A sea ice thickness determination method, program product, device and storage medium

Abstract

Embodiments of the present application disclose a sea ice thickness determination method, a program product, an apparatus and a storage medium, comprising: acquiring three-dimensional point cloud data and regional images of a sea ice region on a side of a ship; determining an upper bottom surface region and a lower bottom surface region of an effective ice block according to the regional images, and based on the three-dimensional point cloud data, the upper bottom surface region and the lower bottom surface region of the effective ice block; the effective point cloud data comprises an upper bottom surface point cloud set and a lower bottom surface point cloud set corresponding to the effective ice block; thickness measurement point selection is performed based on data quality of the upper bottom surface point cloud set and the lower bottom surface point cloud set, to obtain to-be-measured data; the to-be-measured data comprises a single point pair, a multi-point pair set or a locally fitted surface; and the thickness of the effective ice block is calculated based on a point cloud spatial distance calculation mode corresponding to the to-be-measured data and the to-be-measured data. The method of the present application avoids common calibration errors, perspective distortion and attitude sensitivity in visual measurement, and improves the stability of sea ice block thickness calculation.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of marine engineering technology, and in particular to a method, program product, device and storage medium for determining sea ice thickness. Background Technology

[0002] Sea ice thickness is a crucial parameter for ship navigation in ice-covered areas, icebreaking operations, marine engineering support, and polar environmental monitoring. For ships navigating in ice-covered areas, timely information on sea ice thickness near the hull is essential for assessing ice conditions, evaluating icebreaking loads, and supporting navigation decisions. Traditional methods for measuring sea ice thickness include manual drilling and contact measurements, which are inefficient and difficult to implement continuously during ship navigation or icebreaking.

[0003] With the development of artificial intelligence, most current methods for measuring sea ice thickness are based on camera-based visual measurement. This method estimates thickness using the cross-section exposed after sea ice overturns during icebreaking by ships. Such methods typically require the sea ice cross-section to be in a suitable orientation for camera observation and necessitate scale calibration, viewpoint correction, or geometric constraint processing. When the sea ice cross-section does not form an ideal angle with the camera's line of sight, when there is relative motion between the ship and the sea ice, or when the sea ice surface is highly reflective or obstructed, the pixel distance in the visual image cannot directly correspond to the actual spatial thickness, limiting the stability and accuracy of the measurement. Summary of the Invention

[0004] This invention provides a method, program product, device, and storage medium for determining sea ice thickness, which can improve the stability, accuracy, and engineering applicability of side sea ice thickness measurement.

[0005] In a first aspect, embodiments of the present invention provide a method for determining sea ice thickness, comprising: Acquire 3D point cloud data and regional images of the sea ice area on the side of the ship; The upper and lower surface regions of the valid ice block are determined based on the region image, and the valid point cloud data of the valid ice block is obtained based on the three-dimensional point cloud data, the upper and lower surface regions of the valid ice block, and the valid point cloud data includes the upper surface point cloud set and the lower surface point cloud set corresponding to the valid ice block. Based on the data quality of the upper and lower bottom surface point cloud sets, thickness measurement points are selected to obtain the data to be measured; the data to be measured is a single point pair, a multi-point pair set, or a local fitted surface. The thickness of the effective ice block is calculated based on the point cloud spatial distance calculation method corresponding to the test data and the test data; the point cloud spatial distance calculation method is single-point distance calculation, statistical distance calculation or surface distance calculation.

[0006] In a second aspect, embodiments of the present invention provide a sea ice thickness determination device, the device comprising: The data acquisition module is used to acquire three-dimensional point cloud data and regional images of the sea ice area on the side of the ship; The first data processing module is used to determine the upper and lower bottom surface regions of the valid ice block based on the region image, and to obtain the valid point cloud data of the valid ice block based on the three-dimensional point cloud data, the upper and lower bottom surface regions of the valid ice block; the valid point cloud data includes the upper bottom surface point cloud set and the lower bottom surface point cloud set corresponding to the valid ice block. The second data processing module is used to select thickness measurement points based on the data quality of the upper bottom surface point cloud set and the lower bottom surface point cloud set to obtain the data to be measured; the data to be measured is a single point pair, a multi-point pair set, or a local fitting surface. The thickness calculation module is used to calculate the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the test data and the test data; the point cloud spatial distance calculation method is single-point distance calculation, statistical distance calculation or surface distance calculation.

[0007] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the sea ice thickness determination method as described in any of the embodiments of the present invention.

[0008] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the sea ice thickness determination method as described in any of the embodiments of the present invention.

[0009] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements a sea ice thickness determination method as described in any of the embodiments of the present invention.

[0010] The method of this invention, after identifying the upper and lower surfaces of sea ice based on regional images, selects thickness measurement points based on the specific data quality of the corresponding upper and lower surface point cloud sets, without relying on image data. Different quality data corresponds to different test data, flexibly adapting to point cloud data of varying quality. This ensures that optimal test data can be found under every practical condition, avoiding systematic errors caused by forcibly fitting inferior data and preventing accuracy loss due to the crude simplification of high-quality data, thus achieving a balance between accuracy and robustness. Finally, the thickness of the sea ice block is flexibly and accurately determined based on the spatial distance calculation method corresponding to the test data. The calculation of sea ice block thickness is based on the three-dimensional spatial coordinates of the lidar point cloud, avoiding common problems in visual measurement such as calibration errors, perspective distortion, and pose sensitivity, thus improving the stability of sea ice block thickness calculation. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a method for determining sea ice thickness provided in an embodiment of the present invention; Figure 2 A schematic diagram showing the results of identifying the upper and lower bottom surface image regions of sea ice for the purpose of implementing the present invention; Figure 3 This is a schematic diagram of the structure of the sea ice thickness determination device provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the invention and not all structures. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant national laws and regulations. It should be noted that, in the embodiments of this application, certain existing industry solutions such as software, components, and models may be mentioned. These should be considered exemplary, intended only to illustrate the feasibility of implementing the technical solutions of this application, and do not imply that the applicant has already used or necessarily used the relevant content of such solutions.

[0014] Figure 1 This is a flowchart illustrating a method for determining sea ice thickness according to an embodiment of the present invention. The method of this embodiment improves the stability, accuracy, and engineering applicability of side sea ice thickness measurement. This method can be executed by a sea ice thickness determination device provided in this embodiment, which can be implemented in software and / or hardware. The following embodiments will illustrate this using the integration of the device into an electronic device as an example. The electronic device can be a server or computer device, etc., used to implement a sea ice thickness determination method. (Refer to...) Figure 1 The method may specifically include the following steps:

[0015] Step 101: Obtain 3D point cloud data and regional images of the sea ice area on the side of the ship.

[0016] The 3D point cloud data consists of a coordinate dataset composed of discrete points in 3D space. In this scheme, the 3D point cloud data is collected by lidar installed at locations directly observable from the icebreaking area, such as the ship's side, bulwark, or deck edge. The lidar's detection direction covers the area of ​​sea ice that is compressed, broken, overturned, and drifting beside the ship. During ship movement, the lidar can collect 3D point clouds within the sea ice area beside the ship in real time or at regular time intervals. The point cloud includes the sea ice surface, sea ice break edges, the overturned upper and lower surfaces, the area near the water surface, and any possible local ship structures or other interfering targets. The area image is captured by imaging equipment deployed on the ship, which, along with the lidar, is directed towards the observation area beside the ship.

[0017] Step 102: Determine the upper and lower surface regions of the valid ice blocks based on the regional image, and obtain the valid point cloud data of the valid ice blocks based on the 3D point cloud data and the upper and lower surface regions of the valid ice blocks.

[0018] Valid ice blocks are sea ice blocks identified from the regional image that can be used for subsequent thickness measurements. Specifically, after acquiring the regional image, a pre-trained object detection model can be used to identify the bounding boxes of the sea ice blocks in the regional image. The bounding boxes are used to define the approximate location of the ice blocks in the regional image, and the pixel area within the bounding box is the entire region of the valid ice blocks. Alternatively, by utilizing the differences in color, brightness, and texture between sea ice and seawater / sky, sea ice regions can be extracted using methods such as thresholding, edge detection, or region growing.

[0019] After identifying the ice region, the upper and lower surfaces of the sea ice are further determined based on their relative positional relationship with the ship's side. Specifically, using the location of the ship's side or hull plating as a reference, the surface of the ice region closer to the ship's side is defined as the upper surface, and the surface farther from the ship's side and opposite to the upper surface is defined as the lower surface. These upper and lower surfaces are relative surfaces determined based on the ship's side reference direction, used to characterize two boundary surfaces in the sea ice thickness direction, and are not limited to corresponding to the original upper and lower surfaces of the sea ice in its natural floating state.

[0020] For example, Figure 2 This is a schematic diagram illustrating the results of identifying the upper and lower surface image regions of sea ice, as provided for the implementation of this invention. (See diagram below.) Figure 2 As shown, a fracture section appears between the original upper and lower surfaces in the overturned sea ice block. The sea ice is laterally submerged and extracted from this section, and the upper and lower bottom surfaces of the sea ice are identified.

[0021] In one optional implementation, effective point cloud data of the effective ice block is obtained based on three-dimensional point cloud data, the upper and lower surface regions of the effective ice block, including: mapping the upper and lower surface regions based on the three-dimensional point cloud data to obtain a mapped upper surface point cloud set corresponding to the upper surface region and a mapped lower surface point cloud set corresponding to the lower surface region; and obtaining effective point cloud data of the effective ice block in the sea ice region based on the mapped upper surface point cloud set and the mapped lower surface point cloud set.

[0022] Point cloud region mapping is the process of determining the corresponding set of spatial points in 3D point cloud data by transforming the upper and lower surface regions identified in the image through spatial geometric transformation. Specifically, the images acquired by the imaging device use an image coordinate system with pixels as the unit, the origin at the upper left corner of the image, the horizontal axis to the right, and the vertical axis downward. The point clouds acquired by the LiDAR use the LiDAR coordinate system, with the origin at the center of the LiDAR and the three axes pointing in specific directions.

[0023] Before acquiring 3D point cloud data and regional images of the sea ice area on the side of the ship, the transformation relationship between pixels in the image and spatial points in the point cloud can be pre-calibrated. Calibration includes intrinsic parameters of the imaging device, such as focal length, principal point position, and distortion coefficient, which describe the correspondence between image pixels and ray directions in the coordinate system of the imaging device; it also includes extrinsic parameters from the imaging device to the lidar, such as rotation matrix and translation vector, which describe the spatial pose relationship between the coordinate systems of the imaging device and the lidar.

[0024] For each pixel in the upper bottom region, using the intrinsic parameters of the imaging device, the image coordinates of that pixel are converted into a ray in the imaging device's coordinate system. This ray originates from the optical center of the imaging device, passes through the pixel's position on the image plane, and points in a certain direction in three-dimensional space. Using pre-calibrated extrinsic parameters, the ray in the imaging device's coordinate system is transformed into the lidar coordinate system. This results in a ray in the lidar coordinate system originating from a point near the lidar and pointing in a specific direction. This ray represents the observation direction of that pixel in the image in three-dimensional space. In the three-dimensional point cloud data acquired by the lidar, the point cloud point closest to the ray in the ray direction and whose perpendicular distance to the ray is less than a certain threshold is identified. For each pixel in the upper bottom region, the points that satisfy the above conditions are defined as the upper bottom point cloud set. The point cloud mapping process for the lower bottom region is the same in principle as that for the upper bottom region, and will not be described again here.

[0025] Specifically, the obtained mapping upper and lower surface point cloud sets may contain noise and a small amount of erroneous data. Noise may originate from random errors in the lidar measurement itself, echo scattering caused by the roughness of the sea ice surface, false echoes caused by splashes or foam, ranging errors caused by specular reflection, or mapping deviations caused by calibration errors between sensors and differences in viewing angles. After obtaining the mapping upper and lower surface point cloud sets, filtering can be performed on both sets to obtain valid point cloud data for effective ice blocks in the sea ice region. Filtering includes deleting isolated points and removing erroneous mapping points caused by occlusion and reflection. For example, for each point in the mapping upper (or lower) surface point cloud set, several nearest neighboring points are identified within the set, and the average distance from that point to its neighbors is calculated. The average distance distribution of all points in the entire set is statistically analyzed, and the global mean and standard deviation of the average distance are calculated. If the average distance of a point exceeds a preset distance threshold, that point is identified as an outlier and deleted.

[0026] Step 103: Select thickness measurement points based on the data quality of the upper and lower bottom surface point cloud sets to obtain the data to be measured.

[0027] The data to be measured is used to calculate the effective thickness of sea ice. In this scheme, the data quality of ice block point cloud data varies for different types of sea ice. Therefore, different thickness measurement point selection methods are used for different types of sea ice point cloud data, and thus, different data to be measured are used. The data to be measured can be single-point pairs, multi-point pairs, or local fitted surfaces. Data quality includes the number of point clouds, point cloud distribution, and surface smoothness. In this scheme, optionally, thickness measurement points are selected based on the data quality of the upper and lower bottom surface point cloud sets to obtain the data to be measured, including the following steps B1-B5:

[0028] Step B1: Determine the quality type corresponding to the data quality based on the number of point clouds, the distribution of point clouds, and the surface smoothness.

[0029] Quality types include few-point type, uniform multi-point type, smooth surface type, and multi-point curved surface type. The number of point clouds directly determines whether statistical or fitting can be performed; the point cloud distribution determines whether global fitting can be performed, and fitting will be distorted when the distribution is uneven; the surface smoothness determines whether fitting is meaningful, and forcing fitting on a rough surface will introduce systematic errors.

[0030] Specifically, for each valid ice block, the number of points in the point cloud sets of the upper and lower surfaces is first counted. If the number of points on either surface is less than a preset minimum effective threshold, its quality type is directly determined to be a low-point type. If the number of points on both surfaces exceeds the threshold, the point cloud distribution is further checked: the area covered by the point cloud is divided into grids, and the variance of the point density within each grid is calculated. If the variance is small, it indicates that the point distribution is uniform, and its quality type is determined to be a uniform multi-point type. If the variance is large but the number of points is sufficient, a surface flatness check is performed: local plane fitting is performed on the upper and lower surfaces respectively, and the residuals from each point to the fitted surface are calculated. If the root mean square of the residuals is less than a preset threshold, it indicates that the surface is flat, and its quality type is determined to be a flat surface type; if the residuals are large but the number of points is still sufficient, it indicates that the surface is curved, and its quality type is determined to be a multi-point curved surface type.

[0031] Step B2: When the quality type is a few points, determine the upper bottom surface reference point in the upper bottom surface point cloud set, determine the lower bottom surface reference point corresponding to the reference point in the lower bottom surface set, and obtain a single point pair based on the upper bottom surface reference point and the lower bottom surface reference point.

[0032] When the data quality type is "few points," it means that at least one of the upper and lower surface point cloud sets has extremely few points, i.e., the point cloud is so sparse that it cannot support any statistical averaging or surface fitting. When the data quality is "few points," an upper surface reference point can be determined from the upper surface point cloud set. Points with relatively high point density in their local neighborhood can be preferentially selected as the upper surface reference point. After determining the upper surface reference point, a corresponding lower surface reference point is determined from the lower surface point cloud set. The point in the lower surface point cloud set with the closest Euclidean distance to the upper surface reference point can be determined and used as the corresponding point. Furthermore, the single-point pair formed by the upper and lower surface reference points is determined as the data to be measured.

[0033] Step B3: When the quality type is uniform multi-point type, determine the upper bottom surface reference point set according to the distribution position of each point in the upper bottom surface point cloud set, determine the lower bottom surface reference point set corresponding to the upper bottom surface reference point set in the lower bottom surface set, and obtain the multi-point pair set based on the upper bottom surface reference point set and the lower bottom surface reference point set.

[0034] When the data quality type is uniform multi-point, it indicates that the point cloud is relatively evenly distributed in space, without obvious clustering or voids, but it does not yet meet the standard for plane fitting. In this case, multiple upper-ground reference points can be determined from the upper-ground point cloud set to form an upper-ground reference point set. Multiple upper-ground reference points can be selected based on the spatial dispersion of each point to avoid all reference points being crowded together, leading to excessive local representativeness. Specifically, the coverage area of ​​the upper-ground point cloud set can be divided into several sub-regions, and a representative point can be selected from each sub-region as the upper-ground reference point, ultimately obtaining the upper-ground reference point set.

[0035] Furthermore, the lower base reference point corresponding to each reference point in the upper base reference point set is determined, thereby constructing the lower base reference point set. The method for determining the lower base reference point is the same as that in step B1, and will not be repeated here. The upper base reference point set and the lower base reference point set are paired to obtain a set of multi-point pairs as the data to be measured.

[0036] Step B4: When the quality type is surface flat, perform plane fitting on the upper and lower bottom point cloud sets respectively to obtain the upper and lower bottom plane equations; determine the local plane fitting surface based on the upper and lower bottom plane equations.

[0037] When the data quality type is "surface smooth," it indicates that there are sufficient points on both sides and the surface smoothness is high. The residuals of local plane fitting are very small, and plane fitting can be performed directly, using the entire upper and lower surfaces as the data to be measured. Specifically, the least squares method can be used to solve for the plane equation that minimizes the sum of the squares of the distances from all points to the plane. During the fitting process, outliers with excessively large residuals can be iteratively removed, and then the plane can be refitted to improve its representativeness. After fitting, the plane equations of the upper and lower surfaces are obtained. Based on the plane equations of the upper and lower surfaces, the locally fitted plane is determined as the data to be measured.

[0038] Step B5: When the quality type is a multi-point surface, perform surface fitting on the upper and lower base point cloud sets respectively to obtain the upper and lower base surface equations; determine the local fitting surface based on the upper and lower base surface equations.

[0039] When the data quality type is multi-point surface, it indicates that the number of points on both sides is sufficient, but the surface is curved (e.g., sea ice deformed by compression). The planar assumption does not hold, and forcing a planar fit will introduce systematic errors. In this case, surface fitting can be performed separately on the upper and lower base point cloud sets. For example, a quadratic surface model (a surface represented by a ternary quadratic equation) can be used for surface fitting. After fitting, the surface equations of the upper and lower bases are obtained.

[0040] By comprehensively evaluating three dimensions—point cloud quantity, point cloud distribution, and surface smoothness—sea ice data is categorized into few-point, uniform multi-point, smooth-surface, and multi-point curved-surface types. This achieves a match between data quality and processing strategies, avoiding the problems of forcibly processing inferior data in a complex manner or oversimplifying high-quality data. For few-point types, single-point pairs are used as the data to be measured, ensuring thickness results can still be output even when data is extremely scarce, guaranteeing the minimum availability of the system. For uniform multi-point types, by constructing a set of multi-point pairs and introducing statistical averaging, data redundancy is used to suppress single-point noise, improving the robustness of thickness estimation. For smooth-surface types, local planar fitting surfaces are used to replace discrete points. For multi-point curved-surface types, local curved-surface fitting surfaces approximate the actual curvature, avoiding systematic errors caused by planar assumptions, expanding the adaptability of the scheme to complex ice blocks, and ensuring the reliability of subsequent thickness measurement results.

[0041] Step 104: Calculate the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the test data and the test data.

[0042] Point cloud spatial distance calculation methods include single-point pair distance calculation, statistical distance calculation, or surface distance calculation. Based on the point cloud spatial distance calculation method corresponding to the test data and the test data itself, the effective ice block thickness is calculated, including the following steps C1-C4:

[0043] Step C1: When the data to be measured is a single-point pair, calculate the three-dimensional Euclidean distance between the upper and lower bottom reference points to obtain the effective ice block thickness.

[0044] When the data to be measured is a single-point pair, that is, there is only one reference point on the upper surface and one on the lower surface, the three-dimensional Euclidean distance between the two points can be directly determined as the effective thickness of the ice block. For example, the three-dimensional coordinates of the two points can be subtracted respectively to obtain the difference in the three coordinate axes. The square root of the sum of the squares of the three differences is the three-dimensional Euclidean distance between the two points.

[0045] Step C2: When the data to be tested is a set of multiple point pairs, calculate the three-dimensional Euclidean distance of each point pair in the set, and determine the thickness of the effective ice block based on the three-dimensional Euclidean distance of all point pairs.

[0046] When the data to be measured is a set of multiple point pairs, meaning there are multiple pairs of upper and lower base reference points, the three-dimensional Euclidean distance can be calculated for each pair in the set. Then, statistical methods can be used to calculate the three-dimensional Euclidean distance of each pair to obtain the effective ice thickness. Statistical methods include calculating the arithmetic mean or median of the three-dimensional Euclidean distances of all pair pairs.

[0047] Step C3: When the data to be measured is a local plane fitting surface, the thickness direction is determined based on the plane normal of the upper and lower plane equations; the projection distance between the upper and lower planes is calculated in the thickness direction to obtain the effective ice block thickness.

[0048] When the data to be measured is a locally fitted planar surface, meaning the upper and lower base surfaces are each fitted to a separate plane, the thickness can be calculated based on the geometric relationship between the two planes. First, the thickness direction is determined based on the plane normals of the upper and lower base plane equations. Each plane equation contains a normal vector. The average direction of the normal vectors of the upper and lower base plane equations can be taken as the thickness direction; that is, the angle bisector direction is obtained by adding the two unit normal vectors and normalizing. Alternatively, the normal vector of one of the planes can be directly taken as the thickness direction, for example, the normal of the upper base surface can be determined as the thickness direction.

[0049] Furthermore, the projected distance between the upper and lower bottom surfaces is calculated along the thickness direction. For example, a point is selected on the upper bottom surface, and a ray is drawn from this point along the thickness direction to obtain the intersection point of the ray and the lower bottom surface. The directed distance between the selected point on the upper bottom surface and the intersection point is determined as the projected distance. This method utilizes the global smoothing effect brought about by plane fitting, is insensitive to local noise, and yields robust results with clear physical meaning.

[0050] Step C4: When the data to be measured is a local fitting surface of a curved surface, determine the reference point of the curved surface in the point cloud set of the upper bottom surface, and determine the normal ray of the curved surface reference point; calculate the distance from the reference point of the curved surface to the intersection of the normal ray of the curved surface and the lower bottom surface to obtain the thickness of the effective ice block.

[0051] When the data to be measured is a locally fitted surface of a curved surface, a reference point for the curved surface is determined in the point cloud set of the upper base surface. The reference point can be the geometric center of the upper base surface, the center of the region with the smallest curvature, or the point with the highest point cloud density. After determining the reference point, the surface normal at that point is calculated, which is the direction perpendicular to the local tangent plane of the surface at that point. After obtaining the surface normal at the reference point, this normal is used as the ray direction. A ray is drawn from the reference point along this direction, which is the surface normal ray. The intersection point of this ray and the lower base surface is calculated. The ray equation can be substituted into the lower base surface equation to solve for the coordinates of the intersection point. Then, the distance from the reference point to the intersection point is calculated, which is the three-dimensional Euclidean distance between the two points. This three-dimensional Euclidean distance is determined as the effective thickness of the ice block.

[0052] In one alternative implementation, the thickness calculation results at consecutive time points can be processed using time series analysis. Since ship navigation and sea ice drift cause the same sea ice block to continuously change within the observation area, sea ice point clouds from adjacent time points can be correlated to determine if they belong to the same sea ice block, and the thickness results from multiple time points can be fused or filtered. This allows for the selection of times when the upper and lower surfaces are more fully exposed and the point cloud quality is better, as the sea ice flip angle gradually changes, to output the thickness results.

[0053] In this scheme, after determining the thickness of sea ice, the final results are output based on the sea ice thickness. The final results include the thickness of a single piece of sea ice, the thickness of a local area, the thickness sequence over a continuous time period, or the statistical results of the sea ice thickness within the side observation area. The final results may also include the measurement location, measurement time, measurement confidence level, number of valid points, identification results of the upper and lower bottom surfaces, and visualized point cloud results, so as to be used for subsequent ice condition analysis, icebreaking process assessment, or ship navigation assistance.

[0054] The technical solution of this embodiment involves acquiring three-dimensional point cloud data and regional images of the sea ice region on the side of the ship; determining the upper and lower bottom surface regions of the effective ice block based on the regional images; and obtaining the effective point cloud data of the effective ice block based on the three-dimensional point cloud data, the upper and lower bottom surface regions of the effective ice block; the effective point cloud data includes the upper bottom surface point cloud set and the lower bottom surface point cloud set corresponding to the effective ice block; selecting thickness measurement points based on the data quality of the upper and lower bottom surface point cloud sets to obtain the data to be measured; the data to be measured includes single point pairs, multi-point pair sets, or local fitted surfaces; calculating the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the data to be measured and the data to be measured; the point cloud spatial distance calculation method includes single point pair distance calculation, statistical distance calculation, or surface distance calculation. The technical solution of this embodiment, after identifying the upper and lower surfaces of sea ice based on regional images, selects thickness measurement points without relying on image data, based on the specific data quality of the corresponding upper and lower surface point cloud sets. Different quality data corresponds to different test data, flexibly adapting to point cloud data of varying quality. This ensures that the optimal test data can be found under every practical condition, avoiding systematic errors caused by forcibly fitting inferior data and preventing accuracy loss due to the crude simplification of high-quality data, thus achieving a balance between accuracy and robustness. Finally, the thickness of the sea ice is flexibly and accurately determined based on the spatial distance calculation method corresponding to the test data. The calculation of sea ice thickness is based on the three-dimensional spatial coordinates of the lidar point cloud, avoiding common problems in visual measurement such as calibration errors, perspective distortion, and pose sensitivity, thus improving the stability of sea ice thickness calculation.

[0055] Figure 3 This is a schematic diagram of the sea ice thickness determination device provided in an embodiment of the present invention. This device is suitable for executing the sea ice thickness determination method provided in an embodiment of the present invention. Figure 3 As shown, the device may specifically include:

[0056] Data acquisition module 301 is used to acquire three-dimensional point cloud data of the sea ice area on the side of the ship; The first data processing module 302 is used to perform ice block point cloud data filtering and ice block surface geometric segmentation on the three-dimensional point cloud data according to preset thickness measurement conditions and data characteristics of the three-dimensional point cloud data, so as to obtain effective point cloud data of effective ice blocks in the sea ice region; the effective point cloud data includes the upper bottom surface point cloud set and the lower bottom surface point cloud set corresponding to the effective ice blocks. The second data processing module 303 is used to select thickness measurement points based on the data quality of the upper bottom surface point cloud set and the lower bottom surface point cloud set to obtain the data to be measured; the data to be measured is a single point pair, a multi-point pair set, or a local fitting surface. The thickness calculation module 304 is used to calculate the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the test data and the test data; the point cloud spatial distance calculation method is single-point distance calculation, statistical distance calculation or surface distance calculation.

[0057] Optionally, the first data processing module 302 is specifically used to: perform coordinate unification, effective distance filtering, and noise data removal on the three-dimensional point cloud data to obtain candidate point cloud data; Multi-dimensional data feature analysis is performed on the candidate point cloud data to obtain the data features of the candidate point cloud data. The data features include the spatial continuity, surface normal variation features, edge abrupt change features, local planarity, and location information of the candidate point cloud data. Based on the thickness measurement conditions and the data characteristics, the candidate point cloud data is filtered to obtain ice block point cloud data. Based on the local geometric features of the ice block point cloud data, the ice block point cloud data is geometrically segmented on the ice block surface to obtain the upper bottom surface point cloud set and the lower bottom surface point cloud set.

[0058] Optionally, the second data processing module 303 is specifically used to: determine the quality type corresponding to the data quality based on the number of point clouds, the distribution of point clouds, and the surface flatness, wherein the quality type includes a few-point type, a uniform multi-point type, a flat surface type, and a multi-point curved surface type; When the quality type is the few-point type, an upper bottom surface reference point is determined in the upper bottom surface point cloud set, a lower bottom surface reference point corresponding to the reference point is determined in the lower bottom surface set, and the single point pair is obtained based on the upper bottom surface reference point and the lower bottom surface reference point; When the quality type is the uniform multi-point type, the upper bottom surface reference point set is determined according to the distribution position of each point in the upper bottom surface point cloud set, the lower bottom surface reference point set corresponding to the upper bottom surface reference point set is determined in the lower bottom surface set, and the multi-point pair set is obtained according to the upper bottom surface reference point set and the lower bottom surface reference point set.

[0059] Optionally, the local fitting surface includes a planar local fitting surface or a curved surface local fitting surface; the second data processing module 303 is further configured to: when the quality type is the surface flatness type, perform planar fitting on the upper bottom surface point cloud set and the lower bottom surface point cloud set respectively to obtain the upper bottom surface plane equation and the lower bottom surface plane equation; The local fitting surface of the plane is determined based on the equations of the upper and lower base planes; When the quality type is the multi-point surface type, surface fitting is performed on the upper bottom surface point cloud set and the lower bottom surface point cloud set respectively to obtain the upper bottom surface surface equation and the lower bottom surface surface equation. The local fitting surface of the surface is determined based on the equations of the upper and lower base surfaces.

[0060] Optionally, the thickness calculation module 304 is specifically used to: when the data to be measured is the single-point pair, calculate the three-dimensional Euclidean distance between the upper bottom reference point and the lower bottom reference point to obtain the thickness of the effective ice block; When the data to be tested is the set of multiple point pairs, the three-dimensional Euclidean distance of each point pair in the set of multiple point pairs is calculated, and the thickness of the effective ice block is determined based on the three-dimensional Euclidean distance of all point pairs.

[0061] Optionally, the thickness calculation module 304 is further configured to: determine the thickness direction based on the plane normal of the upper base plane equation and the lower base plane equation when the data to be measured is the local fitting surface of the plane; The thickness of the effective ice block is obtained by calculating the projected distance between the upper and lower bottom surfaces in the thickness direction. When the data to be measured is the local fitting surface of the surface, a reference point of the surface is determined in the point cloud set of the upper bottom surface, and the normal ray of the surface reference point is determined. The effective ice block thickness is obtained by calculating the distance from the reference point of the surface to the intersection of the normal ray of the surface and the bottom surface.

[0062] Optionally, the thickness calculation module 304 is further configured to: acquire image data of the sea ice area on the side of the ship, and determine the ice block image data of the valid ice blocks in the image data; The ice block image data is verified based on the ice block point cloud data to obtain a verification result, which is either consistent in recognition or inconsistent in recognition. If the verification result is consistent, then the upper and lower bottom image regions of the valid ice block are determined in the ice block image data; point cloud region mapping is performed on the upper and lower bottom image regions respectively to obtain the mapped upper bottom point cloud set corresponding to the upper bottom image region and the mapped lower bottom point cloud set corresponding to the lower bottom image region. The thickness of the effective ice block is calculated based on the mapping upper and lower surface point cloud sets.

[0063] The sea ice thickness determination device provided in this embodiment of the invention can execute the sea ice thickness determination method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method. Contents not described in detail in this embodiment can be referred to the descriptions in any method embodiment of the invention.

[0064] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, with reference to... Figure 4 , Figure 4 The electronic device 12 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this application. Figure 4 As shown, the electronic device 12 is represented in the form of a general-purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).

[0065] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0066] Electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0067] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4As not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this application.

[0068] A program / utility 40 having a set (at least one) of program modules 46 may be stored, for example, in system memory 28. Such program modules 46 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 46 typically perform the functions and / or methods described in the embodiments of this application.

[0069] Electronic device 12 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 12, and / or with any device that enables electronic device 12 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 12 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. As shown, network adapter 20 communicates with other modules of electronic device 12 via bus 18. It should be understood that, although... Figure 4 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0070] The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, such as implementing a sea ice thickness determination method provided in this embodiment of the invention: acquiring three-dimensional point cloud data and regional images of the sea ice region on the side; determining the upper and lower bottom surface regions of the effective ice block based on the regional images, and obtaining the effective point cloud data of the effective ice block based on the three-dimensional point cloud data, the upper and lower bottom surface regions of the effective ice block; the effective point cloud data includes the upper bottom surface point cloud set and the lower bottom surface point cloud set corresponding to the effective ice block; selecting thickness measurement points based on the data quality of the upper bottom surface point cloud set and the lower bottom surface point cloud set to obtain the data to be measured; the data to be measured is a single point pair, a multi-point pair set, or a local fitting surface; calculating the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the data to be measured and the data to be measured; the point cloud spatial distance calculation method is single point pair distance calculation, statistical distance calculation, or surface distance calculation.

[0071] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements a sea ice thickness determination method as provided in all embodiments of this invention: acquiring three-dimensional point cloud data and a region image of a sea ice region on the side of the ship; determining the upper and lower bottom surface regions of an effective ice block based on the region image, and obtaining effective point cloud data of the effective ice block based on the three-dimensional point cloud data, the upper and lower bottom surface regions of the effective ice block; the effective point cloud data includes the upper and lower bottom surface point cloud sets corresponding to the effective ice block; selecting thickness measurement points based on the data quality of the upper and lower bottom surface point cloud sets to obtain the data to be measured; the data to be measured is a single-point pair, a set of multiple point pairs, or a local fitted surface; calculating the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the data to be measured and the data to be measured; the point cloud spatial distance calculation method is single-point pair distance calculation, statistical distance calculation, or surface distance calculation. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor electronic devices, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires; portable computer disks; hard disks; random access memory (RAM); read-only memory (ROM); erasable programmable read-only memory (EPROM or flash memory); optical fiber; portable compact disk read-only memory (CD-ROM); optical storage devices; magnetic storage devices; or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an electronic device, apparatus, or apparatus that can be executed.

[0072] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in conjunction with an electronic device, apparatus, or device that executes instructions.

[0073] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0074] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0075] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for determining sea ice thickness, characterized in that, The method includes: Acquire 3D point cloud data and regional images of the sea ice area on the side of the ship; The upper and lower surface regions of the valid ice block are determined based on the region image, and the valid point cloud data of the valid ice block is obtained based on the three-dimensional point cloud data, the upper and lower surface regions of the valid ice block, and the valid point cloud data includes the upper surface point cloud set and the lower surface point cloud set corresponding to the valid ice block. Based on the data quality of the upper and lower bottom surface point cloud sets, thickness measurement points are selected to obtain the data to be measured; the data to be measured is a single point pair, a multi-point pair set, or a local fitted surface. The thickness of the effective ice block is calculated based on the point cloud spatial distance calculation method corresponding to the test data and the test data; the point cloud spatial distance calculation method is single-point distance calculation, statistical distance calculation or surface distance calculation.

2. The method according to claim 1, characterized in that, Based on the aforementioned 3D point cloud data and the upper and lower surface regions of the effective ice block, the effective point cloud data of the effective ice block is obtained, including: Based on the three-dimensional point cloud data, point cloud regions are mapped to the upper bottom surface region and the lower bottom surface region respectively, to obtain the mapped upper bottom surface point cloud set corresponding to the upper bottom surface region and the mapped lower bottom surface point cloud set corresponding to the lower bottom surface region. Effective point cloud data of effective ice blocks in the sea ice region are obtained based on the mapping upper bottom surface point cloud set and the mapping lower bottom surface point cloud set.

3. The method according to claim 1, characterized in that, Based on the data quality of the upper and lower surface point cloud sets, thickness measurement points are selected to obtain the data to be measured, including: The quality type corresponding to the data quality is determined based on the number of point clouds, the distribution of point clouds, and the surface smoothness. The quality type includes a few-point type, a uniform multi-point type, a smooth surface type, and a multi-point curved surface type. When the quality type is the few-point type, an upper bottom surface reference point is determined in the upper bottom surface point cloud set, a lower bottom surface reference point corresponding to the reference point is determined in the lower bottom surface set, and the single point pair is obtained based on the upper bottom surface reference point and the lower bottom surface reference point; When the quality type is the uniform multi-point type, the upper bottom surface reference point set is determined according to the distribution position of each point in the upper bottom surface point cloud set, the lower bottom surface reference point set corresponding to the upper bottom surface reference point set is determined in the lower bottom surface set, and the multi-point pair set is obtained according to the upper bottom surface reference point set and the lower bottom surface reference point set.

4. The method according to claim 3, characterized in that, The local fitting surface includes a planar local fitting surface or a curved surface local fitting surface; based on the data quality of the upper bottom surface point cloud set and the lower bottom surface point cloud set, thickness measurement points are selected to obtain the data to be measured, including: When the quality type is the surface flatness type, plane fitting is performed on the upper bottom surface point cloud set and the lower bottom surface point cloud set respectively to obtain the upper bottom surface plane equation and the lower bottom surface plane equation. The local fitting surface of the plane is determined based on the equations of the upper and lower base planes; When the quality type is the multi-point surface type, surface fitting is performed on the upper bottom surface point cloud set and the lower bottom surface point cloud set respectively to obtain the upper bottom surface surface equation and the lower bottom surface surface equation. The local fitting surface of the surface is determined based on the equations of the upper and lower base surfaces.

5. The method according to claim 4, characterized in that, The effective ice block thickness is calculated based on the point cloud spatial distance calculation method corresponding to the test data and the test data, including: When the data to be measured is a single point pair, the three-dimensional Euclidean distance between the upper bottom reference point and the lower bottom reference point is calculated to obtain the thickness of the effective ice block; When the data to be tested is the set of multiple point pairs, the three-dimensional Euclidean distance of each point pair in the set of multiple point pairs is calculated, and the thickness of the effective ice block is determined based on the three-dimensional Euclidean distance of all point pairs.

6. The method according to claim 4, characterized in that, The effective ice block thickness is calculated based on the point cloud spatial distance calculation method corresponding to the test data and the test data, including: When the data to be measured is the local fitting surface of the plane, the thickness direction is determined based on the plane normal of the upper bottom plane equation and the lower bottom plane equation; The thickness of the effective ice block is obtained by calculating the projected distance between the upper and lower bottom surfaces in the thickness direction. When the data to be measured is the local fitting surface of the surface, a reference point of the surface is determined in the point cloud set of the upper bottom surface, and the normal ray of the surface reference point is determined. The effective ice block thickness is obtained by calculating the distance from the reference point of the surface to the intersection of the normal ray of the surface and the bottom surface.

7. A device for determining sea ice thickness, characterized in that, The device includes: The data acquisition module is used to acquire three-dimensional point cloud data and regional images of the sea ice area on the side of the ship; The first data processing module is used to determine the upper and lower bottom surface regions of the valid ice block from the regional image, and to obtain the valid point cloud data of the valid ice block based on the three-dimensional point cloud data and the upper and lower bottom surface regions of the valid ice block; the valid point cloud data includes the upper bottom surface point cloud set and the lower bottom surface point cloud set corresponding to the valid ice block. The second data processing module is used to select thickness measurement points based on the data quality of the upper bottom surface point cloud set and the lower bottom surface point cloud set to obtain the data to be measured; the data to be measured is a single point pair, a multi-point pair set, or a local fitting surface. The thickness calculation module is used to calculate the thickness of the effective ice block based on the point cloud spatial distance calculation method corresponding to the test data and the test data; the point cloud spatial distance calculation method is single-point distance calculation, statistical distance calculation or surface distance calculation.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the sea ice thickness determination method as described in any one of claims 1-6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the sea ice thickness determination method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the sea ice thickness determination method as described in any one of claims 1 to 6.

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