Branch laser scanning quality pre-evaluation and displacement detection analysis method, device, equipment and medium

Abstract

The invention discloses a branch laser scanning quality pre-evaluation and displacement detection analysis method, device, equipment and medium, and the method comprises the steps: carrying out the laser pre-scanning of a to-be-detected target similar to a tree, obtaining point cloud data, carrying out the quality pre-evaluation of the obtained point cloud data, and determining a measurement scheme of laser scanning; scanning the forms of the branches in different seasons based on the determined measurement scheme of laser scanning to obtain point cloud data of the branches in different seasons; preprocessing the obtained point cloud data of the branches in different seasons, and obtaining point cloud body density indication factors based on the preprocessed point cloud data; and comparing the point cloud body density indication factor with a threshold value, and selecting a corresponding branch displacement analysis method based on a comparison result to obtain the displacement of branches in different seasons. Therefore, the displacement of the branches in autumn and winter is compared and analyzed, the change rule of the branches in two seasons is disclosed, and a scientific basis is provided for research and application in related fields.

Description

Technical Field

[0001] This invention relates to a method, apparatus, equipment, and medium for pre-assessment of tree branch quality and displacement detection and analysis using laser scanning, and relates to fields such as forestry, surveying and mapping, and remote sensing. Background Technology

[0002] As an important organ component of trees, tree branches have extremely high ecological, economic, and cultural value in investigation and research. The changes in branch morphology in different seasons not only affect the growth and physiological processes of the trees themselves, but also have multifaceted impacts on ecosystems and human activities. For example, ginkgo trees are lush with leaves in autumn and leafless in winter.

[0003] Handheld SLAM scanning technology can quickly collect point cloud data of trees in different seasons. After noise reduction and cleaning, the point cloud data can be directly compared, but the visual effect is relatively weak. Current research in the forestry industry on the application of 3D laser scanning technology in tree branch surveys is relatively limited, and the technology still has certain limitations. Currently, this technology is mostly used to construct 3D models of individual trees and for forest resource surveys. Professor Feng Zhongke of Beijing Forestry University has conducted an in-depth analysis of the application prospects of terrestrial 3D laser scanning technology in forestry. The results show that the errors in tree diameter at breast height (DBH) and tree height data obtained using 3D laser scanning technology are within the allowable range for forestry tree measurement compared to actual measurements.

[0004] In recent years, terrestrial 3D laser scanning technology has attracted increasing attention from forestry researchers, both in forest resource monitoring and in single-tree model reconstruction. For example, by processing 3D scanning point cloud data of Pinus sylvestris, Wu Chunfeng extracted data such as tree height and diameter at breast height (DBH), and used this data to calculate the standing timber volume of the trees using traditional calculation methods. Compared with the actual measured volume of felled timber, the overall relative average error reached 2.91%, achieving high accuracy. However, the field currently pays relatively little attention to the investigation and analysis of laser scanning at the branch level, and corresponding basic remote sensing survey technologies are still lacking. Summary of the Invention

[0005] This invention aims to at least solve one of the technical problems existing in the prior art. Therefore, in response to the above-mentioned problems, the purpose of this invention is to provide a method, apparatus, device, and medium for laser scanning quality pre-assessment and displacement detection analysis of tree branches, capable of comparative analysis of the displacement of tree branches in different seasons and revealing the changing patterns of tree branches in different seasons.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: In a first aspect, the present invention provides a method for pre-assessment of the quality and displacement detection and analysis of tree branch laser scanning, comprising: Laser pre-scanning is performed on similar tree-like targets to obtain point cloud data. The quality of the obtained point cloud data is pre-assessed, and then the laser scanning measurement scheme is determined. Based on a defined laser scanning measurement scheme, the morphology of tree branches in different seasons is scanned to obtain point cloud data of tree branches in different seasons. The point cloud data of tree branches in different seasons were preprocessed, and the point cloud volume density indicator was obtained based on the preprocessed point cloud data. The point cloud volume density indicator factor is compared with a threshold, and the corresponding tree displacement analysis method is selected based on the comparison results to obtain the tree displacement in different seasons.

[0007] In some possible implementations, laser pre-scanning is performed on the target to be verified, similar to a tree, to obtain point cloud data. The specific process is as follows: The experiment was conducted at calibration sites with distances of 5 meters, 10 meters, and 15 meters between the target to be calibrated and the measuring station. In the calibration sites, 10 cylinders with different diameters and smooth surfaces were arranged horizontally to be calibrated. The targets to be calibrated were arranged at intervals, and several measuring stations were set up at equal intervals on one side of the target to be calibrated. The diameters of the targets to be calibrated were 5 mm, 10 mm, 16 mm, 20 mm, 25 mm, 30 mm, 35 mm, 40 mm, 45 mm, and 50 mm, respectively.

[0008] In some possible implementations, the quality of the acquired point cloud data is pre-assessed to determine the measurement scheme for laser scanning, specifically as follows: In the calibration area with a 5m ranging accuracy, 10 targets to be calibrated with diameters ranging from 5 to 50mm were tested. Among all the tested targets, the largest diameter error occurred on the target with a diameter of 20mm, while the smallest diameter error occurred on the target with a diameter of 10mm. In the calibration site with a 10m ranging accuracy, 10 targets to be calibrated with diameters ranging from 5 to 50mm were tested. The point cloud collected from the target with a diameter of 5mm could not be modeled. Among all the tested targets, the target with the largest diameter error was the target with a diameter of 45mm, while the target with the smallest diameter error was the target with a diameter of 30mm. In a calibration field with a 15m ranging accuracy, 10 targets with diameters ranging from 5 to 50mm were tested. The point clouds collected from targets with diameters of 5mm, 10mm, and 16mm could not be modeled. Among all the tested targets, the target with the largest diameter error was 40mm, while the target with the smallest diameter error was 45mm.

[0009] In some possible implementations, point cloud data of tree branches in different seasons are preprocessed, including point cloud integration and denoising, point cloud data segmentation, and point cloud standardization.

[0010] In some possible implementations, when the point cloud volume density indicator factor is higher than a set threshold, the branch axis of the tree in different seasons is fitted using a cylindrical fitting method, and the displacement of the tree is determined by comparing the fitted branch axes of different seasons; when the point cloud volume density indicator factor is lower than the set threshold, the lowest scanning point of the horizontal segment height value is extracted interactively on the tree and the curves fitted before and after are compared to determine the displacement of the tree.

[0011] In some possible implementations, the specific process of determining the displacement of a tree branch by interactively extracting the lowest scanning point of horizontal segment height and comparing the fitted curves before and after is as follows: Preprocessed point cloud data is imported into Trimble RealWorks software. Based on the tree branch morphology and variation characteristics, at least 20 representative point clouds with equal horizontal intervals are selected for each branch. The representative point clouds with equal horizontal intervals are selected based on the lowest scanning point of horizontal segment height. Point cloud data from different seasonal periods of the branch at the same height are analyzed. The coordinate data of the selected point clouds are organized into a table, recording the X, Y, and H coordinate values ​​of each point cloud in autumn and winter. The tabulated point cloud coordinate data is imported into data analysis software for curve fitting. The fitted curves of the branch in different seasons are compared to determine the displacement of the branch.

[0012] Secondly, the present invention also provides a device for pre-assessment of quality and displacement detection and analysis of tree branch laser scanning, comprising: The quality pre-assessment unit is configured to perform laser pre-scanning on a target similar to a tree to obtain point cloud data, perform quality pre-assessment on the obtained point cloud data, and then determine the measurement scheme for laser scanning. The point cloud acquisition unit is configured to scan the morphology of tree branches in different seasons based on a determined laser scanning measurement scheme, and obtain point cloud data of tree branches in different seasons. The point cloud preprocessing unit is configured to preprocess the acquired point cloud data of tree branches in different seasons and obtain the point cloud volume density indicator based on the preprocessed point cloud data. The displacement analysis unit is configured to compare the point cloud volume density indicator with a threshold, and select the appropriate tree displacement analysis method based on the comparison result to obtain the displacement of trees in different seasons.

[0013] Thirdly, the present invention also provides an electronic device, characterized in that it includes: at least one processor; and a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, the instructions being executed by the processor to enable the processor to perform any of the methods described above.

[0014] Fourthly, the present invention also provides a computer-readable storage medium for storing one or more programs, characterized in that the one or more programs include computer instructions for causing a computer to perform the method according to any one of the present invention.

[0015] This invention, by adopting the above technical solutions, has the following characteristics: It utilizes processed point cloud data for fitting and then performs quantifiable comparative analysis to obtain the branch extension patterns in different seasons. Using the point cloud volume density indicator as a unified bridge, it compares and analyzes the displacement of branches in autumn and winter, revealing the changing patterns of branches in different seasons and providing a scientific basis for research and application in related fields. In summary, this invention can be widely applied to plant morphology research. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. In the drawings: Figure 1 This is a schematic diagram of the test setup according to an embodiment of the present invention; Figure 2 This is a diagram showing the relationship between the target diameter difference and distance at a 5m calibration site according to an embodiment of the present invention. Figure 3 This is a diagram showing the relationship between the target diameter difference and distance at a 10m calibration site according to an embodiment of the present invention. Figure 4 This is a diagram showing the relationship between the target diameter difference and distance at a 15m calibration site according to an embodiment of the present invention. Figure 5 These are point cloud comparison images of tree branches from different angles in winter and autumn according to embodiments of the present invention, wherein (a) point cloud comparison image of autumn and winter from angle 1; and (b) point cloud comparison image of autumn and winter from angle 2. Figure 6 This is a point cloud comparison image of sample tree branches in autumn and winter in an embodiment of the present invention; Figure 7 This is a comparison image of the left-side branches of the sample trees in different seasons, as shown in this embodiment of the invention. Figure 8 This is a comparison image of the right-side branches of the sample trees in different seasons, as shown in this embodiment of the invention. Figure 9 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0017] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0018] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0019] For ease of description, spatial relative terms may be used in the text to describe the relationship of one element or feature relative to another element or feature as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "above," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure.

[0020] The field currently pays relatively little attention to laser scanning at the tree branch level, and corresponding basic remote sensing survey technologies are still lacking. This invention provides a method, apparatus, equipment, and medium for pre-assessment of tree branch laser scanning quality and displacement detection analysis based on point cloud volume density indicator factors. The process includes: pre-scanning a target tree-like object with laser to obtain point cloud data; pre-assessing the quality of the obtained point cloud data to determine the laser scanning measurement scheme; scanning the tree branch morphology in different seasons based on the determined laser scanning measurement scheme to obtain point cloud data for each season; pre-processing the obtained point cloud data for each season and obtaining a point cloud volume density indicator factor based on the pre-processed point cloud data; comparing the point cloud volume density indicator factor with a threshold, and selecting an appropriate tree branch displacement analysis method based on the comparison result to obtain the tree branch displacement in different seasons. Therefore, this invention provides a comparative analysis of tree branch displacement in different seasons, revealing the changing patterns of tree branches in different seasons and providing a scientific basis for research and application in related fields.

[0021] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the invention and to fully convey the scope of the invention to those skilled in the art.

[0022] Example 1: The method for pre-assessment of tree branch laser scanning quality and displacement detection analysis provided in this example includes: S1. Perform laser pre-scanning on similar tree-like targets to obtain point cloud data, conduct quality pre-assessment on the obtained point cloud data, and then determine the measurement scheme for subsequent laser scanning of tree branches.

[0023] In this embodiment, multiple types of laser scanners can be used for scanning. "Multiple types" refers to various models. In the experiment, laser scanners such as the Huace RS10 and Faro350 were used as examples, but are not limited to these.

[0024] In this embodiment, laser pre-scanning is performed on a target similar to a tree to obtain point cloud data. The quality of the obtained point cloud data is pre-assessed to determine the subsequent measurement scheme for laser scanning of tree branches, including: S11. Design a calibration scheme and obtain the point cloud data of the target to be calibrated.

[0025] In this embodiment, the measurement adopts the CGCS2000 coordinate frame to keep the coordinate system consistent at different stations, without the need to set up additional control points or perform coordinate transformation. The horizontal and vertical coordinate values ​​XY and the relative elevation coordinate value H are recorded under this coordinate system. For scenarios requiring high-precision measurement, the real-time point cloud results can be further refined using the Huace CoPre software.

[0026] In this embodiment, to ensure the accuracy of the experiment, the distance between the target to be verified and the station was selected as 5 meters, 10 meters and 15 meters for the experiment. This is just one example and is not limited to this.

[0027] Specifically, such as Figure 1 As shown, in the calibration site, 10 cylinders of varying diameters with smooth surfaces were arranged horizontally as the targets to be calibrated. The diameter of each cylinder differed to facilitate diverse testing and data acquisition. In this embodiment, 10 steel bars of different diameters were arranged at intervals as the targets to be calibrated. The diameters of the steel bars were 5 mm, 10 mm, 16 mm, 20 mm, 25 mm, 30 mm, 35 mm, 40 mm, 45 mm, and 50 mm. This size arrangement is based on both market availability and helps in fitting and analyzing the change pattern of laser scanning performance with the increase of the target diameter. Stations 1, 2, and 3 were equally spaced on one side of the target to be calibrated, for example, at the same horizontal level as target 5, with a distance of 5 m between adjacent stations. This is an example, but not limited to this. Experimenters can adjust the measurements according to... Figure 1 The arrangement determines the specific location and relative distance of each cylinder, such as a relative distance of 1.5 meters. Specific details can be referenced from the experimental site. Figure 1 The modes can be flexibly set to ensure the smooth progress of the experiment and the accuracy of the data.

[0028] S12. Experimental data acquisition and processing.

[0029] In this embodiment, the acquisition and processing of experimental data includes: First, a series of cylinders with varying diameters were precisely scanned using a 350-type scanner to obtain point cloud data of their surface features. The diameter range of these cylinders covers the size variations that may be encountered in practical applications, ensuring the universality of the research results.

[0030] Then, for the point cloud data acquired under different distance conditions, precise modeling of cylinders was performed. Specifically, Trimble RealWorks software was used to process these point cloud data into 3D models. Trimble RealWorks software, with its excellent data processing capabilities and modeling accuracy, can transform point cloud data into accurate 3D models.

[0031] Finally, after the modeling was completed, the diameter of the cylinder model generated by Trimble RealWorks software was compared and analyzed with the actual diameter of the cylinder. To ensure the reliability and accuracy of the data, the actual diameter of each cylinder was measured multiple times, and the average value was calculated as the final reference standard.

[0032] S13. Based on the analysis of experimental results, a preliminary quality assessment of point cloud data is achieved.

[0033] In this embodiment, the pre-assessment of point cloud data quality through comparative analysis aims to explore the correlation between the accuracy of cylindrical point cloud modeling and the scanning distance. This invention hypothesizes that different scanning distances may affect the accuracy of point cloud modeling. Through comparative analysis, it hopes to reveal the intrinsic relationship between the two, providing a theoretical basis for subsequent research and practical applications.

[0034] 1) Analysis of data results from the 5m calibration site.

[0035] Table 15m Calibration Site Data Results

[0036] Within the calibration area with a 5m ranging accuracy, detailed analysis of the chart data clearly revealed that for the 10 targets to be calibrated with diameters ranging from 5 to 50 mm, high-precision fitting of the collected point cloud data allowed for the creation of steel rod models. Further comparison of the fitted cylinder diameters with the actual object diameters revealed a certain deviation. Among all the tested targets, the largest diameter error occurred with a steel rod with a nominal diameter of 20 mm, reaching 12.094 mm; while the smallest diameter error occurred with a steel rod with a nominal diameter of 10 mm, at only 0.192 mm.

[0037] 2) Analysis of data results from the 10m calibration site Table 210m Calibration Site Data Results

[0038] In the calibration field with a 10m ranging accuracy, as shown in the chart, targets 1-9 of different diameters can all be modeled. A steel rod with a nominal diameter of 5mm cannot be modeled due to insufficient point cloud data. Based on the fitting of the scanned point cloud data, the largest difference between the cylinder diameter and the actual diameter is for a steel rod with a nominal diameter of 45mm, with a diameter difference of 8.737mm; the smallest difference is for a steel rod with a nominal diameter of 30mm, with a diameter difference of 0.7mm.

[0039] 3) Analysis of data results from the 15m calibration site Table 315m Calibration Site Data Results

[0040] In the calibration field with a 15m ranging accuracy, as shown in the charts, targets 1-7 of different diameters can all be modeled. Steel bars with nominal diameters of 5mm, 10mm, and 16mm cannot be modeled due to insufficient point cloud data. Based on the fitted cylinder diameter from the scanned point cloud data, the largest difference between the fitted diameter and the actual diameter is for the steel bar with a nominal diameter of 40mm, with a diameter difference of 16.911mm; the smallest difference is for the steel bar with a nominal diameter of 45mm, with a diameter difference of 0.694mm. Through comparative analysis of the mean diameter difference in each group of experiments, it is found that the difference between the fitted diameter and the actual diameter gradually increases with increasing distance.

[0041] S14. Based on the quality assessment results, determine the measurement scheme for laser scanning and carry out subsequent scanning work.

[0042] In this embodiment, three sets of comparative experiments were conducted to explore the impact of scanning distance on the modeling accuracy of steel bars with different diameters. The experimental results show that when the scanning distance reaches 10m for a steel bar with a nominal diameter of 5mm, the point cloud data is too sparse to achieve effective modeling; while when the scanning distance increases to 15m, steel bars with nominal diameters of 5mm, 10mm, and 16mm cannot be accurately modeled.

[0043] Furthermore, comparative analysis revealed that, under similar scanning distances, the difference in modeling diameter did not exhibit a linear relationship with the distance. Data analysis uncovered a correlation between modeling accuracy and the scanner's incident angle. Among all experimental groups, the steel rod with a nominal diameter of 20 mm showed a relatively large diameter difference, which may be related to its poor point cloud scanning effect when the instrument was in a parallel position.

[0044] Based on the above experimental analysis, this invention concludes that when scanning and modeling cylindrical objects, the distance between the object being scanned and the scanner, as well as the scanner's incident angle, must be fully considered. In practical applications, such objects include pipes, tree branches, lampposts, etc., therefore, the conclusions of this invention can provide a reference for related fields.

[0045] S2. Based on a determined laser scanning measurement scheme, the morphology of tree branches in different seasons is scanned to obtain point cloud data of tree branches in different seasons.

[0046] S3. Preprocess the point cloud data of the acquired tree branches in different seasons.

[0047] In this embodiment, the preprocessing of point cloud data includes: 1) Point cloud integration and denoising: Different batches of point cloud data are stitched together and integrated to form a complete tree point cloud dataset. This step ensures the integrity and consistency of the data and provides a reliable foundation for subsequent analysis.

[0048] During scanning, due to instrument errors, environmental factors (such as wind), or other interference, the scan data will contain a large number of irrelevant and noisy points. These irrelevant points may originate from ground weeds, rocks, other trees, buildings, or passersby, while noise points may be anomalous points generated by the laser point colliding with two objects or not colliding with any objects. These redundant points increase the data volume and interfere with subsequent analysis, thus requiring noise reduction. Point cloud processing software filters are used, with specific thresholds set to identify and remove noise points. This method can correct or remove anomalous scan points caused by instrument errors or environmental interference.

[0049] 2) Point cloud data segmentation, specifically including: Use the lasso tool in the point cloud post-processing software to select the area of ​​the target tree, and then delete the point cloud data outside the target area. Alternatively, you can directly select the scan points of surrounding objects and delete them to reduce the interference of irrelevant points on the analysis.

[0050] 3) Point cloud data standardization, specifically: To ensure good comparability and consistency of data at different time points, the processed point cloud data needs to be standardized. The processed point cloud data should be exported to a unified format (such as ASCII) for further analysis and processing in different software.

[0051] The data processing steps described above effectively remove irrelevant and noisy data, decompose complex tree structures into multiple independent parts, and ensure data standardization and consistency. These processing steps provide high-quality data support for subsequent morphological analysis and comparative studies.

[0052] S22. Based on the preprocessed point cloud data, obtain the point cloud volume density indicator factor. When the point cloud volume density indicator factor is greater than the set threshold, use the cylinder fitting tree method to perform tree displacement analysis. When the point cloud volume density indicator factor is lower than the set threshold, use the interactive extraction scheme to perform tree displacement analysis.

[0053] In this embodiment, a point cloud volume density indicator is obtained based on the preprocessed point cloud data. The point cloud volume density indicator is a quantitative index of the number of laser footprints contained within a voxel after point cloud voxelization, primarily used to quantify the distribution density of points within a specified area in three-dimensional space. A method for detecting and analyzing tree branch displacement is selected based on the point cloud volume density indicator. When the point cloud volume density indicator is higher than a set threshold, a cylinder fitting method is used to fit the branch axes of branches in different seasons, and the displacement of the branches is determined by comparing the fitted branch axes of different seasons. When the point cloud volume density indicator is lower than the set threshold, the lowest horizontal segment height value is extracted interactively on the branch, and the displacement of the branches is determined by comparing the fitted curves before and after extraction.

[0054] Furthermore, since the point cloud volume density indicator factor is usually lower than the set threshold during actual measurement, this embodiment provides a detailed explanation of the process of interactively extracting the lowest scanning point of the horizontal segment height value of the tree branch and comparing the fitted curves before and after to determine the displacement of the tree branch. The specific process is as follows: Taking ginkgo trees as an example, in autumn, the branches extend more widely, with wider branching angles, presenting a more expansive overall shape; while in winter, the branches contract relatively, the branching angles become smaller, and the shape appears more compact, such as... Figure 5 The point clouds of tree branches at different angles are shown. The volume density indicator factor of the point cloud for each sample is obtained. It is determined that the displacement of the tree branch is determined by interactively extracting the lowest horizontally segmented scanning point on the tree branch and comparing the fitted curves before and after extraction. The specific analysis process is as follows: 1) Point coordinate selection and fitting, including: A. Trimble RealWorks point coordinate selection includes: The preprocessed point cloud data was imported into Trimble RealWorks software. Based on the tree branch morphology and variation characteristics, at least 20 representative points with equal horizontal intervals were selected for each tree branch. If the tree branch is long, the number of points can be increased appropriately. The selection of these points fully considers the key feature parts of the tree branch to improve measurement accuracy and ensure that the morphological changes of the tree branch can be accurately reflected.

[0055] B. Point coordinate comparison, including: Based on the sample trees, point cloud data of the left and right branches at the same height were selected for analysis during different seasons. For example, 20 points on the left branch were selected and recorded, as shown in Table 4; 28 points on the right branch were selected and recorded, as shown in Table 5. The coordinate data of the selected points were organized into a table, and the X, Y, and H coordinate values ​​of each point in autumn and winter were recorded respectively.

[0056] Table 4 Comparison of locations of tree branches on the left side in autumn and winter

[0057] Table 5 Comparison of location points for tree branches on the right side in autumn and winter. C. Point coordinate display.

[0058] The coordinates of points from autumn and winter were imported into AutoCAD software for display. The 3D rendering provides a more intuitive view of the morphological characteristics of branches in different seasons and accurately compares the differences between them. The results show that there are significant differences in the morphology of branches in autumn and winter. These differences are mainly reflected in the degree of branch extension, branching angle, and overall morphological changes. These seasonal morphological changes are an adaptive response of branches to environmental changes, helping them to better perform physiological activities such as photosynthesis, water regulation, and resistance to adverse external conditions in different seasons. Figure 6 As shown, blue represents the leafless state in winter, and yellow represents the leafy state in autumn. The ability to measure the changes in tree branches further verifies the universality and regularity of these seasonal morphological changes.

[0059] 2) Tree fitting comparison analysis.

[0060] Import the collected point coordinate data into data analysis software such as MATLAB for curve fitting. Compare the fitted curves of tree branches before and after different seasons to determine the displacement of the tree branches. Figure 7 and Figure 8 As shown.

[0061] In summary, this invention uses multi-type laser scanning technology to scan the morphology of tree branches in autumn and winter, obtains high-precision three-dimensional point cloud data, and performs detailed data processing and comparative analysis. The obtained three-dimensional data is used to compare the position of tree branches. The study reveals, for example, whether there are significant differences in the morphology of ginkgo tree branches in autumn and winter. The differences are mainly reflected in the degree of branch extension, upward angle, and overall morphological changes.

[0062] Example 2: Following the method for pre-evaluating the quality and displacement detection of dendritic laser scanning based on point cloud volume density indicator factors provided in Example 1, this example provides a device for pre-evaluating the quality and displacement detection of dendritic laser scanning based on point cloud volume density indicator factors. The device provided in this example can implement the method for pre-evaluating the quality and displacement detection of dendritic laser scanning based on point cloud volume density indicator factors in Example 1. This device can be implemented through software, hardware, or a combination of both. For ease of description, this example is described by dividing the functionality into various units. Of course, in implementation, the functions of each unit can be implemented in one or more software and / or hardware components. For example, the device may include integrated or separate functional modules or units to execute the corresponding steps in the methods of Example 1. Since the device in this example is basically similar to the method example, the description process of this example is relatively simple. For relevant details, please refer to the description in Example 1. The example of the device for pre-evaluating the quality and displacement detection of dendritic laser scanning based on point cloud volume density indicator factors provided by this invention is merely illustrative.

[0063] Specifically, the dendrite laser scanning quality pre-assessment and displacement detection analysis device provided by the present invention includes: The quality pre-assessment unit is configured to perform laser pre-scanning on a target similar to a tree to obtain point cloud data, perform quality pre-assessment on the obtained point cloud data, and then determine the measurement scheme for laser scanning. The point cloud acquisition unit is configured to scan the morphology of tree branches in different seasons based on a determined laser scanning measurement scheme, and obtain point cloud data of tree branches in different seasons. The point cloud preprocessing unit is configured to preprocess the acquired point cloud data of tree branches in different seasons and obtain the point cloud volume density indicator based on the preprocessed point cloud data. The displacement analysis unit is configured to compare the point cloud volume density indicator with a threshold, and select the appropriate tree displacement analysis method based on the comparison result to obtain the displacement of trees in different seasons.

[0064] Example 3: This example provides an electronic device corresponding to the dendritic laser scanning quality pre-assessment and displacement detection analysis method based on point cloud volume density indicator factor provided in Example 1. The electronic device can be an electronic device for the client, such as a mobile phone, laptop, tablet computer, desktop computer, etc., to execute the method of Example 1.

[0065] like Figure 9 As shown, the electronic device includes a processor, a memory, a communication interface, and a bus. The processor, memory, and communication interface are connected via the bus to enable communication between them. The memory stores a computer program that can run on the processor. When the processor runs the computer program, it executes the method of Embodiment 1. The implementation principle and technical effects are similar to those of Embodiment 1, and will not be repeated here. Those skilled in the art will understand that... Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computing device on which the present application is applied. The specific computing device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0066] In a preferred embodiment, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), and optical discs.

[0067] In a preferred embodiment, the processor can be any type of general-purpose processor such as a central processing unit (CPU) or a digital signal processor (DSP), and is not limited thereto.

[0068] Example 4: This example provides a computer-readable storage medium for storing one or more programs, the one or more programs including computer instructions, which, when executed by a computer, cause the computer to perform the method provided in Example 1 above.

[0069] In a preferred embodiment, the computer-readable storage medium may be a tangible device for holding and storing instructions executable, such as, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. The computer-readable storage medium stores computer program instructions that cause a computer to perform the method provided in Embodiment 1 above.

[0070] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0071] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0072] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0073] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In the description of this specification, the terms "a preferred embodiment," "furthermore," "specifically," "in this embodiment," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments in this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for pre-assessment of quality and displacement detection analysis of tree branch laser scanning, characterized in that, include: Laser pre-scanning is performed on similar tree-like targets to obtain point cloud data. The quality of the obtained point cloud data is pre-assessed, and then the laser scanning measurement scheme is determined. Based on a defined laser scanning measurement scheme, the morphology of tree branches in different seasons is scanned to obtain point cloud data of tree branches in different seasons. The point cloud data of tree branches in different seasons were preprocessed, and the point cloud volume density indicator was obtained based on the preprocessed point cloud data. The point cloud volume density indicator factor is compared with a threshold, and the corresponding tree displacement analysis method is selected based on the comparison results to obtain the tree displacement in different seasons.

2. The method for pre-assessment of quality and displacement detection analysis of tree branch laser scanning according to claim 1, characterized in that, When selecting a cylindrical target similar to a tree for verification, the distance between the target and the scanner, as well as the incident angle of the scanner, must be considered when determining the laser scanning measurement scheme.

3. The method for pre-assessment of quality and displacement detection analysis of tree branch laser scanning according to claim 2, characterized in that, To obtain point cloud data for the target object to be verified, which resembles a tree, the specific process is as follows: The experiment was conducted at calibration sites with distances of 5 meters, 10 meters, and 15 meters between the target to be calibrated and the measuring station. In the calibration sites, 10 cylinders with different diameters and smooth surfaces were arranged horizontally to be calibrated. The targets to be calibrated were arranged at intervals, and several measuring stations were set up at equal intervals on one side of the target to be calibrated. The diameters of the targets to be calibrated were 5 mm, 10 mm, 16 mm, 20 mm, 25 mm, 30 mm, 35 mm, 40 mm, 45 mm, and 50 mm, respectively.

4. The method for pre-assessment of quality and displacement detection analysis of tree branch laser scanning according to claim 3, characterized in that, The quality of the obtained point cloud data is pre-assessed to determine the laser scanning measurement scheme, specifically as follows: In the calibration area with a 5m ranging accuracy, 10 targets to be calibrated with diameters ranging from 5 to 50mm were tested. Among all the tested targets, the largest diameter error occurred on the target with a diameter of 20mm, while the smallest diameter error occurred on the target with a diameter of 10mm. In the calibration site with a 10m ranging accuracy, 10 targets to be calibrated with diameters ranging from 5 to 50mm were tested. The point cloud collected from the target with a diameter of 5mm could not be modeled. Among all the tested targets, the target with the largest diameter error was the target with a diameter of 45mm, while the target with the smallest diameter error was the target with a diameter of 30mm. In a calibration field with a 15m ranging accuracy, 10 targets with diameters ranging from 5 to 50mm were tested. The point clouds collected from targets with diameters of 5mm, 10mm, and 16mm could not be modeled. Among all the tested targets, the target with the largest diameter error was 40mm, while the target with the smallest diameter error was 45mm.

5. The method for pre-assessment of quality and displacement detection analysis of tree branch laser scanning according to claim 1, characterized in that, Preprocessing is performed on point cloud data of tree branches in different seasons, including point cloud integration and denoising, point cloud data segmentation, and point cloud standardization.

6. The method for pre-assessment of quality and displacement detection analysis of dendritic laser scanning according to claim 5, characterized in that, When the point cloud volume density indicator factor is higher than the set threshold, the branch axis of the tree in different seasons is fitted using the cylinder fitting method, and the displacement of the tree is determined by comparing the fitted branch axes of different seasons; when the point cloud volume density indicator factor is lower than the set threshold, the lowest scanning point of the horizontal segment height value is extracted interactively on the tree and the displacement of the tree is determined by comparing the fitted curves before and after it.

7. The method for pre-assessment of quality and displacement detection analysis of dendritic laser scanning according to claim 5, characterized in that, The specific implementation process for determining the displacement of tree branches by interactively extracting the lowest scanning point of horizontal segment height values ​​and comparing the fitted curves before and after is as follows: Preprocessed point cloud data is imported into Trimble RealWorks software. Based on the tree branch morphology and variation characteristics, at least 20 representative point clouds with equal horizontal intervals are selected for each branch. The lowest scanning point of horizontal segment height values ​​is selected from these representative point clouds. Point cloud data from different seasonal periods at the same height position on the tree branch are analyzed. The coordinate data of the selected point clouds are organized into a table, recording the X, Y, and H coordinate values ​​of each point cloud in autumn and winter. X and Y are the vertical coordinate values ​​of the point cloud, and H is the relative elevation coordinate value. The tabulated point cloud coordinate data is imported into data analysis software for curve fitting. The fitted curves of the tree branches in different seasons are compared to determine the displacement of the tree branches.

8. A device for pre-assessment of quality and displacement detection and analysis of tree branch laser scanning, characterized in that, include: The quality pre-assessment unit is configured to perform laser pre-scanning on a target similar to a tree to obtain point cloud data, perform quality pre-assessment on the obtained point cloud data, and then determine the measurement scheme for laser scanning. The point cloud acquisition unit is configured to scan the morphology of tree branches in different seasons based on a determined laser scanning measurement scheme, and obtain point cloud data of tree branches in different seasons. The point cloud preprocessing unit is configured to preprocess the acquired point cloud data of tree branches in different seasons and obtain the point cloud volume density indicator based on the preprocessed point cloud data. The displacement analysis unit is configured to compare the point cloud volume density indicator with a threshold, and select the appropriate tree displacement analysis method based on the comparison result to obtain the displacement of trees in different seasons.

9. An electronic device, characterized in that, include: At least one processor; And a memory communicatively connected to the processor; wherein the memory stores instructions executable by the processor, which are executed by the processor to enable the processor to perform the method according to any one of claims 1-7.

10. A computer-readable storage medium for storing one or more programs, characterized in that, One or more programs include computer instructions for causing a computer to perform the method according to any one of claims 1-7.

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