Method for obtaining the burial depth of a submarine pipeline and electronic device

Abstract

The application provides a method for obtaining the burial depth of a submarine pipeline, comprising the following steps: obtaining point cloud data of the pipeline; determining the position and height of the top of the pipeline according to the point cloud data; determining the height of the natural mud surface according to the position of the top of the pipeline; and determining the burial depth of the pipeline according to the height of the top of the pipeline and the height of the natural mud surface. The scheme provided by the application can reflect the change in the burial depth of the pipeline in real time and comprehensively without manual intervention, significantly improves the measurement efficiency and accuracy, reduces human errors, and provides more reliable technical support for the monitoring and maintenance of submarine pipelines.

Description

Technical Field

[0001] This invention relates to the field of marine surveying, and in particular to a method and electronic device for obtaining the burial depth of submarine pipelines. Background Technology

[0002] During the laying of submarine pipelines, the pipeline must first be lowered to the designed location. High-pressure water jets from trenching machines create trenches beneath the pipeline that meet design specifications, allowing the pipeline to sink into the trench under its own weight. During this process, it is crucial to accurately determine whether the pipeline's burial depth meets design requirements. Traditionally, the burial depth is determined using the cross-sectional method, calculating the depth based on the positional relationship between the pipe's top elevation and the natural mud surface. This method typically requires manual assessment of the pipe's top elevation, extracting it piece by piece, and then locating and comparing it with the corresponding natural mud surface height before making the calculation.

[0003] However, the traditional cross-sectional method is cumbersome and inefficient, and its reliance on manual judgment can lead to significant measurement errors. More importantly, this method only reflects the burial depth of a small section of the pipeline, failing to accurately and comprehensively reflect the burial depth of the entire pipeline. Therefore, in complex seabed environments, such as those with large variations in water depth, strong currents, and uneven seabed composition, the accuracy and efficiency of traditional methods are difficult to guarantee.

[0004] Current technologies lack a solution for rapidly, accurately, and continuously monitoring the burial depth of subsea pipelines. Especially in complex environments, existing technologies generally suffer from operational complexity, incomplete data, and unstable measurement accuracy. Therefore, a new method is urgently needed to effectively overcome these limitations and improve the accuracy and efficiency of subsea pipeline burial depth measurement. Summary of the Invention

[0005] This application provides a method and electronic device for obtaining the burial depth of submarine pipelines, which solves the problems of quickly finding and obtaining the top elevation of submarine pipelines, and quickly finding and comparing the top elevation of the pipeline and the corresponding natural mud surface point, and finally quickly obtaining the burial depth of submarine pipelines.

[0006] This application provides a method for obtaining the burial depth of a subsea pipeline, comprising the following steps: acquiring point cloud data of the pipeline; determining the position and height of the pipe top elevation based on the point cloud data; determining the height of the natural mud surface based on the position of the pipe top elevation; and determining the burial depth of the pipeline based on the height of the pipe top elevation and the height of the natural mud surface.

[0007] In one embodiment, the point cloud data includes upper pipeline point cloud data; determining the position and height of the pipe top elevation based on the point cloud data includes: selecting upper pipeline point cloud data that meets a first preset condition as denoised upper pipeline point cloud data by automatically adjusting weights; determining lower pipeline point cloud data based on the pipeline size and the denoised upper pipeline point cloud data; determining a three-dimensional pipeline model based on the lower pipeline point cloud data and the denoised upper pipeline point cloud data; and slicing the three-dimensional pipeline model to determine the position and height of the pipe top elevation.

[0008] In one embodiment, acquiring the point cloud data of the pipeline includes: acquiring a three-dimensional image of the pipeline using a three-dimensional sonar device; converting the three-dimensional image into initial point cloud data; and selecting initial point cloud data that meets a second preset condition as the point cloud data based on the reflection intensity of the pipeline. In another embodiment, determining the lower point cloud data of the pipeline based on the pipeline's dimensions and the upper point cloud data of the denoised pipeline includes: determining interpolation coefficients based on the pipeline's dimensions and the upper point cloud data of the denoised pipeline; and determining the lower point cloud data of the pipeline using spline interpolation based on the interpolation coefficients.

[0009] In one embodiment, determining the pipeline 3D model based on the pipeline lower point cloud data and the denoised pipeline upper point cloud data includes: using the least squares method to match the pipeline lower point cloud data and the denoised pipeline upper point cloud data with a white model to obtain the optimally matched pipeline lower point cloud data and denoised pipeline upper point cloud data; determining the pipeline 3D model based on the optimally matched pipeline lower point cloud data and denoised pipeline upper point cloud data; wherein the white model is a standard pipeline 3D model constructed based on the dimensions of the pipeline.

[0010] In one embodiment, slicing the three-dimensional model of the pipeline to determine the position and height of the pipe top elevation includes: slicing the three-dimensional model of the pipeline to obtain multiple near-circular slices; for each near-circular slice, performing the following operations: drawing multiple tangents on the near-circular slice to obtain multiple tangent points; extracting the position and height of each tangent point, and selecting the three tangent points with the highest heights; calculating the arc formed by the three tangent points; if the arc is less than a preset arc, then selecting the position and height of the tangent point with the highest height among the three tangent points as the position and height of the pipe top elevation.

[0011] In one embodiment, slicing the three-dimensional model of the pipeline to obtain multiple near-circular slices includes: determining the slice spacing according to the shape of the pipeline; and slicing the three-dimensional model of the pipeline according to the slice spacing to obtain multiple near-circular slices.

[0012] In one embodiment, determining the height of the natural mud surface based on the position of the pipe top elevation includes: using a multibeam echo sounder to obtain three-dimensional terrain data of the natural mud surface; performing three-dimensional modeling on the three-dimensional terrain data to obtain a three-dimensional mud surface model; placing the pipe top elevation on the three-dimensional mud surface model, and using the position of the pipe top elevation as the center and a preset radius as the radius, encircling multiple height data of the three-dimensional mud surface model; calculating the average value of the multiple height data as the height of the natural mud surface.

[0013] In one embodiment, determining the pipeline burial depth based on the height of the pipe top elevation and the height of the natural mud surface includes: calculating the difference between the height of the pipe top elevation and the height of the natural mud surface as the initial pipeline burial depth; and determining the pipeline burial depth based on the initial pipeline burial depth and minimizing the error.

[0014] This application also provides an electronic device, which includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-described method for obtaining the burial depth of a subsea pipeline.

[0015] The solution provided in the above embodiments of this application requires no manual intervention. Through automated calculation and optimization, it can reflect the changes in the burial depth of the pipeline in real time and comprehensively, significantly improving measurement efficiency and accuracy, reducing human error, and providing more reliable technical support for the monitoring and maintenance of subsea pipelines. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly described below.

[0017] Figure 1 This is a schematic diagram of the submarine pipeline provided in the embodiments of this application;

[0018] Figure 2 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application;

[0019] Figure 3 This is a flowchart illustrating the method for obtaining the burial depth of submarine pipelines provided in this application embodiment. Detailed Implementation

[0020] The technical solutions in the embodiments of this application will now be described with reference to the accompanying drawings.

[0021] Similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] Figure 1 This is a schematic diagram of a submarine pipeline provided in an embodiment of this application. Figure 1 In submarine pipelines, the pipeline is buried beneath the natural mud surface. The pipeline top elevation is a point on the submarine pipeline, and there can be multiple pipeline top elevations. For example, a pipeline top elevation is determined at intervals along the pipeline. Taking any pipeline top elevation A as an example, the position of the natural mud surface B corresponding to that position can be determined based on the position of the pipeline top elevation, and the position of the sea level C corresponding to that position can also be determined. The height of the pipeline top elevation is the distance from the position of the pipeline top elevation to the corresponding position of the natural mud surface (i.e., AC), the height of the natural mud surface is the distance from the position of the corresponding natural mud surface to the corresponding position of the sea level (i.e., BC), and the pipeline burial depth is the distance from the position of the pipeline top elevation to the corresponding position of the natural mud surface (i.e., AB). Since the natural mud surface is not a smooth plane, and the shape and size of the submarine pipeline may vary at different locations on the seabed, the burial depth of the submarine pipeline may vary at different locations. Generally, multiple pipeline top elevations are needed to obtain a more accurate pipeline burial depth.

[0023] In related technologies, pipeline burial depth is often obtained using the cross-sectional method, which calculates the pipeline burial depth based on the positional relationship between the pipe top elevation and the natural mud surface. This method typically requires manual judgment of the pipe top elevation, extraction of each elevation, and then locating and comparing it with the corresponding natural mud surface height before calculation. However, the cross-sectional method is cumbersome and inefficient, and its reliance on manual judgment can lead to significant measurement errors. More importantly, this method only reflects the burial depth of a small section of the pipeline and cannot accurately and comprehensively reflect the burial depth of the entire pipeline. Therefore, in complex seabed environments, such as those with large variations in water depth, strong currents, and uneven seabed composition, the accuracy and efficiency of traditional methods are difficult to guarantee effectively.

[0024] To address the aforementioned issues, this application provides a method for obtaining the burial depth of a subsea pipeline, comprising: acquiring point cloud data of the pipeline; determining the position and height of the pipe top elevation based on the point cloud data; determining the height of the natural mud surface based on the position of the pipe top elevation; and determining the burial depth of the pipeline based on the height of the pipe top elevation and the height of the natural mud surface. This method requires no manual intervention and, through automated calculation and optimization, can reflect changes in pipeline burial depth in real time and comprehensively, significantly improving measurement efficiency and accuracy, reducing human error, and providing more reliable technical support for subsea pipeline monitoring and maintenance.

[0025] Figure 2 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device 100 can be used to execute the method for obtaining the burial depth of subsea pipelines provided in an embodiment of this application. Figure 1 As shown, the electronic device 100 includes: one or more processors 102 and one or more memories 104 storing processor-executable instructions. The processors 102 are configured to execute the method for obtaining the burial depth of subsea pipelines provided in the following embodiments of this application.

[0026] The processor 102 may be a gateway, a smart terminal, or a device that includes a central processing unit (CPU), a graphics processing unit (GPU), or other forms of processing units with data processing capabilities and / or instruction execution capabilities. It can process data from other components in the electronic device 100 and control other components in the electronic device 100 to perform desired functions.

[0027] The memory 104 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 102 may execute the program instructions to implement the method for obtaining the burial depth of the subsea pipeline described below. Various application programs and various data may also be stored in the computer-readable storage medium, such as various data used and / or generated by the application programs.

[0028] In one embodiment, Figure 1 The illustrated electronic device 100 may further include an input device 106, an output device 108, and a data acquisition device 110, these components being interconnected via a bus system 112 and / or other forms of connection mechanisms (not shown). It should be noted that... Figure 1 The components and structure of the electronic device 100 shown are merely exemplary and not limiting; the electronic device 100 may also have other components and structures as needed.

[0029] The input device 106 may be a device for a user to input commands, and may include one or more of a keyboard, mouse, microphone, and touchscreen. The output device 108 may output various information (e.g., images or sounds) to the outside (e.g., a user), and may include one or more of a display, speaker, etc. The data acquisition device 110 may acquire three-dimensional images of the subsea pipeline and store the acquired data in the memory 104 for use by other components. For example, the data acquisition device 110 may be a three-dimensional sonar device.

[0030] In one embodiment, the components in the example electronic device 100 used to implement the method for obtaining the burial depth of the submarine pipeline in the present application can be integrated or distributed. For example, the processor 102, memory 104, input device 106 and output device 108 can be integrated into one unit, while the data acquisition device 110 can be separated.

[0031] In one embodiment, the example electronic device 100 used to implement the method for obtaining the burial depth of the submarine pipeline in the present application can be implemented as a smart terminal such as a smartphone, tablet computer, desktop computer, server, vehicle-mounted device, etc.

[0032] Figure 3 This is a flowchart illustrating a method for obtaining the burial depth of a subsea pipeline according to an embodiment of this application. Figure 3 As shown, the method includes steps 210-240.

[0033] Step 210: Obtain the point cloud data of the pipeline.

[0034] Point cloud data refers to a set of vectors in a three-dimensional coordinate system, where each point contains at least three coordinates (X, Y, Z), used to accurately describe the geometry of an object's surface. High-resolution 3D images of the upper part of a submarine pipeline can be obtained using 3D sonar equipment. Optimal acquisition parameters, including operating frequency, beam angle, gain, sensitivity, noise suppression, and reflection intensity threshold, are used to acquire these high-resolution 3D images. These parameters have mutual gain or inhibition effects, and their behavior varies under different water depths, turbidity levels, and flow velocities. The acquired high-resolution 3D images are then converted into processable point cloud data of the pipeline.

[0035] Step 210 may specifically include steps 2101-2103.

[0036] Step 2101: Use a three-dimensional sonar device to acquire a three-dimensional image of the pipeline.

[0037] Regarding the process of acquiring three-dimensional images of pipelines, this application describes how to optimize image quality by using adjusted parameters through a simplified model based on sound wave propagation and reflection intensity optimization (hereinafter referred to as the sound wave model).

[0038] Relationship between sound wave propagation model and image quality: The intensity of a signal is estimated using a sound wave propagation model, which is directly related to image sharpness. Signal propagation and reflection intensity are affected by various environmental factors and acquisition parameters. The main influencing factors include:

[0039] Operating frequency (F): Higher frequency results in higher resolution but lower penetration; conversely, lower frequency results in stronger penetration but lower resolution. Beam angle (R): A smaller beam angle allows for a more concentrated signal and higher image resolution, but reduces coverage. Gain (V): Gain affects the intensity of the echo; excessively high gain may increase noise, while excessively low gain leads to a weak signal. Sensitivity (A): Sensitivity determines the receiving system's response to weak signals; excessively low sensitivity will result in a blurry image.

[0040] The relationship between the acquisition parameters and the reflection intensity is described by the following formula:

[0041]

[0042] in, I echo The intensity of reflection reflects the quality of the image. P transmit Let be the power emitted by the sound source, and be the acoustic reflection coefficient of the target object. G This is the gain value. S For sensitivity, T Let be the distance from the target to the receiver, and let be the function relating the beam angle φ to the operating frequency φ.

[0043] The fundamental relationship between operating frequency (F) and beam angle (R): Operating frequency (F) and beam angle (R) play different roles in a sonar system. Although they are usually independent control parameters, they interact in terms of reflection intensity and image quality during sound wave propagation. High frequency and small beam angle: At high frequencies, due to the high energy density of sound waves, a smaller beam angle (narrower beam) focuses the sound wave into a small area, providing higher resolution and a clearer image. Low frequency and large beam angle: At low frequencies, a larger beam angle disperses sound wave energy over a larger area, resulting in a wider detection range, but resolution decreases due to energy dispersion. Therefore, the relationship between operating frequency and beam angle can be described by a function, typically considering reflection intensity, signal dispersion, and energy concentration.

[0044] By adjusting the acquisition parameters, it is possible to avoid excessive noise while ensuring signal strength, thereby obtaining high-definition 3D images.

[0045] In one embodiment, to obtain optimal 3D images in complex seabed environments, an objective function is proposed to comprehensively consider the optimal configuration of various parameters. Objective function J It can be the intensity of reflection. I echo With noise level N The ratio between them can maximize image quality and minimize the impact of noise.

[0046] Define an objective function:

[0047]

[0048] in N Noise level is a measure typically related to environmental factors such as water turbidity and flow velocity. Based on the analysis of the underwater acoustic environment and noise sources, the noise level... N It can be modeled as the combined effect of environmental factors (such as turbidity and flow rate) and system noise:

[0049]

[0050] in, N background The basic noise level (including constants such as thermal noise and equipment electronic noise) is turbidity (units such as NTU or mg / L), reflecting the concentration of suspended particles. v Water flow velocity (unit: m / s), the square term of the velocity reflects the contribution of turbulent noise. α , β These are weighting coefficients (which need to be calibrated using experimental or field data). These parameters related to noise levels are noise suppression parameters.

[0051] By optimizing the objective function J Find the optimal operating frequency (F), beam angle (R), and gain value. G Sensitivity S And noise suppression parameters, thereby improving the quality of 3D images.

[0052] Step 2102: Convert the three-dimensional image into initial point cloud data.

[0053] Step 2103: Based on the reflection intensity of the pipeline, select initial point cloud data that meets the second preset condition as the point cloud data.

[0054] Although the 3D images obtained by the 3D sonar equipment have undergone the above-mentioned series of optimizations to obtain clear images, it cannot be guaranteed that there are no mud surface point cloud data mixed in with the 3D images. Therefore, it is necessary to filter out the point cloud output belonging to the mud surface in the initial point cloud data in order to obtain the point cloud data of the pipeline.

[0055] Based on the different reflection intensity values, the reflection intensity of the pipeline is greater than that of the surrounding mud surface. A threshold is set for the reflection intensity value of the pipeline to filter out the point cloud data of the mud surface and retain only the point cloud data of the pipeline.

[0056] The second preset condition can be that the reflection intensity corresponding to the initial point cloud data is greater than or equal to the reflection intensity threshold. The reflection intensity corresponding to each point cloud data can be calculated by the above formula (1), and then compared with the reflection intensity threshold to filter out the initial point cloud data that is greater than or equal to the threshold. The filtered initial point cloud data is the point cloud data of the pipeline.

[0057] Specifically, regarding the threshold for distinguishing the reflection intensity values ​​of mud surfaces and pipelines, this application proposes a method for dynamically calculating the threshold of reflection intensity. The reflection intensity is obtained through formula (1). I echo Next, a dynamic threshold function based on the parameters in formula (1) is set. I threshold The details are as follows:

[0058]

[0059] in: c It is a constant coefficient used to adjust the sensitivity of the reflection intensity threshold, and is usually adjusted according to the actual environment and experimental results. Δ I=I max -I min It is the difference in reflection intensity between the pipeline and the mud surface, representing the gap between the reflection intensity of the pipeline and the reflection intensity of the mud surface. I max and I min These represent the maximum value of the reflection intensity of the pipeline and the minimum value of the reflection intensity of the mud surface, respectively. I max During the actual measurement process, the reflection intensity value of the pipeline can be sampled, and the maximum value can be selected. I min It is the minimum reflection intensity of the mud surface, which is usually selected from multiple samples to find the minimum reflection intensity of the mud surface area. k It is a modulating factor used to adjust the threshold of reflection intensity and the difference in reflection intensity. Its function is to adjust the sensitivity of the threshold, that is, to control the degree of influence of the difference in reflection intensity between the pipeline and the mud surface on the threshold. k The value needs to be determined through actual testing and optimization, usually based on experimental data comparing different... k The value is selected to make the threshold more adaptable to different seabed environmental conditions.

[0060] Based on the calculated dynamic threshold I threshold Filter point cloud data. Set point cloud data. P i The corresponding reflection intensity is I pointi And perform the following formula judgment:

[0061]

[0062] in, P i For the first i Initially, point cloud data is used to classify points as belonging to pipelines only if their reflection intensity is greater than or equal to a threshold; otherwise, they are considered mud surfaces. Through dynamic threshold calculation, this application can accurately determine the reflection intensity distinction threshold between pipelines and mud surfaces under various environmental conditions. This method integrates the influence of important parameters such as operating frequency, gain, and sensitivity, adapting to different working environments and effectively filtering point cloud data to provide accurate results for subsea pipeline inspection.

[0063] Step 220: Determine the position and height of the top elevation of the pipe based on the point cloud data.

[0064] As mentioned earlier, due to the complexity of the seabed environment, the top elevation of submarine pipelines varies at different locations. Therefore, multiple top elevations are needed to accurately and completely represent the pipeline burial depth. In steps 220-240, we will take the acquisition of a certain top elevation as an example to introduce the pipeline burial depth corresponding to that top elevation. The other top elevations can be obtained by reference.

[0065] Regarding the acquisition of pipe top elevation, this application proposes a pipe top elevation extraction method based on adaptive point cloud repair and optimized matching. The first step is pipeline point cloud data preprocessing: noise filtering and removal are performed on the point cloud data obtained in step 210. An appropriate noise removal threshold is automatically selected using an automatic weight adjustment method. The second step is pipeline geometric model construction and data filling: based on the known pipeline dimensions, blind area data filling is performed using interpolation algorithms (such as spline interpolation) and geometric modeling, and matching is performed in conjunction with the point cloud data. The third step is least squares matching optimization: the filled data is matched with the white model using least squares optimization to further remove external noise and obtain a high-quality 3D pipeline model. The fourth step is adaptive slicing and pipe top elevation extraction: using an adaptive slicing algorithm, combined with tangent extraction and geometric analysis, the resolution of the slices and tangent extraction points are automatically determined, and the pipe top elevation is extracted based on a three-point arc construction method. The fifth step is efficient arc selection and elevation optimization: a dynamic threshold optimization algorithm is used to select the arc constructed from the three points, automatically adjusting the threshold and quickly extracting the pipe top elevation.

[0066] Specifically, step 220 may include steps 2201-2204.

[0067] Step 2201: By automatically adjusting the weights, select the upper point cloud data of the pipeline that meets the first preset condition as the upper point cloud data of the denoised pipeline.

[0068] In one embodiment, the three-dimensional sonar equipment obtains a high-resolution three-dimensional image of the upper part of the subsea pipeline; therefore, the obtained point cloud data includes point cloud data of the upper part of the pipeline.

[0069] The first preset condition can be that the weight corresponding to the upper point cloud data of the pipeline is greater than or equal to the weight threshold. The weight corresponding to each point cloud data can be calculated by the following formula (6), and then compared with the weight threshold to filter out the upper point cloud data of the pipeline that is greater than or equal to the threshold. The filtered upper point cloud data of the pipeline is the denoised upper point cloud data.

[0070] The point cloud data above the pipeline is denoised to obtain denoised point cloud data above the pipeline. As mentioned above, the point cloud data is... P 管线 Noise is removed using an automatic weight adjustment method, and the weight function is defined as follows:

[0071]

[0072] in, d i For the point cloud data i The distance from each point to its nearest neighbor. d The adjustment parameter is used to control the threshold. dthreshold The distance threshold between noise points and real data is used to distinguish between normal and abnormal points, determined based on the local density of the point cloud data. Weight calculation does not directly rely on known noise points, but rather determines whether a point is noise based on its relative distance to its neighborhood. Points with low weights are more likely to be noise, while points with high weights are considered real. Real data refers to point cloud data that conforms to actual physical meaning, typically objects that are close to surrounding points and exist in a real environment. This is achieved through an automatically adjusted weight function. w i The weight of each point is automatically adjusted based on distance, thereby removing noise. Here, weight refers to the "importance" or "reliability" of each point cloud data point, used to quantify the point's influence in the data processing. For each point cloud data point, the weight measures whether the point is valid data or noise.

[0073] Define weight function w i The purpose is to assign a weighted value to each point, thereby determining whether that point is noise. By calculating the weight of each point, we can determine which points are close to other points (potentially real data) and which points are far from other points (potentially noise). Weights w i The size reflects the point i The "credibility" of a point is determined by its distance from surrounding points. Points with higher weights are typically considered "credible" data (potentially representing real objects), while points with lower weights are considered "abnormal" data, possibly noise. The distance between a point and its surrounding points... d i The small value indicates that it may represent real data, hence the weight. w i It will approach 1, indicating that it is reliable. When the point... i Distance to surrounding points d i A larger value indicates that it may be an outlier (i.e., a noise point), therefore the weight should be adjusted accordingly. w i A value close to 0 indicates that it is unreliable. In this way, weight values ​​help us determine which points are reasonable and which are noise. This is achieved by setting a weight threshold. w threshold Points with weights lower than this value are considered noise, thus achieving noise removal.

[0074] Step 2202: Determine the lower point cloud data of the pipeline based on the pipeline size and the upper point cloud data of the denoised pipeline.

[0075] Due to the existence of measurement blind zones, data at the bottom of the pipeline cannot be obtained. Interpolation coefficients can be determined based on the pipeline's dimensions and the point cloud data above the denoised pipeline. Then, using spline interpolation, the point cloud data below the pipeline is determined based on these interpolation coefficients. The pipeline's dimensions are known data, typically expressed as its diameter.

[0076] Specifically, based on the known dimensions of the pipeline D The point cloud data at the top of the denoising pipeline is filled using spline interpolation. The data at the bottom of the pipeline can be supplemented using the spline interpolation formula, as follows:

[0077]

[0078] in, S(x) Indicates the location x The height of the bottom of the pipeline at that location. Specifically, S(x) It is a height function obtained through spline interpolation, which describes the continuity of the bottom point cloud. x It is the independent variable, usually representing the position in the horizontal direction or the coordinates of the pipeline along the pipeline direction. a 0、 a 1. a 2. a 3 represents the interpolation coefficients calculated from known point cloud data. Typically, spline interpolation calculates these coefficients using several known points (in this case, certain known heights of the pipeline). Specifically, these coefficients are determined by solving a system of equations that minimize the error of the interpolation function at the known data points and remain smooth throughout the interval.

[0079] This interpolation function generates supplementary bottom point cloud data. The bottom point cloud data is in three-dimensional coordinates, meaning each point has... (x, y, z) However, spline interpolation functions S(x) It only provides the direction along the pipe. x The height value, that is, it is z This is part of the coordinate system. To convert it into complete 3D point cloud data, it still needs to be combined with the lateral coordinates. y Coordinates. For example, if the geometry of a pipeline at a certain lateral position is known, the final 3D point cloud data can be obtained by... x and y Combined, and utilizing the height values ​​provided by spline interpolation. S(x) To construct three-dimensional coordinate points: (x, y, S(x)) Thus, the result of the interpolation function S(x) Can be compared with the horizontal coordinate of the pipe y By combining these data, complete 3D point cloud data can be obtained.

[0080] Step 2203: Determine the three-dimensional model of the pipeline based on the lower point cloud data of the pipeline and the upper point cloud data of the denoised pipeline.

[0081] After the processing in step 2202, complete point cloud data of the pipeline can be obtained. Therefore, a three-dimensional model of the pipeline can be constructed based on this point cloud data, and then the top elevation of the pipeline can be determined based on the three-dimensional model of the pipeline.

[0082] Specifically, the least squares method is used to match the lower point cloud data of the pipeline and the denoised upper point cloud data of the pipeline with the white model to obtain the optimal matching lower point cloud data of the pipeline and the denoised upper point cloud data of the pipeline. Based on the optimal matching lower point cloud data of the pipeline and the denoised upper point cloud data of the pipeline, the three-dimensional model of the pipeline is determined. The white model is a standard three-dimensional model of the pipeline constructed based on the dimensions of the pipeline.

[0083] The objective function of the least squares method is:

[0084]

[0085] in, P i It consists of point cloud data of the lower part of the pipeline and point cloud data of the upper part of the denoised pipeline. i It is the first of these point cloud data. i A point cloud, M model ( i ) is the first one after matching the white model. i Point cloud data, Q It is the total number of these point cloud data.

[0086] The optimal match is obtained by minimizing the objective function. M model ( i ), and then according to all M model ( i The three-dimensional model of the pipeline is determined. If the error exceeds the set threshold, optimization continues until the error is below the threshold.

[0087] Step 2204: Slice the three-dimensional model of the pipeline to determine the position and height of the top elevation of the pipeline.

[0088] The 3D model of the pipeline is sliced ​​to obtain multiple near-circular slices. For each near-circular slice, the following operations are performed: multiple tangents are drawn on the near-circular slice to obtain multiple tangent points; the position and height of each tangent point are extracted, and the three tangent points with the highest heights are selected; the curvature of the arc formed by the three tangent points is calculated; if the curvature is less than a preset curvature, the position and height of the tangent point with the highest height among the three tangent points are selected as the position and height of the top elevation of the pipe.

[0089] The pipeline 3D model is sliced ​​to obtain multiple near-circular slices, including: determining the slice spacing according to the shape of the pipeline; and slicing the pipeline 3D model according to the slice spacing to obtain multiple near-circular slices.

[0090] Specifically, adaptive slicing is performed based on the 3D model of the pipeline. The slice spacing Δ z The slice spacing will adaptively adjust based on the shape of the pipeline, with smaller slice spacing used for complex pipeline areas and larger slice spacing used for straight sections. The calculation method for adaptive slicing is as follows:

[0091]

[0092] in, L This is the minimum number of points required for slicing, that is, the minimum number of data points needed to complete the slicing. It can be set according to the specific shape of the pipeline and the required accuracy. L It depends on the following factors: (1) Pipeline complexity: For pipelines with complex shapes (such as curved or irregular sections), more point cloud data may be required to ensure accuracy, therefore, L It can be appropriately increased. (2) Density of point cloud data: If the density of point cloud data acquisition is high, L The size can be relatively small; if the data is sparse, it may be necessary to increase it. L To obtain enough points for slicing. (3) Accuracy requirements: If the accuracy requirement for extracting the tube top elevation is high, a smaller slicing spacing (i.e., a larger spacing) may be required. L This is to improve the accuracy of calculations. z max The maximum slice spacing can usually be set based on the following factors: (1) Pipe size and shape: A larger pipe diameter may allow for a larger slice spacing, while a smaller diameter pipe requires a smaller slice spacing to ensure the accuracy of the slices. (2) Point cloud data quality: If the point cloud data is dense and accurate, a larger Δ can be used. z max Otherwise, it is recommended to choose a smaller slice spacing to ensure more accurate extraction of the tube top elevation. (3) Calculation efficiency requirements: A larger slice spacing will reduce the amount of calculation, but may affect the accuracy of the results. Therefore, Δ z max A balance needs to be struck between accuracy and efficiency depending on the specific application requirements.

[0093] For a slice, tangents are drawn at regular intervals, and the positions and heights of the points where the tangents intersect are extracted. The heights of the extracted tangents are compared, and the three points with the highest heights are selected. The three highest points are used to construct an arc. If the arc is less than a set threshold, the highest point is selected as the top elevation of the pipe.

[0094] For an arc formed by three points, its radian can be calculated. e and with dynamic threshold e threshold Compare the two formulas. The formula is as follows:

[0095]

[0096] Where, 𝑅 is the radius calculated using three points, x 1. x 2. x The number 3 represents the coordinates of the three points that make up the arc. These points are used to calculate the tangent and angle of the arc, and thus determine whether this part is the critical part of the pipe top. If e < e threshold If so, the highest point is selected as the pipe top elevation. Dynamic threshold. e threshold Adjustments are made based on the complexity of the sliced ​​region. Through dynamic optimization, the threshold can be automatically adjusted to improve the accuracy and efficiency of elevation extraction. On the cross-section of the pipe, if it is a relatively smooth arc shape with a small curvature (i.e., e A smaller value indicates that this section of the pipe is relatively straight or has a low curvature, meeting the requirements for extracting the pipe top elevation. In this case, the highest point should be the top of the pipe, i.e., the pipe top elevation. If... e Less than the threshold e threshold If the pipe shape in this area is relatively regular (close to a circle or a smooth arc), then it is reasonable to choose the highest point as the pipe top elevation.

[0097] if e Greater than the threshold e threshold This indicates that the pipe shape in this area is relatively complex (possibly with large curvature or irregular shape), and it is no longer suitable to directly extract the pipe top elevation by fitting three points. In this case, the following methods can be used to redetermine the pipe top elevation: (1) More refined slicing: Divide the area into more slices and select a new set of suitable points for calculation. (2) Use other geometric models: The pipe top elevation can be found by using more accurate fitting methods, such as curve fitting or multi-point matching. (3) Local optimization: Perform local optimization processing on complex areas, and improve the elevation extraction accuracy by adjusting the point cloud data or using more data.

[0098] The pipe top elevation extraction algorithm based on adaptive point cloud repair and optimized matching described in this application can extract the pipe top elevation of submarine pipelines more accurately and automatically, while greatly improving efficiency.

[0099] Step 230: Determine the height of the natural mud surface based on the position of the top elevation of the pipe.

[0100] A multibeam echo sounding system is used to obtain three-dimensional topographic data of the natural mud surface; three-dimensional modeling is performed on the three-dimensional topographic data to obtain a three-dimensional model of the mud surface; the pipe top elevation is placed on the three-dimensional model of the mud surface, and multiple height data of the three-dimensional model of the mud surface are circled with the position of the pipe top elevation as the center and a preset radius as the radius; the average value of the multiple height data is calculated as the height of the natural mud surface.

[0101] Regarding the placement of the pipe top elevation on the 3D mud surface model, the position and height of the pipe top elevation have already been obtained in step 220. Software can be used to place the pipe top elevation on the 3D model by plotting or marking points. Regarding the preset radius, based on experience, it can generally be set to 5cm. Theoretically, one pipe top elevation corresponds to one mud surface point. However, considering that the height of the mud surface point is obtained through measuring equipment and has a certain deviation, to reduce this deviation, 5cm is specified as the radius centered on the characteristic point of the pipe top elevation. If it were specified as 1-2cm, the points within this range might be sparse; if it were too large, it wouldn't reflect the actual situation. Based on experience, 5cm is specified. Thus, multiple pipe top elevations can exist within a 5cm range. Therefore, multiple natural mud surfaces corresponding to multiple pipe top elevations can be determined. The average height of these natural mud surfaces is taken as the height of the natural mud surface.

[0102] To address the non-uniformity of seabed topography and measurement errors, this application provides an adaptive elevation correction method based on local regional weighted averaging. This method dynamically adjusts the weights of mud surface points based on their distance from the pipe top elevation, assigning lower weights to mud surface points farther from the pipe top elevation and higher weights to those closer. This allows for accurate natural mud surface correction based on the local environment of the pipeline. The formula for correcting the natural mud surface height using weighted averages, derived by analyzing local variations in seabed topography, is as follows:

[0103]

[0104] in, l i It is the first i The weight of each mud surface point is usually related to the measurement accuracy of that point, or to the relative distance between that point and the top elevation of the pipe. h i For the first i The height of each mud dot is obtained through the steps described above. K This represents the total number of mud dots.

[0105] The weight value can be calculated using the following formula:

[0106]

[0107] in, u i It is the distance between the nth mud surface point and the elevation of the top of the pipe. u pipe This refers to the reference distance from the top elevation of the pipe to a certain point. Specifically, u pipe This is the distance from a specified reference point (usually the center of the pipe top elevation or the nearest known elevation) to a given measurement point. This distance is used to calculate the relative distance between each point and the pipe top elevation, thus providing a basis for weighted averaging. φ is the weighted standard deviation, which is adjusted using different φ values ​​in different regions. For more complex seabed environments, the correction strategy within the region can be dynamically adjusted to make the correction results more accurate.

[0108] Step 240: Determine the burial depth of the pipeline based on the elevation of the top of the pipe and the height of the natural mud surface.

[0109] Calculate the difference between the height of the top of the pipe and the height of the natural mud surface, and use it as the initial pipeline burial depth; determine the pipeline burial depth based on the initial pipeline burial depth and the minimum error.

[0110] Specifically, this application provides a global pipeline burial depth optimization algorithm for calculating pipeline burial depth. By accurately matching the adaptively corrected natural mud surface height and the pipe top elevation, the burial depth data of the entire pipeline is optimized.

[0111] In measuring the burial depth of subsea pipelines, measurement errors may occur due to the complex seabed environment and the influence of various factors such as water depth and turbidity. To compensate for these errors, traditional burial depth calculation methods are usually based on the natural mud surface and pipe top elevation data at a certain point, but these methods may be inaccurate or inefficient. Therefore, a global pipeline burial depth optimization algorithm based on minimizing errors provides a more accurate calculation method. The core idea of ​​this algorithm is to match the adaptively corrected natural mud surface height and pipe top elevation data by minimizing global errors, and then calculate the optimal burial depth value.

[0112] Global pipeline burial depth optimization algorithm steps:

[0113] (1) Difference between pipe top elevation and natural mud surface height: First, calculate the difference between the pipe top elevation and the corresponding corrected natural mud surface height. For point cloud data of a pipeline, calculate the height difference of each point. h 管线 - h 修正自然泥面高度 This allows us to obtain the burial depth information for that point.

[0114] (2) Error Minimization: In order to obtain the globally optimal burial depth data, the algorithm is optimized by minimizing the error. The error function is defined as follows:

[0115]

[0116] in, O i It is the first i The actual burial depth of each point h 管线 It is the height of the top of the pipe. h 修正自然泥面高度 It is the height of the corrected natural mud surface. or It represents the total number of points.

[0117] (3) Global optimization: The algorithm adjusts the burial depth value of each point. O i To minimize the total error, the least squares method or other optimization methods are used to calculate the optimal burial depth at each point, thereby obtaining globally optimized pipeline burial depth data.

[0118] Through this optimization process, the algorithm can accurately calculate the burial depth of the entire pipeline based on the difference between the adaptively corrected natural mud surface and the top elevation of the pipe. Furthermore, by using a global optimization method, it reduces local errors and ensures the accuracy and reliability of the burial depth calculation, thus obtaining the precise burial depth of the subsea pipeline.

[0119] This application's embodiments, by introducing various optimization schemes such as adaptive point cloud repair, global burial depth optimization, and high-precision 3D modeling, can efficiently and accurately acquire pipeline burial depth data. Compared with existing technologies, this application requires no manual intervention; through automated calculation and optimization, it can reflect pipeline burial depth changes in real time and comprehensively, significantly improving measurement efficiency and accuracy, reducing human error, and providing more reliable technical support for subsea pipeline monitoring and maintenance.

[0120] The devices and methods disclosed in the several embodiments provided in this application can also be implemented in other ways. The device and method embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0121] In addition, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0122] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A method for determining the burial depth of a submarine pipeline, characterized in that, Includes the following steps: Obtain the point cloud data of the pipeline; Based on the point cloud data, determine the position and height of the pipe top elevation; The height of the natural mud surface is determined based on the position of the top elevation of the pipe; The burial depth of the pipeline is determined based on the elevation of the top of the pipe and the height of the natural mud surface. The point cloud data includes the point cloud data above the pipeline; Determining the position and height of the pipe top elevation based on the point cloud data includes: By automatically adjusting the weights, pipeline upper point cloud data that meets the first preset condition is selected as denoised pipeline upper point cloud data. The lower point cloud data of the pipeline is determined based on the pipeline size and the upper point cloud data of the denoised pipeline. Based on the point cloud data of the lower part of the pipeline and the point cloud data of the upper part of the denoised pipeline, a three-dimensional model of the pipeline is determined; The three-dimensional model of the pipeline is sliced ​​to determine the position and height of the top elevation of the pipeline; The method of removing noise by automatically adjusting weights is defined as follows: Where, d i Let d be the distance from the i-th point in the point cloud data to its nearest neighbor, δ be the adjustment parameter for the control threshold, and d be the distance from the i-th point to its nearest neighbor. threshold The distance threshold between noise points and real data is a distance threshold used to distinguish between normal and abnormal points, determined based on the local density of the point cloud data; it is achieved by setting a weighted threshold w. threshold Points with weights lower than this value are considered noise, thereby achieving noise removal; The acquisition of point cloud data of the pipeline includes: Three-dimensional images of the pipeline were obtained using a three-dimensional sonar device. The 3D image is converted into initial point cloud data; Based on the reflection intensity of the pipeline, initial point cloud data that meets the second preset condition is selected as the point cloud data; The second preset condition is that the reflection intensity corresponding to the initial point cloud data is greater than or equal to a reflection intensity threshold, wherein the reflection intensity is: Among them, P transmit Let σ be the power emitted by the sound source, G be the acoustic reflection coefficient of the target object, S be the gain value, T be the sensitivity, and f(θ) be the function relationship between the beam angle θ and the operating frequency f. The reflection intensity threshold is: I threshold =γ·I echo ·(1+κ·ΔI) Where γ is a constant coefficient used to adjust the sensitivity of the reflection intensity threshold, ΔI is the difference in reflection intensity between the pipeline and the mud surface, representing the gap between the reflection intensity of the pipeline and the reflection intensity of the mud surface, and κ is an adjustment factor used to adjust the threshold of reflection intensity and the factor of the difference in reflection intensity. The step of slicing the three-dimensional model of the pipeline to determine the position and height of the top elevation of the pipeline includes: The three-dimensional model of the pipeline is sliced ​​to obtain multiple near-circular slices; For each near-circular slice, perform the following operations: Draw multiple tangents on the near-circular slice to obtain multiple tangent points; Extract the position and height of each cut point, and select the three cut points with the highest heights. Calculate the radian of the arc formed by the three tangent points; If the arc is less than the preset arc, then the position and height of the highest tangent point among the three tangent points are selected as the position and height of the top elevation of the pipe; The step of slicing the three-dimensional model of the pipeline to obtain multiple near-circular slices includes: The slice spacing is determined based on the shape of the pipeline; Based on the slice spacing, the three-dimensional model of the pipeline is sliced ​​to obtain multiple near-circular slices; Determining the slice spacing based on the shape of the pipeline includes: The minimum value between the ratio of pipeline diameter to the minimum number of points required for slicing and the maximum slicing spacing is selected as the slicing spacing; Determining the height of the natural mud surface based on the position of the pipe top elevation includes: A multibeam echo sounding system was used to obtain three-dimensional topographic data of the natural mud surface; The three-dimensional terrain data is used to perform three-dimensional modeling to obtain a three-dimensional model of the mud surface; The pipe top elevation is arranged on the mud surface three-dimensional model, and multiple height data of the mud surface three-dimensional model are circled with the position of the pipe top elevation as the center and the preset radius as the radius. Calculate the average of the multiple height data points as the height of the natural mud surface; The burial depth of the pipeline is determined based on the elevation of the top of the pipe and the height of the natural mud surface, including: Calculate the difference between the height of the top of the pipe and the height of the natural mud surface, and use it as the initial pipeline burial depth; The pipeline burial depth is determined based on the initial pipeline burial depth and the minimized error.

2. The method for obtaining the burial depth of submarine pipelines according to claim 1, characterized in that, The step of determining the lower point cloud data of the pipeline based on the pipeline size and the upper point cloud data of the denoised pipeline includes: The interpolation coefficients are determined based on the dimensions of the pipeline and the point cloud data above the denoising pipeline. Based on the interpolation coefficients, the point cloud data below the pipeline is determined using spline interpolation.

3. The method for obtaining the burial depth of submarine pipelines according to claim 1, characterized in that, The step of determining the 3D model of the pipeline based on the lower point cloud data of the pipeline and the denoised upper point cloud data of the pipeline includes: Using the least squares method, the lower point cloud data of the pipeline and the upper point cloud data of the denoised pipeline are matched with the white model to obtain the optimal matching lower point cloud data of the pipeline and the upper point cloud data of the denoised pipeline. The three-dimensional model of the pipeline is determined based on the optimally matched lower point cloud data of the pipeline and the denoised upper point cloud data of the pipeline. The white model is a standard three-dimensional model of the pipeline constructed based on the dimensions of the pipeline.

4. An electronic device, characterized in that, The electronic device includes: processor; Memory used to store processor-executable instructions; The processor is configured to execute the method for obtaining the burial depth of submarine pipelines as described in any one of claims 1-3.

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