A multi-beam and side-scan sonar fused offshore station facility inspection method

Abstract

This invention discloses a method for inspecting offshore facilities using a fusion of multibeam and side-scan sonar, relating to the field of sonar detection signal processing technology. This method acquires multibeam and side-scan sonar data and establishes a unified spatial coordinate system. Based on the multibeam sonar data, a three-dimensional point set is constructed, and each data point is used as a reference point to determine the spatial neighborhood range of each reference data point and its corresponding side-scan sonar data point. Based on the spatial distance difference and intensity difference between data points within the spatial neighborhood, the degree of intensity change per unit spatial distance is calculated to determine anomaly status. The abnormal reference data points are spatially aggregated to determine the spatial range of the abnormal area and generate anomaly inspection results. This method introduces spatial neighborhood constraints to quantitatively analyze spatial change characteristics, achieving accurate identification of abnormal areas and improving the stability and reliability of inspection results.

Description

Technical Field

[0001] This invention relates to the field of sonar detection signal processing technology, specifically to a method for inspecting offshore facilities using a fusion of multibeam and side-scan sonar. Background Technology

[0002] In current underwater inspections of offshore facilities, multibeam sonar is typically used to acquire three-dimensional structural morphology information of the target area, while side-scan sonar is used to acquire echo intensity information of the target surface. By aligning the two sets of data in a unified spatial coordinate system, the correspondence between spatial location and intensity information is achieved, providing data support for subsequent anomaly identification. Based on this, existing processing methods mainly rely on intensity characteristics or image analysis of side-scan sonar data to determine the condition of the facility. For example, CN116105701A discloses a topographic survey system for offshore wind turbine pile foundations based on an unmanned semi-submersible vessel. This system uses a multibeam echo sounder and side-scan sonar mounted on an unmanned semi-submersible vessel to match and fuse measurement data in a unified coordinate system to form a complete seabed topographic map for the survey of offshore wind turbine pile foundations. CN112837252A discloses a method and system for image fusion of common coverage areas of side-scan sonar strip images. This method improves processing quality by fusing texture and intensity information from side-scan sonar images to analyze underwater topography and seabed distribution during offshore wind power construction and operation.

[0003] Existing methods for anomaly detection mostly rely on the intensity value of a single data point or local statistical characteristics, which constitutes a direct judgment based on discrete points and fails to fully characterize the spatial relationships between adjacent data points. Especially when there are structural changes, material differences, or localized damage on the surface of offshore facilities, anomalies often manifest as continuous or abrupt phenomena within a certain spatial range, rather than isolated abrupt changes in intensity at a single point. Simply relying on the intensity value of a single point is insufficient to accurately capture such spatially correlated changes, resulting in insufficient sensitivity and reliability in anomaly identification.

[0004] Existing methods lack joint analysis of spatial distance changes and intensity changes between adjacent data points, making it difficult to establish a quantitative correspondence between "spatial location differences and intensity differences" and effectively quantify the degree of intensity change within a unit spatial distance. When faced with complex scenarios such as gradual changes in facility surfaces, abrupt boundary changes, or minor defects, problems such as missed detections, misjudgments, or unstable judgment results are prone to occur, affecting the overall accuracy and stability of offshore facility inspections. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a method for inspecting offshore facilities by fusing multibeam and side-scan sonar. By introducing spatial neighborhood constraints, it jointly analyzes the spatial distance and intensity changes between adjacent data points and determines abnormal states based on the degree of intensity change within a unit spatial distance. This enables accurate identification of abnormal areas in offshore facilities and improves the reliability and stability of anomaly determination.

[0006] To achieve the above objectives, this invention provides a method for inspecting marine station facilities using a fusion of multibeam and side-scan sonar, comprising the following steps:

[0007] The system moves along a preset inspection path and simultaneously acquires multibeam sonar data, side-scan sonar data, and corresponding pose information. It also establishes the time correspondence between multibeam sonar data and pose information, as well as the time correspondence between side-scan sonar data and pose information.

[0008] A unified spatial coordinate system is established based on multibeam sonar data and pose information. Each beam data point in the multibeam sonar data is mapped to the unified spatial coordinate system to determine the spatial position of each beam data point in the unified spatial coordinate system. Based on the spatial position of each beam data point in the unified spatial coordinate system, a three-dimensional point set representing the structural morphology of the offshore station facility is constructed.

[0009] Map each side-scan sonar data point in the side-scan sonar data to a unified spatial coordinate system, and determine the spatial position of each side-scan sonar data point in the unified spatial coordinate system.

[0010] In a unified spatial coordinate system, each data point in the three-dimensional point set is used as a reference data point to determine the spatial neighborhood range of each reference data point and the side-scan sonar data points that fall within the spatial neighborhood range of each reference data point.

[0011] For each reference data point, the side-scan sonar data points falling within the spatial neighborhood of the reference data point are sorted according to their spatial distance from the reference data point. Based on the difference in spatial distance and intensity value between adjacent side-scan sonar data points after sorting, the abnormal state of the reference data point is determined.

[0012] Based on the spatial distribution of the reference data points identified as abnormal in a unified spatial coordinate system, the abnormal inspection results of offshore station facilities are determined.

[0013] The technical solution provided in this invention has at least the following technical effects or advantages: addressing the problem of lack of spatial correlation and quantification of change features in the anomaly identification process of multibeam sonar and side-scan sonar data, by constructing a unified spatial coordinate system, spatial alignment of multibeam sonar data and side-scan sonar data is achieved, and on this basis, a spatial neighborhood correlation relationship based on a three-dimensional point set is established, and the change features between data points in the spatial neighborhood are analyzed, thereby realizing the determination of anomaly status. Specifically, a three-dimensional point set reflecting the structural morphology of offshore facilities is constructed using multibeam sonar data, providing a foundation for spatial location analysis. By mapping side-scan sonar data to a unified spatial coordinate system, a correspondence is established between the spatial location and intensity information of each data point. Using data points in the three-dimensional point set as reference data points, a spatial neighborhood relationship is constructed between the reference data points and the side-scan sonar data points, transforming anomaly detection from a single-point-based analysis to a spatial neighborhood-based analysis. By calculating the spatial distance difference and intensity difference between adjacent side-scan sonar data points within the spatial neighborhood, and constructing the intensity variation within a unit spatial distance based on these two factors, a quantitative description of spatial variation characteristics is achieved, thereby improving the sensitivity and stability of anomaly identification. Furthermore, by spatially clustering the reference data points identified as anomalous, the anomalous data points are divided into regions based on spatial distance. Combining the spatial range and number of data points in each anomalous region, the anomaly results are spatially expressed, realizing the transformation from discrete anomaly points to anomalous regions with spatial distribution characteristics.

[0014] Compared with existing methods, this invention introduces spatial neighborhood constraints to jointly analyze changes in spatial distance and intensity, and uses the degree of intensity change within a unit spatial distance as the criterion for judgment. This can more accurately reflect the abnormal characteristics caused by structural changes or changes in surface properties. At the same time, by expressing abnormal regions based on spatial distribution, it improves the intuitiveness of anomaly location and the completeness of result expression, thereby improving the accuracy and reliability of anomaly inspection of offshore facilities. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This application provides a method for inspecting offshore station facilities by fusing multibeam and side-scan sonar. Detailed Implementation

[0017] This invention introduces a joint analysis mechanism of spatial distance change and intensity change under spatial neighborhood constraints, and proposes a method for inspecting marine station facilities by fusing multibeam and side-scan sonar. This solves the problems of existing methods lacking the ability to characterize the relationship between adjacent data points and having difficulty quantifying the intensity change within a unit spatial distance during anomaly identification, which leads to insufficient accuracy in anomaly identification and incomplete expression of anomaly results.

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] It should be noted that the terms "first," "second," etc., used in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices.

[0020] Example 1, as Figure 1 As shown, a method for inspecting marine station facilities using multibeam and side-scan sonar fusion includes the following steps:

[0021] S1. Move along the preset inspection path and simultaneously acquire multibeam sonar data, side-scan sonar data, and corresponding pose information, and establish the time correspondence between multibeam sonar data and pose information, and the time correspondence between side-scan sonar data and pose information, respectively.

[0022] Furthermore, establishing the time correspondence between multibeam sonar data and pose information, as well as the time correspondence between side-scan sonar data and pose information, includes the following steps:

[0023] Timestamps are added to multibeam sonar data, side-scan sonar data, and pose information respectively, so that multibeam sonar data, side-scan sonar data, and pose information are on the same time reference.

[0024] The multibeam sonar data and side-scan sonar data are sorted according to timestamps, and a continuous time axis is constructed based on the time series of pose information.

[0025] For each frame of multibeam sonar data and side-scan sonar data, the pose information data with the closest timestamp is found in the continuous time axis. The time difference between the multibeam sonar data and the pose information data, and between the side-scan sonar data and the pose information data, are calculated respectively. Based on the time difference, the time correspondence between the multibeam sonar data and the pose information, and the time correspondence between the side-scan sonar data and the pose information are established.

[0026] Specifically, before the inspection begins, the starting and ending points of the inspection are determined based on the known location range of the offshore facilities. A continuous inspection path is generated according to the distribution direction of the facilities. The inspection path adopts a parallel, back-and-forth path along the extension direction of the facilities, with the spacing between adjacent paths controlled within 0.6 to 0.8 times the coverage width of the multibeam sonar to ensure overlapping areas between adjacent scanning regions. After the mobile carrier starts, it moves along the inspection path at a constant speed, controlled within the range of 1.5 to 3 knots. The multibeam sonar transmission frequency is set to 5 Hz to 20 Hz, and the side-scan sonar transmission frequency is set to 2 Hz to 10 Hz. This ensures that the spacing between adjacent sonar sampling positions along the inspection path is controlled within the range of 0.04 meters to 0.8 meters, thereby ensuring the continuity of the structural data acquired by the multibeam sonar and the spatial correspondence of the intensity information from the side-scan sonar. Among them, multibeam sonar is used to construct a set of structural points. Its sampling density directly affects the continuity of the structural representation. Therefore, its transmission frequency is higher than that of side-scan sonar. Side-scan sonar is mainly used to obtain intensity information. Its spatial distribution is constrained by establishing a correlation with the structural point set data. Therefore, its transmission frequency can be relatively low.

[0027] During the data acquisition process, pose information is collected synchronously, including three-dimensional position data and attitude angle data. The three-dimensional position data is obtained through a combination of satellite positioning and inertial measurement, while the attitude angle data is obtained in real time through an inertial measurement device. The sampling frequency of the pose information is set to 50Hz to 200Hz to ensure that the pose angle data has a higher time resolution than the sonar data.

[0028] During the data acquisition process, timestamps are added to the multibeam sonar data, side-scan sonar data, and pose information. The timestamps are generated by a unified clock source, so that the multibeam sonar data, side-scan sonar data, and pose information are on the same time base, and the time stamping accuracy is controlled within the millisecond level.

[0029] After data acquisition was completed, the multibeam sonar data and side-scan sonar data were sorted according to timestamps, and a continuous time axis was constructed based on the time series of pose information.

[0030] For each frame of multibeam sonar data, the pose information data with the closest timestamp is found in the continuous time axis, and the time difference between the two is calculated. When the time difference is less than the multibeam time matching threshold, the multibeam sonar data is mapped to the pose information, thereby establishing a one-to-one correspondence between the multibeam sonar data and the pose information in the time dimension.

[0031] When the time difference between the multibeam sonar data and its corresponding pose information is greater than the multibeam time matching threshold, time interpolation is performed on the pose information of two adjacent frames before and after the timestamp of the multibeam sonar data to obtain the pose information corresponding to the timestamp of the multibeam sonar data, and a one-to-one correspondence is established between the multibeam sonar data and the interpolated pose information.

[0032] For each frame of side-scan sonar data, the pose information data with the closest timestamp is found in the continuous time axis, and the time difference between the two is calculated. When the time difference is less than the side-scan time matching threshold, the side-scan sonar data is mapped to the pose information, thereby establishing a one-to-one correspondence between the side-scan sonar data and the pose information in the time dimension.

[0033] When the time difference between the side-scan sonar data and its corresponding pose information is greater than the side-scan time matching threshold, time interpolation is performed on the pose information of two adjacent frames before and after the timestamp of the side-scan sonar data to obtain the pose information corresponding to the timestamp of the side-scan sonar data, and a one-to-one correspondence is established between the side-scan sonar data and the interpolated pose information.

[0034] The multi-beam time matching threshold and the side-scan time matching threshold are determined based on the sampling period of the multi-beam sonar and the sampling period of the side-scan sonar, respectively. The sampling period of the multi-beam sonar and the sampling period of the side-scan sonar are the reciprocals of the corresponding sonar transmission frequency. The multi-beam time matching threshold and the side-scan time matching threshold are set to within 0.5 times their respective sampling periods to ensure that each frame of sonar data is matched only with the pose information within its adjacent sampling periods on the continuous time axis, thereby avoiding the correspondence error caused by the matching result crossing adjacent sampling periods.

[0035] Through the above processing, each frame of multibeam sonar data and each frame of side-scan sonar data corresponds to a unique pose information, thereby completing the time alignment between multibeam sonar data, side-scan sonar data and pose information, so that each sonar data has a unique corresponding pose information.

[0036] S2. Establish a unified spatial coordinate system based on multibeam sonar data and pose information, and map each beam data point in the multibeam sonar data to the unified spatial coordinate system. Determine the spatial position of each beam data point in the unified spatial coordinate system, and construct a three-dimensional point set representing the structural morphology of the offshore station facility based on the spatial position of each beam data point in the unified spatial coordinate system.

[0037] Further, determining the spatial position of each beam data point in a unified spatial coordinate system includes the following steps:

[0038] The sound path distance and transmission direction angle of each beam in each frame of multibeam sonar data are obtained, and the spatial coordinate position of each beam data point relative to the multibeam sonar transmission position is determined based on the sound path distance and transmission direction of each beam.

[0039] Based on the directional deflection of the multibeam sonar transmission direction relative to the carrier reference direction and the spatial offset of the multibeam sonar transmission position relative to the carrier reference position, the spatial coordinate positions of each beam data point relative to the multibeam sonar transmission position are replaced with directional correspondence and numerically corrected to obtain the spatial coordinate positions of each beam data point relative to the carrier reference position.

[0040] Based on the pose information corresponding to each beam data point, the spatial coordinate position of each beam data point relative to the carrier reference position is transformed and numerically corrected to obtain the spatial coordinate position of each beam data point in a unified spatial coordinate system.

[0041] Specifically, after completing the time alignment between multibeam sonar data and pose information, the pose information corresponding to each frame of multibeam sonar data is determined based on the alignment result, and a unified spatial coordinate system is established based on the pose information. The origin of the unified spatial coordinate system is taken as the carrier position at the start of the inspection, the heading direction at the start of the inspection is taken as the first coordinate axis direction, the direction perpendicular to the first coordinate axis direction and located in the horizontal plane is taken as the second coordinate axis direction, and the direction perpendicular to the horizontal plane is taken as the third coordinate axis direction, thus determining the spatial direction of the unified spatial coordinate system.

[0042] After establishing a unified spatial coordinate system, the spatial position of each beam data point in the multibeam sonar data is determined within the unified spatial coordinate system. Specifically, for each frame of multibeam sonar data, the path distance and transmission direction corresponding to each beam in that frame are obtained, and the transmission direction angle is used as the spatial transmission direction parameter corresponding to each beam. The spatial transmission direction parameter is obtained by calibrating the beam direction of the multibeam sonar device and establishing a one-to-one correspondence with each beam. For each beam, the transmission position of the multibeam sonar is used as a reference point, and the spatial position is calculated based on the path distance and corresponding spatial transmission direction parameter of that beam to determine the spatial position of that beam along the corresponding transmission direction, thereby obtaining the spatial coordinate position of each beam data point relative to the transmission position of the multibeam sonar.

[0043] After obtaining the spatial coordinates of each beam data point relative to the multi-beam sonar transmission position, the installation parameters of the multi-beam sonar equipment are read, and the spatial offset of the multi-beam sonar transmission position relative to the carrier reference position and the directional deflection of the multi-beam sonar transmission direction relative to the carrier reference direction are obtained from the installation parameters. For each beam data point, the spatial coordinates of that beam data point relative to the multi-beam sonar transmission position are obtained, and these spatial coordinates are represented as x-axis, y-axis, and z-axis coordinate components. Based on the spatial offset of the multi-beam sonar transmission position relative to the carrier reference position, a coordinate system rotation method is used to replace the directional correspondence of the x-axis, y-axis, and z-axis coordinate components, so that the x-axis, y-axis, and z-axis coordinate components correspond to the three coordinate directions under the carrier reference direction, respectively. After completing the directional correspondence replacement, a coordinate translation method is used to add the directional deflection of the multi-beam sonar transmission direction relative to the carrier reference direction to the x-axis, y-axis, and z-axis coordinate components, respectively, to obtain the corrected x-axis, y-axis, and z-axis coordinate components. These corrected coordinate components are then used as the spatial coordinates of the beam data point relative to the carrier reference position.

[0044] Based on this, using the attitude angle data in the pose information corresponding to the beam data point, a coordinate system rotation method is used to transform the x-axis, y-axis, and z-axis coordinate components, making them correspond to the three coordinate directions of a unified spatial coordinate system. After the direction transformation is completed, based on the three-dimensional position data in the pose information corresponding to the beam data point, a coordinate translation method is used to superimpose the x-axis, y-axis, and z-axis coordinate components with the three-dimensional position data to obtain the corrected x-axis, y-axis, and z-axis coordinate components. The corrected three coordinate components are then used as the spatial coordinate position of the beam data point in the unified spatial coordinate system.

[0045] This allows us to obtain the spatial coordinates of each beam data point in a unified spatial coordinate system, thus enabling the mapping of each beam data point in multi-beam sonar data to a unified spatial coordinate system.

[0046] After obtaining the spatial coordinates of each beam data point in a unified spatial coordinate system, for each frame of multibeam sonar data, the beam data points in that frame are traversed according to the data acquisition order. For each beam data point, its x-axis, y-axis, and z-axis coordinate components in the unified spatial coordinate system are read and recorded as a set of coordinate data. After traversing all beam data points in that frame of multibeam sonar data, the corresponding sets of coordinate data are sequentially stored in a data set, where each set of coordinate data corresponds to one beam data point. After processing all frames of multibeam sonar data, all coordinate data in the data set is used as a three-dimensional point set to characterize the structural morphology of the offshore station facility.

[0047] S3. Map each data point in the side-scan sonar data to a unified spatial coordinate system, and determine the spatial position of each data point in the unified spatial coordinate system.

[0048] Further, determining the spatial position of each side-scan sonar data point in a unified spatial coordinate system includes the following steps:

[0049] The corresponding sound path distance and emission direction angle of each side-scan sonar data point in each frame of side-scan sonar data are obtained, and the spatial coordinate position of each side-scan sonar data point relative to the side-scan sonar emission position is determined based on the sound path distance and emission direction angle of each side-scan sonar data point.

[0050] Based on the spatial offset of the side-scan sonar transmission position relative to the carrier reference position and the directional deflection of the side-scan sonar transmission direction relative to the carrier reference direction, the spatial coordinate positions of each side-scan sonar data point relative to the side-scan sonar transmission position are replaced with directional correspondence and numerically corrected to obtain the spatial coordinate positions of each side-scan sonar data point relative to the carrier reference position.

[0051] Based on the pose information corresponding to each side-scan sonar data point, the spatial coordinate position of each side-scan sonar data point relative to the carrier reference position is transformed and numerically corrected to obtain the spatial coordinate position of each side-scan sonar data point in a unified spatial coordinate system.

[0052] Specifically, for each frame of side-scan sonar data, the path distance and emission direction angle corresponding to each side-scan sonar data point in that frame are obtained, and the emission direction angle is used as the spatial emission direction parameter corresponding to each side-scan sonar data point. The spatial emission direction parameter is obtained by directional calibration of the side-scan sonar equipment and corresponds one-to-one with each side-scan sonar data point during data output. For each side-scan sonar data point, the emission position of the side-scan sonar is used as a reference point, and the spatial position is calculated based on the path distance and corresponding spatial emission direction parameter of the side-scan sonar data point to determine the spatial position of the side-scan sonar data point along the corresponding emission direction, thereby obtaining the spatial coordinate position of each side-scan sonar data point relative to the side-scan sonar emission position.

[0053] After obtaining the spatial coordinates of each side-scan sonar data point relative to the side-scan sonar transmission position, the installation parameters of the side-scan sonar equipment are read, and the spatial offset of the side-scan sonar transmission position relative to the carrier reference position and the directional deflection of the side-scan sonar transmission direction relative to the carrier reference direction are obtained from the installation parameters. For each side-scan sonar data point in the side-scan sonar data, the spatial coordinates of the data point relative to the side-scan sonar transmission position are obtained, and these spatial coordinates are represented as x-axis, y-axis, and z-axis coordinate components. Based on the spatial offset of the side-scan sonar transmission position relative to the carrier reference position, a coordinate system rotation method is used to replace the directional correspondence of the x-axis, y-axis, and z-axis coordinate components, so that the x-axis, y-axis, and z-axis coordinate components correspond to the three coordinate directions under the carrier reference direction, respectively. After completing the directional correspondence replacement, a coordinate translation method is used to add the directional deflection of the side-scan sonar transmission direction relative to the carrier reference direction to the x-axis, y-axis, and z-axis coordinate components, respectively, to obtain the corrected x-axis, y-axis, and z-axis coordinate components. These three corrected coordinate components are then used as the spatial coordinates of the side-scan sonar data point relative to the carrier reference position.

[0054] Based on this, using the attitude angle data in the pose information corresponding to the side-scan sonar data point, a coordinate system rotation method is used to transform the x-axis, y-axis, and z-axis coordinate components, making them correspond to the three coordinate directions of a unified spatial coordinate system. After the direction transformation is completed, based on the three-dimensional position data in the pose information corresponding to the side-scan sonar data point, a coordinate translation method is used to superimpose the x-axis, y-axis, and z-axis coordinate components with the three-dimensional position data to obtain the corrected x-axis, y-axis, and z-axis coordinate components. The corrected three coordinate components are then used as the spatial coordinate position of the side-scan sonar data point in the unified spatial coordinate system.

[0055] This allows us to obtain the spatial coordinates of each side-scan sonar data point in a unified spatial coordinate system, thus enabling us to map each side-scan sonar data point in the side-scan sonar data to a unified spatial coordinate system.

[0056] S4. In a unified spatial coordinate system, using each data point in the three-dimensional point set as a reference data point, determine the spatial neighborhood range of each reference data point and the side-scan sonar data points falling within the spatial neighborhood range of each reference data point.

[0057] Further, the spatial neighborhood of each reference data point is determined, including the following steps:

[0058] Perform statistical processing on the distances between data points in the 3D point set, calculate the average proximity distance between data points in the 3D point set, and use the average proximity distance as the initial radius of the spatial neighborhood.

[0059] The side-scan sonar data points are statistically analyzed using the initial radius of the spatial neighborhood range, and the spatial neighborhood range of each reference data point is determined based on the statistical results.

[0060] Furthermore, the side-scan sonar data points are statistically analyzed using the initial radius of the spatial neighborhood range, and the spatial neighborhood range of each reference data point is determined based on the statistical results. This includes using the spatial coordinate position of each reference data point in a unified spatial coordinate system as the center point, and the initial radius of the spatial neighborhood range as the effective distance, to count the number of side-scan sonar data points falling within the current spatial neighborhood range. When the statistical result is zero, the distance of the initial radius of the spatial neighborhood range is gradually increased by an expansion step size. After each increase in the expansion step size, the number of side-scan sonar data points falling within the current spatial neighborhood range is recounted until the statistical result is greater than zero. At this point, the expansion step size is stopped, and the current spatial neighborhood range is taken as the spatial neighborhood range of each reference data point.

[0061] Furthermore, the expansion step size is determined by sequentially selecting each data point in the three-dimensional point set, calculating the spatial distance between the data point and its neighboring data points in the three-dimensional point set, and recording the minimum nearest neighbor distance corresponding to each data point. After completing the calculation of the minimum nearest neighbor distance of all data points, the minimum value among the minimum nearest neighbor distances of all data points in the three-dimensional point set is determined as the expansion step size.

[0062] Specifically, in a unified spatial coordinate system, the spatial coordinate positions of each data point in the three-dimensional point set are read in the unified spatial coordinate system, and each data point is used as a reference data point in turn.

[0063] Statistical processing of inter-point distances is performed on each data point in the 3D point set. For each data point in the 3D point set, the spatial distance between that data point and its adjacent data points in the 3D point set is calculated, and the minimum nearest neighbor distance for each data point is recorded. Specifically, the spatial distance between any two data points is calculated based on Euclidean distance. This involves obtaining the coordinate values ​​of the two data points in three directions in a unified spatial coordinate system, calculating the coordinate differences between the two data points in the three directions, squaring each of the three coordinate differences, summing the sums, and then taking the square root of the sum to obtain the spatial distance between the two data points. After calculating the minimum nearest neighbor distance for all data points, statistical processing is performed on all minimum nearest neighbor distances to calculate the average nearest neighbor distance of the 3D point set. This average nearest neighbor distance is used as the initial radius of the spatial neighborhood. Simultaneously, the minimum value among the minimum nearest neighbor distances of all data points in the 3D point set is determined as the expansion step size, ensuring that the expansion step size of the spatial neighborhood of the reference data point does not exceed the minimum distance between any data points in the 3D point set.

[0064] Using the spatial coordinates of each reference data point in a unified spatial coordinate system as the center point, and the initial radius of the spatial neighborhood as the effective distance, the side-scan sonar data points are traversed. The number of side-scan sonar data points falling within the current spatial neighborhood is counted. Specifically, the spatial distance between each side-scan sonar data point and each reference data point is calculated, and the number of side-scan sonar data points whose spatial distance is less than or equal to the initial radius of the spatial neighborhood is counted. When the count result is zero, an expansion step size is added to the initial radius of the spatial neighborhood as the effective distance. Based on the updated effective distance, the traversal of the side-scan sonar data points is performed again, and the number of side-scan sonar data points falling within the current spatial neighborhood is counted again until the count result is greater than zero. At this point, the expansion step size is stopped, and the current spatial neighborhood is taken as the spatial neighborhood of each reference data point.

[0065] After determining the spatial neighborhood range of each reference data point, the side-scan sonar data points falling within the spatial neighborhood range of each reference data point are counted to form a set of side-scan sonar data points within the spatial neighborhood range of each reference data point.

[0066] S5. For each reference data point, sort the side-scan sonar data points falling within the spatial neighborhood of that reference data point according to their spatial distance from the reference data point. Then, determine the abnormal state of the reference data point based on the difference in spatial distance and intensity value between adjacent side-scan sonar data points after sorting.

[0067] Furthermore, based on the spatial distance difference and intensity difference between adjacent side-scan sonar data points after sorting, the abnormal state of the reference data point is determined, including:

[0068] The rate of change of intensity between adjacent side-scan sonar data points is calculated based on the spatial distance difference and intensity value difference.

[0069] Based on the intensity change rate results corresponding to the benchmark data point, the abnormal state of the benchmark data point is determined.

[0070] Furthermore, based on the intensity change rate corresponding to the benchmark data point, the abnormal state of the benchmark data point is determined, including:

[0071] Statistical processing is performed on the intensity change rate corresponding to all benchmark data points, the average intensity change rate is calculated, and the average intensity change rate is used as the anomaly judgment threshold.

[0072] When the intensity change rate result corresponding to the benchmark data point contains a value greater than the anomaly judgment threshold, the benchmark data point is judged to be in an abnormal state.

[0073] When all values ​​in the intensity change rate results corresponding to the benchmark data point are less than the anomaly detection threshold, the benchmark data point is determined to be in a normal state.

[0074] Specifically, for each reference data point, the spatial distance between the reference data point and each side-scan sonar data point falling within its spatial neighborhood is obtained. The side-scan sonar data points falling within the spatial neighborhood of the reference data point are then sorted in ascending order of spatial distance, resulting in the sorted result, denoted as { , , ..., }, where n represents the number of side-scan sonar data points falling within the spatial neighborhood of the reference data point. For each side-scan sonar data point, the spatial distance between the record and the reference data point is d, and the corresponding intensity value is l, and the sorting result satisfies .

[0075] After obtaining the sorting results, adjacent side-scan sonar data points are selected sequentially from the sorting results. For any two adjacent data points... and Calculate the spatial distance difference and intensity difference separately, where the spatial distance difference is denoted as... The difference in strength values ​​is denoted as ,in, This represents the i-th side-scan sonar data point after sorting. This represents the spatial distance between the i-th side-scan sonar data point and the reference data point corresponding to its spatial neighborhood range. This represents the intensity value corresponding to the i-th side-scan sonar data point.

[0076] Based on spatial distance difference Difference from strength value Calculate the rate of change of intensity between adjacent side-scan sonar data points, denoted as The intensity change rates between adjacent side-scan sonar data points are aggregated to form the intensity change rate set corresponding to the reference data point. , , ..., Meanwhile, all intensity change rate data in the intensity change rate set corresponding to all benchmark data points are statistically analyzed, the average intensity change rate is calculated, and the average intensity change rate is used as the anomaly judgment threshold.

[0077] Each intensity change rate in the intensity change rate set is compared with an anomaly detection threshold. If there is a value in the intensity change rate result that is greater than the anomaly detection threshold, the benchmark data point is determined to be in an abnormal state; if all values ​​in the intensity change rate result are less than the anomaly detection threshold, the benchmark data point is determined to be in a normal state.

[0078] During the scanning of a continuous spatial region by side-scan sonar, when the surface state of the detected object is continuous and without abrupt changes, the echo intensity corresponding to adjacent positions usually exhibits a smooth change. As the spatial distance increases, the intensity value change is continuous and gradual, keeping the intensity change rate between adjacent side-scan sonar data points within a small range. However, when there are structural abrupt changes, obstructed boundaries, or material differences in space, the echo intensity at adjacent positions will change significantly within a short spatial distance, resulting in a significant increase in the degree of intensity change per unit distance, thus causing a sudden increase in the corresponding intensity change rate. Therefore, by comparing the intensity change rate between adjacent side-scan sonar data points with a threshold, the location of abnormal changes in space can be effectively identified, thereby enabling the determination of abnormal states of reference data points.

[0079] S6. Based on the spatial distribution of the reference data points identified as abnormal in a unified spatial coordinate system, determine the abnormal inspection results of the offshore station facilities.

[0080] Furthermore, based on the spatial distribution of the reference data points identified as abnormal in a unified spatial coordinate system, the abnormal inspection results of the offshore station facilities are determined, including:

[0081] The reference data points that are determined to be in an abnormal state are used as abnormal reference data points, and based on the spatial distance between abnormal reference data points, abnormal reference data points that meet the preset conditions are divided into the same abnormal region.

[0082] Extract the spatial coordinates of all abnormal reference data points within each abnormal region, determine the spatial range of each abnormal region in a unified spatial coordinate system, and count the number of abnormal reference data points within each abnormal region.

[0083] The spatial range of each abnormal area in a unified spatial coordinate system is used as the abnormal location identifier, and the number of abnormal reference data points is used as the abnormality index to generate corresponding abnormal inspection result data.

[0084] Specifically, in a unified spatial coordinate system, the spatial coordinates of all reference data points identified as abnormal are obtained. Each abnormal reference data point is then used as a current processing point. For each current processing point, the spatial distance between that current processing point and the other abnormal reference data points is calculated. Abnormal reference data points whose spatial distance from the current processing point is less than or equal to a preset clustering distance are grouped into the same abnormal region. After processing all abnormal reference data points, each abnormal reference data point is assigned to its corresponding abnormal region, resulting in several abnormal regions. The preset clustering distance is obtained by statistically processing the inter-point distances of the data points in the 3D point set, using the average proximity distance of the 3D point set as the preset clustering distance.

[0085] For each abnormal region, the spatial coordinates of all abnormal reference data points within that region are extracted. The maximum and minimum coordinate values ​​of all abnormal reference data points in the x-axis, y-axis, and z-axis directions are determined. The spatial range of each abnormal region in a unified spatial coordinate system is defined by the maximum and minimum coordinate values ​​of each abnormal reference data point in the three directions. At the same time, the number of abnormal reference data points within that region is counted.

[0086] The spatial range of each abnormal area in a unified spatial coordinate system is used as the abnormal location identifier, and the number of each abnormal baseline data point is used as the abnormality index to generate and output the corresponding abnormal inspection result data.

[0087] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for inspecting offshore station facilities by multi-beam and side-scan sonar fusion, characterized in that, Includes the following steps, The system moves along a preset inspection path and simultaneously acquires multibeam sonar data, side-scan sonar data, and corresponding pose information. It also establishes the time correspondence between multibeam sonar data and pose information, as well as the time correspondence between side-scan sonar data and pose information. A unified spatial coordinate system is established based on multibeam sonar data and pose information. Each beam data point in the multibeam sonar data is mapped to the unified spatial coordinate system to determine the spatial position of each beam data point in the unified spatial coordinate system. Based on the spatial position of each beam data point in the unified spatial coordinate system, a three-dimensional point set representing the structural morphology of the offshore station facility is constructed. Map each side-scan sonar data point in the side-scan sonar data to a unified spatial coordinate system, and determine the spatial position of each side-scan sonar data point in the unified spatial coordinate system. In a unified spatial coordinate system, each data point in the three-dimensional point set is used as a reference data point to determine the spatial neighborhood range of each reference data point and the side-scan sonar data points that fall within the spatial neighborhood range of each reference data point. For each reference data point, the side-scan sonar data points falling within the spatial neighborhood of the reference data point are sorted according to their spatial distance from the reference data point. Based on the difference in spatial distance and intensity value between adjacent side-scan sonar data points after sorting, the abnormal state of the reference data point is determined. Based on the spatial distribution of the reference data points identified as abnormal in a unified spatial coordinate system, the abnormal inspection results of offshore station facilities are determined.

2. The method according to claim 1, wherein, Establishing the time correspondence between multibeam sonar data and pose information, as well as the time correspondence between side-scan sonar data and pose information, includes the following steps: Timestamps are added to multibeam sonar data, side-scan sonar data, and pose information respectively, so that multibeam sonar data, side-scan sonar data, and pose information are on the same time reference. The multibeam sonar data and side-scan sonar data are sorted according to timestamps, and a continuous time axis is constructed based on the time series of pose information. For each frame of multibeam sonar data and side-scan sonar data, the pose information data with the closest timestamp is found in the continuous time axis. The time difference between the multibeam sonar data and the pose information data, as well as between the side-scan sonar data and the pose information data, is calculated respectively. Based on the time difference, the time correspondence between the multibeam sonar data and the pose information, as well as the time correspondence between the side-scan sonar data and the pose information, is established.

3. The method according to claim 2, wherein, The process involves determining the spatial position of each beam data point in a unified spatial coordinate system. Includes the following steps, The sound path distance and transmission direction angle of each beam in each frame of multibeam sonar data are obtained, and the spatial coordinate position of each beam data point relative to the multibeam sonar transmission position is determined based on the sound path distance and transmission direction of each beam. Based on the directional deflection of the multibeam sonar transmission direction relative to the carrier reference direction and the spatial offset of the multibeam sonar transmission position relative to the carrier reference position, the spatial coordinate positions of each beam data point relative to the multibeam sonar transmission position are replaced with directional correspondence and numerically corrected to obtain the spatial coordinate positions of each beam data point relative to the carrier reference position. Based on the pose information corresponding to each beam data point, the spatial coordinate position of each beam data point relative to the carrier reference position is transformed and numerically corrected to obtain the spatial coordinate position of each beam data point in a unified spatial coordinate system.

4. The method according to claim 3, wherein, Determining the spatial position of each side-scan sonar data point in a unified spatial coordinate system includes the following steps: The sound path distance and emission direction angle of each side-scan sonar data point in each frame of side-scan sonar data are obtained, and the spatial coordinate position of each side-scan sonar data point relative to the side-scan sonar emission position is determined based on the sound path distance and emission direction angle of each side-scan sonar data point. Based on the spatial offset of the side-scan sonar transmission position relative to the carrier reference position and the directional deflection of the side-scan sonar transmission direction relative to the carrier reference direction, the spatial coordinate positions of each side-scan sonar data point relative to the side-scan sonar transmission position are replaced with directional correspondence and numerically corrected to obtain the spatial coordinate positions of each side-scan sonar data point relative to the carrier reference position. Based on the pose information corresponding to each side-scan sonar data point, the spatial coordinate position of each side-scan sonar data point relative to the carrier reference position is transformed and numerically corrected to obtain the spatial coordinate position of each side-scan sonar data point in a unified spatial coordinate system.

5. The method according to claim 4, wherein, Determining the spatial neighborhood range of each reference data point includes the following steps: Perform statistical processing on the distances between data points in the 3D point set, calculate the average nearest neighbor distance between data points in the 3D point set, and use the average nearest neighbor distance as the initial radius of the spatial neighborhood. The side-scan sonar data points are statistically analyzed using the initial radius of the spatial neighborhood range, and the spatial neighborhood range of each reference data point is determined based on the statistical results.

6. The method for inspecting marine station facilities using multibeam and side-scan sonar fusion as described in claim 5, characterized in that, The process involves statistically analyzing the side-scan sonar data points using an initial radius of spatial neighborhood, and determining the spatial neighborhood of each reference data point based on the statistical results. Using the spatial coordinates of each reference data point in a unified spatial coordinate system as the center point, and the initial radius of the spatial neighborhood as the effective distance, the number of side-scan sonar data points falling into the current spatial neighborhood is counted. When the count result is zero, the distance of the initial radius of the spatial neighborhood is gradually increased by the expansion step size. After each increase in the expansion step size, the number of side-scan sonar data points falling into the current spatial neighborhood is counted again until the count result is greater than zero. Then, the expansion step size is stopped, and the current spatial neighborhood is taken as the spatial neighborhood of each reference data point.

7. The method for inspecting marine station facilities using multibeam and side-scan sonar fusion as described in claim 6, characterized in that, The expansion step size is determined by sequentially selecting each data point in the three-dimensional point set, calculating the spatial distance between the data point and its neighboring data points in the three-dimensional point set, and recording the minimum nearest neighbor distance corresponding to each data point. After completing the calculation of the minimum nearest neighbor distance of all data points, the minimum value among the minimum nearest neighbor distances of all data points in the three-dimensional point set is determined as the expansion step size.

8. The method for inspecting marine station facilities using multibeam and side-scan sonar fusion according to claim 7, characterized in that, The step of determining the abnormal state of the reference data point based on the spatial distance difference and intensity difference between adjacent side-scan sonar data points after sorting includes: The rate of change of intensity between adjacent side-scan sonar data points is calculated based on the spatial distance difference and intensity value difference. Based on the intensity change rate results corresponding to the benchmark data point, the abnormal state of the benchmark data point is determined.

9. The method for inspecting marine station facilities using multibeam and side-scan sonar fusion as described in claim 8, characterized in that, The step of determining the abnormal state of the benchmark data point based on the intensity change rate result corresponding to the benchmark data point includes: Statistical processing is performed on the intensity change rate corresponding to all benchmark data points, the average intensity change rate is calculated, and the average intensity change rate is used as the anomaly judgment threshold. When the intensity change rate result corresponding to the benchmark data point contains a value greater than the anomaly judgment threshold, the benchmark data point is judged to be in an abnormal state. When all values ​​in the intensity change rate results corresponding to the benchmark data point are less than the anomaly detection threshold, the benchmark data point is determined to be in a normal state.

10. The method for inspecting marine station facilities using multibeam and side-scan sonar fusion according to claim 9, characterized in that, The process of determining the abnormal inspection results of offshore station facilities based on the spatial distribution of reference data points identified as abnormal in a unified spatial coordinate system includes: The reference data points that are determined to be in an abnormal state are used as abnormal reference data points, and based on the spatial distance between abnormal reference data points, abnormal reference data points that meet the preset conditions are divided into the same abnormal region. Extract the spatial coordinates of all abnormal reference data points within each abnormal region, determine the spatial range of each abnormal region in a unified spatial coordinate system, and count the number of abnormal reference data points within each abnormal region. The spatial range of each abnormal area in a unified spatial coordinate system is used as the abnormal location identifier, and the number of abnormal reference data points is used as the abnormality index to generate corresponding abnormal inspection result data.

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