Computer vision-based offshore platform mooring force detection method, system and device

Abstract

The method comprises the following steps: obtaining relevant parameters of a mooring chain; collecting a position change video of a mooring point, reading an image and pre-processing to obtain a target image set; analyzing images in the image set to obtain a coordinate set of position changes of feature points in a pixel coordinate system; converting the feature point coordinates in the pixel coordinate system into coordinates in a world coordinate system to obtain a coordinate set of the mooring point changing with time; establishing a quasi-static mathematical model satisfied by a catenary segment of the mooring chain to obtain an equation group for solving the mooring force through the mooring point coordinates; and substituting the relevant parameters and the mooring point coordinates into the equation group to obtain time-history change data of the mooring force. The application also discloses a mooring force detection system and device for offshore platforms based on computer vision. The application aims to overcome the limitations of traditional detection methods and improve the economy, adaptability, convenience and flexibility of the mooring force detection for offshore platforms.

Description

Technical Field

[0001] This invention relates to offshore platform inspection technology, and more particularly to a method, system, and apparatus for detecting the mooring force of offshore platforms based on computer vision. Background Technology

[0002] With the continued increase in global demand for both renewable and traditional energy sources, the construction and development of offshore platforms, including those for offshore solar power, offshore wind power, and oil and gas extraction, are becoming increasingly important. Offshore platforms are typically connected to the seabed by mooring cables, ensuring they remain fixed in a specific location or confined to a certain area throughout their lifecycle. The mooring safety of offshore platforms is crucial for ensuring structural safety and maintaining normal platform operation. Therefore, efficient, convenient, and accurate detection of the mooring forces of offshore platforms is essential, providing strong support for their operation and maintenance.

[0003] Currently, there are two main methods for detecting mooring forces on offshore platforms: one is through tension / compression sensors that directly contact the anchor chain, and the other is by accurately capturing the anchor chain's shape for calculation. Clearly, existing mooring force detection methods all rely on sensors, either directly or indirectly, which has certain limitations. First, installation and maintenance are inconvenient; the complex and ever-changing marine environment presents significant difficulties for the installation and subsequent maintenance of traditional detection equipment. Second, traditional detection technologies typically require direct contact with the physical components of the mooring system, which not only increases system complexity but also makes the system susceptible to negative impacts from marine environmental factors such as seawater corrosion and biofouling, thus affecting the accuracy and reliability of the detection results. Furthermore, traditional methods also suffer from significant shortcomings in terms of flexibility and economy, limiting their efficiency and practicality in large-scale applications.

[0004] In recent years, computer vision has been widely used in fields such as medical care, intelligent driving, security and human-computer interaction. It has advantages such as automation, intelligence, high accuracy, strong adaptability and strong scalability. Based on this, computer vision has been gradually applied to the field of structural inspection, involving many aspects such as deformation, displacement, response and stress. Summary of the Invention

[0005] To address the limitations of traditional mooring force detection methods in the background art, this invention proposes a computer vision-based method, system, and device for detecting mooring force of offshore platforms. This overcomes the limitations of traditional detection methods and improves the economy, adaptability, convenience, and flexibility of offshore platform mooring force detection.

[0006] To achieve the above objectives, this invention proposes a computer vision-based method, system, and apparatus for detecting mooring forces on offshore platforms, the specific details of which are as follows:

[0007] Firstly, this invention discloses a computer vision-based method for detecting mooring forces on offshore platforms, specifically including the following steps:

[0008] Step S1: Obtain the relevant parameters of the mooring anchor chain;

[0009] Step S2: Acquire video of the position change of the mooring point, read the images in the video at appropriate frame intervals, and preprocess them according to the image quality to obtain the target image set;

[0010] Step S3: Analyze the images in the image set one by one, identify the coordinates of feature points in the pixel coordinate system, and obtain the coordinate set of feature point position changes in the pixel coordinate system;

[0011] Step S4: Transform the feature point coordinates in the pixel coordinate system into coordinates in the world coordinate system, and form a sequence based on the image acquisition time to obtain the coordinate set of the mooring point changing over time;

[0012] Step S5: Based on the catenary equation, the overall force analysis of the anchor chain and the infinitesimal element, establish a quasi-static mathematical model that satisfies the length, vertical length and horizontal length of the catenary segment of the anchor chain, and obtain the equation set for solving the mooring force through the coordinates of the mooring point;

[0013] Step S6: Substitute the relevant parameters and mooring point coordinates into the system of equations to solve the problem, calculate the mooring force value corresponding to each image one by one, and obtain the time history variation data of the mooring force.

[0014] Furthermore, in step S1, the relevant parameters include the anchor chain length and the buoyancy of the anchor chain in the target water environment.

[0015] Furthermore, the specific process of step S2 is as follows:

[0016] Step S2.1: When there are no natural landmarks at the mooring point, add artificial landmarks; when the lighting conditions are insufficient, add appropriate light sources; adjust the position of the high-precision camera to ensure that the platform does not exceed the video shooting range during movement; adjust the camera aperture and lens parameters to ensure that the mooring point is clearly visible in the captured image; set appropriate resolution and capture frame rate to continuously capture video of the position change of the mooring point.

[0017] Step S2.2: Set the reading frame interval according to the camera's acquisition frame rate and required accuracy to improve processing efficiency and data storage economy while ensuring recognition accuracy;

[0018] Step S2.3: Preprocess the read image by converting the color image to grayscale using a weighted average method; select frequency domain method and spatial domain method to filter noise based on the actual image quality, enhance the image, and improve recognition accuracy and precision.

[0019] Furthermore, in step S3, the feature point coordinates in the pixel coordinate system are identified using a sub-pixel level corner detection or edge detection method.

[0020] Furthermore, the relationship applied in step S4 to transform the feature point coordinates in the image coordinate system into the mooring point coordinates in the world coordinate system is as follows:

[0021]

[0022] Where s is the depth of the mooring point from the camera, u and v are the coordinates of the feature point in the pixel coordinate system, and f x and f y It is a physical quantity that describes the focal length along the x and y axes in pixels, where u0 and v0 are the coordinates of the principal point in the pixel coordinate system, and R and t are the rotation and translation matrices, respectively. w y w and z w Here are the coordinates of the mooring point in the world coordinate system, and A is the camera intrinsic parameter matrix, which was obtained through calibration tests.

[0023] Furthermore, in step S5, based on the catenary equation and the overall and infinitesimal force analysis of the anchor chain, the process of establishing a quasi-static mathematical model satisfying the catenary segment length, vertical length, and horizontal length of the anchor chain is as follows:

[0024] Step S5.1: Derive the relationship that the length of the catenary segment of the anchor chain must satisfy:

[0025]

[0026] Where L is the length of the anchor chain, X1 is the horizontal coordinate of the point where the anchor chain separates from the seabed, and γ is the wet unit weight of the anchor chain. Let be the angle between any point on the anchor chain and the horizontal direction. The angle between the point where the anchor chain separates from the seabed and the horizontal direction. Let T' be the angle between the top of the anchor chain and the horizontal direction, and T' be the equivalent cross-sectional tension. s For mooring force;

[0027] Step S5.2: Derive the relationship that the vertical length of the catenary segment of the anchor chain must satisfy:

[0028]

[0029] Where D is the vertical length of the catenary segment, and Z2 is the vertical coordinate of the top of the anchor chain;

[0030] Step S5.3: Determine the relationship that the horizontal length of the catenary segment of the anchor chain must satisfy:

[0031]

[0032] Where X2 is the horizontal coordinate of the top of the anchor chain;

[0033] Step S5.4: Combining equations (2), (3), and (4), we obtain the system of equations for solving the mooring force using the coordinates of the mooring point:

[0034]

[0035] Furthermore, in step S6, the specific method for substituting the relevant parameters and mooring point coordinates into the system of equations to solve the problem is as follows:

[0036] Subtract equation (7) from equation (5):

[0037]

[0038] Divide equation (8) by equation (6):

[0039]

[0040] Solving Substituting into equation (8), we can solve for the mooring force T. s .

[0041] Furthermore, the calibration experiment is specifically conducted as follows: the black and white checkerboard calibration board is placed in different positions and at different angles and postures, and no fewer than 10 images are taken; corner information is initially extracted; sub-pixel information is further extracted to reduce camera calibration deviation; and the camera intrinsic parameter matrix is ​​calculated.

[0042] Secondly, this invention discloses a computer vision-based offshore platform mooring force detection system, comprising:

[0043] The information acquisition module is used to acquire relevant parameters of the mooring anchor chain;

[0044] The image acquisition module is used to acquire video of the position changes of the mooring point and read the images in the video at appropriate frame intervals;

[0045] The image preprocessing module is used to preprocess the images in the video, convert the color images to grayscale using a weighted average method, filter noise using frequency domain and spatial domain methods based on the actual image quality, enhance the image, improve recognition accuracy and precision, and obtain the target image set.

[0046] The feature point recognition module is used to analyze the images in the target image set one by one, identify the coordinates of feature points in the pixel coordinate system, and obtain the coordinate set of the feature point position changes in the pixel coordinate system.

[0047] The coordinate transformation module is used to transform the feature point coordinates in the pixel coordinate system into coordinates in the world coordinate system, and to form a sequence based on the image acquisition time to obtain the coordinate set of the mooring point changing over time.

[0048] The mooring force calculation module is used to substitute the relevant parameters and mooring point coordinates into the system of equations to solve the problem, calculate the mooring force value corresponding to each image one by one, and obtain the mooring force time history variation data.

[0049] Thirdly: This invention discloses a computer vision-based offshore platform mooring force detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the computer vision-based offshore platform mooring force detection method described in this invention.

[0050] Beneficial effects of the invention:

[0051] This invention discloses a computer vision-based method, system, and apparatus for detecting mooring forces on offshore platforms. This overcomes the limitations of traditional detection methods and improves the economy, adaptability, convenience, and flexibility of offshore platform mooring force detection. The method involves: acquiring relevant parameters of the mooring anchor chain; collecting video footage of mooring point position changes; reading and preprocessing the images to obtain a target image set; analyzing the images in the image set to identify the coordinates of feature points in the pixel coordinate system, obtaining a set of coordinates showing the position changes of feature points in the pixel coordinate system; converting the feature point coordinates in the pixel coordinate system to coordinates in the world coordinate system, obtaining a set of coordinates showing the mooring point changes over time; establishing a quasi-static mathematical model satisfying the length, vertical length, and horizontal length of the anchor chain catenary segment, obtaining a set of equations for solving the mooring force using the mooring point coordinates; substituting the relevant parameters and mooring point coordinates into the equations to solve for the mooring force value corresponding to each image, obtaining the time-history variation data of the mooring force. This invention does not use sensors found in traditional detection methods, thus avoiding the problems of sensor damage and inaccuracy that are prone to occur in marine environments. At the same time, it overcomes the difficulties of cumbersome sensor replacement procedures and high uncertainty in offshore operations, and solves the problems of low efficiency and poor cost in mooring force detection. It has many advantages such as low economic cost, strong adaptability, convenient use and flexible expansion and migration, and has broad application prospects. Attached Figure Description

[0052] Figure 1 A flowchart of a computer vision-based method for detecting mooring forces of offshore platforms provided for this application;

[0053] Figure 2 A schematic diagram of the anchor chain under stress, derived from the mathematical model of a computer vision-based method for detecting mooring forces on offshore platforms provided in this application;

[0054] Figure 3 A schematic diagram of a computer vision-based offshore platform mooring force detection system provided for this application; Detailed Implementation

[0055] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings:

[0056] The present invention describes a computer vision-based method for detecting mooring forces on offshore platforms, with reference to... Figure 1 Specifically, it includes the following steps:

[0057] Step S1: Obtain the relevant parameters of the mooring anchor chain;

[0058] Step S2: Acquire video of the position change of the mooring point, read the images in the video at appropriate frame intervals, and preprocess them according to the image quality to obtain the target image set;

[0059] Step S3: Analyze the images in the image set one by one, identify the coordinates of feature points in the pixel coordinate system, and obtain the coordinate set of feature point position changes in the pixel coordinate system;

[0060] Step S4: Transform the feature point coordinates in the pixel coordinate system into coordinates in the world coordinate system, and form a sequence based on the image acquisition time to obtain the coordinate set of the mooring point changing over time;

[0061] Step S5: Based on the catenary equation, the overall force analysis of the anchor chain and the infinitesimal element, establish a quasi-static mathematical model that satisfies the length, vertical length and horizontal length of the catenary segment of the anchor chain, and obtain the equation set for solving the mooring force through the coordinates of the mooring point;

[0062] Step S6: Substitute the relevant parameters and mooring point coordinates into the system of equations to solve the problem, calculate the mooring force value corresponding to each image one by one, and obtain the time history variation data of the mooring force.

[0063] Furthermore, in step S1, the relevant parameters include the anchor chain length and the buoyancy of the anchor chain in the target water environment.

[0064] Furthermore, the specific process of step S2 is as follows:

[0065] Step S2.1: When there are no natural landmarks at the mooring point, add artificial landmarks; when the lighting conditions are insufficient, add appropriate light sources; adjust the position of the high-precision camera to ensure that the platform does not exceed the video shooting range during movement; adjust the camera aperture and lens parameters to ensure that the mooring point is clearly visible in the captured image; set appropriate resolution and capture frame rate to continuously capture video of the position change of the mooring point.

[0066] Step S2.2: Set the reading frame interval according to the camera's acquisition frame rate and required accuracy to improve processing efficiency and data storage economy while ensuring recognition accuracy;

[0067] Step S2.3: Preprocess the read image by converting the color image to grayscale using a weighted average method; select frequency domain method and spatial domain method to filter noise based on the actual image quality, enhance the image, and improve recognition accuracy and precision.

[0068] Specifically, artificial markers are generally objects with a clear color contrast to the background and distinct shape features, facilitating feature point recognition during image processing. The frame interval should be adjusted according to the actual situation; generally, acquiring one image per second is sufficient for recognition accuracy. The formula for converting a color image to grayscale using the weighted average method is as follows:

[0069] Y = 0.299R + 0.587G + 0.114B

[0070] Furthermore, in step S3, the feature point coordinates in the pixel coordinate system are identified using a sub-pixel level corner detection or edge detection method.

[0071] Specifically, sub-pixel level detection methods mainly include interpolation, geometric moment search, and fitting methods. Gaussian fitting algorithms are often used to improve recognition accuracy, and the principle is as follows:

[0072] Experimental data can be described using a Gaussian function:

[0073]

[0074] in, Taking the logarithm of both, we get:

[0075]

[0076] Here, n0, n1, n2, and n3 are four unknown parameters to be determined, and x0 and y0 are the sub-pixel coordinates of the extreme points. Based on this principle, the sub-pixel level feature point coordinates can be obtained through an algorithm.

[0077] Furthermore, the relationship applied in step S4 to transform the feature point coordinates in the image coordinate system into the mooring point coordinates in the world coordinate system is as follows:

[0078]

[0079] Where s is the depth of the mooring point from the camera, u and v are the coordinates of the feature point in the pixel coordinate system, and f x and f y It is a physical quantity that describes the focal length along the x and y axes in pixels, where u0 and v0 are the coordinates of the principal point in the pixel coordinate system, and R and t are the rotation and translation matrices, respectively. w y w and z w Here are the coordinates of the mooring point in the world coordinate system, and A is the camera intrinsic parameter matrix, which was obtained through calibration tests.

[0080] Further details can be found in the appendix. Figure 2 In step S5, based on the catenary equation and the overall and infinitesimal force analysis of the anchor chain, the process of establishing a quasi-static mathematical model satisfying the catenary segment length, vertical length, and horizontal length of the anchor chain is as follows:

[0081] Step S5.1: Derive the relationship that the length of the catenary segment of the anchor chain must satisfy:

[0082]

[0083] Where L is the length of the anchor chain, X1 is the horizontal coordinate of the point where the anchor chain separates from the seabed, and γ is the wet unit weight of the anchor chain. Let be the angle between any point on the anchor chain and the horizontal direction. The angle between the point where the anchor chain separates from the seabed and the horizontal direction. Let T' be the angle between the top of the anchor chain and the horizontal direction, and T' be the equivalent cross-sectional tension. s For mooring force;

[0084] Step S5.2: Derive the relationship that the vertical length of the catenary segment of the anchor chain must satisfy:

[0085]

[0086] Where D is the vertical length of the catenary segment, and Z2 is the vertical coordinate of the top of the anchor chain;

[0087] Step S5.3: Determine the relationship that the horizontal length of the catenary segment of the anchor chain must satisfy:

[0088]

[0089] Where X2 is the horizontal coordinate of the top of the anchor chain.

[0090] Step S5.4: Combining equations (2), (3), and (4), we obtain the system of equations for solving the mooring force using the coordinates of the mooring point:

[0091]

[0092] Furthermore, in step S6, the specific method for substituting the relevant parameters and mooring point coordinates into the system of equations to solve the problem is as follows:

[0093] Subtract equation (7) from equation (5):

[0094]

[0095] Divide equation (8) by equation (6):

[0096]

[0097] It can be solved Substituting into equation (8), we can solve for the mooring force T. s .

[0098] Furthermore, the calibration experiment is specifically conducted as follows: the black and white checkerboard calibration board is placed in different positions and at different angles and postures, and no fewer than 10 images are taken; corner information is initially extracted; sub-pixel information is further extracted to reduce camera calibration deviation; and the camera intrinsic parameter matrix is ​​calculated.

[0099] This invention discloses a computer vision-based system for detecting mooring forces on offshore platforms, with reference to... Figure 3 ,include:

[0100] The information acquisition module is used to acquire relevant parameters of the mooring anchor chain;

[0101] The image acquisition module is used to acquire video of the position changes of the mooring point and read the images in the video at appropriate frame intervals;

[0102] The image preprocessing module is used to preprocess the images in the video, convert the color images to grayscale using a weighted average method, filter noise using frequency domain and spatial domain methods based on the actual image quality, enhance the image, improve recognition accuracy and precision, and obtain the target image set.

[0103] The feature point recognition module is used to analyze the images in the target image set one by one, identify the coordinates of feature points in the pixel coordinate system, and obtain the coordinate set of the feature point position changes in the pixel coordinate system.

[0104] The coordinate transformation module is used to transform the feature point coordinates in the pixel coordinate system into coordinates in the world coordinate system, and to form a sequence based on the image acquisition time to obtain the coordinate set of the mooring point changing over time.

[0105] The mooring force calculation module is used to substitute the relevant parameters and mooring point coordinates into the system of equations to solve the problem, calculate the mooring force value corresponding to each image one by one, and obtain the mooring force time history variation data.

[0106] This invention discloses a computer vision-based offshore platform mooring force detection device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the computer vision-based offshore platform mooring force detection method of this invention.

[0107] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Any modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A computer vision-based method for detecting mooring forces on offshore platforms, characterized in that, Includes the following steps: Step S1: Obtain the relevant parameters of the mooring anchor chain; Step S2: Acquire video of the position change of the mooring point, read the images in the video at appropriate frame intervals, and preprocess them according to the image quality to obtain the target image set; Step S3: Analyze the images in the image set one by one, identify the coordinates of feature points in the pixel coordinate system, and obtain the coordinate set of feature point position changes in the pixel coordinate system; Step S4: Transform the feature point coordinates in the pixel coordinate system into coordinates in the world coordinate system, and form a sequence based on the image acquisition time to obtain the coordinate set of the mooring point changing over time; Step S5: Based on the catenary equation, the overall force analysis of the anchor chain and the infinitesimal element, establish a quasi-static mathematical model that satisfies the length, vertical length and horizontal length of the catenary segment of the anchor chain, and obtain the equation set for solving the mooring force through the coordinates of the mooring point; Step S6: Substitute the relevant parameters and mooring point coordinates into the system of equations to solve the problem, calculate the mooring force value corresponding to each image one by one, and obtain the mooring force time history variation data; In step S5, based on the catenary equation and the overall and infinitesimal force analysis of the anchor chain, the process of establishing a quasi-static mathematical model satisfying the catenary segment length, vertical length, and horizontal length of the anchor chain is as follows: Step S5.1: Derive the relationship that the length of the catenary segment of the anchor chain must satisfy: in, The length of the anchor chain. The horizontal coordinates of the point where the anchor chain separates from the seabed. The wet weight of the anchor chain. Let be the angle between any point on the anchor chain and the horizontal direction. The angle between the point where the anchor chain separates from the seabed and the horizontal direction. The angle between the top of the anchor chain and the horizontal direction. For equivalent cross-sectional tension, For mooring force; Step S5.2: Derive the relationship that the vertical length of the catenary segment of the anchor chain must satisfy: in, Let be the vertical length of the catenary segment. The vertical coordinate of the top of the anchor chain; Step S5.3: Determine the relationship that the horizontal length of the catenary segment of the anchor chain must satisfy: in, The horizontal coordinate of the top of the anchor chain; Step S5.4: Combined , , The equations yield the system of equations for solving the mooring force using the coordinates of the mooring point: 。 2. The computer vision-based method for detecting mooring force of offshore platforms according to claim 1, characterized in that: In step S1, the relevant parameters include the anchor chain length and the buoyancy of the anchor chain in the target water environment.

3. The computer vision-based method for detecting mooring force of offshore platforms according to claim 1, characterized in that: The specific process of step S2 is as follows: Step S2.1: When there are no natural landmarks at the mooring point, add artificial landmarks; when the lighting conditions are insufficient, add appropriate light sources; adjust the position of the high-precision camera to ensure that the platform does not exceed the video shooting range during movement; adjust the camera aperture and lens parameters to ensure that the mooring point is clearly visible in the captured image; set appropriate resolution and capture frame rate to continuously capture video of the position change of the mooring point. Step S2.2: Set the reading frame interval according to the camera's acquisition frame rate and required accuracy to improve processing efficiency and data storage economy while ensuring recognition accuracy; Step S2.3: Preprocess the read image by converting the color image to grayscale using a weighted average method; select frequency domain method and spatial domain method to filter noise based on the actual image quality, enhance the image, and improve recognition accuracy and precision.

4. The computer vision-based method for detecting mooring force of offshore platforms according to claim 1, characterized in that: In step S3, the feature point coordinates in the pixel coordinate system are identified using a sub-pixel level corner detection or edge detection method.

5. The computer vision-based method for detecting mooring forces of offshore platforms according to claim 1, characterized in that: In step S4, the relationship for converting the feature point coordinates in the image coordinate system to the mooring point coordinates in the world coordinate system is as follows: in, The distance from the mooring point to the camera. and The coordinates of the feature point in the pixel coordinate system. and Described in pixels shaft and The physical quantity of focal length along the axial direction. and These are the coordinates of the principal point in the pixel coordinate system. and These are rotation and translation matrices, respectively. , and These are the coordinates of the mooring point in the world coordinate system. This is the camera's intrinsic parameter matrix, obtained through calibration experiments.

6. The computer vision-based method for detecting mooring forces of offshore platforms according to claim 1, characterized in that: In step S6, the specific method for substituting the relevant parameters and mooring point coordinates into the system of equations to solve the problem is as follows: Will subtract Mode: Will Divide by Mode: Solving Substitute The formula can be used to solve for the mooring force. .

7. The computer vision-based method for detecting mooring forces of offshore platforms according to claim 5, characterized in that: The specific method for the calibration test is as follows: Place the black and white checkerboard calibration board in different positions and take no less than 10 pictures at different angles and postures; initially extract corner information; further extract sub-pixel information to reduce camera calibration deviation; calculate the camera intrinsic parameter matrix.

8. A computer vision-based offshore platform mooring force detection system, used to implement the method described in any one of claims 1-7, characterized in that, include: The information acquisition module is used to acquire relevant parameters of the mooring anchor chain; The image acquisition module is used to acquire video of the position changes of the mooring point and read the images in the video at appropriate frame intervals; The image preprocessing module is used to preprocess the images in the video, convert the color images to grayscale using a weighted average method, filter noise using frequency domain and spatial domain methods based on the actual image quality, enhance the image, improve recognition accuracy and precision, and obtain the target image set. The feature point recognition module is used to analyze the images in the target image set one by one, identify the coordinates of feature points in the pixel coordinate system, and obtain the coordinate set of the feature point position changes in the pixel coordinate system. The coordinate transformation module is used to transform the feature point coordinates in the pixel coordinate system into coordinates in the world coordinate system, and to form a sequence based on the image acquisition time to obtain the coordinate set of the mooring point changing over time. The mooring force calculation module is used to substitute the relevant parameters and mooring point coordinates into the system of equations to solve the problem, calculate the mooring force value corresponding to each image one by one, and obtain the mooring force time history variation data.

9. A computer vision-based offshore platform mooring force detection device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the computer vision-based offshore platform mooring force detection method as described in any one of claims 1 to 7.

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