A data processing method and device based on a smart city

Abstract

The application provides a kind of data processing method and device based on wisdom city, it is related to the technical field of data processing, method includes: with unmanned aerial vehicle tilt photography constructs city-level space data base, through generation real scene three-dimensional model, digital orthophoto and digital elevation model, form unified, real space foundation, on this basis, extract building base and building white mode, and implement spatial logic verification to multi-source business basic data, further construct and building three-dimensional entity stable association standard address system, realize the accurate space anchoring of population, housing and unit and other governance objects, finally, associated data is uniformly gathered to city information model platform, provide reliable, renewable spatial data support for wisdom city application.The application can improve the timeliness of wisdom city data processing under the dynamic ground deformation scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a data processing method and device based on a smart city. BACKGROUND

[0002] The existing smart city data processing usually takes multi-department business systems and industry platforms as data sources, collects business data such as population, housing, units, roads, and pipe networks in a decentralized manner, and completes preliminary sorting and application within each system. Overall, it relies more on business attribute association rather than unified spatial constraints. Data is often aggregated through interface connection or manual comparison. There is a lack of unified constraints on coordinate systems, address standards, coding rules, and update mechanisms, resulting in inconsistent locations, inconsistent semantics, or repeated expressions of the same object in different systems. At the same time, the association between spatial data and business data is mostly limited to two-dimensional layer superimposition or text address matching, lacking a stable anchoring mechanism centered on three-dimensional spatial entities. This makes the data fusion result highly sensitive to scene changes and data updates, making it difficult to support fine governance, cross-department collaboration, and rapid response in complex emergency scenarios. Overall, it still mainly relies on "data aggregation and display", and has not yet formed a data-driven smart city processing system centered on unified spatial foundation and systematic data governance.

[0003] In addition, in the prior art, when geological disasters or extreme weather cause ground deformation and change the road traffic boundaries and water body river shorelines in a short period of time, the existing smart city data processing system usually relies on pre-constructed static road traffic data, water body river data, and standard address data. These data have a long update cycle after generation, and lack real-time or quasi-real-time linkage verification mechanisms with digital elevation models and digital orthophotos, resulting in new digital elevation models and digital orthophotos generated or updated after a disaster reflecting the true topographic changes, while existing road traffic data and water body river data still maintain their pre-disaster spatial positions, thus forming systematic misplacement in space. In this case, the prior art mostly uses simple layer superimposition or manual verification to identify abnormalities, making it difficult to timely and automatically transmit topographic changes to road traffic relationships, water body boundaries, and standard address spatial anchors. This in turn causes standard address units to remain anchored at the original road centerline or building base position, even if this position has been submerged by water, collapsed or become an inaccessible area. The system still cannot identify and correct this spatial distortion in a short period of time, resulting in serious risks such as positioning errors, path planning failures, and resource delivery biases in emergency command, personnel evacuation, and rescue dispatch operations based on distorted address spatial carriers at critical moments. This is a major deficiency of the prior art in dynamic ground change scenarios.

[0004] Therefore, there is a need for a method to improve the timeliness of smart city data processing in dynamic ground deformation scenarios. SUMMARY

[0005] The application provides a data processing method and device based on a smart city, which can improve the timeliness of smart city data processing in a dynamic ground surface deformation scenario.

[0006] In a first aspect of the application, a data processing method based on a smart city is provided, which comprises:

[0007] Carrying out basic data collection around a city-level spatial data base, and obtaining original image data under a preset coordinate system through unmanned aerial vehicle oblique photogrammetry;

[0008] Generating point cloud data of a real scene three-dimensional model based on the original image data through aerial triangulation and dense matching;

[0009] Synchronously generating a digital orthophoto map and a digital elevation model under the constraint of the point cloud data;

[0010] Extracting a building contour to form building base data with reference to the real scene three-dimensional model, and generating building white model data based on the building base data;

[0011] Introducing a plurality of source business basic data into the preset coordinate system around the city digital public infrastructure construction demand, and verifying the spatial logical relationship between the plurality of source business basic data and the building base data and terrain data based on the digital orthophoto map and the digital elevation model;

[0012] Based on the spatial logical relationship, generating a standard address unit on the basis of a road space entity around a unified standard address system, establishing a spatial correlation relationship between the standard address unit and the building white model data, and forming an address space carrier;

[0013] Data correlating real population, real housing and real units with the address space carrier to form correlation data;

[0014] Processing the correlation data according to a unified data format specification and data slicing rule and converging to a city information model platform, and outputting to a smart city application.

[0015] In a second aspect of the application, a data processing device based on a smart city is provided, which is used for executing any one of the data processing methods based on a smart city as described above, and comprises an acquisition module, a processing module and an output module, wherein:

[0016] The acquisition module is used for carrying out basic data collection around a city-level spatial data base, and obtaining original image data under a preset coordinate system through unmanned aerial vehicle oblique photogrammetry;

[0017] The processing module is configured to generate point cloud data of a real scene three-dimensional model through aerial triangulation and dense matching based on the original image data;

[0018] The processing module is configured to generate a digital orthographic image and a digital elevation model synchronously under the constraint of the point cloud data;

[0019] The processing module is configured to extract a building contour to form building base data with reference to the real scene three-dimensional model, and generate building white model data based on the building base data;

[0020] The processing module is configured to introduce the preset coordinate system around the construction demand of a city digital public infrastructure to multi-source business basic data, and verify the spatial logical relationship between the multi-source business basic data and the building base data and terrain data based on the digital orthographic image and the digital elevation model;

[0021] The processing module is configured to generate a standard address unit based on a road space entity around a unified standard address system based on the spatial logical relationship, establish a spatial correlation relationship between the standard address unit and the building white model data, and form an address space carrier;

[0022] The processing module is configured to perform data correlation between actual population, actual housing and actual units and the address space carrier to form associated data;

[0023] The output module is configured to process and converge the associated data to a city information model platform according to a unified data format specification and data slice rule, and output to a smart city application.

[0024] In a third aspect of the application, an electronic device is provided, comprising a processor, a memory, a user interface and a network interface, the memory is configured to store instructions, the user interface and the network interface are configured to communicate with other devices, and the processor is configured to execute the instructions stored in the memory to enable the electronic device to perform the method according to any one of the preceding aspects.

[0025] In a fourth aspect of the application, a non-transitory computer readable storage medium is provided, which stores instructions, when the instructions are executed, the method according to any one of the preceding aspects is performed.

[0026] In summary, the one or more technical solutions provided in the embodiments of the application have at least the following technical effects or advantages:

[0027] 1. The present application builds a city-level spatial data base by starting with unmanned aerial photography, so that the original image, real scene three-dimensional model, digital orthographic image and digital elevation model form a high consistency of spatial expression in the same preset coordinate system, and on this basis, multi-source business basic data, building base data and building white model data are uniformly included in the spatial logic verification framework, so that key spatial elements such as roads, water areas, buildings and standard addresses are no longer dependent on static attribute association, but are constrained by the latest landform reflected by digital orthographic image and digital elevation model; when dynamic landform changes occur, new images and point cloud data can quickly generate updated digital orthographic image and digital elevation model, and immediately trigger automatic verification and callback correction of the plan position, elevation relationship and spatial topology of multi-source business basic data, thereby synchronously adjusting the spatial anchoring relationship of road space entities, standard address units and address space carriers, so that the spatial association of real population, real housing and real unit is updated in time with the change of landform, avoiding relying on manual check or periodic update mechanism, and thereby significantly improving the data processing timeliness and business response capability of smart city in dynamic landform scenarios such as geological disasters and extreme weather.

[0028] 2. By uniformly constraining outliers, mismatched points and point cloud density at the point cloud data level, the point cloud data forms a stable and reliable spatial reference, and the generation of digital orthographic image and digital elevation model is completed synchronously under the spatial reference, so that the plan position expression of image and the elevation expression of terrain are consistent in source and mutually verified, thereby avoiding the spatial deviation problem caused by the separate generation of orthographic image and elevation model, and providing consistent two-dimensional and three-dimensional basic data support for subsequent spatial logic verification.

[0029] 3. By introducing the combined processing mode of aerial triangulation and dense matching, the image exterior orientation elements are accurately calculated under the joint constraint of photo control points and image connection relationship, and on this basis, the point cloud data with spatial continuity and controlled density is formed, so that the real scene three-dimensional model not only has true spatial position and attitude accuracy, but also can reflect the continuous structure characteristics of urban space, providing high-precision three-dimensional geometric basis for subsequent orthographic correction, terrain modeling and building modeling.

[0030] 4. By extracting building contours and generating building white model data with real scene three-dimensional model as reference, the plan area range, elevation reference and three-dimensional volume of the building are all derived from real spatial data constraint, and through contour consistency checking and spatial consistency callback correction mechanism, systematic deviation of building model from real scene is avoided, thereby constructing a building space entity that can stably support address anchoring and governance object association.

[0031] 5. By introducing multi-source business basic data into a preset coordinate system and performing spatial logical verification from multiple dimensions of plane position, elevation relationship, spatial topology and terrain constraint, the multi-source business basic data is no longer dependent on business attribute consistency, but is jointly constrained by digital orthophoto, digital elevation model and building base data, thereby significantly reducing the risk of spatial mispositioning and semantic conflict caused by historical data lag or departmental differences.

[0032] 6. By generating a standard address unit based on the spatially logically verified road space entity and spatially associating the standard address unit with building base data and building white model data, the address is upgraded from a traditional road text description to a spatial carrier that can be accurately positioned in three-dimensional space, thereby improving the stability and traceability of the address in a dynamic scene and providing reliable spatial anchor points for emergency command and fine management. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of a data processing method based on a smart city according to an embodiment of the present application;

[0034] Figure 2 is a module schematic diagram of a data processing device based on a smart city according to an embodiment of the present application;

[0035] Figure 3 is a structural schematic diagram of an electronic device according to an embodiment of the present application.

[0036] Legend: 201, acquisition module; 202, processing module; 203, output module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0037] In order for those skilled in the art to better understand the technical solutions in the specification, the technical solutions in the specification will be clearly and completely described below in conjunction with the drawings in the embodiments of the specification. Obviously, the described embodiments are only some of the embodiments of the present application, not all.

[0038] In the description of the embodiments of the present application, the words such as "for example" or "for instance" are used to represent an example, illustration or description. Any embodiment or design scheme described as "for example" or "for instance" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the words such as "for example" or "for instance" are intended to present the relevant concept in a specific manner.

[0039] In the description of the embodiments of the present application, the term "a plurality of" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first", "second" are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Therefore, the features defined with "first", "second" can be explicitly or implicitly included one or more features. The terms "include", "contain", "have" and their variants mean "include but are not limited to", unless otherwise specifically emphasized.

[0040] The existing smart city data processing technology generally takes multi-department business systems and industry platforms as data sources, focuses on business attribute association and interface aggregation, lacks unified spatial base constraints, resulting in non-unified coordinate system, address standard and coding rules, causing inconsistent spatial position and semantic expression of the same object in different systems, and the data fusion is mostly limited to two-dimensional superposition or text matching level, lacking stable anchoring mechanism with three-dimensional spatial entity as the core; in the dynamic scenario of geological disasters or extreme weather causing ground deformation, data such as roads, water areas and standard addresses are prone to systematic mispositioning with the real terrain after the disaster due to update lag and lack of linkage verification with digital elevation model and digital orthographic image, and the existing technology mainly relies on manual verification or static layer comparison, which is difficult to timely transmit the terrain changes to the road traffic relationship and address spatial anchor point, and further cause the emergency command and rescue dispatch to operate based on the distorted address spatial carrier, which exposes the prominent deficiencies of the current smart city data processing system in unified spatial constraints and dynamic change response capability.

[0041] The embodiment discloses a data processing method based on a smart city, referring to Figure 1 , comprising the following steps S110-S180:

[0042] S110, basic data collection is carried out around the city-level spatial data base, and original image data under a preset coordinate system is obtained through unmanned aerial vehicle oblique photogrammetry.

[0043] The data processing method based on the smart city disclosed by the embodiment of the present application is applied to a server, and the server includes but is not limited to electronic devices such as mobile phones, tablet computers, wearable devices, PC (Personal Computer, personal computer) and the like, and can also be a background server running a data processing method based on a smart city. The server can be realized by an independent server or a server cluster composed of multiple servers.

[0044] In the process of collecting basic data around the city-level spatial data base, first of all, based on the scope of urban digital public infrastructure construction, the spatial boundary of the target area is determined, and under the constraint of the spatial boundary, the preset coordinate system and the height datum are uniformly selected, so that all the spatial data collected subsequently have consistent spatial reference basis in the plane position and height expression, thereby avoiding the inherent inconsistency of multi-source data in the coordinate datum.

[0045] After the target area and the preset coordinate system are determined, the aerial photography scheme design is performed for the unmanned aerial vehicle oblique photogrammetry task, the flight height, the heading overlap, the lateral overlap and the oblique photography angle combination are determined by combining the terrain undulation characteristics, the building density distribution and the urban space complexity of the target area, so that the aerial photography parameters can meet the requirements of image resolution and geometric stability for subsequent three-dimensional reconstruction and spatial analysis while ensuring the completeness of coverage.

[0046] After the aerial photography scheme design is completed, the photograph control points are arranged around the preset coordinate system, the high-precision spatial coordinates of the photograph control points are obtained by using global satellite navigation positioning measurement, and the distribution rationality of the photograph control points is verified, so that the photograph control points form a control network with uniform coverage and sufficient spatial constraint ability in the target area, thereby providing reliable external constraint conditions for the spatial positioning of the original image data.

[0047] After the photograph control point arrangement and measurement are completed, the unmanned aerial vehicle is controlled to perform oblique photogrammetry operation according to the aerial photography scheme, the oblique images covering the target area are synchronously collected through multiple perspectives, so that the same ground object is imaged multiple times under different viewing angles, thereby obtaining a set of original image data with uniform temporal characteristics, and the attitude parameters and time information corresponding to the images are recorded during the collection process to support subsequent image geometric relationship solving.

[0048] After the unmanned aerial vehicle oblique photogrammetry operation is completed, the quality of the obtained original image data is checked and screened, the abnormal image frames with blur, occlusion or insufficient overlap are removed through checking the image clarity, exposure consistency, viewing angle integrity and image coverage continuity, so as to ensure that the original image data participating in the subsequent processing maintains consistency in imaging quality and temporal conditions.

[0049] After the quality screening of the original image data is completed, the original image data and the photograph control point measurement results are jointly arranged, and the initial spatial position of the image is constrained based on the preset coordinate system, so that the original image data has a unified spatial datum and clear geometric constraint relationship before entering the subsequent aerial triangulation and three-dimensional reconstruction processing, thereby forming the basic image data results that can directly support the construction of the city-level spatial data base.

[0050] S120, generating point cloud data of the real scene three-dimensional model based on the original image data through aerial triangulation and dense matching.

[0051] In a possible implementation, the point cloud data of the real scene three-dimensional model is generated based on the original image data through aerial triangulation and dense matching, and specifically includes: removing abnormal images with blur, occlusion or time offset from the original image data to obtain a photo control result; performing aerial triangulation processing on the original image data based on a preset coordinate system and the photo control result, determining spatial position parameters and attitude parameters of each image in the preset coordinate system by extracting corresponding feature points between images and establishing image connection relationships, and solving image exterior orientation elements through joint adjustment of the photo control points, to obtain the image exterior orientation elements; performing dense matching preparation processing on the original image data based on the image exterior orientation elements, determining image combination relationships that meet the dense matching conditions by analyzing and screening the image angle differences, overlapping area ranges and baseline lengths; performing dense matching processing on the original image data based on the image combination relationships, obtaining a matching point set with spatial correspondence by performing point-by-point matching on corresponding regions of multi-view images at a pixel scale; inversely calculating matching points of the matching point set to three-dimensional spatial positions in the preset coordinate system in combination with the image exterior orientation elements, to form an initial three-dimensional point set; and performing point cloud consistency constraint processing on the initial three-dimensional point set, identifying and removing abnormal points caused by occlusion, reflection or matching errors, and correcting point cloud density distribution and spatial continuity, to generate the point cloud data.

[0052] Specifically, when performing abnormal image removal on the original image data, image quality evaluation records are established for the original image data in units of image frames, the image quality evaluation records at least include a definition evaluation value, an occlusion proportion evaluation value and a time offset evaluation value, and image frames with a definition evaluation value lower than a definition threshold, an occlusion proportion evaluation value higher than an occlusion threshold or a time offset evaluation value higher than an offset threshold are determined as abnormal images to be removed, to form an effective image set for subsequent aerial triangulation; wherein the definition evaluation value is used to represent the distinguishable degree of image detail texture, the occlusion proportion evaluation value is used to represent the area proportion of the image covered by clouds, fog, strong shadows or foreground objects, and the time offset evaluation value is used to represent the deviation degree of the image shooting time relative to a unified time reference, and the photo control result is used to represent the spatial coordinate result of the photo control points in the preset coordinate system and the corresponding relationship with the image frames, and the photo control points are ground control points arranged in the target area and measured to obtain accurate coordinates through global satellite navigation positioning. The result obtained after removing the abnormal images is defined as the effective image set, and the photo control result is limited to the set of control point coordinates and corresponding relationships.

[0053] When aerial triangulation is performed based on a preset coordinate system and photo control results, same-name feature points are extracted in an effective image set in an image overlapping area and an image connection relationship is established, the same-name feature points refer to image feature points corresponding to a same ground object point in different images, and the image connection relationship is used to represent overlapping and matching constraints between images; on this basis, a photo control point is introduced as an absolute space constraint into joint adjustment solving, the joint adjustment solving is used to simultaneously solve a spatial position parameter and a posture parameter of each image and a three-dimensional spatial position corresponding to the same-name feature points in the sense of minimizing a re-projection residual, so that image exterior orientation elements have a unified spatial reference under the preset coordinate system; the spatial position parameter is used to represent a three-dimensional coordinate of an image photographing center under the preset coordinate system, the posture parameter is used to represent a rotation relationship of an image camera coordinate system relative to the preset coordinate system, and the image exterior orientation elements are a set composed of the spatial position parameter and the posture parameter; in order to quantify a constraint target of the joint adjustment, an observed pixel coordinate of the same-name feature point in the i-th image is a three-dimensional point is projected to an image plane to obtain a predicted pixel coordinate by the image exterior orientation elements and a camera interior orientation parameter , a re-projection residual square sum is taken as an optimization target to minimize overall error, and a calculation formula is as follows:

[0054]

[0055] , wherein represents a value of a target function of the joint adjustment, represents a number of the same-name feature points participating in adjustment, represents an image index set visible to the k-th same-name feature point, represents an observed pixel coordinate of the k-th same-name feature point in the i-th image, represents a predicted pixel coordinate obtained by projection of the image exterior orientation elements, represents a coefficient of a control photo control point constraint weight and takes a non-negative value, represents a number of photo control points, represents a three-dimensional coordinate of the j-th photo control point obtained by adjustment solving, represents a three-dimensional coordinate of the j-th photo control point obtained by global satellite navigation positioning measurement; the formula simultaneously minimizes pixel residuals between image observation and projection prediction, and the coordinate deviation of the photo control point is included in a penalty term, so that the solving result meets image geometric consistency and absolute spatial consistency, so that stable image exterior orientation elements are obtained.

[0056] When performing the dense matching preparation processing based on the image exterior orientation elements, the geometric matchability of the image pairs in the effective image set is screened, and the screening is based on the image view angle difference, the overlapping area range and the baseline length. The image view angle difference is used to represent the angle between the observation directions of the two images to avoid the depth insensitivity caused by too small view angle difference or the serious occlusion caused by too large view angle difference. The overlapping area range is used to represent the common coverage ratio of the two images on the ground projection to ensure sufficient matching area. The baseline length is used to represent the spatial distance between the photographic centers of the two images to provide sufficient stereo intersection geometric strength. In the implementation, the photographic center connecting vector and the line of sight vector of the image pair are calculated based on the image exterior orientation elements to obtain the image view angle difference. The overlapping area range is obtained by intersecting the ground coverage polygons of the two images in the preset coordinate system. The baseline length is calculated from the photographic center coordinates of the two images. Then, the image pairs that do not meet the conditions are excluded according to the preset threshold interval, and the retained image pair set is the image combination relationship. The image combination relationship is used to limit the candidate image set for subsequent dense matching, so that the dense matching is performed under the premise of controllable geometric conditions, thereby reducing the probability of false matching and improving the spatial uniformity of point cloud density.

[0057] When performing the dense matching processing based on the image combination relationship, each image pair or multi-view image group in the image combination relationship is matched point by point at the pixel scale. The dense matching is used to establish the corresponding relationship across the image for as many pixels as possible in the overlapping area, so that the three-dimensional reconstruction is expanded from sparse points to high-density points. In the implementation, the pixels are traversed in the overlapping area of each reference image according to the preset step size or the pixel blocks are traversed according to the superpixel block, and the matching positions that meet the similarity constraints are searched in the corresponding target image. The similarity constraints can be jointly defined by the gray consistency, the gradient consistency or the local texture consistency. At the same time, the epipolar constraint is introduced to reduce the search range. The epipolar constraint is determined by the image exterior orientation elements and is used to reduce the matching search from a two-dimensional area to a one-dimensional line segment. Through the above matching process, a matching point set is obtained. The matching point set refers to the pixel point pairs or pixel point groups that have established the corresponding relationship between at least two images. The matching point set internally includes the reference image pixel coordinates, the target image pixel coordinates and the matching confidence value. The matching confidence value is used to represent the reliability of the matching result and is used for subsequent outlier rejection.

[0058] In the process of inversely calculating the initial three-dimensional point set from the matching point set combined with the exterior orientation elements of the images, a space intersection solution is performed for each matching record in the matching point set, and the space intersection solution is used to determine the three-dimensional spatial position of the feature point in the preset coordinate system by using the intersection of the lines of sight of the multi-view images. Specifically, the matching pixel coordinates are converted into normalized line of sight directions in the camera coordinate system according to the exterior orientation elements of the images, and the line of sight directions are transformed into space rays in the preset coordinate system through the attitude parameters. In an ideal case, the space rays corresponding to two or more images intersect at a point. When there are noises and errors, the three-dimensional point closest to each ray is obtained by solving in the least square sense as the intersection result, so as to obtain the initial three-dimensional point set. The initial three-dimensional point set is a set composed of a large number of three-dimensional points, each three-dimensional point corresponds to a matching record in the matching point set and has three-dimensional coordinates and a confidence attribute, and the confidence attribute can be determined by the matching confidence value and the intersection residual. In order to clarify the intersection process, the two space rays can be expressed as and wherein are the coordinates of the centers of the two cameras, are the unit vectors of the corresponding line of sight directions, and the parameters that minimize the distance between the two rays are solved, and the midpoint is taken as the three-dimensional point coordinates, so as to convert the two-dimensional matching relationship into a three-dimensional spatial expression, and ensure that the initial three-dimensional point set maintains a consistent geometric constraint relationship with the exterior orientation elements of the images in the preset coordinate system.

[0059] When the point cloud consistency constraint processing is performed on the initial three-dimensional point set, first, the abnormal points are identified based on the spatial statistical characteristics. By calculating the distance distribution, local density and local normal consistency between each three-dimensional point and its neighborhood three-dimensional points, the three-dimensional points obviously deviating from the neighborhood structure are determined as abnormal points and removed. The abnormal points are caused by errors caused by occlusion, texture confusion caused by reflection and intersection deviation caused by matching errors. Then, the remaining points are subjected to point cloud density distribution correction. By constructing a voxel grid or a two-dimensional grid in the preset coordinate system and counting the number of points in each grid, the area with too high point number is subjected to down-sampling to avoid redundancy, and the area with too low point number is triggered to perform matching backfilling or interpolation to avoid holes, so as to improve the spatial uniformity of the point cloud density. At the same time, the spatial continuity of the point cloud is corrected. By detecting the fracture zone caused by occlusion or weak texture features and performing smooth connection on the boundary of the fracture zone, the point cloud forms a continuous expression on the building surface and the ground surface. After the above abnormal point removal, density distribution correction and spatial continuity correction, the point cloud data satisfying the unified spatial reference, stable geometric structure and being available for subsequent orthorectification and elevation inversion is output, wherein the point cloud data refers to the three-dimensional point set obtained after the consistency constraint, which includes three-dimensional coordinates, point density identifier and quality identifier, and is used to represent the reliability and availability of each three-dimensional point in the spatial expression.

[0060] S130, synchronously generating a digital orthographic image and a digital elevation model under the constraint of point cloud data.

[0061] In a possible implementation, the method for synchronously generating a digital orthographic image and a digital elevation model under the constraint of point cloud data specifically comprises: performing quality shaping processing on the point cloud data by identifying and removing outliers, correcting mismatched points in a blocked area, and unifying point cloud density distribution, thereby obtaining a unified spatial constraint reference; under the unified spatial constraint reference, performing pixel correction on the tilt distortion of original image data by using ground elevation information and ground object height information in the point cloud data based on the geometric correspondence between the point cloud data and the original image data, thereby generating a digital orthographic image in a preset coordinate system; performing ground object point and ground point separation processing on the point cloud data based on the elevation distribution characteristics of the point cloud data, forming ground point cloud data for terrain expression by identifying building points and vegetation points and retaining a ground point set; and performing spatial interpolation and gridding processing based on the ground point cloud data, thereby generating a digital elevation model.

[0062] Specifically, before the spatial constraint is applied to the point cloud data, quality shaping processing is performed on the spatial reliability of the point cloud data, outliers that are significantly deviated from their neighborhood points in spatial position and height variation are identified by statistically analyzing the spatial distribution characteristics of each three-dimensional point in the point cloud data, and the outliers are removed from the point cloud data; meanwhile, for mismatched points caused by multi-view matching blocking relationship or reflection interference, three-dimensional points with height mutation and lack of neighborhood support in the blocked area are corrected or deleted in combination with the height continuity and surface normal consistency of the point cloud data in the local area; after the abnormal point processing is completed, density distribution unification processing is performed on the point cloud data, a spatial grid is constructed under a preset coordinate system, and the point density of the point cloud in each grid element is adjusted, so that the point cloud data forms a uniform, continuous and stable spatial reference for subsequent image correction and terrain inversion in the overall spatial range, thereby enabling the processed point cloud data to serve as a unified spatial constraint source for subsequent generation of a digital orthographic image and a digital elevation model.

[0063] In generating the digital orthographic image under the unified spatial constraint, the orthographic correction processing is performed on the original image data based on the established geometric correspondence between the point cloud data and the original image data, the tilt distortion in the original image due to the terrain undulation and the height of the ground objects is corrected pixel by pixel by using the real ground elevation information and the height information of the ground objects such as buildings reflected in the point cloud data; specifically, each pixel point in the original image is projected to the three-dimensional surface position expressed by the point cloud data along the corresponding imaging line-of-sight direction, and the pixel point is repositioned in space according to the real spatial coordinates of the three-dimensional surface position in the preset coordinate system, so as to eliminate the displacement error caused under the tilt photography condition, make the corrected image one-to-one correspond to the real ground in the plane position, and finally form the digital orthographic image in the preset coordinate system which can truly reflect the texture distribution of the ground.

[0064] Before generating the digital elevation model, the ground point and ground object point separation processing is performed based on the elevation distribution characteristics in the point cloud data, the non-ground points belonging to the ground objects such as buildings and vegetation are identified by analyzing the height difference, slope change and surface roughness characteristics of the point cloud data in the local neighborhood, and are removed from the point cloud data, only the ground point set capable of continuously expressing the terrain undulation characteristics is reserved; wherein the ground point is used to represent the spatial position close to the bare ground or the ground, and the elevation change thereof should satisfy the terrain continuity constraint, while the building point and the vegetation point are characterized by the height mutation relative to the surrounding ground points, and are stably distinguished by the above-mentioned characteristic difference, so as to obtain the ground point cloud data used for subsequent terrain modeling.

[0065] The spatial interpolation and gridding processing are performed based on the ground point cloud data, the ground point cloud data is projected into a regular grid structure in the preset coordinate system, and the elevation value of the adjacent ground point is interpolated in each grid cell to generate the elevation grid continuously covering the target area; the spatial interpolation is used to complete the elevation information in the area where the ground points are sparse or unevenly distributed, so that the elevation expression remains continuous in space, and the gridding processing is used to convert the discrete ground point cloud data into a regularly arranged elevation unit set, so as to form the digital elevation model capable of systematically expressing the terrain undulation characteristics.

[0066] S140, the building contour is extracted with reference to the real three-dimensional model to form building base data, and the building white model data is generated based on the building base data.

[0067] In a possible implementation, the building contour is extracted with reference to the real three-dimensional model to form building base data, and the building white model data is generated based on the building base data, and specifically, the building separation enhancement processing is performed on the real three-dimensional model, the height mutation, the normal vector difference and the slope difference between the building and the ground surface are identified to form a building candidate region set; the building contour extraction processing is performed based on the building candidate region set, the building contour line set is generated through plane projection, boundary identification and topology checking, and the contour consistency checking is performed in combination with the digital orthographic image; the building base data is constructed based on the building contour line set, the building base surface set is formed through closed surface construction and surface topology checking, and the ground elevation fitting is completed in combination with the digital elevation model; the building height analysis processing is performed based on the building base data and the real three-dimensional model, and the building height value is obtained through the height difference calculation between the roof point set and the ground point set; the three-dimensional stretching is performed based on the building base data and the building height value to generate the building white model data; the spatial consistency checking is performed on the building white model data and the real three-dimensional model, and the building contour and the building height are recalled and corrected when the deviation exceeds the limit, so as to output the building base data and the building white model data consistent with the real three-dimensional model.

[0068] Specifically, when the building separation enhancement processing is performed on the real three-dimensional model, the distinguishable features of the spatial structure of the ground surface and the ground surface in the real three-dimensional model are jointly judged, the local height gradient is calculated on the point cloud or the grid surface of the real three-dimensional model to identify the height mutation zone, the local height gradient is used to represent the elevation change rate of adjacent positions and to depict the mutation boundary at the junction of the building outer wall and the ground surface; at the same time, the local normal vector of the surface of the real three-dimensional model is calculated and the adjacent normal vector angles are compared, the normal vector is used to represent the local surface orientation, and the normal vector difference is used to represent the significant change in orientation between the building outer wall surface and the ground surface; and the local slope is calculated in the terrain undulating area and the slope change amplitude is compared, the slope difference is used to represent the geometric difference between the ground slope and the building roof or platform surface; the spatial segments satisfying the height mutation, the normal vector difference and the slope difference are aggregated into connected regions, and the connected regions are subjected to hole completion and boundary smoothing, so as to form the building candidate region set which can be used for contour extraction, and the building candidate region set is used to limit the possible building area and reduce the subsequent false detection probability.

[0069] When performing building contour extraction processing based on a building candidate region set, the building candidate region set is first projected onto a horizontal plane under a preset coordinate system, and the planar projection is used to convert the three-dimensional candidate region into a two-dimensional footprint expression so as to extract the boundary; then the outer boundary is identified in the projection result to form an initial boundary line, and the boundary identification is used to extract the outer envelope line from the point or surface set; topology checking is performed on the initial boundary line, and the topology checking is used to check the closure, self-intersection and overlap with adjacent boundaries of the boundary line, so as to eliminate the geometric abnormalities caused by breaks, burrs and self-intersections and form a building contour line set; after obtaining the building contour line set, profile consistency checking is performed in combination with a digital orthographic image, the building contour line set is superimposed on the digital orthographic image, and the gradient distribution of the roof texture boundary and the shadow boundary near the contour line is detected, so that the position of the contour line is consistent with the roof texture boundary, and the false boundary caused by tree canopy shielding or strong shadow is removed, thereby improving the consistency of the building contour line set with the actual building footprint range.

[0070] When constructing building base data based on the building contour line set, each closed contour line in the building contour line set is converted into a closed surface element to form a building base surface set, and the closed surface construction is used to convert the linear boundary into a surface geometry that can express the building footprint range; surface topology checking is performed on the building base surface set, and the surface topology checking is used to check whether there is overlap, gap, hole and boundary inconsistency between the surfaces, so as to ensure that the building base surface set is mutually exclusive and complete in spatial expression; after completing the surface topology checking, ground elevation fitting is completed in combination with a digital elevation model, the ground elevation within the coverage range of the building base surface set is sampled in the digital elevation model, and the sampling result is statistically fitted, so that the building base surface set obtains a ground elevation reference that is consistent with the ground surface, and the ground elevation fitting is used to avoid the building base from appearing suspended or sunken in the three-dimensional scene, and to provide a consistent ground reference surface for building height analysis.

[0071] When performing building height analysis processing based on the building base data and the real scene three-dimensional model, for each building base surface, a point cloud subset within its coverage range is extracted in the real scene three-dimensional model, and a roof point set and a ground point set are identified in the point cloud subset, the roof point set is used to represent the point cloud on the upper surface of the building, and the ground point set is used to represent the point cloud of the ground surface within the range of the building base; the building height value is obtained by calculating the difference between the representative elevation of the roof point set and the representative elevation of the ground point set, and the representative elevation can be quantile statistics to reduce the influence of local structures or noise; the building height value can be in the form of height difference, and the calculation formula is:

[0072]

[0073] wherein H represents the building height value, a representative elevation value representing a set of roof points, the representative elevation can be determined by the median elevation of the set of roof points or the upper quantile elevation to suppress the influence of roof equipment and noise points, a representative elevation value representing a set of ground points, the representative elevation can be determined by the median elevation of the set of ground points to suppress the influence of local undulations and abnormal points; the formula obtains the effective height of the building relative to the ground by subtracting the overall elevation level of the set of ground points from the overall elevation level of the set of roof points, so that the building height value can be stabilized for subsequent three-dimensional stretching.

[0074] When generating building white model data based on building base data and building height values by performing three-dimensional stretching, each building base surface is stretched in a direction perpendicular to the horizontal plane, and the stretching distance is the corresponding building height value, thereby forming a building white model body, which is used to express the three-dimensional volume of the building in a regular geometric body; geometric constraints are applied to the building white model body during the stretching process to stabilize the geometry of the white model body by keeping the sides vertical and unifying the surface normal direction, and consistency processing is performed on the contact boundaries between adjacent white model bodies to avoid interpenetration or gaps; when a building has obvious height segmentation characteristics in a real scene three-dimensional model, the building base surface is split according to the height segmentation boundary and stretched separately, so that the building white model body can express complex volume structures such as tower and podium, thereby obtaining building white model data that can be used for spatial correlation and scene bearing.

[0075] When performing spatial consistency checking on building white model data and a real scene three-dimensional model, the spatial deviation between the outer surface of the building white model body and the corresponding point cloud subset in the real scene three-dimensional model is quantitatively evaluated, the deviation statistics are obtained by calculating the distance distribution of the point cloud subset to the outer surface of the building white model body, and the deviation statistics are compared with the deviation threshold to determine whether the deviation is out of limit; when the deviation is out of limit, a callback correction is triggered, which includes local adjustment of the boundary position of the building contour line set and re-estimation of the building height value, so that the building base surface set is more consistent with the building footprint expression of the real scene three-dimensional model on the plane boundary, and the building white model body is more consistent with the true elevation level of the set of roof points in height; the deviation statistics used in the spatial consistency checking can use the root mean square distance to take into account the influence of the overall deviation level and abnormal deviation, and the calculation formula is:

[0076]

[0077] wherein, the root mean square distance, N represents the number of three-dimensional points in the point cloud subset participating in the checking and takes a positive integer, represents the shortest distance from the i-th three-dimensional point to the outer surface of the building white model, the shortest distance is calculated by the point-to-surface distance and can take a non-negative value; the formula obtains the quantitative index of the overall fitting degree by averaging the square of the distance of all points to the outer surface of the white model and taking the square root, and when When the deviation exceeds the threshold value, it indicates that the white model and the real three-dimensional model have systematic inconsistency, thereby triggering the callback correction of the contour and the height and outputting the building base data and the building white model data consistent with the real three-dimensional model.

[0078] In S150, a preset coordinate system is introduced to the multi-source business basic data around the demand of urban digital public infrastructure construction, and the spatial logical relationship between the multi-source business basic data and the building base data and the terrain data is verified based on the digital orthophoto map and the digital elevation model.

[0079] In a possible implementation, a preset coordinate system is introduced to the multi-source business basic data around the demand of urban digital public infrastructure construction, and the spatial logical relationship between the multi-source business basic data and the building base data and the terrain data is verified based on the digital orthophoto map and the digital elevation model, specifically including: performing coordinate system unification processing on the multi-source business basic data to make the multi-source business basic data form a unified spatial reference under the preset coordinate system; performing plane position consistency verification on the multi-source business basic data based on the digital orthophoto map to identify the plane offset relationship between the multi-source business basic data and the real ground texture; performing elevation logical relationship verification on the multi-source business basic data based on the digital elevation model to determine the rationality of the multi-source business basic data in the vertical expression; performing spatial inclusion relationship and spatial adjacency relationship verification on the multi-source business basic data around the building base data to identify abnormal relationships that do not conform to the urban spatial organization logic; performing terrain adaptability verification on the multi-source business basic data based on the terrain data to verify the spatial distribution rationality of the multi-source business basic data under the terrain constraint; classifying and marking various spatial logical abnormalities identified through verification and determining the correction or constraint strategy, so that the multi-source business basic data form the spatial logical relationship with the digital orthophoto map, the digital elevation model, the building base data and the terrain data.

[0080] Specifically, when performing the coordinate system unification processing on the multi-source business basic data, the coordinate reference information and the data collection method information of the multi-source business basic data are respectively combed, the coordinate reference information at least includes an original coordinate system type, projection parameters, ellipsoid parameters and an elevation reference type, and when the coordinate reference information is missing, the coordinate deviation parameters are inversely deduced through spatial fitting with a known control point set; then, for each type of multi-source business basic data, the coordinate conversion parameters from the original coordinate system to a preset coordinate system are solved, so that the geometric objects of the multi-source business basic data are uniformly expressed in the preset coordinate system, and the conversion result is subjected to reference consistency checking to eliminate systematic translation, rotation and scale distortion; wherein, the coordinate system unification processing is used to solve the spatial misalignment problem caused by inconsistent historical surveying and mapping reference of different data sources, the preset coordinate system is used as a unified spatial reference, and the unified spatial reference is used to represent the spatial state of the multi-source business basic data that can be directly superimposed and compared under the same coordinate reference; when the control point set exists, the plane conversion relationship can be estimated through a similarity transformation model to minimize the residual error of the control points before and after conversion, and the calculation formula is:

[0081]

[0082] wherein, represents the planar coordinate value of the geometric object in the original coordinate system, represents the planar coordinate value of the geometric object in the preset coordinate system, s represents the scale coefficient value and is a positive number, represents the rotation angle value and can change in the range of , and represents the translation value and is used to compensate for the origin difference; the formula maps the geometric object in the original coordinate system to the preset coordinate system through the combination of scale, rotation and translation, thereby realizing the planar unification of the multi-source business basic data, and then the conversion quality is constrained by control point residual error statistics, so that the unified spatial reference has verifiable accuracy guarantee.

[0083] When performing plane position consistency check on multi-source business basic data based on digital orthophoto pair, the geometric boundaries of the multi-source business basic data are superimposed on the ground texture expression corresponding to the digital orthophoto pair, and alignment constraints are constructed around the texture structure elements that can be stably identified, including at least road boundary texture, water area shoreline texture and building roof texture boundary; the plane offset distribution is obtained by extracting the edge lines of the texture structure elements in the digital orthophoto pair and measuring the distance with the corresponding boundaries of the multi-source business basic data, and the plane position abnormality is determined when the plane offset distribution exceeds the offset threshold; wherein the plane position consistency check is used to judge whether the plane geometry of the multi-source business basic data is consistent with the real ground texture, and the plane offset relationship is used to represent the systematic displacement direction and displacement amplitude of the geometric object relative to the texture boundary; in order to quantify the offset degree, the average offset distance can be used as an indicator, and the calculation formula is:

[0084]

[0085] wherein, represents the average offset distance value, M represents the number of sampling points participating in the alignment evaluation and is a positive integer, represents the shortest plane distance value between the multi-source business basic data boundary and the digital orthophoto texture boundary of the i th sampling point and is non-negative; the formula forms a stable evaluation of the overall offset level by averaging the boundary distances of multiple sampling points, which is used to quickly identify systematic misplacement in a large area and as the basis for subsequent correction strategies.

[0086] When performing elevation logical relationship check on multi-source business basic data based on digital elevation model, the geometric objects of the multi-source business basic data are projected to the grid space corresponding to the digital elevation model, and the high sequences of the digital elevation model are extracted within the coverage of the geometric objects to construct the elevation logical constraints; for road traffic data, the continuity of the high sequence is used to judge whether there is unreasonable mutation or reverse slope section in the road longitudinal profile, for water area river data, the decreasing trend of the high sequence is used to judge whether the water area shoreline and the river center line meet the elevation constraints of the natural flow direction, for the building base data associated with the real estate registration data, the high sequence is used to judge whether the ground elevation within the building base range is consistent with the ground elevation fitting result of the building base; wherein the elevation logical relationship check is used to judge whether the vertical expression conforms to the basic law of terrain undulation and hydrological flow direction, and the vertical expression is used to represent the consistency and rationality of the multi-source business basic data in the elevation dimension; the slope change rate can be used to evaluate the elevation mutation degree of the road, and the calculation formula is:

[0087]

[0088] wherein, denotes the slope value of the kth segment, denotes the kth elevation value sampled along the road, denotes the k+1th elevation value, denotes the planar distance value of the kth segment and is positive; the formula obtains the local slope by the ratio of the elevation difference between adjacent sampling points and the distance, and is used to identify the sudden change of slope, the abnormality of slope sign or slope amplitude, so as to form a calculable basis for the vertical rationality of road traffic data.

[0089] When performing spatial inclusion relationship and spatial adjacency relationship verification on multi-source business basic data around building base data, a spatial relationship verification domain centered on building base data is constructed, and the topological relationship of multi-source business basic data in the spatial relationship verification domain is identified for consistency; the spatial inclusion relationship is used to determine whether a geometric object is located inside or covered by another geometric object, and the spatial adjacency relationship is used to determine whether the geometric objects form a reasonable adjacent relationship under the constraints of boundary or distance; for road traffic data, it is verified whether the road boundary and the building base boundary maintain the minimum distance required for accessibility and avoid the road crossing the building base, for water area river data, it is verified whether the water area shoreline and the building base satisfy the avoidance constraint and avoid the water covering the building base, for administrative division data, it is verified whether the administrative division surface forms complete coverage to the building base and whether the building base cross-surface drift causes breakage; the minimum distance can be used to identify the adjacency rationality, and the calculation formula is:

[0090]

[0091] wherein, denotes the minimum distance value between the boundary of geometric object A and geometric object B and is non-negative, denotes the boundary point set of geometric object A, denotes the boundary point set of geometric object B, p denotes the boundary point selected from , and q denotes the boundary point selected from denotes the Euclidean distance value of two points; the formula quantifies the adjacency degree by finding the minimum distance in the boundary point pair of two objects, and compares it with the accessibility threshold or avoidance threshold, to identify the topological abnormal relationship that does not conform to the urban space organization logic such as penetration, coverage or distance abnormality.

[0092] ​When performing terrain adaptability verification on multi-source business foundation data based on terrain data, the slope distribution, aspect distribution, and terrain confluence paths derived from the digital elevation model are used as terrain constraint features. The slope distribution characterizes changes in terrain steepness, the aspect distribution characterizes the surface inclination direction, and the terrain confluence paths characterize the spatial channels through which surface water may converge and flow. For road traffic data, the verification examines whether the relationship between road orientation and slope distribution avoids large-scale reverse slope crossings or unreasonable steep inclines. For water and river data, the verification examines whether the water body shoreline and terrain confluence paths are consistent and avoids long-term overlap between water body boundaries and elevation ridges. For administrative division data, the verification examines whether the natural boundary characteristics of administrative divisions near ridgelines or valley lines conform to the spatial distribution patterns under terrain constraints. Terrain adaptability verification is used to identify hidden anomalies where geometric objects satisfy planar superposition but do not conform to terrain control laws, thereby improving the interpretability and reliability of spatial logical relationships. The terrain confluence consistency index can be used to assess the degree of fit between water body boundaries and confluence paths; the calculation formula is as follows:

[0093]

[0094] Where C represents the value of the consistency index and can be used... Variation within the range, This represents the number of sampling points along the water boundary and is a positive integer. The value of represents the shortest distance from the i-th sampling point to the nearest topographic confluence path and is non-negative. r represents the distance attenuation coefficient and is positive, used to control the attenuation rate of the distance's contribution to consistency. This formula exponentially attenuates the distance from the sampling point to the confluence path and averages it, so that the smaller the distance, the greater the contribution to consistency. This quantifies the overall fit between the water boundary and the topographic control path and is used to identify abnormal areas where the water boundary deviates from the confluence path.

[0095] When the identified spatial logical anomalies are classified and labeled and the correction or constraint strategy is determined, the residual features output by the coordinate system processing, the plane offset distribution output by the plane position consistency verification, the slope anomaly and trend anomaly output by the elevation logical relationship verification, the topological conflict type output by the spatial inclusion relationship and spatial adjacency relationship verification, and the terrain consistency index output by the terrain adaptability verification are used to construct a spatial logical anomaly label system and each anomaly object is given a corresponding label; the anomaly label at least includes a plane offset anomaly, an elevation anomaly, a topological conflict anomaly, and a terrain inadaptation anomaly, and each anomaly label is associated with a correction strategy or a constraint strategy; the correction strategy is used to position callback, boundary reconstruction, or attribute correction of the geometric object under the condition of meeting the precision threshold and consistency threshold, and the constraint strategy is used to generate a risk shielding condition associated with the subsequent standard address unit when the anomaly cannot be immediately corrected or there is uncertainty; the anomaly label and the strategy result are written into the quality attribute field of the multi-source business basic data, so that the multi-source business basic data has traceable verification basis and executable correction path when used with digital orthophoto, digital elevation model, building base data, and terrain data.

[0096] In one possible implementation, based on the spatial logical relationship, the standard address unit is generated around the unified standard address system based on the road space entity, the spatial correlation between the standard address unit and the building white model data is established, and the address space carrier is formed. Specifically, the topological specification confirmation processing is performed on the road space entity after the spatial logical relationship verification, and the effective road space entity set is formed; the road attribution determination is performed based on the spatial adjacency relationship between the effective road space entity set and the building base data to determine the road space entity corresponding to the building base data; the standard address unit is generated based on the road space entity, so that the standard address unit reflects the road attribute and the building space position relationship; the unique identification code is generated for each standard address unit according to the unified identification code coding rule; the spatial correlation between the standard address unit and the corresponding building base data and building white model data is established; the consistency check is performed on the spatial correlation, thereby forming the address space carrier.

[0097] In one possible implementation, based on the spatial logical relationship, the standard address unit is generated around the unified standard address system based on the road space entity, the spatial correlation between the standard address unit and the building white model data is established, and the address space carrier is formed. Specifically, the topological specification confirmation processing is performed on the road space entity after the spatial logical relationship verification, and the effective road space entity set is formed; the road attribution determination is performed based on the spatial adjacency relationship between the effective road space entity set and the building base data to determine the road space entity corresponding to the building base data; the standard address unit is generated based on the road space entity, so that the standard address unit reflects the road attribute and the building space position relationship; the unique identification code is generated for each standard address unit according to the unified identification code coding rule; the spatial correlation between the standard address unit and the corresponding building base data and building white model data is established; the consistency check is performed on the spatial correlation, thereby forming the address space carrier.

[0098] Specifically, when performing the topological normative confirmation processing on the road space entity after the spatial logical relationship verification, the system checks the integrity and availability of the road space entity in geometry and topological relationship, identifies and removes the abnormal road segments with broken, overlapping, self-intersection or hanging nodes by checking the continuity of the road center line, the segmented connection relationship and the connectivity relationship between the road nodes, and excludes the logically incomplete road space entity caused by the missing or conflicting attributes by combining the consistency verification of the road grade attribute, the road name attribute and the road coding attribute, so that the remaining road space entity meets the stability requirements of address generation reference in geometry and semantic attribute, thereby forming an effective road space entity set that can be used for subsequent address anchoring; wherein the road space entity is used to represent the continuous geometric expression of the urban road in space, and the topological normative is used to represent the state that the road space entity meets the organization rules of urban road network in connection relationship and structure relationship.

[0099] When performing road attribution determination based on the spatial adjacency relationship between the effective road space entity set and the building base data, the road influence range is constructed around the road space entity, and the minimum distance relationship, orientation relationship and accessibility relationship between the building base data and the road space entity in the road influence range are analyzed to determine which road space entity forms the most direct service relationship with the building base data in space; the spatial adjacency relationship is used to represent the proximity and accessibility of the building base data and the road space entity in space position, and by comparing the distance from the building base boundary to the boundaries of multiple road space entities and combining the road grade and road direction, the road space entity corresponding to each building base data is determined, thereby establishing a clear road attribution basis for the generation of subsequent standard address units.

[0100] After determining the road space entity corresponding to the building base data, the standard address unit is generated based on the road space entity around the unified standard address system, and by combining the standard name of the road space entity, the road attribute information and the relative ordering relationship of the building base data in the road direction, the address expression is constructed for the building base data according to the unified address arrangement rule, so that the generated standard address unit can reflect the road name, road attribute and building space position relationship in semantics; wherein the standard address unit is used as the smallest granularity address expression unit with clear spatial direction in the city, and the unified standard address system is used to constrain the consistency of the address generation rule and the address expression structure, thereby ensuring the comparability of the structure and meaning of the standard address generated in different regions and at different times.

[0101] After the standard address unit is generated, the identification code generation process is performed on each standard address unit according to the unified identification code coding rule, the identification code that can uniquely identify the standard address unit is generated by combining the administrative division identifier, the road space entity identifier and the address sequence information according to the preset coding structure, so that the standard address unit has uniqueness, stability and traceability at the system level; the identification code is used as a unique index in the cross-system data exchange and subsequent governance object association process to avoid ambiguity and conflicts caused by relying only on text address description.

[0102] After obtaining the standard address unit with the unique identification code, the standard address unit is associated with the corresponding building base data and building white model data in space, the position of the standard address unit in the two-dimensional space is mapped to the corresponding building base surface, and the mapping relationship is further extended to the building white model data corresponding to the building base data, so that the standard address unit can be accurately anchored to the specific building white model entity in the three-dimensional space; wherein the building white model data is used to express the three-dimensional volume structure of the building, and the spatial association relationship is used to represent the binding relationship between the address and the building in the spatial position, so that the address is changed from the two-dimensional road reference to the spatial carrier with the three-dimensional building entity as the core.

[0103] After the spatial association of the standard address unit with the building base data and the building white model data is completed, the consistency of the spatial association relationship is checked, whether the standard address unit is uniquely associated with a single building white model data, whether the road ownership relationship is logically consistent, and whether the three-dimensional spatial position falls within the reasonable range of the building white model data are checked to identify abnormal conditions such as one address multiple bodies, multiple addresses one body or sequential misplacement; when an abnormality is found, the corresponding road ownership determination or address arrangement rule is modified, so that the finally formed address spatial carrier maintains consistency in spatial orientation, semantic expression and system identification, thereby outputting the address spatial carrier that is locatable, traceable and can be used for governance object data association.

[0104] S170, data association is performed between the actual population, the actual house and the actual unit and the address spatial carrier to form associated data.

[0105] In a possible implementation, the real population, the real house and the real unit are data-associated with the address space carrier to form associated data, specifically including: performing governance object standardization processing on the real population data, the real house data and the real unit data to form a governance object set; performing initial matching processing on the governance object set based on the unique identification code of the address space carrier to form a candidate association relationship between the governance object and the standard address unit; performing spatial consistency verification on the candidate association relationship based on the building white model data associated with the address space carrier to eliminate abnormal associations that do not have spatial rationality, and obtaining a processing association relationship; performing spatial hierarchical relationship analysis processing around the building white model data to refine and bind the real house data, and establishing a hierarchical association relationship between the real population data, the real unit data and the real house data; based on the processing association relationship and the hierarchical association relationship, associating the governance object that passes the verification with the corresponding address space carrier and outputting the associated data.

[0106] Specifically, when performing governance object standardization processing on the real population data, the real house data and the real unit data, the identification field, the address field, the business attribute field and the source field of various governance objects are uniformly arranged and verified to solve the problems of field heterogeneity, coding heterogeneity and state heterogeneity of the governance objects in the cross-department gathering scene, so that the governance objects of different sources meet the comparability and connectivity requirements in structure; the identification field is used to carry the unique identification or unique identification of the governance object, the address field is used to carry the address description information and the doorplate information of the governance object, the business attribute field is used to carry the population attribute, the house attribute or the unit attribute, and the source field is used to carry the data source and the update time and other meta information; for the governance object with missing identification field or conflicting identification field, a multi-field joint unique rule is used to generate a supplementary identification, and the address field is standardized and cleaned to eliminate the expression inconsistency caused by synonyms, abbreviations, misspelled words and sequence differences, so as to form a governance object set that can directly participate in subsequent matching and verification.

[0107] In the initial matching process of the governance object set based on the unique identification code of the address space carrier, the address field of each governance object record in the governance object set is parsed into a comparable address element sequence, and the address element sequence is jointly compared with the unique identification code corresponding to the standard address unit and its address semantic elements. A candidate association relationship between the governance object and the standard address unit is established through coding matching priority and semantic matching supplement. Coding matching is used to directly establish a deterministic association when the governance object record carries a unique identification code or can be mapped to a unique identification code. Semantic matching is used to determine the similarity between the address elements and the standard address unit according to the road name, house number, building number, etc. when the governance object record lacks a unique identification code. In the semantic matching process, the address similarity between the address element sequence and the standard address element sequence is calculated, and the candidate pairs with an address similarity exceeding the matching threshold are included in the candidate association relationship. The calculation formula is:

[0108]

[0109] wherein S represents the address similarity value and can vary in the range of , represents the number of address element categories and is a positive integer, represents the weight value of the th address element category and satisfies , represents the similarity value of the th address element category and can vary in the range of The address element categories include at least administrative division elements, road elements, house number elements, and building elements. The similarity value can be obtained by complete consistency determination or edit distance normalization determination. This formula weights and fuses the similarity of multiple address elements, so that key elements contribute more to the overall matching, thereby forming a candidate association relationship that can be used for subsequent spatial verification when the unique identification code is missing.

[0110] When performing spatial consistency verification on the candidate association relationship based on the building white model data associated with the address space carrier, the standard address unit in the candidate association relationship is mapped to its corresponding building white model data, and the spatial clues available in the governance object record are verified for consistency. The spatial clues include at least the building number, floor number, room number registered in the governance object record, and the spatial range description related to the house or unit. The spatial consistency verification is used to determine whether the candidate association relationship is established in the three-dimensional spatial direction. By checking whether the spatial clues indicated by the governance object fall within the spatial range corresponding to the building white model data, abnormal associations that are similar only at the address text level but contradictory in spatial direction are identified. For governance object records that cannot provide spatial clues, indirect verification is performed using the business association clues of the real house data or real unit data, and when the verification fails, the candidate association relationship is marked as an abnormal association and is removed, thereby outputting the processing association relationship that does not contain abnormal associations.

[0111] When performing spatial hierarchical relationship analysis processing around the building white model data, the building's building structure, floor structure, and room structure are analyzed using the geometric structure and attribute structure of the building white model data, and the real house data is refined and bound according to the hierarchical elements of building number, floor number, and room number, so that each piece of real house data can be positioned to a unique spatial hierarchical position in the building white model data. The spatial hierarchical relationship analysis processing is used to refine the building white model data from a whole volume expression to a hierarchical expression that can carry house units and unit units. The hierarchical association relationship is used to represent the ownership path and mapping path between the real population data, real unit data, and real house data. After the refinement and binding are completed, the real population data is associated with the corresponding real house data according to the residence relationship or use relationship, and the real unit data is associated with the corresponding real house data according to the business relationship or office relationship, so that the real population data and the real unit data can indirectly obtain the hierarchical position in the building white model data through the real house data, thereby forming a complete hierarchical association relationship.

[0112] When the association data is output based on the processing association relationship and the hierarchical association relationship, first, consistency check is performed on the processing association relationship and the hierarchical association relationship. Through checking whether the same governance object is associated to multiple address space carriers, whether the same real house data corresponds to multiple standard address units, and whether the same real population data falls into mutually exclusive house hierarchical positions, inconsistent conditions such as multi-address integration, integrated multi-address, or hierarchical conflict are identified and removed. Then, the governance object and the corresponding address space carrier that pass the check are finally associated, and the association result is written into the association data, so that the association data contains governance object identifier, unique identification code, standard address unit identifier, building white model identifier, and hierarchical elements corresponding to building structure, floor structure, and room structure, so that the association data has a traceable spatial pointing chain and can be directly used for subsequent city information model platform aggregation and smart city business calling.

[0113] S180, the association data is processed according to the unified data format specification and data slicing rule and is aggregated to the city information model platform and is output to the smart city application.

[0114] Before the association data is aggregated, first, around the unified data bearing requirement of the city information model platform, the data structure standardization processing is performed on the association data. Through field verification, type unification and constraint completion on the governance object identifier, standard address unit identifier, unique identification code, building white model identifier and spatial hierarchical elements contained in the association data, the association data meets the unified data format specification in field structure, naming rule and association semantics, so as to ensure that the association data generated at different times from different sources has consistent data organization form before entering the city information model platform, avoiding data parsing ambiguity caused by structural differences.

[0115] After completing the data structure standardization processing, the spatial index construction processing is performed on the association data according to the unified data format specification. By configuring the spatial index parameters of the spatial objects in the association data consistent with the preset coordinate system, each piece of association data can be quickly positioned to the corresponding spatial range. At the same time, attribute index is constructed for non-spatial attribute fields to support subsequent quick retrieval according to governance object type, address type or business attribute, so that the association data is adapted to the efficient scheduling needs of the city information model platform in logical structure and spatial structure.

[0116] After the index construction is completed, the data slicing processing is performed on the associated data around the city-level large-scale data bearing and distributed scheduling demand, the associated data is blocked according to the spatial range, the building white model data range or the administrative division range, so that each data slice covers a clear and continuous spatial area; the data slicing rule is used to control the data size of a single data unit, and the seamless connection between the data slices is maintained on the spatial boundary, so as to support the city information model platform to load and update the associated data on demand under different spatial scales, and avoid the performance pressure caused by the overall loading.

[0117] After the data slicing processing is completed, the processed associated data is subjected to data aggregation processing according to the data access specification of the city information model platform, the data slices are written into the data storage unit corresponding to the spatial range, and the metadata information of the associated data is registered on the platform side, so that the city information model platform can identify the spatial coverage range, data version state and update time of the associated data; the metadata information is used to support the unified management, version control and incremental update of the platform to the data, so that the associated data has maintainability and evolvability in the platform.

[0118] After the data aggregation is completed, the service encapsulation processing is performed on the associated data around the calling demand of the smart city application, the associated data is published in the form of a standardized data service interface, so that the smart city application can obtain the governance object information, address space carrier information and building white model associated information in a specified spatial range based on the unified interface; the data service interface is consistent with the spatial index and data slicing rule of the city information model platform, so that the smart city application can directly use the spatial scheduling capability of the platform when calling the associated data, thereby realizing real-time loading and updating in linkage with the three-dimensional scene.

[0119] Finally, through the continuous processing of the above data format standardization, data slicing processing, data aggregation and service output, the associated data forms a data result that can be uniformly carried, dynamically scheduled and stably output in the city information model platform, so that the smart city application can run under the same spatial data foundation and unified standard data system, thereby supporting the fine governance of the city, cross-departmental collaboration and business linkage in complex scenarios.

[0120] The embodiment also discloses a data processing device based on a smart city, referring to Figure 2 , comprising an acquisition module 201, a processing module 202 and an output module 203, and the device is used for executing any one of the data processing methods based on the smart city.

[0121] The acquisition module 201 is used for carrying out basic data collection around the city-level spatial data foundation, and acquiring original image data under a preset coordinate system through unmanned aerial vehicle oblique photogrammetry.

[0122] The processing module 202 is configured to generate point cloud data of a real scene three-dimensional model based on original image data through aerial triangulation and dense matching;

[0123] The processing module 202 is configured to synchronously generate a digital orthographic image and a digital elevation model under the constraint of the point cloud data;

[0124] The processing module 202 is configured to extract a building contour to form building base data with reference to the real scene three-dimensional model, and generate building white model data based on the building base data;

[0125] The processing module 202 is configured to introduce a preset coordinate system around the construction demand of a city digital public infrastructure to multi-source business basic data, and verify the spatial logical relationship between the multi-source business basic data and the building base data and the terrain data based on the digital orthographic image and the digital elevation model;

[0126] The processing module 202 is configured to generate a standard address unit based on a road space entity around a unified standard address system based on the spatial logical relationship, establish a spatial correlation relationship between the standard address unit and the building white model data, and form an address space carrier;

[0127] The processing module 202 is configured to perform data correlation between the actual population, the actual housing and the actual unit and the address space carrier to form correlation data;

[0128] The output module 203 is configured to process and converge the correlation data to a city information model platform according to a unified data format specification and a data slice rule, and output to a smart city application.

[0129] It should be noted that the apparatus provided in the above embodiments is only used as an example to divide the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0130] The embodiment also discloses an electronic device, which refers to Figure 3 The electronic device can include at least one processor 301, at least one communication bus 302, a user interface 303, a network interface 304, and at least one memory 305.

[0131] The communication bus 302 is used to realize the connection and communication between the components.

[0132] The user interface 303 can include a display screen, a camera, and optionally a standard wired interface and a wireless interface.

[0133] The network interface 304 can optionally include a standard wired interface and a wireless interface (e.g., a WI-FI interface).

[0134] The processor 301 can include one or more processing cores. The processor 301 connects various parts of the server through various interfaces and lines, executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 301 can be integrated with a combination of one or more of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU is mainly used to process operating systems, user interfaces, and application programs. The GPU is used to render and draw the content to be displayed on the display screen. The modem is used to process wireless communication. It can be understood that the above-mentioned modem can also not be integrated into the processor 301, but can be implemented by a separate chip.

[0135] The memory 305 can include a random access memory (RAM) and can also include a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 can include a program storage area and a data storage area, where the program storage area can store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, etc.; the data storage area can store data involved in the various method embodiments described above, etc. The memory 305 can also be at least one storage device located away from the aforementioned processor 301. The memory 305, as a computer storage medium, can include an operating system, a network communication module, a user interface 303 module, and an application program of a data processing method based on a smart city.

[0136] In Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an interface for user input and obtain data input by the user; and the processor 301 can be used to call an application program of a data processing method based on a smart city stored in the memory 305, and when executed by one or more processors 301, the electronic device performs the method of one or more of the above embodiments.

[0137] It should be noted that, for the above-mentioned method embodiments, in order to simply describe, they are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification all belong to preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0138] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0139] In several embodiments provided by the present application, it should be understood that the disclosed apparatus can be implemented in other manners. For example, the division of the apparatus embodiments is merely illustrative, and the units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0141] In addition, each functional unit in each embodiment of the present application can be integrated into a processing unit, or each unit can be physically present separately, or two or more units can be integrated into one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0142] If the integrated unit is realized in the form of 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 solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium 305 and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method of each embodiment of the present application. The aforementioned storage medium 305 includes: a U disk, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0143] The present application also discloses a non-transitory computer readable storage medium, which stores instructions. When executed by one or more processors 301, the electronic device executes one or more methods as in the above embodiments.

[0144] The above merely show example embodiments of the present disclosure, and cannot limit the scope of the present disclosure. That is, equivalent changes and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the specification and practice of the present disclosure. The present disclosure is intended to cover any variations, uses, or adaptive changes of the present disclosure following the general principles thereof and including those art-known or customary practices not recited in the present disclosure. The specification and examples are to be regarded as merely illustrative, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A data processing method based on smart cities, characterized in that, The method includes: Basic data collection was carried out around the city-level spatial data base, and raw image data under the preset coordinate system was obtained by UAV oblique photogrammetry. Point cloud data of a real-world 3D model is generated based on the original image data through aerial triangulation and dense matching. Digital orthophotos and digital elevation models are generated synchronously under the constraints of the point cloud data. Using the real-world 3D model as a reference, the building outline is extracted to form building base data, and building white model data is generated based on the building base data; To meet the needs of urban digital public infrastructure construction, the preset coordinate system is introduced into the multi-source business basic data, and the spatial logical relationship between the multi-source business basic data, the building base data, and the terrain data is verified based on the digital orthophoto and the digital elevation model. Based on the aforementioned spatial logical relationship, standard address units are generated on the basis of road spatial entities around a unified standard address system. Spatial association is established between the standard address units and the building white model data to form an address space carrier. The actual population, actual housing, and actual units are associated with the address space carrier to form associated data; The associated data is processed according to a unified data format specification and data slicing rules, aggregated into the city information model platform, and output to smart city applications.

2. The data processing method based on smart cities according to claim 1, characterized in that, The process of synchronously generating digital orthophotos and digital elevation models under the constraints of the point cloud data specifically includes: By identifying and removing outliers, correcting mismatched points in occluded areas, and unifying the point cloud density distribution, a unified spatial constraint benchmark is obtained from the point cloud data. Under the unified spatial constraint benchmark, based on the geometric correspondence between point cloud data and the original image data, the tilt distortion of the original image data is corrected by pixel by utilizing the surface elevation information and ground feature height information in the point cloud data, thereby generating a digital orthophoto image in the preset coordinate system. Based on the elevation distribution characteristics in the point cloud data, the point cloud data is processed to separate ground features from ground points. By identifying building points and vegetation points and retaining the ground point set, ground point cloud data for terrain representation is formed. Spatial interpolation and gridding are performed on the ground point cloud data to generate the digital elevation model.

3. The data processing method based on smart cities according to claim 1, characterized in that, The point cloud data for generating a real-world 3D model based on the original image data through aerial triangulation and dense matching specifically includes: Abnormal images with blurriness, occlusion, or temporal shift are removed from the original image data to obtain image control results; Based on the preset coordinate system and the image control results, aerial triangulation processing is performed on the original image data. By extracting the same feature points between images and establishing image connection relationships, and combining the image control points, joint adjustment is performed on the image exterior orientation elements to determine the spatial position parameters and attitude parameters of each image under the preset coordinate system, thereby obtaining the image exterior orientation elements. Based on the exterior orientation elements of the images, dense matching preparation processing is performed on the original image data. By analyzing and filtering the differences in image viewpoints, the range of overlapping areas, and the baseline length, the image combination relationship that meets the dense matching conditions is determined. Based on the image combination relationship, dense matching processing is performed on the original image data. By performing point-by-point matching on the corresponding regions of the multi-view images at the pixel scale, a set of matching points with spatial correspondence is obtained. By combining the image exterior orientation elements, the matching points of the matching point set are back-calculated to their three-dimensional spatial positions under the preset coordinate system, thereby forming an initial three-dimensional point set; The initial three-dimensional point set is subjected to point cloud consistency constraint processing. This process involves identifying and removing outliers caused by occlusion, reflection, or matching errors, and correcting the point cloud density distribution and spatial continuity to generate the point cloud data.

4. The data processing method based on smart cities according to claim 1, characterized in that, The step of extracting building outlines to form building base data based on the real-world 3D model, and generating white model data of the building based on the building base data, specifically includes: The building separation enhancement process is performed on the real-scene 3D model to identify height abrupt changes, normal vector differences, and slope differences between buildings and the ground surface to form a set of candidate building regions. Building contour extraction is performed based on the candidate building region set. A set of building contour lines is generated through planar projection, boundary recognition and topology verification. Contour consistency verification is then performed in conjunction with the digital orthophoto. Based on the set of building outlines, the building base data is constructed. The building base surface set is formed by constructing closed surfaces and checking surface topology. The ground elevation is then fitted by combining the digital elevation model. Based on the building base data and the real-scene 3D model, a building height analysis is performed, and the building height value is calculated by the height difference between the roof point set and the ground point set. Based on the building base data and the building height value, perform three-dimensional extrusion to generate white model data of the building; The spatial consistency check is performed between the building white model data and the real-world 3D model. When the deviation exceeds the limit, the building outline and building height are corrected by callback, thereby outputting building base data and building white model data that are consistent with the real-world 3D model.

5. The data processing method based on smart cities according to claim 1, characterized in that, The process involves introducing multi-source business foundation data into the preset coordinate system based on the needs of urban digital public infrastructure construction, and verifying the spatial logical relationship between the multi-source business foundation data, building base data, and terrain data based on the digital orthophoto and the digital elevation model. Specifically, this includes: The coordinate system is unified by performing coordinate system processing on the multi-source business basic data, so that the multi-source business basic data forms a unified spatial reference under the preset coordinate system; Based on the digital orthophoto, a planar position consistency check is performed on the multi-source service basic data to identify the planar offset relationship between the multi-source service basic data and the real surface texture. Based on the digital elevation model, the elevation logic relationship of the multi-source business basic data is verified to determine the rationality of the vertical expression of the multi-source business basic data. Spatial inclusion and spatial adjacency checks are performed on the multi-source business basic data based on the building base data to identify abnormal relationships that do not conform to the logic of urban spatial organization. Based on the terrain data, a terrain adaptability check is performed on the multi-source business basic data to verify the rationality of the spatial distribution of the multi-source business basic data under terrain constraints. Various spatial logic anomalies identified through verification are classified and marked, and correction or constraint strategies are determined, thereby enabling the multi-source business basic data to form the spatial logic relationship with the digital orthophoto, the digital elevation model, the building base data, and the terrain data.

6. The data processing method based on smart cities according to claim 1, characterized in that, Based on the spatial logical relationship, standard address units are generated on the road spatial entity around the unified standard address system. A spatial association is established between the standard address units and the building white model data to form an address space carrier, specifically including: The road spatial entities that have been verified by the aforementioned spatial logical relationship are subjected to topological normalization confirmation processing to form a valid set of road spatial entities; Based on the spatial adjacency relationship between the set of valid road spatial entities and the building base data, a road attribution determination is performed to identify the road spatial entities corresponding to the building base data; The standard address unit is generated based on the road space entity, so that the standard address unit reflects the relationship between road attributes and building spatial location; A unique identification code is generated for each of the standard address units according to the unified identification code encoding rules; Establish a spatial association between the standard address unit and the corresponding building foundation data and building white model data; A consistency check is performed on the spatial association to form the address space carrier.

7. The data processing method based on smart cities according to claim 1, characterized in that, The step of associating the actual population, actual housing, and actual units with the address space carrier to form associated data specifically includes: The data on the actual population, actual housing, and actual units are processed to standardize the governance objects in order to form a set of governance objects; Based on the unique identification code of the address space carrier, an initial matching process is performed on the set of governance objects to form a candidate association relationship between the governance objects and standard address units; Based on the building white model data associated with the address space carrier, spatial consistency verification is performed on the candidate associations to eliminate abnormal associations that do not have spatial rationality, and processed associations are obtained. Spatial hierarchy relationship parsing is performed on the white model data of the building to refine and bind the actual housing data, and establish hierarchical association between the actual population data, the actual unit data and the actual housing data; Based on the processing association and the hierarchical association, the governance objects that have passed the verification are associated with the corresponding address space carriers and the associated data is output.

8. A data processing device based on smart cities, characterized in that, The apparatus is used to execute a data processing method based on a smart city as described in any one of claims 1-7, the apparatus comprising an acquisition module, a processing module, and an output module, wherein: The acquisition module is used to collect basic data around the city-level spatial data base and acquire raw image data under a preset coordinate system through UAV oblique photogrammetry. The processing module is used to generate point cloud data of a real-world 3D model based on the original image data through aerial triangulation and dense matching. The processing module is used to synchronously generate digital orthophotos and digital elevation models under the constraints of the point cloud data. The processing module is used to extract the building outline to form building base data based on the real scene 3D model, and generate building white model data based on the building base data; The processing module is used to introduce multi-source business basic data into the preset coordinate system based on the construction needs of urban digital public infrastructure, and to verify the spatial logical relationship between the multi-source business basic data and the building base data and terrain data based on the digital orthophoto and the digital elevation model. The processing module is used to generate standard address units on the basis of road spatial entities based on the spatial logical relationship and around the unified standard address system, and to establish a spatial association relationship between the standard address units and the building white model data to form an address space carrier. The processing module is used to associate the actual population, actual housing and actual units with the address space carrier to form associated data; The output module is used to process the associated data according to a unified data format specification and data slicing rules, aggregate it to the city information model platform, and output it to smart city applications.

9. An electronic device, characterized in that, The device includes a processor, a communication bus, a user interface, a network interface, and a memory. The memory is used to store instructions. The user interface and the network interface are both used to communicate with other devices. The communication bus is used to enable communication between the components within the electronic device. The processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method as described in any one of claims 1-7.

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