3D positioning installation method and device of square center flower basket and electronic equipment

Abstract

The embodiment of the application relates to the technical field of large-scale landscape decoration engineering, in particular to a 3D positioning and installation method and device for a square center flower basket and electronic equipment. The method comprises the following steps: 3D scanning a small sample of the flower basket to obtain 3D original data generated by scanning; performing structured geometric data construction on the 3D original data, and performing digital modeling based on the generated structured data to generate a small sample digital model; enlarging the small sample digital model by a preset multiple to obtain an enlarged digital model; extracting the center space coordinates and the orientation angle parameters of each flower from the enlarged digital model, and positioning and installing the flowers according to the center space coordinates and the orientation angle parameters. The method can realize high-precision spatial positioning and installation of the flowers, and guarantee the structural consistency between the actual flower basket and the small sample.

Description

Technical Field

[0001] This application relates to the field of large-scale landscape decoration engineering technology, and in particular to a 3D positioning and installation method, device and electronic equipment for a central flower basket in a square. Background Technology

[0002] The central flower basket in the plaza, as a core landscape decoration, carries significant symbolic meaning and visual presentation requirements. Its production process involves first creating a small-scale model (usually at a 1:20 scale), then precisely enlarging it to its actual size (typically 18 meters high at the top, 16 meters high in the basket body, 12 meters in diameter of the basket tray, and approximately 15 meters long after the tray is inserted). The final product is composed of sloping flower beds, fiberglass flower baskets, and numerous artificial flowers. The core technical challenge of this landscape design lies in ensuring that the actual flower basket and the model are highly consistent in overall structural proportions, the spatial coordinates of the flower's center, and its orientation angle, thus guaranteeing overall aesthetics and design fidelity. However, due to the enormous size of the flower basket, the large number of flowers, and the complex spatial layout, balancing positioning accuracy and construction efficiency during the enlargement process from model to finished product becomes a key technical challenge for this type of large-scale landscape decoration project.

[0003] In existing technologies, the production and positioning of large-scale landscape decorations mainly rely on two types of solutions: one is the traditional manual measurement and visual experience judgment method, which involves construction workers measuring on-site, comparing with small samples to enlarge the scale and install flowers; the other is the 3D modeling direct generation method applied in some areas (focal flowers and outline flowers), which involves designing flower shapes and layouts through software modeling, and then guiding the actual production.

[0004] However, existing technologies have significant drawbacks: First, traditional manual measurement and experience-based judgment methods suffer severe accuracy loss during the scaling-up process from small samples to the actual product, resulting in large errors in flower spatial position and orientation positioning, making it difficult to guarantee consistency between the actual flower basket and the sample, directly affecting the overall visual effect; Second, reliance on manual comparison and adjustment leads to lengthy production and installation cycles, resulting in low efficiency and failing to meet the tight schedule requirements for landscape decoration of major events; Third, traditional methods lack standardized operating procedures, and construction quality is greatly affected by personnel experience, resulting in insufficient stability; Fourth, some 3D modeling-generated schemes are prone to problems such as flower proportion imbalance and insufficient structural strength due to the lack of physical prototype verification, and traditional mold-making is costly and difficult to adapt to the needs of rapid design changes.

[0005] Therefore, how to achieve a precise scale-up of the flower basket from the sample to the actual size, ensuring accurate replication of the spatial coordinates and orientation angle of the flower center of each flower, while improving the standardization and efficiency of construction, is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0006] The purpose of this application is to provide at least one 3D positioning and installation method, device and electronic equipment for a flower basket in the center of a square, which can achieve high-precision spatial positioning and installation of flowers and ensure the consistency of the actual flower basket with the simulated flower structure of the sample.

[0007] To address the aforementioned technical problems, at least one embodiment of this application provides a 3D positioning and installation method for a flower basket in the center of a plaza, comprising: Perform a 3D scan on the flower basket sample to obtain the raw 3D data generated by the scan; The original 3D data is used to construct structured geometric data, and digital modeling is performed based on the generated structured data to generate a sample digital model; The small digital model is enlarged by a preset ratio to obtain the enlarged digital model; The spatial coordinates of the flower center and the orientation angle parameters of each flower are extracted from the magnified digital model, and the flowers are positioned and installed according to the spatial coordinates of the flower center and the orientation angle parameters.

[0008] In one embodiment, scaling up the small digital model by a preset factor includes: Read the basic structural data of the sample digital model, and based on the basic structural data, establish the Z-axis upward along the central axis of the flower basket (i.e., the center point of the bottom of the flower basket) as the origin, establish the X-axis horizontally pointing to the front of the flower basket, and establish the Y-axis according to the right-hand coordinate system rule to generate the sample coordinate system; With the center point of the bottom of the actual flower basket as the origin, and the coordinate axis direction consistent with the small sample coordinate system, establish the actual size coordinate system; Record the reference alignment parameters between the sample coordinate system and the actual size coordinate system; Extract all geometric elements and feature parameters from the sample digital model to generate a structured data list; Based on the structured data list, the orientation angle parameter of each flower in the sample digital model is locked, and the spatial coordinates of each geometric element are linearly enlarged according to the preset multiple based on the reference alignment parameter. The feature parameters are scaled according to the preset multiple to generate the enlarged digital model.

[0009] In one embodiment, after generating the magnified digital model, the method further includes: For the easily deformable target areas in the magnified digital model, coordinate correction is performed based on the material deformation compensation coefficient; Structural parameters of the target object with slender structure are extracted from the magnified digital model, and the structural parameters are adjusted according to the structural stability coefficient to improve the structural load-bearing capacity of the target object. The coordinates of the positioning core parts and structural connection parts in the magnified digital model are corrected according to the geometric error correction coefficient.

[0010] In one embodiment, extracting the spatial coordinates of the flower center and the orientation angle parameters of each flower from the magnified digital model includes: Extract the disk structure corresponding to the center of each flower from the magnified digital model; Measure the spatial coordinates of three non-collinear points on the disk structure; Based on the spatial coordinates of the three non-collinear points, the system of equations constructed according to the constraints is solved to obtain the coordinates of the target point; the constraints include: the three points are coplanar and the three points are equidistant from the target point; Extract the spatial coordinates of the uppermost and lowermost points of the disk structure, and calculate the orientation angle using the coordinate difference; The orientation angle is converted into orientation angle parameters that include pitch angle, azimuth angle, and rotation angle.

[0011] In one embodiment, the digital modeling based on the generated structured data includes: The generated structured data is decomposed into several geometric units based on geometric elements; the geometric units include: flowers and baskets; The feature parameters of each geometric unit are defined according to its geometric shape and functional attributes; wherein, the feature parameters of the flower include: number of petals, size, and curvature; the feature parameters of the basket include: weaving density and texture features; Establish the mapping relationship between each of the feature parameters and the corresponding geometric unit, as well as the constraint relationship between components, to generate the sample digital model.

[0012] In one embodiment, the process of constructing structured geometric data from the raw 3D data includes: Based on the statistical outlier detection algorithm, noise data in the original 3D data is identified and removed to obtain denoised 3D data. The denoised 3D data is then aligned and fused with multiple sets of data to generate a single point cloud model. The single-point cloud model is subjected to mesh generation, edge repair, and hole filling to generate the triangular mesh model of the flower basket sample.

[0013] In one embodiment, after generating the triangular mesh model of the flower basket sample, the method further includes: Based on the curvature values ​​of each region in the triangular mesh model, the mesh structure of different curvature regions is adaptively simplified; wherein, the high curvature region retains more mesh cells than the low curvature region.

[0014] In one embodiment, the 3D scanning of the flower basket sample includes: At a first distance from the flower basket sample, the overall outline of the flower basket sample is rotated and scanned; wherein the overlap rate of adjacent scan areas of the rotational scan is not less than the overlap threshold. At a second distance from the flower basket sample, a multi-angle detailed scan is performed on each flower in the flower basket sample; the first distance is greater than the second distance. Supplementary scanning was performed on the internal fine structure and obstructed areas of the flower basket sample.

[0015] At least one embodiment of this application also provides a 3D positioning and installation device for a flower basket in the center of a square, comprising: The scanning unit is used to perform 3D scanning on the flower basket sample to obtain the raw 3D data generated by the scan. The digital modeling unit is used to construct structured geometric data from the original 3D data and perform digital modeling based on the generated structured data to generate a sample digital model. The model scaling unit is used to enlarge the small digital model by a preset ratio to obtain the enlarged digital model. The parameter extraction unit is used to extract the spatial coordinates of the flower center and the orientation angle parameters of each flower from the magnified digital model, and to position and install the flower according to the spatial coordinates of the flower center and the orientation angle parameters.

[0016] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, which are then executed to enable the at least one processor to perform the 3D positioning and installation method for the flower basket in the center of the square.

[0017] The 3D positioning and installation method for a flower basket in the center of a plaza provided in this application involves 3D scanning of a flower basket sample. Digital scanning technology comprehensively captures the three-dimensional geometric shape and detailed features of the flower basket sample, resulting in more complete data acquisition and less error compared to traditional manual measurement. Then, structured geometric data is constructed from the raw 3D data. Digital modeling is performed based on the generated structured data. The structured processing clarifies the boundaries and relationships of geometric units such as flowers and the basket body, and defines quantifiable characteristic parameters such as petal size and weaving density, transforming the physical sample's shape and structure into a standardized digital benchmark. Finally, a unified preset multiplier is used to synchronize all elements of the sample's digital model. The process involves enlarging the model to ensure that the dimensions and spatial relationships of each component are completely consistent with the prototype. This avoids problems such as disproportion and structural misalignment that are common with traditional manual enlargement, ensuring that the actual size model closely matches the original design intent of the prototype. Then, parameters are extracted from the enlarged digital model, and the spatial coordinates and orientation angles of the flower center in the enlarged digital model serve as the sole construction benchmark. The installation position and posture of each flower have clear numerical basis, eliminating the need for subjective judgment by construction personnel. They only need to perform positioning according to the parameters, which reduces the time cost of on-site adjustments and ensures the consistency of the installation of all flowers, avoiding problems such as uneven height and chaotic orientation, and significantly improving installation accuracy and overall landscape harmony.

[0018] This method achieves high precision, high consistency, and high efficiency in the process of flower basket design and on-site installation, effectively solving the core pain points of insufficient precision, distorted proportions, and cumbersome installation in traditional methods. Attached Figure Description

[0019] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0020] Figure 1 This is a flowchart illustrating a 3D positioning and installation method for a flower basket in the center of a plaza, as provided in one embodiment of this application. Figure 2 This is a schematic diagram of flower center positioning based on three-point measurement of a disk, provided in one embodiment of this application; Figure 3 This is a schematic diagram illustrating the measurement of flower tilt angle based on the upper and lower points of a disc, provided in one embodiment of this application. Figure 4 This is a schematic diagram of a curved stem flower positioning adapter provided in one embodiment of this application; Figure 5 This is a schematic diagram of the module division of a 3D positioning and installation device for a flower basket in the center of a square, provided in one embodiment of this application; Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical solutions claimed in this application can be implemented. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0022] The following is a detailed description of the implementation details of the 3D positioning and installation method for the flower basket in the center of the square in this embodiment. The following content is only for the convenience of understanding and is not necessary for implementing this solution. Example 1:

[0023] The specific process of the 3D positioning and installation method for the flower basket in the center of the square in this embodiment can be described as follows: Figure 1 As shown, it includes: Step 101: Perform a 3D scan on the flower basket sample to obtain the generated 3D raw data.

[0024] Using professional 3D scanning equipment (such as handheld 3D scanners, large depth-of-field scanners, etc.), comprehensive data collection is carried out on a flower basket sample made at a preset scale (such as 1:20) in a stable scanning environment. The three-dimensional geometric shape, surface structure features (such as flower shape, basket weaving texture) and spatial positional relationship of each component (flowers, basket body, base) of the sample are accurately captured. Finally, 3D raw data is generated, and the physical form of the flower basket sample is digitized and transformed into a three-dimensional data carrier that can be processed later.

[0025] Traditional methods such as manual measurement and hand-drawing are difficult to accurately capture the complex three-dimensional structure of a flower basket sample (such as the curvature of petals and the spatial form of the basket's weave texture), and are prone to human error. In this step, 3D scanning can directly convert the physical form into digital data, which can comprehensively capture the three-dimensional geometric features and spatial relationships of the flower basket sample, providing data assurance for the consistency of the actual flower basket with the sample in terms of proportion and form.

[0026] Step 102: Construct structured geometric data from the original 3D data, and perform digital modeling based on the generated structured data to generate a sample digital model.

[0027] The raw 3D data is organized, optimized, and logically reorganized into an ordered set of data with clear geometric shapes, characteristic parameters, and relationships—that is, structured geometric data. Based on this structured geometric data, digital modeling technology is used to virtually assemble the geometric units according to constraints, constructing a virtual digital carrier that can completely replicate the physical shape, structural features, and component constraints of the flower basket prototype, ultimately forming a prototype digital model. During the modeling process, the characteristic parameters and geometric relationships in the structured data must be strictly followed to ensure that the digital model accurately matches the size, shape, and component relationships of the physical prototype. The final prototype digital model carries the three-dimensional geometric information, characteristic parameters, and constraint relationships of the flower basket prototype. As a virtual carrier, the prototype digital model can be directly used for digital scaling, parameter extraction, and virtual verification, without relying on repeated measurements of the physical prototype, significantly reducing the operational difficulty of subsequent processes and improving overall efficiency.

[0028] The process of constructing structured geometric data mainly includes: first, performing basic optimization processing on the original 3D data (such as removing redundant noise and integrating multi-source scan data) to ensure data accuracy; then, decomposing the data into independent geometric units such as flowers, baskets, and bases according to geometric attributes, clarifying the boundaries and ranges of each unit; subsequently, defining the core feature parameters of each geometric unit (such as the number, size, and curvature of petals in the flower, and the weaving density and texture features of the basket), and establishing the mapping relationship between each parameter and the geometric shape; finally, sorting out the assembly constraint relationships between each geometric unit (such as the connection position between the flower and the basket, and the support relationship between the base and the basket), forming logically coherent and clearly defined structured geometric data. This embodiment uses the above construction process as an example, but it is not limited to this. Other methods can refer to the description in this embodiment, and will not be elaborated here.

[0029] Step 103: Enlarge the small digital model by a preset ratio to obtain the enlarged digital model.

[0030] The physical model is only a scaled-down version of the actual flower basket and cannot directly guide on-site construction. In this step, the digital model of the model generated in the previous step is scaled up in all dimensions and its geometric features are transferred according to the preset ratio determined by the design ratio relationship between the actual flower basket and the model (such as 20 times magnification corresponding to a 1:20 scaling ratio). While keeping the original geometric shape, component constraint relationship and feature parameter ratio (such as the matching relationship between flower structure, basket weaving density and size) of the digital model of the model unchanged, the model size is accurately enlarged to the design size of the actual flower basket, and finally an enlarged digital model that can be directly used for on-site construction and parameter extraction is generated.

[0031] The process of enlarging the prototype digital model by a preset magnification ratio mainly includes three parts: First, based on the coordinate system of the prototype digital model, the origin and axis directions of the coordinate system remain unchanged to ensure that the spatial relative positions of each component do not shift during the enlargement process; second, based on the preset magnification ratio, all geometric units (flowers, baskets, bases, etc.) and feature parameters (petal size, basket diameter, weaving spacing, etc.) of the prototype digital model are simultaneously enlarged to ensure that the geometric shape and parameter ratio are completely consistent with the prototype; third, the assembly constraint relationship between each component is preserved (such as the connection position between the flower and the basket, and the support angle between the base and the basket). After enlargement, the coherence and geometric accuracy of the overall structure of the model are verified to avoid structural misalignment or feature distortion caused by the method, and finally, a complete enlarged digital model is generated.

[0032] Traditional manual enlargement is prone to problems such as local size deviations and component proportion mismatches (e.g., the compatibility between the enlarged flower pattern and the basket body becomes poor). Digital proportional enlargement, on the other hand, ensures that each component and each feature parameter is enlarged synchronously by the same factor, directly transferring the precision advantage of the sample to the actual size model, providing a core guarantee for the consistency of the actual flower basket with the sample.

[0033] Step 104: Extract the spatial coordinates and orientation angle parameters of the flower center of each flower from the magnified digital model, and position and install the flowers according to the spatial coordinates and orientation angle parameters of the flower center.

[0034] From the digital model enlarged to actual size, the core positioning information of each flower is extracted, including the precise coordinates of the flower center in three-dimensional space (X, Y, Z axis coordinates), and the orientation angle parameters of the flower in space (such as pitch angle, azimuth angle, rotation angle), etc. This positioning information is precisely matched with the coordinate system of the actual flower basket construction site, directly corresponding to the physical position of the installation on site. The extracted coordinates and angle parameters serve as the sole execution benchmark to guide the physical installation of the flowers on site, ensuring that the actual installation position and spatial orientation of each flower are completely consistent with the design state in the enlarged digital model. All are based on unified digital parameters, with no individual differences. Based on this, the installation error can be controlled within a preset range (such as within 5 cm), meeting the high precision requirements of large-scale landscape decoration projects.

[0035] During positioning and installation, a high-precision total station (angle accuracy ≤1", distance accuracy ≤1mm+1ppm) can be used, along with auxiliary equipment such as a data acquisition controller, a 360° omnidirectional prism, and a laser pointer, to ensure that the on-site positioning accuracy meets the requirements. AR technology can also be used to overlay the design model with the actual scene in real time, intuitively displaying the flower's installation position, orientation, and deviation adjustment direction, reducing communication errors and lowering the technical threshold for construction.

[0036] The total station positioning relies on the following core algorithms and engineering optimization techniques to ensure the accuracy of both dynamic and static positioning. The basic measurement algorithms and error corrections are as follows: 3D coordinate calculation model: The coordinates of the measured point P are calculated using the following formula:

[0037] Where (X0, Y0, Z0) are the center coordinates of the total station (calibrated via resection), S is the slope distance, and θ is the horizontal angle. is the vertical angle, and k is the prism constant (calibrated on-site, ranging from -30mm to +30mm).

[0038] Multi-dimensional error compensation: Atmospheric refraction correction: , where n is the real-time atmospheric refractive index; Earth curvature and refraction correction: horizontal distance correction Where R = 6371000m is the Earth's radius; Elevation correction: Where R = 6371000m is the Earth's radius; Shaft error calibration: The vertical axis tilt error i is corrected in real time by a dual-axis compensator, and the horizontal and vertical angle measurement results are automatically adjusted.

[0039] High-precision dynamic tracking algorithm: Weighted adjustment by multiple measurements: This involves performing n measurements on the same flower reference point to obtain the observed values. Calculate the weights for each iteration: Where σ is the instrument's nominal accuracy, and the final coordinates are taken as a weighted average to improve the stability of single-point measurements; Kalman filter dynamic tracking: Establishing a state vector for the flower component during hoisting. (Position + Velocity), through the state equation and observation equations It enables pose prediction and real-time updates, adapting to dynamic positioning scenarios.

[0040] Engineering optimization techniques: Redundancy calculation for multi-station joint measurement: At least 3 total stations are deployed in the square to form a side intersection network, and the least squares adjustment formula is used. Solving for coordinates (A is the design matrix, P is the weight matrix, and l is the observation vector) reduces the error by 40%; Temperature deformation compensation: Real-time acquisition of ambient temperature T, using a formula Correction for thermal deformation, where T0 is the standard temperature and L is the length of the steel structure. is the coefficient of linear expansion of steel.

[0041] Establishing a dual coordinate system: Plaza coordinate system: with the fixed point at the center of the plaza as the origin, the Y-axis points due north, the X-axis points due east, and the Z-axis points vertically upward; Flower basket coordinate system: The origin is the center point of the bottom of the flower basket, the Z-axis points upward along the central axis, and the X-axis points to the front of the flower basket (south). It is transformed from the square coordinate system through translation vectors and rotation matrices, with a transformation accuracy of ≤±2mm.

[0042] Establishment of a three-tiered control network: Baseline control network: ≥4 fixed control points (concrete base + stainless steel markers) are set around the square, and the coordinates are determined by GPS-RTK, with a horizontal accuracy of ≤5mm and an elevation accuracy of ≤8mm; Encrypted control network: Set up 8-12 encrypted control points around the flower basket, and use a total station closed traverse survey to achieve a horizontal accuracy of ≤3mm and an elevation accuracy of ≤5mm. Temporary working reference points: set at intervals of ≤30 meters, coordinates determined by polar coordinate method, with a plane accuracy of ≤5mm and an elevation accuracy of ≤8mm.

[0043] The following construction preparations will be carried out: Site survey: Assess the terrain and interference factors (tourists, wind), and develop contingency plans; Equipment calibration: Verify the angle / distance accuracy of the total station and the prism reflection performance, and prepare backup equipment; Data preparation: Convert the flower coordinates from the flower basket coordinate system to the square coordinate system, export the data by area (CSV / TXT format), and optimize the installation sequence (layered and zoned installation). Select control points with a wide field of view for station setup, determine the instrument position using the resection method, observe ≥3 known points for orientation, and ensure angle error ≤5mm and coordinate error ≤5mm.

[0044] Positioning and installation can be performed in the following steps: Coarse positioning: Following the optimized sequence, hoist the flower to 50cm above the installation position and adjust its approximate orientation; Precise positioning: Use a total station to lay out the coordinates of the flower center, mark the position with a laser pointer, and monitor the deviation in real time to ensure it is ≤±2.5cm; Orientation adjustment: Set the pitch, azimuth, and rotation angles using the angle adjuster, with an error of ≤±2°, and use a digital level for calibration; Fixing and recording: After temporary fixing, verify the position and orientation, and permanently lock it in place after confirmation. Record the flower ID, actual coordinates, installation personnel, and time, and upload the data to the central database in real time.

[0045] Actual performance indicators: Using a high-precision total station such as the Leica TS60, the single-point measurement accuracy within a distance of 50m is ≤1.5mm, and the dynamic tracking frequency reaches 5Hz under stable working conditions; after multi-station joint measurement, the positioning error can be controlled within ±2mm. Combined with the above algorithm, the design requirement of flower installation positioning accuracy ≤5cm is finally achieved.

[0046] After all flowers are installed, a comprehensive accuracy verification can be performed: 1. Full data acquisition: Use a 3D laser scanner to perform a 360° full-dimensional scan of the flower basket to obtain a complete point cloud model of the actual installation, with a scanning accuracy ≤0.5mm; 2. Model comparison: Import the actual point cloud model and the magnified digital design model into comparison software such as Geomagic Control, set the comparison accuracy threshold to ±5mm, and generate a deviation heatmap; 3. Quality report: Statistically analyze the position and orientation deviations of each flower, mark the parts with deviations >5cm and propose rectification suggestions, evaluate whether the overall installation accuracy meets the design requirements, and form the "Plaza Center Flower Basket Installation Quality Acceptance Report". The following are the handling methods for special situations: For obstructed views, use auxiliary stations, conduct multi-station joint observations, and temporarily adjust the flower basket structure and compensate for coordinates if necessary; In severe weather, increase the number of measurements and take the average value when the wind force is >4, add rain protection facilities in rainy weather, avoid midday when temperatures are high, and supplement lighting at night; In case of equipment failure, use a backup total station and power supply, restore backup data through the central database to ensure continuous construction.

[0047] Based on the above introduction, the 3D positioning and installation method for the flower basket in the center of the square provided in this embodiment involves 3D scanning of the flower basket sample. Digital scanning technology comprehensively captures the three-dimensional geometric shape and detailed features of the flower basket sample, resulting in more complete data acquisition and less error compared to traditional manual measurement. Then, structured geometric data is constructed from the original 3D data. Digital modeling is performed based on the generated structured data. The structured processing clarifies the boundaries and relationships of geometric units such as flowers and the basket body, and defines quantifiable characteristic parameters such as petal size and weaving density, transforming the physical sample's shape and structure into a standardized digital benchmark. Finally, a unified preset multiplier is used to perform full-element analysis on the sample's digital model. Synchronous scaling ensures that the size proportions and spatial relationships of each component are completely consistent with the prototype after enlargement, avoiding problems such as disproportion and structural misalignment that are prone to occur in traditional manual enlargement. This ensures that the actual size model is highly consistent with the original design intention of the prototype. Then, parameters are extracted from the enlarged digital model. The spatial coordinates and orientation angle parameters of the flower center in the enlarged digital model are used as the sole construction benchmark. The installation position and posture of each flower have clear digital basis. Construction personnel do not need to make subjective judgments. They only need to perform positioning according to the parameters. This reduces the time cost of on-site adjustments and ensures the consistency of the installation of all flowers, avoiding problems such as uneven height and chaotic orientation. This greatly improves the installation accuracy and overall landscape coordination.

[0048] This method achieves high precision, high consistency, and high efficiency in the process of flower basket design and on-site installation, effectively solving the core pain points of insufficient precision, distorted proportions, and cumbersome installation in traditional methods.

[0049] It should be noted that this method is not only applicable to the production and installation of flower baskets in central squares, but can also be extended to: 1. Large-scale landscape decoration projects (city squares, theme parks, exhibition installations); 2. Architectural decoration (complex facades, large-scale interior artworks, specially shaped components); 3. Cultural heritage protection and replication (digitization of cultural relics, restoration of historical buildings, reproduction); 4. Large-scale industrial manufacturing (equipment assembly and positioning, complex structure manufacturing, mold verification). Its widespread application will significantly improve work efficiency and product quality in related fields, establish standardized processes, and possess broad application prospects and economic value.

[0050] Example 2: The above embodiments do not limit the steps for enlarging the small digital model. For example, conventional methods such as simple scaling or manual decomposition and calculation can be used. However, such methods are prone to problems such as coordinate reference confusion, asynchronous scaling of feature parameters, and distortion of flower orientation. In order to achieve precise control and deviation-free transmission of the enlargement process, this embodiment proposes a standardized enlargement operation procedure. By establishing a unified coordinate reference, clarifying the reference association parameters, and implementing the technical logic of synchronous enlargement of all elements, it can be ensured that the geometric shape, relative position of components, and feature ratio of the enlarged model are highly consistent with the small digital model, and that the flower orientation is without deviation, providing a high-precision digital reference for subsequent positioning and installation.

[0051] Step 103: Enlarge the sample digital model by a preset magnification ratio. This can be done by following these steps: Step 31: Read the basic structural data of the sample digital model, and based on the basic structural data, establish the Z-axis upward along the central axis of the flower basket with the center point of the basket as the origin, establish the X-axis horizontally pointing to the front of the flower basket, and establish the Y-axis according to the right-hand coordinate system rule to generate the sample coordinate system.

[0052] Read the basic structural data of the sample digital model (such as the bottom outline, the position of the central axis, etc.), take the center point of the sample flower basket tray as the origin, and the center point of the sample flower basket tray as the geometric reference point of the flower basket structure to ensure the consistency of the coordinate calculation of each component; establish the Z-axis upward along the central axis of the flower basket, which fits the vertical force and shape extension direction of the flower basket, and establish the X-axis horizontally pointing to the front of the flower basket to clarify the main visual orientation of the flower basket and ensure the consistency of the installation direction. Then, establish the Y-axis according to the right-hand coordinate system rule, and finally generate the sample coordinate system.

[0053] Step 32: Establish an actual size coordinate system with the center point of the actual flower basket as the origin and the coordinate axis direction consistent with the sample coordinate system.

[0054] Using the center point of the actual flower basket as the origin, the coordinate axes of the sample coordinate system are completely followed (X-axis pointing, Y-axis regular, and Z-axis extension direction are all consistent), so that the two coordinate systems form a relationship of corresponding origins and parallel axes. This eliminates the influence of coordinate direction deviation on the magnification result, ensures that the relative positional relationship of each component in the sample is synchronously preserved in the actual size, and avoids structural misalignment problems such as the flower being on the left side of the basket in the sample but shifting to the right side after magnification.

[0055] Furthermore, an independent local coordinate system can be established for each flower, with the origin at the flower center, the Z' axis along the central axis of the flower, and the X' axis as the main horizontal direction of the flower. The system is then linked to the global coordinate system through a transformation matrix to record the position vector and rotation matrix R = Rz (γ) × Ry (β) × Rx (α) of each flower.

[0056] Step 33: Record the reference alignment parameters between the sample coordinate system and the actual size coordinate system.

[0057] The datum alignment parameters include, but are not limited to: the origin offset between the sample coordinate system and the actual size coordinate system (clarifying the spatial relationship between the origins of the two coordinate systems), the axis direction consistency verification result (verifying that the coordinate axes are not deflected), and the scale datum calibration data (confirming the application datum for the preset magnification). By recording these parameters, a precise datum association is provided for the subsequent coordinate magnification of geometric elements, ensuring that the magnification of each geometric element is based on a unified coordinate mapping rule, and avoiding local magnification imbalances caused by ambiguity in the relationship between the two coordinate systems.

[0058] Step 34: Extract all geometric elements and feature parameters from the sample digital model to generate a structured data list.

[0059] By extracting all geometric units (flowers, baskets, bases, etc.) and their corresponding feature parameters (number, size, and curvature of flower petals, weaving density, diameter, and wall thickness of baskets) from the sample digital model, and organizing them into a structured data list, the list clearly defines the boundary range, feature parameter values, and constraint relationships of each geometric unit. This ensures that the scaling operation can cover all core elements of the model, without missing small structures (such as flower stamens and basket weaving nodes) or causing partial scaling or omission of feature parameters, thus providing data support for the synchronous scaling of all elements.

[0060] Step 35: Based on the structured data list, lock the orientation angle parameters of each flower in the sample digital model, and linearly enlarge the spatial coordinates of each geometric element according to the baseline alignment parameters by a preset factor, and scale the feature parameters by a preset factor to generate the enlarged digital model.

[0061] Based on a structured data list, the orientation angle parameters (such as pitch angle, azimuth angle, and rotation angle) of each flower are locked. The reason for locking is that the flower orientation directly affects the overall landscape effect of the flower basket and must be strictly consistent with the sample to avoid posture deviation during the enlargement process. Then, according to the benchmark alignment parameters, the spatial coordinates of all geometric elements are linearly enlarged at a preset multiple (ensuring that the coordinate enlargement is based on a unified benchmark and without deviation), while the feature parameters are scaled by the same multiple (e.g., if the petal length in the sample is 2cm, it will be 40cm after a preset 20x enlargement, keeping the feature proportions unchanged). The final enlarged digital model completely retains the geometric shape, component constraint relationship, feature proportions, and flower orientation of the sample digital model, realizing accurate proportional transfer and deviation-free replication from the sample to the actual size.

[0062] Specifically, linear amplification can be achieved using matrix transformations, applying the transformation to any point in the sample. The orientation angle of the flowers in the sample Actual size corresponding points The actual size corresponds to the angle of the flower's orientation. One calculation method is as follows:

[0063] In this context, the unit vector describing direction retains its direction but is magnified in length; the flower's orientation angle... Fully locked, remains after zooming in .

[0064] Small Flower Heart After magnification The actual flower rotation matrix is ​​consistent with the sample. .

[0065] This enlargement method eliminates the spatial offset problem caused by coordinate datum confusion by establishing a coordinate system consistent between the sample and the actual size, using the center point of the flower basket as a reference. It achieves unbiased mapping between the two coordinate systems through datum alignment parameter recording, providing a precise reference for the enlargement operation. By extracting a structured data list, it ensures that all geometric elements and feature parameters are included, and by combining a linear enlargement algorithm with a synchronous scaling mechanism for feature parameters, it guarantees consistency in the size ratio, relative position, and height of each component with the sample. Simultaneously, it locks the flower orientation angle parameter to avoid posture distortion during enlargement. Ultimately, it achieves accurate replication of the geometric shape, structural constraints, feature ratios, and flower posture of the sample digital model. The enlargement process is highly controllable and has minimal error, laying a highly reliable digital foundation for subsequent extraction of flower center coordinates and precise on-site positioning and installation.

[0066] The above method achieves the proportional transfer from the small-scale digital model to the actual size. However, large-scale landscape decoration projects face complex actual working conditions and constraints. Problems such as irreversible deformation of materials due to environmental factors like gravity and wind load, insufficient load-bearing capacity and stability of slender structures after being enlarged to a fixed scale, and the accumulation and amplification of scanning and modeling errors during the enlargement process can arise. These issues lead to deviations between the enlarged digital model and actual construction requirements, affecting installation accuracy and structural reliability. To overcome the limitations of linear enlargement and further improve the engineering practicality and accuracy reliability of the digital model, this embodiment proposes a nonlinear correction process. After step 35, the following steps can be further executed to combine the digital model with the material properties and structural mechanics requirements of the actual construction. This eliminates accumulated errors during the enlargement process and proactively avoids structural risks and accuracy deviations after actual installation. Step 36: For the easily deformable target area in the magnified digital model, perform coordinate correction based on the material deformation compensation coefficient.

[0067] First, identify the easily deformable target areas, namely the parts of the flower basket that are significantly affected by gravity and wind loads, such as the upper dense flower area, the overhanging edges of the basket, and the connection points of large petals. The material deformation compensation coefficient can be determined through material mechanics models and finite element simulation analysis, taking into account the elastic modulus, tensile strength, and wind load resistance of the materials used in the flower basket (such as fiberglass, metal supports, and artificial flower materials), as well as the load parameters such as gravity and wind in the actual environment. This embodiment does not impose any limitations on this. In specific implementation, the spatial coordinates of the easily deformable target areas are reversed according to the material deformation compensation coefficient. For example, for the edges of the basket that are drooping due to gravity, the coordinates are adjusted upwards appropriately so that the material deformation after actual installation exactly offsets the correction amount. Ultimately, this ensures that the geometry of the physical entity is completely consistent with the design model, avoiding problems such as flower position displacement and overall landscape incoordination caused by material deformation.

[0068] Step 37: Extract the structural parameters of the slender target object from the magnified digital model, and adjust the structural parameters according to the structural stability coefficient to improve the structural load-bearing capacity of the target object.

[0069] Extract slender structural target objects from the magnified digital model, such as flower stems, basket support bars, petal connecting rods, etc., whose structural parameters include, but are not limited to, diameter, wall thickness, length, cross-sectional shape, etc.

[0070] The structural stability coefficient is determined based on the results of structural mechanics analysis. It is derived by calculating critical loads, deflection, and other indicators, taking into account the aspect ratio, material strength, and stress conditions (such as load-bearing capacity and wind resistance) of the slender structure. The specific calculation algorithm is not limited in this embodiment. In actual implementation, structural parameters are adjusted according to the structural stability coefficient. For example, the diameter of the flower stem may be increased, the wall thickness may be increased, or the cross-sectional shape may be optimized (from a circle to a polygon). Without compromising the consistency with the prototype's proportions, this improves the bending and torsional resistance and overall load-bearing capacity of the slender structure, avoiding safety risks such as structural fracture and excessive deformation after actual installation, and ensuring the stability of the flower basket during long-term use.

[0071] Step 38: For the positioning core parts and structural connection parts in the enlarged digital model, the coordinates are corrected according to the geometric error correction coefficient.

[0072] The core positioning parts refer to the key locations that directly affect the installation accuracy of the flowers (such as the flower center, installation reference point, and petal positioning groove), while the structural connection parts refer to the key nodes for force transmission between various components (such as the connection between the petals and the flower base, the connection between the flower stem and the basket, and the connection between the basket and the base).

[0073] The geometric error correction coefficient is determined based on the error prediction model. It comprehensively considers the cumulative effects of scanning error, modeling error, and amplification algorithm error, and is quantified using methods such as Monte Carlo simulation and error ellipse analysis. This embodiment does not limit the specific algorithm used. In practical implementation, the spatial coordinates of the aforementioned key components are precisely corrected based on the geometric error correction coefficient, eliminating accumulated errors one by one. This ensures that the coordinate accuracy of the core positioning components is controlled at the millimeter level, and that the assembly gaps and positional deviations of the structural connection parts meet engineering requirements. This provides a highly reliable digital reference for subsequent extraction of the flower center coordinates and on-site positioning and installation.

[0074] To ensure overall positioning accuracy, after nonlinear correction, further error budget allocation and accuracy verification can be performed. Specifically, for example, the total error can be controlled within 5 cm, where scanning error ≤ 1 cm, magnification algorithm error ≤ 2 cm, and implementation / installation error ≤ 2 cm; based on the error propagation model ( , Due to the measurement error of small samples, To quantify the cumulative effect of algorithm errors, the overall accuracy is evaluated through Monte Carlo simulation. An error ellipse is established to quantify the accuracy level at different locations, ensuring that the model and installation accuracy meet the standards. The model is continuously corrected until the error budget allocation standard is reached.

[0075] To improve the dynamic accuracy and environmental adaptability of on-site positioning, the following algorithm strategy can be adopted: 1. Multi-measurement weighted adjustment: Perform n measurements on the same flower installation reference point and calculate the weight of each measurement. (σ is the instrument's nominal accuracy, Si is the slope distance, θi is the horizontal angle, and φi is the vertical angle), the final coordinates are taken as a weighted average; 2. Kalman filter dynamic tracking: For the flower component during hoisting, a state vector is established. Through state equations and observation equations Achieve pose prediction and real-time updates; 3. Redundancy calculation for multi-station joint measurement: Deploy 3 or more total stations to form a side intersection network, and use the least squares adjustment formula. Solving for coordinates reduces system errors; 4. Temperature deformation compensation: Real-time acquisition of ambient temperature T, and compensation using formulas. ( (Standard temperature, L is the structural length) Corrects for thermal deformation of the steel structure to ensure positioning accuracy.

[0076] Based on the above introduction, this embodiment establishes a dual coordinate system with consistent axes and records the benchmark alignment parameters, fundamentally ensuring the uniformity of the coordinate benchmark during the enlargement process. Combined with synchronous linear enlargement of all geometric elements and all feature parameters, it ensures a high degree of consistency between the sample and the actual size model in terms of structural form, relative position of components, and feature proportions, completely avoiding the problems of scale misalignment and spatial offset in traditional enlargement. Secondly, subsequent nonlinear correction specifically compensates for the limitations of pure linear enlargement. Material deformation compensation predicts and corrects geometric deviations under actual working conditions in advance. Adjustment of slender structural parameters enhances the load-bearing capacity and stability of the actual flower basket. Error correction of positioning core parts and structural connection parts accurately eliminates the cumulative errors during the enlargement process, upgrading the digital model from accurate scale to engineering practicality. Ultimately, it achieves multiple goals: high-precision scale transfer from sample to actual size, enhanced structural reliability, and guaranteed installation accuracy. It maintains consistency with the sample while fully adapting to the actual working conditions of large-scale landscape decoration projects, laying a high-quality digital benchmark with both accuracy and reliability for subsequent precise on-site positioning and installation.

[0077] Example 3: The above embodiments do not limit the specific extraction logic and operation method for extracting parameters from the magnified digital model to support installation. Traditional parameter extraction often relies on manually marking geometric features or making rough geometric judgments. This can easily lead to deviations in flower center coordinates and distortions in orientation angles due to fuzzy extraction benchmarks and inconsistent calculation logic. For example, flower center positioning lacks clear constraints, which may result in misalignment between the visual center and the actual geometric center. Orientation angle extraction has not established standardized conversion rules, resulting in no unified benchmark for the angle parameters of different flowers. This directly affects the consistency and accuracy of subsequent positioning and installation, and cannot meet the core requirement of millimeter-level parameter accuracy for large-scale landscape decoration projects.

[0078] To address the accuracy concerns in the parameter extraction process and achieve standardized and high-precision acquisition of core positioning parameters, this embodiment proposes a parameter extraction method. By locking the disc structure corresponding to the flower's center as a unified extraction benchmark, constructing an equation system based on dual constraints to solve for the flower's center coordinates, and standardizing the orientation angle conversion, this method ensures that the core positioning parameter extraction process for each flower is repeatable, the accuracy is controllable, and the parameter format is uniformly adapted to subsequent construction needs.

[0079] Step 104: Extract the spatial coordinates and orientation angle parameters of the flower center for each flower from the magnified digital model. This can be done by following these steps: Step 41: Extract the disk structure corresponding to the center of each flower from the magnified digital model.

[0080] The disc-shaped structure at the center of a flower exhibits clear geometric symmetry and structural stability, making its geometric features easier to accurately identify compared to complex and irregular parts such as petals. Targeted extraction of this structure from the magnified digital model avoids feature recognition biases caused by the flower's complex shape, providing a unified and stable geometric basis for subsequent coordinate measurements and angle calculations, ensuring consistent parameter extraction benchmarks across all flowers.

[0081] Step 42: Measure the spatial coordinates of three non-collinear points on the disk structure.

[0082] Three non-collinear points are selected on the extracted disk structure and their spatial coordinates (X, Y, Z) are measured to avoid the problem of no solution or multiple solutions to the system of equations caused by collinear points.

[0083] Step 43: Based on the spatial coordinates of the three non-collinear points, solve the system of equations constructed according to the constraints to obtain the coordinates of the target point.

[0084] The constraints include: the three points are coplanar and the three points are equidistant from the target point. The coplanar constraint conforms to the geometric characteristics of the disk structure, ensuring that the target point and the three points are in the same spatial plane, which is consistent with the physical position relationship of the flower center at the center of the disk. The equidistant constraint clearly defines the target point as the center of the disk (i.e., the position of the flower center).

[0085] Based on this dual constraint, a system of linear algebraic equations is constructed. By substituting the spatial coordinates of three non-collinear points into the equations, the coordinates of the unique target point can be obtained directly. This process relies entirely on mathematical operations, eliminating subjective human judgment and achieving high-precision, unbiased extraction of the flower center coordinates.

[0086] To clarify the construction logic and solution process of the system of equations, the specific derivation is as follows: Let the spatial coordinates of the three non-collinear points be respectively... The target point (center of the flower) has coordinates O(x,y,z) and a radius of R.

[0087] Three-point coplanar constraint: According to the principles of spatial geometry, the equation of the plane determined by three points is... (4).

[0088] in,

[0089]

[0090]

[0091]

[0092] Constraint that the distances from three points to the target point are equal:

[0093]

[0094]

[0095] Solving the system of equations: By combining (1)-(2) and (2)-(3) and eliminating R², we obtain the linear equation:

[0096]

[0097] By combining the plane equations (4) and (5) and (6), and solving the system of three linear equations in three variables by elimination, we can obtain the coordinates of the unique target point O(x,y,z), which is the coordinates of the flower center.

[0098] like Figure 2 The diagram shows a method for locating the center of a flower based on three-point measurement on a disc. Three non-collinear points A, B, and C are selected on the disc structure corresponding to the flower's center. The plane determined by these three points coincides with the plane of the disc. By solving a system of linear equations that show the distances from the three points to the target point being equal, the coordinates of the center O can be uniquely determined. This center is the location of the flower's center.

[0099] Step 44: Extract the spatial coordinates of the top and bottom points of the disk structure, and calculate the orientation angle by the coordinate difference.

[0100] The line connecting the top and bottom points of the disc structure directly reflects the direction of the flower's central axis. By extracting the spatial coordinates of the two points and calculating the coordinate differences (such as the Z-axis height difference and the XY-plane projection offset), the initial tilt angle of the flower can be derived. This method fully utilizes the symmetry and geometric stability of the disc structure, has a simple calculation logic, and is highly accurate. It avoids angle measurement errors caused by the identification of complex parts such as petals, providing accurate initial data for subsequent standardization conversion.

[0101] Figure 3 The diagram shows a method for measuring the tilt angle of a flower based on the top and bottom points of a disc. The diameter of the disc is d (100cm in the actual flower basket). The uppermost point D and the lowermost point E are extracted, and the line connecting the two points is the direction of the flower's central axis. Through the geometric relationship h=d×sinα (α is the design tilt angle), when α=39°, h≈63cm. Construction workers can directly measure the h value with a steel tape measure to quickly calibrate the angle without repeating coordinate calculations.

[0102] On-site construction can utilize rapid positioning techniques to improve efficiency: Let the diameter of the disc be d (e.g., the actual diameter of the flower basket disc is 100cm). If the designed flower tilt angle is α (e.g., 39°), the vertical distance h from the top to the bottom of the disc can be calculated using geometric relationships: h = d × sinα (e.g., 100cm × sin39° ≈ 63cm). Construction workers can directly measure the actual distance between the top and bottom of the disc with a steel tape measure to quickly calibrate the flower tilt angle, eliminating the need for repeated coordinate calculations and balancing accuracy and efficiency.

[0103] Step 45: Convert the orientation angle into orientation angle parameters including pitch angle, azimuth angle, and rotation angle.

[0104] The initial tilt angle is converted into industry-standard three-dimensional angle parameters: pitch angle (vertical tilt angle), azimuth angle (horizontal rotation angle), and rotation angle (rotation angle around the central axis), unifying the parameter format and definition standards. The converted parameters can be directly imported into construction equipment and software such as total stations and CAD systems without additional format conversion, reducing communication errors during construction. Simultaneously, it ensures that all flower orientation parameters have a unified engineering interpretation standard, providing direct and usable digital data for precise on-site installation. Specifically, the triangular mesh model can be converted into a parametric CAD model, extracting key features such as the flower centerline and basket outline to establish a feature tree.

[0105] Positioning adaptation for curved stem flowers: If the actual flower installation uses a curved stem structure, its positioning logic is completely consistent with that of a straight stem flower. Figure 4The diagram illustrates a positioning adaptation method for curved-stem flowers. Both straight-stem and curved-stem flowers share the same circular disk structure. Only the three-point coordinate measurement and the uppermost / lowermost angle measurement steps of the disk structure need to remain unchanged. The curvature of the curved stem does not affect the calculation results of the flower center coordinates and orientation angle, eliminating the need for additional adjustments to the parameter extraction logic and ensuring consistent positioning for flowers with different structures. It should be noted that this embodiment introduces a parameter extraction method. This method uses the corresponding circular disk structure as a unified extraction benchmark, combines the coplanarity of three points with the equidistant distances from the three points to the target point as a double constraint equation to solve for the flower center coordinates, and then calculates and standardizes the orientation angle using the coordinate differences of the disk's feature points. This achieves high-precision, repeatable, and standardized extraction of core positioning parameters, ensuring consistent parameter extraction logic and controllable errors for each flower. However, this is not the only method. For example, based on the petal outline features of the flower, the coordinates of the flower center can be identified through edge detection and geometric center fitting algorithms, and the orientation angle can be calculated by combining the normal direction of the petal surface; or the parametric modeling attributes of the digital model can be used to directly read the preset flower center coordinates and posture parameters when modeling the flower; or the principles of photogrammetry can be used to perform multi-view virtual shooting and image matching on the magnified digital model to deduce the spatial position and orientation angle of the flower center. Other extraction methods can be referred to the description in this embodiment, and will not be repeated here.

[0106] Example 4: Structured geometric data is essentially an integrated set of three-dimensional information. If it is directly used for modeling, problems such as fuzzy geometric unit division, missing key feature parameters, and chaotic relationships between components may easily occur. For example, it may be impossible to clearly distinguish the boundary range between flowers and baskets, omit core features such as petal curvature and basket weaving density, or ignore the assembly constraint relationship between flowers and baskets. As a result, the generated sample digital model cannot accurately replicate the geometric shape and functional attributes of the physical sample, which in turn affects the accuracy of subsequent scaling and parameter extraction. It cannot meet the core requirements of high precision and traceability of digital models for large-scale landscape decoration projects.

[0107] To address the issues of standardization and accuracy in the modeling process and achieve unbiased conversion of structured data into digital models, this embodiment proposes a modeling method. By clearly defining geometric unit divisions, precisely defining feature parameters, and establishing stable constraint relationships, this method ensures that the prototype digital model can fully retain all key features and component associations of the physical prototype, providing a high-precision and highly reliable digital benchmark for subsequent scaling up and parameter extraction.

[0108] Step 102 involves digital modeling based on the generated structured data, which can be performed as follows: Step 21: Decompose the generated structured data into several geometric units based on geometric elements.

[0109] The structured data contains complete 3D information of the flower basket sample. It is decomposed into independent geometric units such as flowers and the basket body, based on the differences in the geometric boundaries and functional attributes of each component. For example, the flowers serve as the core decorative component, while the basket body serves as the supporting component; the two are clearly distinguished in terms of structural form and function. The decomposed geometric units include, but are not limited to, the flowers and the basket body. This decomposition method avoids interference between geometric information from different structures, allowing subsequent modeling to address the details of each unit specifically, ensuring accurate replication of the shape of each component, and providing clear processing objects for defining feature parameters and establishing relationships.

[0110] Step 22: Define the characteristic parameters of each geometric unit according to its geometric shape and functional attributes.

[0111] For each decomposed geometric unit, unique feature parameters are defined based on its geometric shape and functional attributes. Specifically, the feature parameters of a flower include: number of petals, size, and curvature; the feature parameters of a basket include: weaving density and texture features.

[0112] For flowers, the number and size of petals directly determine their visual form, and curvature affects the natural arc and beauty of the petals. These parameters are key to replicating the details of flowers. For baskets, weaving density is related to their structural stability and visual texture. Texture features are the core of restoring the craftsmanship details of baskets.

[0113] By clearly defining these quantifiable characteristic parameters, subjective judgments about geometric shapes are avoided during the modeling process. This ensures that the details of each geometric unit are traceable and adjustable, guaranteeing that the digital model can accurately match the shape and functional characteristics of the physical prototype.

[0114] Step 23: Establish the mapping relationship between each feature parameter and the corresponding geometric unit, as well as the constraint relationship between components, and generate a sample digital model.

[0115] A mapping relationship is established between each feature parameter and its corresponding geometric unit, so that the adjustment of each parameter can be directly linked to the morphological change of the geometric unit. For example, modifying the petal size parameter can synchronously update the 3D shape of the flower. Secondly, the constraint relationship between components is established, clarifying the assembly position and connection method of the flower and the basket, such as the fixed point of the flower on the basket and the installation angle, to ensure that each geometric unit is integrated according to the actual structure of the physical prototype. Through the establishment of these two layers of relationships, the independent geometric units and feature parameters are integrated into a complete prototype digital model, which not only ensures the accuracy of each component, but also ensures the coherence and consistency of the overall structure, achieving high-precision digital replication of the physical prototype.

[0116] The digital modeling method provided in this embodiment decomposes structured data into independent geometric units such as flowers and baskets according to geometric elements, clarifying the boundaries and processing objects of each component and avoiding interference between geometric information of different structures. Secondly, it defines exclusive feature parameters for the geometric shape and functional attributes of each geometric unit, quantifying key details such as the number, size, and curvature of flower petals and the weaving density and texture features of baskets, making model details traceable and adjustable, completely eliminating the ambiguity of relying on subjective judgment in traditional modeling, and ensuring a high degree of consistency between the digital model and the physical prototype in terms of details. Finally, by establishing the mapping relationship between feature parameters and geometric units and the constraint relationship between components, it achieves precise linkage between parameter adjustment and geometric shape change, and clarifies the core associations such as the assembly position and connection method of flowers and baskets, avoiding structural misalignment during the modeling process, and ensuring that the generated prototype digital model has both complete geometric accuracy and stable structural coherence, providing a high-precision and high-reliability digital benchmark for subsequent scaling up and core parameter extraction.

[0117] Example 5: The raw data acquired by 3D scanning inevitably contains noise data caused by environmental interference and equipment errors. Furthermore, multiple sets of scan data are prone to spatial misalignment and overlapping inconsistencies. At the same time, the raw point cloud data may have structural defects such as edge breaks and holes. If structured geometric data is directly constructed based on such data, it may lead to a decrease in the accuracy of subsequent digital modeling and distortion of details. It may be impossible to accurately replicate the geometric shape and structural features of the flower basket sample, thereby affecting the accuracy control of subsequent stages such as enlargement and installation.

[0118] To address the issues of purity and integrity in 3D raw data and achieve standardized, high-quality construction of structured geometric data, this embodiment proposes a method for constructing structured geometric data. By specifically eliminating interference factors in the raw data, integrating multi-source data, and improving the model structure, this method ensures that the structured geometric data can accurately reflect the true shape of the flower basket sample, providing a reliable data foundation for subsequent digital modeling.

[0119] Step 102: Construct structured geometric data from the raw 3D data. This can be done by following these steps: Step 24: Based on the statistical outlier detection algorithm, identify and remove noisy data in the original 3D data to obtain denoised 3D data.

[0120] The raw data acquired by 3D scanning inevitably contains discrete noise data caused by environmental interference (such as light reflection and dust) and equipment errors. This noise can interfere with subsequent geometric feature recognition and model construction. By using statistical outlier detection algorithms, outliers that deviate from the normal data distribution can be accurately identified and removed. This avoids the subjectivity and inefficiency of manual denoising while preserving the true geometric features of the flower basket sample to the greatest extent. The result is denoised 3D data that is clean and has clear features, ensuring the accuracy of structured construction from the source.

[0121] Step 25: Align and fuse multiple sets of data on the denoised 3D data to generate a single point cloud model.

[0122] Due to the complex structure and occlusions of the flower basket sample, scanning needed to be performed in batches from multiple angles, resulting in multiple independent sets of denoised 3D data with potentially misaligned spatial positions. Data alignment technology was used to calibrate multiple sets of data to the same spatial coordinate system, using key geometric features of the flower basket sample (such as bottom positioning marks and basket outlines) as a benchmark. Then, a fusion algorithm was used to integrate them into a single point cloud model, ensuring spatial consistency and avoiding structural breaks, overlaps, and redundancy. This provides a complete and unified data carrier for subsequent structured geometric morphology construction.

[0123] Step 26: Perform mesh generation, edge repair, and hole filling on the single point cloud model to generate a triangular mesh model of the flower basket sample.

[0124] A single point cloud model is meshed, and discrete point cloud data is transformed into a mesh structure with a defined geometric shape by generating triangular patches. This transforms the originally disordered point data into a quantifiable and analyzable structured geometry. To address structural defects in the point cloud data, such as edge breaks and holes caused by scan occlusion, edge repair algorithms are used to fill broken geometric boundaries, and hole filling algorithms are used to fill missing data areas (elastic body models or symmetry can also be used to complete the data). This ensures that the generated triangular mesh model has a complete structure, regular contours, and a smooth surface. The final triangular mesh model fully preserves the geometric features and structural details of the basket pattern, providing a high-precision and highly complete core geometric carrier for subsequent digital modeling based on structured data.

[0125] During mesh generation, the size of the triangular facets can be set to 0.2-0.5mm to repair edge breaks in the single point cloud model, fill holes smaller than 2mm, and ensure the integrity of the triangular mesh model structure and the smoothness of the surface.

[0126] During the data structuring process, Artec Studio software can be used for point cloud processing, combined with triangular mesh generation software and texture mapping software to complete model optimization. The point cloud registration accuracy is controlled within 0.1mm, and the texture resolution is set to 4K (4096×4096 pixels).

[0127] In the actual process of constructing structured geometric data, a unified mesh processing strategy is adopted, which does not take into account the differences in geometric features of different regions of the flower basket sample. High curvature areas (such as the edges of petals and the details of the basket's woven texture) require dense meshes to accurately preserve the shape details, while maintaining the same mesh density in low curvature smooth areas (such as the sides of the basket and the base plane) will cause a lot of data redundancy. This not only increases the computing power consumption for subsequent data storage and processing, but may also cause key details and redundant data to be confused due to unreasonable mesh distribution, affecting the accuracy and efficiency of subsequent digital modeling.

[0128] To further optimize the quality of geometric data and address the balance between accuracy and lightweight design in the triangular mesh model, the following steps can be performed after step 26: Step 27: Based on the curvature values ​​of each region in the triangular mesh model, perform adaptive differential simplification processing on the mesh structure of different curvature regions.

[0129] First, the curvature value of each region in the triangular mesh model is calculated by an algorithm. The magnitude of the curvature value directly reflects the complexity of the geometric shape. High curvature regions (such as the edges of petals, the raised texture of the basket weave, and the folds of the petals) mean that the geometric shape changes drastically, and sufficient mesh units are needed to accurately reproduce the detailed features. Low curvature regions (such as the smooth parts of the side of the basket, the base plane, and the large flat areas of the petals) have gentle geometric shapes. Excessive mesh units will cause data redundancy and increase the computational power consumption of subsequent processing.

[0130] The optimized triangular mesh model is exported in multiple formats. OBJ format retains complete geometric and texture information, STL format is used for 3D printing and engineering analysis, PLY format retains point cloud color information, and STEP / IGES format is compatible with CAD software import. Original accuracy is maintained during export without additional simplification. A unified naming convention can be adopted, such as the format: Project Name_Part Name_Version Number_Date. The original scan data and processed model are stored separately. At least three physical backups are saved (stored on different devices), with additional backups via cloud storage services. Scanning parameters, data processing flow, and algorithm usage instructions are recorded. A model index is created for easy retrieval later. For example, a three-level index can be established based on the flower basket area (top / middle / bottom) - flower type (peony / rose / chrysanthemum) - flower ID (F001-FXXX). Each index is associated with the flower coordinate table, orientation angle parameters, installation record table, and acceptance deviation values ​​for quick querying and traceability.

[0131] Based on the curvature analysis described above, this step employs a differentiated simplification strategy: more grid cells are retained in high-curvature areas to ensure that detailed features are not lost and the shape is not distorted; the number of grid cells is appropriately reduced in low-curvature areas to significantly reduce the amount of data without affecting the overall geometric shape restoration. For example, more than 90% of the geometric information is retained in the flower detail area, 80% of the geometric information is retained in the basket structure, and 60% of the geometric information is retained in the smooth area.

[0132] This adaptive processing method abandons the limitations of unified mesh simplification, ensuring the accuracy of key model details while achieving data lightweighting. This allows the optimized triangular mesh model to combine high-precision details with efficient processing performance, laying a better data foundation for subsequent digital modeling, scaling, and core parameter extraction based on this model, effectively improving the overall processing efficiency and final replication accuracy.

[0133] To reproduce the surface texture details of the flower basket sample (such as the basket's weave texture and the color and texture of the petals), further refined texture processing can be performed: 1. Texture acquisition: Capture high-resolution texture images using the scanner's built-in camera, ensuring uniform lighting in the shooting environment and avoiding interference from shadows and strong highlights; 2. Texture mapping: Accurately map the captured texture images onto the triangular mesh model, setting the texture resolution to 4K (4096×4096 pixels), and using a seamless stitching algorithm to process the texture boundaries, ensuring no stitching marks; 3. Texture optimization: Adjust the color balance and contrast of the texture, repair defects, scratches, and discontinuous areas in the texture, making the texture highly consistent with the geometric model and improving the visual fidelity of the digital model.

[0134] Based on the above introduction, this embodiment accurately removes noise points through a statistical outlier detection algorithm, avoiding the impact of interfering data on subsequent processing and ensuring data purity. The alignment and fusion of multiple sets of data solves the fragmentation problem of multi-view scanning, generating a coherent and unified single point cloud model, ensuring the spatial consistency of the data. Mesh generation, edge repair, and hole filling processes fill in the data structure defects, transforming the discrete point cloud into a complete and regular triangular mesh model. The mesh unit density is adjusted based on the regional curvature value. More meshes are retained in high curvature areas to accurately restore details, while low curvature smooth areas are appropriately simplified to reduce redundant data. This achieves a balance between not losing detail accuracy and reducing data volume. The final generated structured geometric data has high precision, high integrity, and high adaptability.

[0135] Example 6: The above embodiments do not limit the specific scanning process for 3D scanning of the flower basket sample. Traditional scanning methods involve scanning at a fixed distance. If the scanning distance is too far, it is difficult to capture detailed features such as the curvature of flower petals and the weaving texture of the basket. If the distance is too close, it is impossible to completely cover the overall outline of the flower basket sample, resulting in missing details or overall breaks in the scanned data. At the same time, small internal structures of the flower basket sample (such as flower stamens and internal connecting nodes of the basket) and occluded parts (such as overlapping areas of flowers and shadow areas under the basket) are easily missed due to the limited scanning angle, which ultimately affects the integrity and accuracy of the original 3D data, and thus restricts the effect of subsequent structured construction and digital modeling.

[0136] To address the issues of inconsistent overall scan data, inaccurate details, and incomplete coverage, and to achieve high-quality acquisition of raw 3D data, this embodiment proposes a scanning strategy that combines differentiated distance, multi-angle coverage, and targeted supplementation. This strategy balances the overall outline integrity of the flower basket sample with the accuracy of local details, ensuring that the scan data is complete and distortion-free.

[0137] Step 101: Perform a 3D scan of the flower basket sample. This can be done by following these steps: Step 11: At a distance of the first interval from the flower basket sample, perform a rotational scan of the overall outline of the flower basket sample.

[0138] Setting the first distance to a relatively large value (such as 80 cm) can cover the overall outline of the flower basket sample and avoid the limitation of the scanning range caused by the distance being too close.

[0139] By using rotational scanning (e.g., rotating 15° each time with an automatic turntable), overall data can be collected from 360° without blind spots, ensuring complete recording of macroscopic dimensions such as the basket's outline, overall height, and basket diameter. A minimum overlap rate of 30% between adjacent scanned areas is required to achieve precise stitching of multiple sets of scanned data through feature matching of overlapping areas, avoiding problems such as structural breaks and spatial misalignment. This ultimately generates coherent and complete overall outline data, providing a unified spatial reference for detailed and supplementary scans. For example, the automatic turntable can be controlled to rotate 15° each time, completing 24 scans in one revolution, with an overlap rate of ≥30% between adjacent scanned areas. During scanning, the scanner is kept perpendicular to the basket's surface, and point cloud generation in Artec Studio is monitored in real-time, allowing for immediate supplementation of any missed areas.

[0140] Step 12: At a second distance from the flower basket sample, perform a multi-angle detailed scan of each flower in the flower basket sample.

[0141] The second distance is smaller than the first distance (e.g., 40 cm). Shortening the scanning distance can improve local scanning accuracy and meet the needs of capturing details such as the curvature of flower petals and the shape of stamens. For each flower, an Artec Eva scanner (3D point accuracy 0.1 mm, resolution 0.2 mm, working distance 0.4-1 m, data capture speed 18 million points / second) can be used for multi-angle scanning. Each flower should be scanned from at least 3 different angles, focusing on capturing the curvature of petals and the structure of the receptacle, and recording the number and position information of each flower. The basket structure scanning distance is 50 cm. The scanning angle is maintained at 60-90° to the surface to ensure complete capture of the woven texture. The base scanning focuses on the connection between the base and the basket to ensure the flatness and symmetry data are complete. This effectively avoids details that may be missed due to the flower's own structure obscuring the view, and ensures that key features such as the number, size, and curvature of the petals are recorded completely. Through differentiated distance scanning, layered acquisition is achieved, combining macroscopic control of the overall outline with precise capture of flower details. This ensures the integrity of the overall data while highlighting the detail accuracy of the flower as the core positioning object, providing high-quality detailed data support for subsequent extraction of flower center coordinates and orientation angle parameters.

[0142] Step 13: Perform supplementary scanning of the internal fine structure and obscured areas of the flower basket sample.

[0143] The internal details of the flower basket sample (such as the stamens and connecting nodes inside the basket) and obscured areas (such as overlapping areas of flowers and shadowed areas below the basket) are easily missed during overall and detailed scanning due to the limited field of view. If supplementary scanning is not performed, structured data will be missing, affecting the accuracy of subsequent modeling. Supplementary scanning can be performed using high-precision industrial-grade scanners. The scanning angle can be adjusted for obscured areas. If necessary, the sample can be split and scanned in parts. After scanning, the parts can be stitched together using software. Highly reflective areas should be sprayed with matte spray to reduce the scanner sensitivity. Multiple scans should be performed and the average value should be taken. After scanning, obvious noise and abnormal data should be deleted, and multiple sets of scan data should be initially aligned.

[0144] This step further improves the integrity of the scanned data, avoiding morphological differences between the digital model and the physical sample due to missing local data, and ensuring the accuracy and reliability of the entire digital processing process from the source.

[0145] To enhance the artistic expression and structural strength of flowers, focal flowers (such as peonies and roses) can be replaced and calibrated using 3D printing: 1. Flower shape optimization and printing: Optimize the small flower model in MAYA software, increase the petal fold density by 20%, and optimize the topology of load-bearing parts such as the flower base (reducing weight by 15%); use photosensitive resin material for printing, with a layer thickness ≤0.5mm, and spray the surface with the original flower color; 2. Positioning and replacement: Fix the 3D printed flower to the original plastic flower position using clips, with a deviation tolerance of ≤1mm; 3. Secondary scanning: Use the Metra SCAN photogrammetry system to verify the spatial coordinates and posture of the printed flower and generate a correction offset table; 4. BIM data mapping: Import the optimized flower shape, position information, and hoisting path into the BIM system, automatically generate the layered hoisting sequence (such as prioritizing the installation of top flowers to avoid obstruction), and guide on-site construction. 5. Accuracy Verification: Key dimensions are measured using digital calipers and compared with the 3D model; the error is ≤ ±0.2mm. The 3D printed model is compared with the original sample, and a deviation heatmap is generated using 3D comparison software. The model is checked for defects, non-manifold edges, and all flowers and key structures are completely captured. This optimized process can improve the flower pattern matching accuracy to ≥95%, control the positioning error within ±1mm, and shorten the design iteration cycle from 2-3 weeks to 3-5 days, effectively solving the problems of large differences between prototypes and finished products, insufficient accuracy of manual replacement, and low efficiency in traditional solutions.

[0146] Based on the above description, this embodiment uses a first distance (farther value) for overall rotational scanning while ensuring an overlap rate of no less than the overlap threshold. This not only fully captures the global spatial shape and macroscopic dimensions of the flower basket sample but also achieves seamless stitching of multiple sets of scan data through feature matching of overlapping areas, avoiding overall structural breaks or spatial misalignments and ensuring data continuity. A second distance (closer value) is used for multi-angle detailed scanning of each flower, adapting to the need to capture microscopic features such as petal curvature and stamen morphology, significantly improving the detail replication accuracy of the core positioning object and providing accurate data support for subsequent extraction of flower center coordinates and orientation angle measurement. Supplementary scanning of internal small structures and occluded parts effectively avoids the blind spots of conventional scanning, fills data gaps in easily missed areas such as stamens, internal connection nodes of the basket, and overlapping areas of flowers, and ensures that the scan data covers all key structures of the flower basket sample. The three processes work in a progressive and complementary manner, resulting in raw 3D data that possesses a complete global form, contains precise details, and has no obvious data blind spots. This provides high-quality data support for subsequent stages such as structured geometric data construction, digital modeling, and scaling, laying a solid foundation for precision control throughout the entire process.

[0147] Example 7: This embodiment relates to a 3D positioning and installation device for a central flower basket in a plaza. A schematic diagram of the 3D positioning and installation device for the central flower basket in this embodiment is shown below. Figure 5 As shown, it includes: a scanning unit 201, a digital modeling unit 202, a model scaling unit 203, and a parameter extraction unit 204.

[0148] The scanning unit 201 is used to perform 3D scanning on the flower basket sample to obtain the 3D raw data generated by the scanning.

[0149] The scanning unit employs a multi-level scanning device combination, including the Artec Eva handheld 3D scanner (3D point accuracy 0.1mm), a large depth-of-field scanner (measuring range 70-100cm), and a high-precision industrial-grade scanner (accuracy 0.03mm), paired with an automatic rotating stage and a high-performance computer (configured with at least an i7 processor, 64GB of memory, and a professional graphics card) to ensure high-precision acquisition of scanned data. The specific working process of one scanning unit is as follows: Before scanning, the indoor temperature is controlled at 20-25℃ and the humidity at 40-60%; the flower basket sample is placed in the center of the automatic rotating stage, and the large depth-of-field scanner is used to perform a full scan from 80cm. The rotating stage rotates 15° each time, completing a full rotation of 24 positions. The overlap rate between adjacent scanned areas is no less than 30%, ensuring continuous data stitching. The scanned area is at least 4m × 4m free of obstructions.

[0150] The digital modeling unit 202 is used to construct structured geometric data from the original 3D data and perform digital modeling based on the generated structured data to generate a sample digital model.

[0151] The model scaling unit 203 is used to enlarge the sample digital model by a preset ratio to obtain the enlarged digital model.

[0152] The parameter extraction unit 204 is used to extract the spatial coordinates and orientation angle parameters of the flower center of each flower from the magnified digital model, and to position and install the flower according to the spatial coordinates and orientation angle parameters of the flower center.

[0153] Furthermore, a data management and information system can be built into the 3D positioning and installation device of the flower basket in the center of the square. This includes establishing a centralized database as the central database, storing design data (CAD models, coordinate data), measurement data (control network data, positioning data), construction data (installation records, quality inspection results), and management data (progress plans, personnel and equipment information) in a categorized manner. Data access control will be implemented, with regular backups of three physical devices plus a cloud backup, and data transmission will be encrypted. A real-time monitoring system will be configured to display installation progress and equipment operating status, with automatic alarms for abnormal situations. A collaborative work platform will be built to enable team task allocation, information sharing, problem recording, and solution management. A dedicated mobile app will be developed to support on-site data collection and uploading, installation guidance, and offline working modes.

[0154] The 3D positioning and installation device for the flower basket in the center of the square can also be equipped with a risk management system, which mainly includes the following functional modules: risk identification, used to cover technical risks (insufficient equipment accuracy, data loss), environmental risks (severe weather, electromagnetic interference), and management risks (progress delays, poor communication); emergency plans, used for rapid replacement of equipment failures, suspension of construction / equipment protection in severe weather, and recovery of data loss through backup; and continuous improvement, used for daily summarization of construction problems, optimization of processes and parameters, and the formation of a closed-loop improvement mechanism.

[0155] In the 3D positioning and installation device for the flower basket in the center of the square provided in this embodiment, the scanning unit focuses on the 3D scanning of the flower basket sample, which can comprehensively capture the 3D original data of the overall outline, flower details, internal fine structures and obstructed parts, providing a complete and high-quality data source for subsequent processing; the digital modeling unit transforms the original data into a digital model that accurately replicates the geometric shape and feature parameters of the sample through structured geometric data construction and digital modeling, which not only removes data noise and fills in structural defects, but also ensures that the model details are traceable and quantifiable through geometric unit decomposition and feature parameter definition, providing a high-precision digital carrier for enlargement and installation; when the model scaling unit enlarges the model according to a preset ratio, it relies on a unified coordinate system and nonlinear correction logic to ensure the accurate transmission of the overall ratio, while avoiding problems such as material deformation and insufficient structural stability, so that the enlarged digital model has both proportional consistency and engineering practicality; the parameter extraction unit accurately extracts the spatial coordinates and orientation angle parameters of the flower center of each flower, directly providing a clear and unique positioning benchmark for on-site installation, replacing the subjective judgment and experience reliance of traditional manual measurement.

[0156] This device automates the entire process from data acquisition to installation, ensuring high consistency between the actual flower basket and the sample in terms of shape, proportion, flower position, and orientation. It also controls the positioning accuracy within 5 centimeters, significantly improving construction efficiency and completely solving the core pain points of traditional methods, such as low accuracy, poor efficiency, and insufficient consistency. It provides reliable equipment support for the high-precision implementation of large-scale landscape decoration projects.

[0157] Additionally, it should be noted that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units.

[0158] Furthermore, in order to highlight the innovative aspects of this application, no units that are not closely related to solving the technical problems proposed in this application are introduced in this embodiment, but this does not mean that there are no other units in this embodiment.

[0159] Example 8: Another embodiment of this application relates to an electronic device, such as... Figure 6 As shown, it includes: at least one processor 301; and a memory 302 communicatively connected to at least one processor 301; wherein the memory 302 stores instructions executable by at least one processor 301, the instructions being executed by at least one processor 301 to enable at least one processor 301 to perform the steps of the 3D positioning and installation method for the central flower basket in the above embodiments.

[0160] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0161] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0162] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A 3D positioning and installation method for a flower basket in the center of a square, characterized in that, include: Perform a 3D scan on the flower basket sample to obtain the raw 3D data generated by the scan; The original 3D data is used to construct structured geometric data, and digital modeling is performed based on the generated structured data to generate a sample digital model; The small digital model is enlarged by a preset ratio to obtain the enlarged digital model; The spatial coordinates of the flower center and the orientation angle parameters of each flower are extracted from the magnified digital model, and the flowers are positioned and installed according to the spatial coordinates of the flower center and the orientation angle parameters.

2. The 3D positioning and installation method for the flower basket in the center of the square according to claim 1, characterized in that, Enlarging the small digital model by a preset ratio includes: Read the basic structural data of the sample digital model, and based on the basic structural data, establish the Z-axis upward along the central axis of the flower basket with the center point of the basket as the origin, establish the X-axis horizontally pointing to the front of the flower basket, and establish the Y-axis according to the right-hand coordinate system rule to generate the sample coordinate system; With the center point of the actual flower basket tray as the origin, and the coordinate axis direction consistent with the small sample coordinate system, establish the actual size coordinate system; Record the reference alignment parameters between the sample coordinate system and the actual size coordinate system; Extract all geometric elements and feature parameters from the sample digital model to generate a structured data list; Based on the structured data list, the orientation angle parameter of each flower in the sample digital model is locked, and the spatial coordinates of each geometric element are linearly enlarged according to the preset multiple based on the reference alignment parameter, and the feature parameters are enlarged according to the preset multiple to generate the enlarged digital model.

3. The 3D positioning and installation method for the flower basket in the center of the square according to claim 2, characterized in that, Following the generation of the magnified digital model, the process also includes: For the easily deformable target areas in the magnified digital model, coordinate correction is performed based on the material deformation compensation coefficient; Structural parameters of the target object with slender structure are extracted from the magnified digital model, and the structural parameters are adjusted according to the structural stability coefficient to improve the structural load-bearing capacity of the target object. The coordinates of the positioning core parts and structural connection parts in the magnified digital model are corrected according to the geometric error correction coefficient.

4. The 3D positioning and installation method for the flower basket in the center of the square according to claim 1, characterized in that, The spatial coordinates and orientation angle parameters of the flower center of each flower are extracted from the magnified digital model, including: Extract the disk structure corresponding to each flower from the magnified digital model; Measure the spatial coordinates of three non-collinear points on the disk structure; Based on the spatial coordinates of the three non-collinear points, the system of equations constructed according to the constraints is solved to obtain the coordinates of the target point; the constraints include: the three points are coplanar and the three points are equidistant from the target point; Extract the spatial coordinates of the uppermost and lowermost points of the disk structure, and calculate the orientation angle using the coordinate difference; The orientation angle is converted into orientation angle parameters that include pitch angle, azimuth angle, and rotation angle.

5. The 3D positioning and installation method for the flower basket in the center of the square according to claim 1, characterized in that, The digital modeling based on the generated structured data includes: The generated structured data is decomposed into several geometric units based on geometric elements; the geometric units include: flowers and baskets; The feature parameters of each geometric unit are defined according to its geometric shape and functional attributes; wherein, the feature parameters of the flower include: number of petals, size, and curvature; the feature parameters of the basket include: weaving density and texture features; Establish the mapping relationship between each of the feature parameters and the corresponding geometric unit, as well as the constraint relationship between components, to generate the sample digital model.

6. The 3D positioning and installation method for the flower basket in the center of the square according to claim 1, characterized in that, The process of constructing structured geometric data from the raw 3D data includes: Based on the statistical outlier detection algorithm, noise data in the original 3D data is identified and removed to obtain denoised 3D data. The denoised 3D data is then aligned and fused with multiple sets of data to generate a single point cloud model. The single-point cloud model is subjected to mesh generation, edge repair, and hole filling to generate the triangular mesh model of the flower basket sample.

7. The 3D positioning and installation method for the flower basket in the center of the square according to claim 6, characterized in that, After generating the triangular mesh model of the flower basket sample, the following steps are also included: Based on the curvature values ​​of each region in the triangular mesh model, the mesh structure of different curvature regions is adaptively simplified; wherein, the high curvature region retains more mesh cells than the low curvature region.

8. The 3D positioning and installation method for the flower basket in the center of the square according to claim 1, characterized in that, The flower basket sample was 3D scanned, including: At a first distance from the flower basket sample, the overall outline of the flower basket sample is rotated and scanned; wherein the overlap rate of adjacent scan areas of the rotational scan is not less than the overlap threshold. At a second distance from the flower basket sample, a multi-angle detailed scan is performed on each flower in the flower basket sample; the first distance is greater than the second distance. Supplementary scanning was performed on the internal fine structure and obstructed areas of the flower basket sample.

9. A 3D positioning and installation device for a flower basket in the center of a square, characterized in that, include: The scanning unit is used to perform 3D scanning on the flower basket sample to obtain the raw 3D data generated by the scan. The digital modeling unit is used to construct structured geometric data from the original 3D data and perform digital modeling based on the generated structured data to generate a sample digital model. The model scaling unit is used to enlarge the small digital model by a preset ratio to obtain the enlarged digital model. The parameter extraction unit is used to extract the spatial coordinates of the flower center and the orientation angle parameters of each flower from the magnified digital model, and to position and install the flower according to the spatial coordinates of the flower center and the orientation angle parameters.

10. An electronic device, characterized in that, include: At least one processor; And, a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the 3D positioning and installation method for the central flower basket of the plaza as described in any one of claims 1 to 8.

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