Intelligent measurement method and device based on urban planning and architectural design

Abstract

The application relates to the technical field of measurement, in particular to an intelligent measurement method and device based on urban planning and architectural design. The method comprises the following steps: setting a flight route of a flying device, a target scanning area and a scanning position. Then, according to the measurement characteristics of a target measurement object in the target scanning area, a target wave band is selected from preset wave bands as a scanning wave band of a sub-area; the sub-area in the scanning range of the flying device at each scanning position is grouped, the sub-area is scanned according to the corresponding scanning wave band of each group, and point cloud data is acquired; the actual point cloud quantity in each grid space after fusion is compared with a standard point cloud quantity, the flight route of the flying device and the actual point cloud data are adjusted according to the comparison result; and a three-dimensional model is constructed according to the adjusted point cloud data and corresponding image data. The application solves the problem of low measurement precision and realizes efficient and accurate measurement of a target measurement area.

Description

Technical Field

[0001] This application relates to the field of measurement technology, and in particular to intelligent measurement methods and devices based on urban planning and architectural design. Background Technology

[0002] With the acceleration of urbanization and the increasing complexity of architectural design, the demand for accurate and efficient measurement technology is becoming increasingly urgent. Traditional measurement methods mostly rely on manual on-site measurement or data collection using single measuring devices. These methods are not only inefficient, but also often fail to obtain comprehensive and accurate data when facing complex urban environments and large buildings. A similar prior art is Chinese Patent Publication No. CN111750830B, which proposes a land parcel mapping method and system, including: a server acquiring images of the land parcel to be analyzed taken by a drone, along with the spatial location and status information of the drone at the time of image capture; the server processing the images based on the spatial location and status information to generate terrain data for the land parcel to be analyzed; upon receiving a data acquisition request from a client, the server obtaining the target terrain data and converting it into a digital terrain model, returning it to the client; and the client generating a mapping map of the land parcel or target area based on the digital terrain model. Compared with existing technologies, this method can accurately and quickly complete land parcel mapping. Furthermore, a similar prior art exists in Chinese Patent Publication No. CN113358093A, which discloses a measurement method for topographic surveying, including: placing a theodolite at a survey station, centering and leveling it, measuring the height of the theodolite, and using a control point near the backsight as the starting direction; placing a small plane table at any position near the survey station, fixing the mapping board, inserting a needle at the survey station position on the map, and fitting the protractor's central hole onto the needle to draw a direction line from the survey station to the backsight point; using the theodolite to observe the horizontal angle, vertical angle, and stadia distance of detail points; this measurement method can accurately measure the terrain of both large and small areas, is simple, and yields highly accurate results. While both patents address the measurement of buildings or terrain, they do not consider the potential for uniform laser variations during laser scanning, which can cause ranging errors and reduce the accuracy of point cloud data. Additionally, in terms of data processing, it is difficult to ensure the uniformity and density of point cloud data during data acquisition. These factors are key constraints on the further development of measurement technology. Summary of the Invention

[0003] This application provides an intelligent measurement method and system based on urban planning and architectural design. It acquires point cloud data by obtaining each measurement target within the target measurement range, setting the flight path and scanning position according to the scanning range of an aircraft, and utilizing a laser emission module onboard the aircraft. This achieves efficient and accurate measurement of the target area. The method includes:

[0004] Based on the scanning range of each target object in the target measurement area and each of the multiple aircraft, a corresponding flight path, target scanning area and scanning position are set, wherein the aircraft is equipped with a laser measurement module;

[0005] Based on the measurement characteristics of the target object in the target scanning area, the target scanning area is divided into multiple sub-regions, and a target band is selected from preset bands as the scanning band of the sub-region;

[0006] The sub-regions within the scanning range of each scanning position of the aircraft are grouped, and the sub-regions are scanned according to the scanning band corresponding to each group to obtain the corresponding point cloud data.

[0007] The point cloud data acquired by each aircraft is mapped to a three-dimensional space and fused in real time. The actual number of point clouds in each grid space in the three-dimensional space is compared with the standard number of point clouds to obtain the comparison results. The flight path and actual point cloud data of the aircraft are adjusted according to the comparison results and point cloud density.

[0008] A 3D model is constructed based on the point cloud data of the adjusted grid space and the corresponding 3D image.

[0009] As a preferred embodiment of the present invention, selecting a target band from a preset band based on the measurement characteristics includes:

[0010] Based on the measurement characteristics of the target object in the target scanning area, the target scanning area is divided into multiple sub-regions. The measurement characteristics include vegetation cover, reflectivity, and measurement accuracy requirements. The optimal wavelength corresponding to each sub-region is obtained based on the measurement characteristics, and the target band is selected based on the optimal wavelength. The target band includes the optimal wavelength.

[0011] As a preferred embodiment of the present invention, the acquisition of point cloud data for each group corresponding to a sub-region includes:

[0012] The aircraft flies along the flight path, acquires the scanning range of the target area corresponding to each scanning position, groups the sub-regions according to the scanning band corresponding to each sub-region within the scanning range, and uses each independent sub-region or multiple connected sub-regions in the group as a scanning unit. When scanning the scanning unit in each group, the reflector current of the measurement module is adjusted according to the scanning band corresponding to each group so that the output beam of the laser measurement module is coarsely tuned to the scanning band. Then, the phase bias current is adjusted according to the scanning sequence to generate a phase-continuous scanning wave. The scanning wave is used to scan each scanning unit to acquire the corresponding point cloud data. The point cloud data of each scanning unit in the group is acquired by adjusting the emission angle of the measurement module.

[0013] As a preferred embodiment of the present invention, the calculation of the standard point cloud quantity includes:

[0014] The target measurement object is divided into grids, and the size data of the target measurement object is obtained based on the fused point cloud data. The structural features, size data, preset accuracy and corresponding three-dimensional image of the target measurement object for each grid space are input into the estimation model to obtain the number of standard point clouds corresponding to each grid space.

[0015] As a preferred embodiment of the present invention, adjusting the actual point cloud data based on the comparison result includes:

[0016] When the comparison result is that the difference between the two is less than or equal to the set difference and the difference between the point cloud density and the average density in any subspace of the grid space is less than the set density, the actual number of point cloud data in the grid space does not need to be adjusted.

[0017] When the comparison result shows that the difference between the two is greater than the set difference, the point cloud density in each subspace is compared with the average density, and a first subspace with a point cloud density greater than the average density is obtained. In the first subspace, the distance between other point clouds except for the key point cloud is calculated, and the point cloud data between point cloud pairs with a distance less than or equal to the set distance is deleted. This step is repeated until the difference between the point cloud density of the first subspace and the average density is less than or equal to the set density.

[0018] As a preferred embodiment of the present invention, adjusting the flight path of the aircraft based on the comparison results includes:

[0019] A second subspace with a point cloud density less than the average density is obtained in the grid space. When the point cloud density is greater than a preset density, point cloud pairs with an Euclidean distance greater than a set distance are interpolated using a linear interpolation algorithm based on the attributes and coordinates of the point cloud pairs. When the point cloud density in the second subspace is less than the preset density, the aircraft closest to the second subspace is taken as the target aircraft. Point cloud data in the second subspace is re-acquired through the target aircraft, and the method of adjusting the point cloud data in the grid space is repeated until the difference between the point cloud density and the average density in the second subspace is less than or equal to the set density.

[0020] As a preferred embodiment of the present invention, the construction of the three-dimensional model includes:

[0021] The point cloud data of the adjusted grid space is preprocessed, and combined with the three-dimensional image data corresponding to the grid space, a three-dimensional model of the target measurement area is constructed using a surface reconstruction algorithm and a voxelization algorithm based on point cloud data.

[0022] As a preferred technical solution of the present invention, the measurement features include at least vegetation coverage, reflectivity, and measurement accuracy requirements.

[0023] The present invention also provides an intelligent measurement device based on urban planning and architectural design for implementing the above-mentioned method, the device comprising:

[0024] The setting unit is used to set the flight path, target scanning area and scanning position of each aircraft according to the scanning area of ​​each target object in the target measurement area and the scanning range of each aircraft among the multiple aircraft.

[0025] The selection unit is used to divide the target scanning area into multiple sub-regions according to the measurement characteristics of the target measurement object in the target scanning area, and select the target band from the preset bands as the scanning band of the sub-region;

[0026] The scanning unit is used to group the sub-regions within the scanning range of each scanning position of the aircraft, and scan the sub-regions according to the scanning band corresponding to each group to obtain the corresponding point cloud data.

[0027] The adjustment unit is used to map the point cloud data acquired by each of the aircraft to a three-dimensional space for real-time fusion, compare the actual number of point clouds in each unit space after fusion with the standard number of point clouds, obtain the comparison result, and adjust the flight path and actual point cloud data of the aircraft according to the comparison result.

[0028] The building unit is used to construct a 3D model based on the adjusted point cloud data and the corresponding image data.

[0029] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0030] Effect

[0031] This invention achieves efficient and accurate measurement of the target area by acquiring each target object within the target measurement range, setting the flight path and scanning position according to the scanning range of the aircraft, and using the laser emission module onboard the aircraft to acquire point cloud data. By selecting the optimal wavelength for scanning based on measurement characteristics, the accuracy and efficiency of laser scanning are effectively improved, and measurement errors caused by laser wavelength jumps are reduced, ensuring the accuracy of the point cloud data. Simultaneously, by mapping the point cloud data to three-dimensional space for real-time fusion and comparing it with the standard point cloud data, the flight path of the aircraft and the actual point cloud data are adjusted based on the comparison results, further optimizing the quality of the measurement data. Furthermore, by preprocessing and three-dimensional modeling the adjusted point cloud data, a high-quality three-dimensional model is generated, providing intuitive and accurate model support for urban planning and architectural design. This method and device not only improve measurement efficiency but also enhance the reliability and accuracy of measurement data, effectively meeting the requirements of urban planning and architectural design for data accuracy and completeness. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the intelligent measurement method based on urban planning and architectural design in the embodiment.

[0034] Figure 2 This is a flowchart illustrating the method for acquiring point cloud data for each sub-region corresponding to each group in this embodiment.

[0035] Figure 3 This is a flowchart illustrating the method for adjusting actual point cloud data based on comparison results in this embodiment.

[0036] Figure 4 This is a structural diagram of the intelligent measurement device based on urban planning and architectural design in the embodiment. Detailed Implementation

[0037] This application provides an intelligent measurement method and apparatus based on urban planning and architectural design. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0038] For ease of understanding, the specific process of the embodiments of this application is described below, such as... Figure 1 As shown, one embodiment of the intelligent measurement method based on urban planning and architectural design in this application includes:

[0039] Step S1: Based on the scanning range of each target object in the target measurement area and each of the multiple aircraft, set the corresponding flight path, target scanning area and scanning position, wherein the aircraft is equipped with a laser measurement module;

[0040] Specifically, firstly, the terrain, landforms, and distribution of measurement targets in the target measurement area are analyzed to determine the scanning area for each target object. These target objects include at least buildings, terrain, and landforms. Simultaneously, considering the aircraft's performance parameters, such as flight speed and scanning range, a flight path is planned that covers the entire target measurement area and can be effectively executed by the aircraft. Furthermore, the target scanning areas of adjacent aircraft overlap to facilitate subsequent point cloud fusion. The aircraft's scanning range is the maximum area of ​​point cloud data it can acquire at a given location by adjusting its flight attitude and scanning angle. The scanning position is also set based on the corresponding target scanning area and scanning range. This technical solution, by rationally setting the flight path, target scanning area, and scanning position, ensures that the aircraft efficiently covers the entire target measurement area, avoids missing important measurement areas, and improves the comprehensiveness and accuracy of the measurement. At the same time, optimizing the flight path can reduce the aircraft's flight time and energy consumption, improving measurement efficiency.

[0041] Step S2: Based on the measurement characteristics of the target object in the target scanning area, divide the target scanning area into multiple sub-regions, and select the target band from the preset bands as the scanning band of the sub-region;

[0042] Specifically, the measurement characteristics of the target object are analyzed, such as vegetation cover, reflectivity, and measurement accuracy requirements. Different measurement characteristics are suitable for different laser wavelengths. For example, a laser with a wavelength of 1555nm can penetrate light vegetation cover and has good reflectivity to building walls. Based on these measurement characteristics, the target scanning area is divided into multiple sub-regions, and the optimal scanning wavelength is selected for each sub-region. This technical solution improves the accuracy and efficiency of laser scanning by selecting the optimal scanning wavelength based on the measurement characteristics, ensuring that the acquired point cloud data accurately reflects the characteristics of the target object. At the same time, selecting appropriate wavelengths for different sub-regions can reduce phase discontinuities caused by sudden laser wavelength jumps and improve measurement reliability.

[0043] Step S3: Divide the sub-regions within the scanning range of each scanning position of the aircraft into groups, and scan the sub-regions according to the scanning band corresponding to each group to obtain the corresponding point cloud data;

[0044] Specifically, based on the scanning range of the aircraft at each scanning position, the sub-regions are grouped according to the scanning bands. When scanning the scanning unit in each group, the current control unit first outputs the mirror current corresponding to the scanning band, so that the output beam of the laser measurement module is coarsely adjusted to the specified scanning band. Then, the phase bias current is adjusted according to the scanning sequence to generate a phase-continuous scanning wave in sequence, ensuring that the laser wavelength transitions smoothly during the scanning process and avoiding phase discontinuities and measurement errors caused by sudden wavelength changes. The above technical solution can reduce the number of scanning band switching and improve scanning efficiency by using grouped scanning. At the same time, by precisely controlling the mirror current and phase bias current, the phase continuity of the scanning wave is ensured, measurement errors are avoided, and the accuracy of point cloud data acquisition is improved.

[0045] Step S4: Map the point cloud data acquired by each aircraft to a three-dimensional space for real-time fusion, compare the actual number of point clouds in each grid space in the three-dimensional space with the standard number of point clouds, obtain the comparison result, and adjust the flight path and actual point cloud data of the aircraft according to the comparison result and point cloud density.

[0046] Specifically, point cloud data acquired by different aircraft undergoes coordinate transformation and is fused into a unified three-dimensional coordinate system. Then, based on the fused point cloud data, the actual number of points in each grid space is calculated and compared with the standard point cloud number. The standard point cloud number is the optimal number calculated based on the structural characteristics, dimensional data, and preset accuracy of the target object into an estimation model. By fusing point cloud data acquired by different aircraft in real time, more comprehensive and accurate three-dimensional information of the target measurement area can be obtained. By comparing the actual point cloud number with the standard point cloud number and adjusting the aircraft's flight path and the actual point cloud data based on the comparison results, this technical solution ensures that the acquired point cloud data meets the preset accuracy and density requirements, improving the reliability and effectiveness of the measurement.

[0047] Step S5: Construct a 3D model based on the point cloud data of the adjusted grid space and the corresponding 3D image.

[0048] Specifically, the adjusted point cloud data is preprocessed, including noise removal and filtering, to improve data quality. Then, combined with 3D image data, 3D modeling algorithms, such as point cloud-based surface reconstruction and voxelization algorithms, are used to construct a 3D model of the target measurement area. This technical solution, by constructing a 3D model, can intuitively display the geospatial information of the target measurement area, providing accurate model support for urban planning and architectural design. A high-quality 3D model helps to better understand and analyze the characteristics of the target scanning area, improving the scientificity and rationality of urban planning and architectural design.

[0049] Further, selecting a target band from the preset bands based on the measurement characteristics includes:

[0050] Based on the measurement characteristics of the target object in the target scanning area, the target scanning area is divided into multiple sub-regions. The measurement characteristics include vegetation cover, reflectivity, and measurement accuracy requirements. The optimal wavelength corresponding to each sub-region is obtained based on the measurement characteristics. The target band is selected from a preset band based on the optimal wavelength. The target band includes the optimal wavelength.

[0051] Specifically, since the measurement characteristics of the target objects in the target scanning area are different, for example, if the target object is a building and is partially obscured by vegetation, a laser with a wavelength of 1555nm can penetrate light vegetation cover and has good reflection characteristics on the building's walls. Therefore, the vegetation-obscured area can be divided into a sub-region. Furthermore, based on the reflection characteristics of the doors, windows, or glass materials on the exterior of the building, the reflection characteristics of the corresponding area are selected, and the wavelength with the best reflection characteristics is chosen. The selected wavelength is taken as the optimal wavelength, and the preset band containing the optimal wavelength is obtained. This preset band is then taken as the target band. For example, the wavelength range of the target band is 1554.5nm-1555.5nm. The wavelength difference between the minimum and maximum wavelengths in the target band is less than or equal to a set threshold of 1nm. Through the above technical solution, an optimal wavelength can be assigned to each sub-region in the target scanning area corresponding to each aircraft, laying the foundation for obtaining high-precision point cloud data.

[0052] Furthermore, the acquisition of point cloud data for each sub-region corresponding to each group, such as... Figure 2 As shown, it includes:

[0053] The aircraft flies along the flight path, acquires the scanning range of the target scanning area corresponding to each scanning position, groups the sub-regions according to the scanning band corresponding to each sub-region within the scanning range, and uses each independent sub-region or multiple connected sub-regions in the group as a scanning unit. When scanning the scanning unit in each group, the reflector current of the measurement module is adjusted according to the scanning band corresponding to each group so that the output beam of the laser measurement module is coarsely tuned to the scanning band. Then, the phase bias current is adjusted according to the scanning sequence to generate a phase-continuous scanning wave. The scanning wave is used to scan each scanning unit to acquire the corresponding point cloud data. The point cloud data of each scanning unit in the group is acquired by adjusting the emission angle of the measurement module.

[0054] Specifically, the aircraft flies along the flight path and scans the sub-regions within the scanning range at each scanning position along the flight path. Since different sub-regions correspond to different scanning bands, when scanning the sub-regions in sequence, especially at the boundary between two adjacent sub-regions with different scanning bands, it is necessary to adjust the scanning bands using the mirror current and the phase bias current. This can cause sudden wavelength jumps, leading to phase discontinuities. These jumps can affect the phase relationship between the transmitted and transmitted signals of the laser beam (i.e., the frequency-modulated continuous wave) during ranging, resulting in ranging errors. To prevent this problem, the sub-regions within the scanning range are grouped according to their corresponding scanning bands. Each group contains sub-regions with the same corresponding scanning band. Connected and independent sub-regions within each group are used as scanning units. Independent sub-regions are those not connected to the laser beam. The scanning units within each group are then scanned... During scanning, the current control unit first outputs the current of the reflector corresponding to the scanning band to acquire the first laser wave. The phase bias current, controlled by the scanning timing, can precisely change the wavelength of the first laser wave to acquire the second laser wave. This allows for a smooth transition of the laser wavelength within the scanning band, avoiding phase discontinuities and measurement errors caused by abrupt wavelength changes. The scanning timing refers to the scanning time and wavelength change sequence for each wavelength, and the scanning time is the duration for acquiring each point cloud data. Based on the second laser wave (i.e., the scanning wave), each scanning unit in the group is scanned to acquire the point cloud data corresponding to each scanning unit. Similarly, point cloud data corresponding to scanning units in other groups are acquired. This technical solution reduces the switching of scanning bands through group scanning and fine-tunes the wavelength of the first laser wave using the phase bias current, ensuring continuous phase of the scanning wave without abrupt changes when acquiring point cloud data via laser scanning. This improves the accuracy of point cloud data acquisition and lays the foundation for improving measurement accuracy.

[0055] Furthermore, the calculation of the standard point cloud quantity includes:

[0056] The target measurement object is divided into grids, and the size data of the target measurement object is obtained based on the fused point cloud data. The structural features, size data, preset accuracy and corresponding three-dimensional image of the target measurement object for each grid space are input into the estimation model to obtain the number of standard point clouds corresponding to each grid space.

[0057] Specifically, by dividing the target object into grids, when the aircraft acquires point cloud data of the target object, it can also acquire three-dimensional image data of the target object. By analyzing the three-dimensional image data corresponding to each grid of the target object, the structural features of the target object within the grid space can be obtained. The structural features include structural complexity and surface texture features. The higher the structural complexity, the greater the number of standard point clouds; the richer the texture features, the greater the number of standard point clouds, and vice versa. The structural features, size data, three-dimensional image, and preset accuracy corresponding to each grid are input into the estimation model. The above-mentioned standard point cloud quantity is the optimal number of point clouds that ensures the three-dimensional model corresponding to the above-mentioned grid unit can achieve the above-mentioned preset accuracy. The above-mentioned preset accuracy is the accuracy of the three-dimensional model corresponding to the target measurement area set by the user according to actual needs. The above-mentioned estimation model is a machine learning model trained with historical data. The above-mentioned historical data includes the structural features, size, three-dimensional image, different accuracies, and the optimal number of point clouds under different accuracies of the sample measurement object. Through the above technical solution, the standard point cloud quantity that meets the above-mentioned preset accuracy for each unit space can be accurately calculated, laying the foundation for obtaining a high-precision and uniform three-dimensional model of the target measurement area.

[0058] Furthermore, the actual point cloud data is adjusted based on the comparison results, such as... Figure 3 As shown, it includes:

[0059] When the comparison result is that the difference between the two is less than or equal to the set difference and the difference between the point cloud density and the average density in any subspace of the grid space is less than the set density, the actual number of point cloud data in the grid space does not need to be adjusted.

[0060] When the comparison result shows that the difference between the two is greater than the set difference, the point cloud density in each subspace is compared with the average density, and a first subspace with a point cloud density greater than the average density is obtained. In the first subspace, the distance between other point clouds except for the key point cloud is calculated, and the point cloud data between point cloud pairs with a distance less than or equal to the set distance is deleted. This step is repeated until the difference between the point cloud density of the first subspace and the average density is less than or equal to the set density.

[0061] Specifically, the actual point cloud quantity in each of the aforementioned grid spaces is compared with the standard point cloud quantity. If the difference between the two is less than a predetermined difference, indicating that although the quantities are similar, the actual point cloud data may not be uniformly distributed, with some spatial regions having missing or excessive point cloud data, resulting in insufficient accuracy and uniformity of the formed 3D model, and the difference between the point cloud density of any subspace in the aforementioned grid space and the aforementioned average density is less than the predetermined density, then the actual point cloud data is considered uniformly distributed. In this case, the aforementioned grid space... The actual point cloud data and the number of actual point cloud data mentioned above do not need to be adjusted. The average point cloud density is the ratio of the standard point cloud quantity to the corresponding grid space volume. When the difference between the two is greater than the set difference, it indicates that there are subspaces in the grid space with point cloud densities greater than or less than the average density. For the first subspace with a point cloud density greater than the average density, especially in areas with overlapping point cloud data acquired by adjacent aircraft, the distances between other point clouds (excluding the key point clouds) are calculated. The key point clouds are used for target measurement. Points that significantly influence the shape, features, or structure of an object are considered. For example, point cloud data can represent the curvature, density, and location of the edge of the target object. Points with greater curvature are typically located at the object's edges or corners and are more important for describing the shape. By retaining these key point cloud data, the overall characteristics and detailed information of the point cloud can be preserved as much as possible while reducing the number of point cloud data. Point cloud data between point cloud pairs whose distance is less than or equal to a set distance (twice the average distance), where the average distance is the number of standard point cloud data points evenly distributed in the grid space, is considered to be the number of point cloud data points. Based on the average distance between them, which is the Euclidean distance, and repeating this step, the point cloud density in the first subspace is made close to the average density. Through the above technical solution, redundant and non-critical point cloud data can be removed, and more representative and critical point cloud data can be retained. This makes the point cloud data more evenly distributed in the grid space, avoiding the situation where the point cloud density in local areas is too high. The uniform point cloud distribution helps to improve the quality of 3D model construction, making the model surface smoother and more complete, and the details richer and clearer, thereby better meeting the requirements of urban planning and architectural design for data accuracy and integrity.

[0062] The aforementioned grid space is divided into multiple subspaces, wherein the set density is less than the average density.

[0063] Furthermore, the flight path of the aircraft is adjusted based on the comparison results, including:

[0064] A second subspace with a point cloud density less than the average density is obtained in the grid space. When the point cloud density is greater than a preset density, point cloud pairs with an Euclidean distance greater than a set distance are interpolated using a linear interpolation algorithm based on the attributes and coordinates of the point cloud pairs. When the point cloud density in the second subspace is less than the preset density, the aircraft closest to the second subspace is taken as the target aircraft. Point cloud data in the second subspace is re-acquired through the target aircraft, and the method of adjusting the point cloud data in the grid space is repeated until the difference between the point cloud density and the average density in the second subspace is less than or equal to the set density.

[0065] Specifically, when the difference between the two results is greater than the set difference, the second subspace in the grid space where the point cloud density is less than the average density is obtained. When the point cloud density is greater than the preset density (i.e., the difference between the point cloud density and the average density is not particularly large), and the preset density is less than the average density, and the Euclidean distance is greater than the set distance, new point cloud data is generated using a linear interpolation algorithm to fill the data gaps in areas with low point cloud density. This makes the point cloud data in the entire grid space more complete, avoiding model reconstruction errors caused by missing data. When the point cloud density is less than the preset density, it indicates a large difference between the point cloud density and the average density. In this case, due to insufficient available point cloud data samples, [the algorithm is used to...]. Point cloud data generated by interpolation algorithms has low accuracy. Therefore, by selecting the aircraft closest to the second subspace as the target aircraft, it is controlled to rescan the subspace and acquire new point cloud data. Since the number of point cloud data cannot be precisely controlled when acquiring point cloud data through the target aircraft, the point cloud density in the second subspace needs to be repeatedly adjusted to make it close to the average density. Through the above technical solution, the interpolation or reacquisition of data method can be flexibly selected according to different point cloud density conditions, which enhances the system's adaptability to different measurement environments and data quality. By selecting the closest aircraft to reacquire data, the flight distance and time of the aircraft are reduced, the efficiency of data acquisition is improved, and the energy consumption of the aircraft is also reduced.

[0066] Furthermore, the construction of the three-dimensional model includes:

[0067] The point cloud data of the adjusted grid space is preprocessed, and combined with the three-dimensional image data corresponding to the grid space, a three-dimensional model of the target measurement area is constructed using a surface reconstruction algorithm and a voxelization algorithm based on point cloud data.

[0068] Specifically, the adjusted point cloud data undergoes preprocessing, including noise removal and filtering, to improve data quality. Preprocessing steps include outlier removal and smoothing, aiming to enhance the accuracy and reliability of the point cloud data. The preprocessed point cloud data is then combined with 3D image data. 3D image data provides visual information such as texture and color of the target object. By spatially registering and fusing point cloud data with image data, the generated 3D model not only possesses geometric shape but also rich visual features. 3D modeling algorithms are then used to construct the 3D model. Commonly used algorithms include point cloud-based surface reconstruction algorithms and voxelization algorithms. Surface reconstruction algorithms generate smooth surfaces by fitting point cloud data, while voxelization algorithms convert point cloud data into voxel representations before generating the 3D model.

[0069] Furthermore, the measurement characteristics include at least vegetation cover, reflectivity, and measurement accuracy requirements.

[0070] This invention also provides an intelligent measuring device based on urban planning and architectural design, used to implement the above-mentioned method, such as... Figure 4 As shown, the device includes:

[0071] The setting unit is used to set the flight path, target scanning area and scanning position of each aircraft according to the scanning area of ​​each target object in the target measurement area and the scanning range of each aircraft among the multiple aircraft.

[0072] The selection unit is used to divide the target scanning area into multiple sub-regions according to the measurement characteristics of the target measurement object in the target scanning area, and select the target band from the preset bands as the scanning band of the sub-region;

[0073] The scanning unit is used to group the sub-regions within the scanning range of each scanning position of the aircraft, and scan the sub-regions according to the scanning band corresponding to each group to obtain the corresponding point cloud data.

[0074] The adjustment unit is used to map the point cloud data acquired by each of the aircraft to a three-dimensional space for real-time fusion, compare the actual number of point clouds in each unit space after fusion with the standard number of point clouds, obtain the comparison result, and adjust the flight path and actual point cloud data of the aircraft according to the comparison result.

[0075] The building unit is used to construct a 3D model based on the adjusted point cloud data and the corresponding image data.

[0076] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0077] In summary, this invention achieves efficient and accurate measurement of the target area by acquiring each target object within the target measurement range, setting the flight path and scanning position according to the scanning range of the aircraft, and utilizing the laser emission module onboard the aircraft to acquire point cloud data. By selecting the optimal wavelength for scanning based on measurement characteristics, the accuracy and efficiency of laser scanning are effectively improved, and the phase discontinuity problem caused by laser wavelength jumps is reduced, ensuring the accuracy of the point cloud data. Furthermore, by mapping the point cloud data to three-dimensional space for real-time fusion and comparing it with the standard point cloud quantity, the flight path of the aircraft and the actual point cloud data are adjusted based on the comparison results, further optimizing the quality of the measurement data. In addition, by preprocessing and three-dimensional modeling the adjusted point cloud data, a high-quality three-dimensional model is generated, providing intuitive and accurate model support for urban planning and architectural design. This method and device not only improve measurement efficiency but also enhance the reliability and accuracy of measurement data, effectively meeting the requirements of urban planning and architectural design for data accuracy and completeness.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0079] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0080] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. An intelligent measurement method based on urban planning and architectural design, characterized in that, The method includes: Based on the scanning range of each target object in the target measurement area and each of the multiple aircraft, a corresponding flight path, target scanning area and scanning position are set, wherein the aircraft is equipped with a laser measurement module; Based on the measurement characteristics of the target object in the target scanning area, the target scanning area is divided into multiple sub-regions, and a target band is selected from preset bands as the scanning band of the sub-region; The sub-regions within the scanning range of each scanning position of the aircraft are grouped, and the sub-regions are scanned according to the scanning band corresponding to each group to obtain the corresponding point cloud data. The point cloud data acquired by each aircraft is mapped to a three-dimensional space and fused in real time. The actual number of point clouds in each grid space in the three-dimensional space is compared with the standard number of point clouds to obtain the comparison results. The flight path and actual point cloud data of the aircraft are adjusted according to the comparison results and point cloud density. A three-dimensional model is constructed based on the point cloud data of the adjusted grid space and the corresponding three-dimensional image. The calculation of the standard point cloud quantity includes: dividing the target measurement object into grids, obtaining the size data of the target measurement object based on the fused point cloud data, inputting the structural features, size data, preset accuracy and corresponding three-dimensional image of the target measurement object into the estimation model, and obtaining the standard point cloud quantity corresponding to each grid space. Adjusting the actual point cloud data based on the comparison results and point cloud density includes: when the difference between the two is less than or equal to a set difference and the difference between the point cloud density and the average density in any subspace within the grid space is less than the set density, the actual number of point cloud data in the grid space does not need to be adjusted; when the difference between the two is greater than the set difference, the point cloud density in each subspace is compared with the average density, and a first subspace with a point cloud density greater than the average density is obtained. In the first subspace, the distance between other point clouds excluding key point clouds is calculated, and point cloud data between point cloud pairs with a distance less than or equal to a set distance is deleted. This step is repeated until the difference between the point cloud density and the average density in the first subspace is less than or equal to the set density. Adjusting the flight path of the aircraft based on the comparison results and point cloud density includes: acquiring a second subspace in the grid space where the point cloud density is less than the average density; when the point cloud density is greater than a preset density, interpolating between point cloud pairs with Euclidean distances greater than a set distance using a linear interpolation algorithm based on the attributes and coordinates of the point cloud pairs; when the point cloud density in the second subspace is less than the preset density, taking the aircraft closest to the second subspace as the target aircraft; reacquiring point cloud data in the second subspace through the target aircraft; and repeating the method of adjusting the point cloud data in the grid space until the difference between the point cloud density and the average density in the second subspace is less than or equal to the set density.

2. The method according to claim 1, characterized in that, Selecting a target band from a preset band based on the measured characteristics includes: Based on the measurement characteristics of the target object in the target scanning area, the target scanning area is divided into multiple sub-regions. The measurement characteristics include vegetation cover, reflectivity, and measurement accuracy requirements. The optimal wavelength corresponding to each sub-region is obtained based on the measurement characteristics, and the target band is selected based on the optimal wavelength. The target band includes the optimal wavelength.

3. The method according to claim 1, characterized in that, The acquisition of point cloud data for each sub-region corresponding to each group includes: The aircraft flies along the flight path, acquires the scanning range of the target area corresponding to each scanning position, groups the sub-regions according to the scanning band corresponding to each sub-region within the scanning range, and uses each independent sub-region or multiple connected sub-regions in the group as a scanning unit. When scanning the scanning unit in each group, the reflector current of the measurement module is adjusted according to the scanning band corresponding to each group so that the output beam of the laser measurement module is coarsely tuned to the scanning band. Then, the phase bias current is adjusted according to the scanning sequence to generate a phase-continuous scanning wave. The scanning wave is used to scan each scanning unit to acquire the corresponding point cloud data. The point cloud data of each scanning unit in the group is acquired by adjusting the emission angle of the measurement module.

4. The method according to claim 1, characterized in that, The construction of the three-dimensional model includes: The point cloud data of the adjusted grid space is preprocessed, and combined with the three-dimensional image data corresponding to the grid space, a three-dimensional model of the target measurement area is constructed using a surface reconstruction algorithm and a voxelization algorithm based on point cloud data.

5. The method according to claim 1, characterized in that, The measurement characteristics include at least vegetation cover, reflectivity, and measurement accuracy requirements.

6. An intelligent measuring device based on urban planning and architectural design, used to implement the method as described in any one of claims 1-5, characterized in that, The device includes: The setting unit is used to set the flight path, target scanning area and scanning position of each aircraft according to the scanning area of ​​each target object in the target measurement area and the scanning range of each aircraft among the multiple aircraft. The selection unit is used to divide the target scanning area into multiple sub-regions according to the measurement characteristics of the target measurement object in the target scanning area, and select the target band from the preset bands as the scanning band of the sub-region; The scanning unit is used to group the sub-regions within the scanning range of each scanning position of the aircraft, and scan the sub-regions according to the scanning band corresponding to each group to obtain the corresponding point cloud data. The adjustment unit is used to map the point cloud data acquired by each of the aircraft to a three-dimensional space for real-time fusion, compare the actual number of point clouds in each unit space after fusion with the standard number of point clouds, obtain the comparison result, and adjust the flight path and actual point cloud data of the aircraft according to the comparison result and the point cloud density. The building unit is used to construct a 3D model based on the adjusted point cloud data and the corresponding image data.

7. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-5.

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