterline monitoring according to claim 1, characterized in that: In step S4, the underwater robot (4) collects its own position, speed and posture data, as well as the waterline image credibility, and performs joint posture correction, so that the waterline image credibility reaches a threshold and the posture is stabilized. The optimization function is expressed as follows: in, is the minimization objective function of the underwater robot (4), n is the total number of time steps, v i (t) is the actual speed of the i-th underwater robot at time t, is the predicted velocity of the ith underwater robot (4) at time t, q is the current posture quaternion, q d is the desired attitude quaternion, λ is the weight coefficient, and C is the waterline image credibility.

8. A joint design method for water flow estimation and ship waterline monitoring according to claim 1, characterized in that: In step S5, the edge detection algorithm is used to identify the edge in the preprocessed waterline image, and double threshold segmentation is used to separate the waterline in the image, which specifically includes the following steps: S5.1, calculating the gradient magnitude and direction of each pixel in the waterline image; S5.2, apply non-maximum suppression to refine the edges; S5.3, apply double threshold detection to determine real and potential edges; S5.

4. Determine the edge by edge tracking and hysteresis threshold.

9. A system for water flow estimation and ship waterline monitoring, characterized in that: The joint design method according to any one of claims 1 to 8 comprises a perception module, a propeller module, a control module and a wireless communication module located on the underwater robot (4).

10. A system for water flow estimation and ship waterline monitoring according to claim 9, characterized in that: The sensing module comprises an onboard camera for collecting the waterline of the ship on the water surface and a Doppler velocimeter for obtaining the water flow velocity; the propeller module comprises an underwater propeller for providing power and a drive module thereof; the control module comprises a processing unit for data processing and model deployment and an inertial navigation positioning device for obtaining the position and posture of the underwater robot (4); the wireless communication module comprises a wireless radio frequency communicator for communication between the underwater robots (4) and an electromagnetic communicator for communication between the shore-based control center (1) and the underwater robot (4).

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