Optimization method after generation of three-dimensional house type model based on house surveying and mapping

Abstract

The invention relates to the technical field of model design, in particular to a method for optimizing a three-dimensional house type model after generation based on house surveying and mapping, which comprises the following steps of: performing multi-stage combination on edge point coordinates in a weight conversion mode to obtain a combined contour edge line; analyzing the contour edge line, dividing a plurality of house units, and obtaining a logic sub-region corresponding to each house unit according to the overlapping region and the broken line region corresponding to each house unit in combination with the projection area of the contour edge line; identifying interval areas of the logic sub-regions, identifying boundary conditions of the logic sub-regions according to the interval areas, and determining target boundary conditions of the logic sub-regions; based on a target boundary condition of the logic sub-region, judging an offset region of each house unit; differential accumulation is carried out on the offset areas of the adjacent house units, and a reference model of the current three-dimensional house model is constructed according to a differential accumulation path; the optimization efficiency and the area positioning accuracy of the house model are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of model design, and in particular to a post-generation optimization method for a three-dimensional apartment model based on house surveying and mapping. Background Art

[0002] In the field of three-dimensional house model generation, house surveying is used to match house architectural parameter information, house structure, geometric topology and house composition to form a house model. However, the current method tends to rely on manual surveying and empirical description of the positions of individual units in the house structure, which makes it easy for the house model to be confused during identification, resulting in partial coordinate errors during house identification and reducing the accuracy of house model establishment.

[0003] In the field of interior design, applying relevant technologies to achieve the desired home decoration has become an emerging trend. By combining architectural parameter information, key structural point identification, geometric topology analysis, and decorative element matching, more intelligent, efficient, and personalized interior design solutions can be achieved.

[0004] For example, Chinese patent publication number CN116702298A discloses a model construction method and system for interior decoration design, which is used to realize online processing of interior decoration design and improve the efficiency of interior decoration design. The method includes: obtaining architectural parameter information and performing data classification to obtain parameter identification data and perform key point identification to obtain a set of structural key points; performing topological structure analysis on the target house to generate the target house geometric topological structure and perform geometric structure generation to obtain the target geometric structure; performing initial model construction to obtain an initial house model and obtain indoor space information; performing decorative element matching on the indoor space information to generate a decorative element set; performing decorative scheme matching to generate a target decorative scheme; performing element screening on the decorative element set to generate a target element set; performing spatial position matching on the target element set to generate a spatial position set; and adjusting parameters of the initial house model to generate a target house model.

[0005] For example, Chinese patent publication number CN112948933A discloses a method for constructing a house model, a display method, a management device, and a storage medium, wherein the method for constructing the house model includes: obtaining spatial data of the house, and obtaining attribute data of the house; wherein the spatial data corresponds to each floor and each household, and the spatial data and attribute data of each household in the house correspond one-to-one, and the attribute data includes at least resident information of each household; and generating a data model using the spatial data and attribute data.

[0006] The existing technologies separately describe the relative positions of various decorative elements used in house layout recognition, as well as the coordinate differences displayed when the house model is used with a perspective command. These house models primarily demonstrate the accuracy of the current house model through the coordinate differences between the decorative elements and the house after disassembly. However, these methods still suffer from logical errors in the edge line configuration when the house model is disassembled, resulting in unclear divisions between the house units, which ultimately increases errors in each area and reduces model accuracy. Summary of the Invention

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a post-generation optimization method for a three-dimensional apartment model based on house surveying, including: S1, based on the connection properties of the three-dimensional house model elements, extracting the edge point coordinates and topological relationships on multiple cross sections, and through weight conversion, performing multi-level combination of the edge lines of the model at different positions to obtain the combined contour edge lines.

[0008] S2: Analyze the contour edge lines to divide multiple housing units and calculate the three-dimensional overlap of the contour edge lines of adjacent housing units to obtain the overlapping area and broken area corresponding to each housing unit. Combined with the projected area of ​​the contour edge lines, the logical sub-area corresponding to each housing unit is obtained.

[0009] S3, based on the logical sub-areas of each housing unit, identifying the interval area of ​​each logical sub-area, and identifying the boundary conditions of the current logical sub-area based on the interval area, and determining the target boundary conditions of the current logical sub-area.

[0010] S4, based on the target boundary conditions of the current logical sub-area, perform spatial solution on the currently accessed three-dimensional house model, and determine the offset area of ​​each house unit based on the mapping area of ​​each target boundary condition after spatial solution.

[0011] S5, performing differential accumulation on the offset areas of adjacent house units, and constructing a reference model of the current three-dimensional house model using the differential accumulation path.

[0012] The beneficial effects of the present invention are: 1. The present invention re-extracts the edge points on multiple cross sections based on connection attributes to re-obtain the contour edge lines of the corresponding positions, and identifies the problem of blurred model edges when the house model is set; and based on the projected area and density of the contour edge lines, splices and generates house units, and divides the overlapping areas and broken areas within the house units into multiple logical sub-units in turn to check whether the configuration of each position in the house model is reasonable and whether the edge line configuration of the generated area part is accurate.

[0013] 2. The present invention triggers boundary condition screening based on the interval area of ​​logical sub-areas, determines the target boundary conditions through similarity calculation and splicing relationship judgment, and further determines the setting accuracy of structural features such as walls, doors and windows in the house model, as well as the reliability of the boundary conditions after the target boundary conditions are checked.

[0014] 3. The present invention determines the bias area through the mapping area of ​​the target boundary conditions, through data point comparison (and problem type classification), and generates a differential accumulation path through differential accumulation of adjacent unit bias areas. It combines the single visit duration and the number of accumulations to dynamically adjust the traversal cycle, improve the update of the model under multi-scenario processing, reduce manual intervention in model configuration, shorten the cycle of model optimization processing, and improve efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below with reference to the accompanying drawings and examples.

[0016] Figure 1 The present invention is a flowchart of a method for optimizing a three-dimensional apartment model after generation based on house surveying and mapping.

[0017] Figure 2 The present invention is a flow chart of step S1 of a method for optimizing a three-dimensional apartment model after generation based on house surveying.

[0018] Figure 3 The present invention is a flow chart of step S2 of a method for optimizing a three-dimensional apartment model after generation based on house surveying.

[0019] Figure 4 The present invention is a flow chart of step S3 of a method for optimizing a three-dimensional apartment model after generation based on house surveying and mapping.

[0020] Figure 5 The present invention is a flow chart of step S4 of a method for optimizing a three-dimensional apartment model after generation based on house surveying.

[0021] Figure 6 The present invention is a flow chart of step S5 of a method for optimizing a three-dimensional apartment model after generation based on house surveying. DETAILED DESCRIPTION

[0022] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0023] See Figure 1A post-generation optimization method for a three-dimensional house model based on house surveying and mapping comprises: S1, based on the connection properties of the three-dimensional house model elements, extracting the edge point coordinates and topological relationships on multiple cross sections, and performing multi-level combination of the edge lines at different positions of the model through weight conversion to obtain the combined contour edge lines.

[0024] S2: Analyze the contour edge lines to divide multiple housing units and calculate the three-dimensional overlap of the contour edge lines of adjacent housing units to obtain the overlapping area and broken area corresponding to each housing unit. Combined with the projected area of ​​the contour edge lines, the logical sub-area corresponding to each housing unit is obtained.

[0025] S3, based on the logical sub-areas of each housing unit, identifying the interval area of ​​each logical sub-area, and identifying the boundary conditions of the current logical sub-area based on the interval area, and determining the target boundary conditions of the current logical sub-area.

[0026] S4, based on the target boundary conditions of the current logical sub-area, perform spatial solution on the currently accessed three-dimensional house model, and determine the offset area of ​​each house unit based on the mapping area of ​​each target boundary condition after spatial solution.

[0027] S5, performing differential accumulation on the offset areas of adjacent house units, and constructing a reference model of the current three-dimensional house model using the differential accumulation path.

[0028] The core goal of the current solution is to optimize the area of ​​each area marked within the house model, detect and correct potential errors by reassembling and reassembling them; and synchronize these identified problems to multiple 3D models after house mapping to identify areas that are prone to problems during the current mapping.

[0029] Preferably, the connection properties of the three-dimensional house model represent the wall endpoints, corner points, and other points that need to be paid attention to when the model is set up. These points are marked, and by identifying the current model connection relationship, it is checked whether there is a problem of reducing the shared area after the model is built.

[0030] like Figure 2 As shown, the implementation method of step S1 includes: S11, obtaining the curve of the three-dimensional house model at the connection position as the basic line segment, and determining whether the position of the basic line segment includes multiple endpoints and multiple corner points.

[0031] S12: If so, use the multiple endpoints and the multiple corner points as the coordinates of the identified edge points, and record the topological relationship of each edge point.

[0032] If no such feature exists, the foundation line segment is projected onto the centerline of the building structure at the connection location. The intersection of the projected centerline and the current foundation line segment is used as the edge point coordinate. The topological relationship between the projection plane and the plane containing the current foundation line segment is recorded as the edge point. When there are no obvious geometric features on the foundation line segment, edge points are constructed using projection to ensure that valid contour points can be extracted in all cases.

[0033] With the current basic line segment as the center, construct a central projection line perpendicular to the plane where the line segment is located; calculate the intersection of the projection line and the adjacent structural surface such as another wall or roof; record the intersection as the edge point, and then record the relationship between the projection line and the plane where the original basic line segment is located; the three-dimensional coordinates of the edge point and the structural surface to which it belongs; this information is also stored together with the normally extracted edge point part to achieve consistency in the current plane setting.

[0034] Preferably, the above-mentioned weight conversion method connects the edge points, sets the weight according to the connection attributes of the position of the connected edge points, and further sets the weighted value for the distance after the edge points are connected to illustrate the geometric contour relationship of the house model in three-dimensional space, and outputs the connected boundary points as contour edge lines.

[0035] That is, the implementation method of obtaining the combined contour edge line through weight conversion includes: connecting the edge points to form several candidate edges, setting weights according to the structure and length corresponding to the candidate edges; using the weight of each candidate edge to correct the candidate edge with the shortest path, and using the corrected candidate edge as the output contour edge line.

[0036] At this time, the weight is set based on the structure of the candidate edge and the length value after connection. For example, the basic weight value is set according to the different structures, and the inverse of the length of the candidate edge is used as the weight related to its length. The sum of these two weights is used as the weight of the candidate edge. The calculated value at this time will be standardized and its dimension will be eliminated before setting. As for the basic weight, the weight value corresponding to each structure of the current house model will be set in advance.

[0037] When using the shortest path to modify candidate edges, the weights of the candidate edges are used to determine a set of paths with the smallest sum of the candidate edge weights, ensuring that the current candidate edges, after combination, conform to the main structural direction. This set of paths is considered the contour edge line of the current output. Multiple planes and positions of the house model are then traversed to complete the division of the house units and output the edge contour.

[0038] In one embodiment of the present invention, the logical sub-area includes an indoor exclusive area, a shared area, and a separation area. This step requires obtaining a clear outline of the house boundary without overlapping gaps, calculating the projected area of ​​each area, and completing the logical area division of the model space to form a house area distribution map with multiple areas such as exclusive areas, shared areas, and separation areas. This diagram will further verify whether there are any abnormal problems in some area types of the logical sub-area after the current house model is continuously generated.

[0039] like Figure 3 As shown, the implementation of step S2 includes: S21, based on the acquired contour edge line, generating the projection area of ​​the contour edge line at the position of the contour edge line.

[0040] S22 , using the density of the center points and the density of the vertices after the projection of the outline edge line, splicing the plane where the outline edge line is located according to the projected area to obtain multiple housing units.

[0041] S23 , distinguishing the overlapping areas and the disconnected areas of the house units according to the transition values ​​of the common contour edge lines of the adjacent house units, and calculating the three-dimensional overlapping degree of the house units.

[0042] S24 , dividing the housing units into logical sub-areas of the housing units in a secondary combination of overlapping areas and disconnected areas according to the three-dimensional overlap of the housing units.

[0043] When generating the projected area, the three-dimensional contour edge line can be projected onto the XY plane, i.e., the horizontal plane relative to the house. Then, the projected area of ​​each closed area formed by the contour edge line is calculated using coordinate points, and the density of each projected center coordinate and vertex coordinate is calculated. For example, the coordinates of multiple projected points on the contour edge line are clustered to determine the coordinates of the current center point, and the number of corresponding points in the neighborhood of the center point is used as the density of the center point. The density of the vertices is similarly calculated. In this case, the identified vertices are more likely to be vertices of a quadrilateral or other polygonal shape mapped on a two-dimensional plane after projection, relative to the three-dimensional structure. In this case, the density value is determined by the number of points at the center of the plane and the number of vertices of the contour edge line. That is, after the density value is determined by density clustering, the contour edge line that can form a closed plane is divided into house units according to the selection of the corresponding density value. Each house unit is regarded as an area within the current house, or the house unit can be regarded as the area after the entire house model is projected. The neighborhood radius used in density clustering can be set based on the average value of the spacing between multiple planes of the current house model to obtain multiple house units after the current house model is projected.

[0044] Preferably, after obtaining the density of the projected center point and the density of the vertex, it is also necessary to verify the type of the area where the original contour edge line is located, that is, whether the current house model is a separate room, corridor or public area and other types of house mapping.

[0045] Preferably, when calculating the transition value of the common contour edge line, when it is an overlapping area, the coordinate difference of the two end points on the common contour edge line is first used for processing, that is, the coordinate difference is calculated in sequence, the average value of the coordinate difference is obtained, and the angle between the adjacent house units at the boundary is determined. If the average value and the angle of the coordinate difference both meet the requirements when the current house unit is combined, that is, when the adjacent house units are at the values ​​set for normal housing, the current house unit is marked, and the ratio of its overlapping volume to the minimum bounding box volume is calculated, and this value is output as the three-dimensional overlap; if the average value and the angle of the coordinate difference do not meet the requirements, the corresponding house unit is directly output and the three-dimensional overlap is marked as 0 to indicate that there is a problem of excessive gap or abnormal angle setting when the current house is spliced. At this time, the judgment that the average value and the angle of the coordinate difference meet the requirements is based on the distance and angle between the corresponding house units during house mapping, that is, the current value is identified by the confidence interval of the average value and the angle of the coordinate difference in historical data, and the confidence interval is set at a 95% confidence level.

[0046] It should be noted that the overlapping volume represents the intersection, and the minimum bounding box refers to the minimum bounding box of the house unit, which represents the volume of an enclosing outline, that is, the relative three-dimensional union, that is, the ratio of the three-dimensional area projected onto the two-dimensional area. It is used to segment the overlap of the three-dimensional outline in the broken lines and overlapping parts, and to explain the relative area ratio after projection. In essence, it uses the density clustering and historical data statistics after projection to quickly identify the house unit, so as to realize the recognition of multi-position splicing and aggregation in the house unit. The use of three-dimensional projection to two dimensions is used to quickly determine the broken lines and overlapping parts that exist in the recognition model and quickly generate preview scenes.

[0047] When it is a broken line area, the endpoint check is performed with the common contour edge line. If the breakpoint distance is greater than the maximum allowable distance, it is considered to be broken, and the three-dimensional overlap at the corresponding position is marked as 0. At this time, the maximum allowable distance is the upper limit of the current coordinate difference average value to determine whether the break is reasonable.

[0048] Preferably, the implementation method of step S24 includes: judging the number of overlapping areas in the current house unit; if the number of overlapping areas is greater than or equal to two, calculating the area ratio of adjacent broken areas to overlapping areas; if the area ratio of adjacent broken areas to overlapping areas is less than a preset area ratio, deleting the corresponding broken areas, and using the remaining overlapping areas and broken areas as output logical sub-areas.

[0049] If the area ratio of adjacent broken line areas to overlapping areas is greater than a preset area ratio, the corresponding overlapping areas are deleted, and the remaining overlapping areas and broken line areas are used as output logical sub-areas.

[0050] If the number of overlapping areas is less than two, the current overlapping area is expanded, and the broken area and the overlapping area are combined at the intersection of the expanded overlapping area and the broken area to obtain the logical sub-area of ​​the current housing unit.

[0051] Preferably, the above-mentioned preset area ratio ranges from 0.2 to 0.5. When the current scene requires rich details, a value of 0.2 will be used. If it is necessary to avoid deleting the valid area, a value of 0.5 will be used. At this time, if it tends to identify residential house models, a value of 0.3 will be used as the current threshold to ensure that small broken line areas are deleted and the main structure is retained.

[0052] In one embodiment of the present invention, after obtaining the logical sub-areas of each housing unit, the area size of the logical sub-areas is identified, and the data form of the combination of the type, boundary conditions, etc. of each logical sub-area is determined. After processing these data forms, the target boundary conditions of each logical sub-area are marked.

[0053] The output boundary conditions describe the current house model settings, including ownership types such as private or shared, functional types such as corridors and elevator shafts, and coordinates and connection angles at corresponding locations. The boundaries within each logical sub-area are labeled, and the boundary conditions are numerically displayed. Using the difference between multiple boundary conditions, target boundary conditions with obvious anomalies are selected.

[0054] After obtaining the gap area, the abnormal intervals are identified to eliminate the invalid interval areas, such as Figure 4 As shown, the implementation method of step S3 also includes: S31, using the spacing area of ​​each logical sub-region to check the abnormal spacing between the logical sub-regions, and triggering each boundary condition with the abnormal spacing to form an spacing check sequence.

[0055] S32, determining the splicing relationship between the interval check sequences. When the splicing relationship of any group of logical sub-regions in the interval check sequence is consistent, the logical sub-regions are spliced ​​in a continuous hierarchical form, and the boundary conditions of the spliced ​​logical sub-regions are used as the target boundary conditions of the current logical sub-region.

[0056] S33, when the splicing relationship is inconsistent, any group of logical sub-regions are spliced ​​in a cyclic splicing manner, the boundary conditions of the spliced ​​logical sub-regions are intersected, and the corresponding intersection is output as the target boundary condition of the current logical sub-region.

[0057] Preferably, the above-mentioned splicing relationship is generally expressed as a relative relationship during various splicing, such as nested sub-regions, obvious boundaries between sub-regions, intersections or overlaps between sub-regions, grid-differentiation of sub-regions, and closed loop formation of sub-regions. If the extracted logical sub-regions meet these splicing relationships and any set of extracted data can achieve consistent representation content, the combination of logical sub-regions is completed according to their relative relationships and multi-level splicing. The boundary conditions finally output by each logical sub-region are relative to the overall boundary conditions under the splicing at this time; as for inconsistent scenarios, it is necessary to loop the splicing multiple times to obtain the form of intersection to illustrate the target splicing conditions at the current splicing.

[0058] Preferably, the above-mentioned abnormal intervals are determined by comparing the interval area with the initial size of the current three-dimensional house model. If the currently extracted interval area is inconsistent with the initial size, it is considered that an abnormal interval exists, and the currently generated three-dimensional model may have problems such as size marking errors.

[0059] That is, the implementation method of the boundary condition check in step S31 includes: based on the boundary conditions of each logical sub-region, using the boundary type corresponding to the boundary conditions, calculating the maximum difference of the logical sub-region under the same boundary type, and based on the maximum difference under the same boundary type, performing similarity calculation on each logical sub-region.

[0060] The minimum difference value under the same boundary type is determined based on the calculated multiple logical sub-regions, and the logical sub-regions corresponding to the minimum difference value are combined to obtain an interval checking sequence.

[0061] Preferably, the above-mentioned maximum difference can be calculated based on the interval area to obtain a group of logical sub-regions with a large distance between them, and these logical sub-regions are calculated using cosine similarity according to the calculated maximum difference, and multiple logical sub-regions with similarity values ​​greater than 0.6 are used as the calculated logical sub-regions, and the corresponding logical sub-regions are divided into multiple groups of data according to the minimum difference under the same boundary type, and these combined logical sub-regions are regarded as interval screening sequences.

[0062] At this point, similarity is used to quantify the correlation between logical sub-regions in terms of boundary conditions, providing a basis for combination. When identifying the same boundary type using the maximum difference in area, the focus is on identifying flaws in the current model for different area markers. Similarity clustering is then used to identify these flaws, highlighting issues with the boundary conditions in the current scenario. After sequentially processing similarity values, areas with the smallest differences are prioritized, reducing model recognition costs and ultimately achieving corrections to the 3D house model.

[0063] In one embodiment of the present invention, Figure 5 As shown, the implementation method of step S4 includes: S41, identifying the mapping area of ​​each target boundary condition, and traversing the mapping area based on the ratio of the number of contour edge lines of the mapping area to the total number of contour edge lines; at this time, the target boundary condition with a high ratio can be used as the priority identification data for subsequent house model identification, and the identified data includes problems such as boundary condition redundancy, insufficient boundary condition identification, and conflicting boundary condition identification of different functional areas. These problems are concentratedly reflected and a bias area related to the corresponding problem is generated. When the ratio of the number of contour edge lines of the mapping area to the total number of contour edge lines is large, it means that the current mapping area is a relatively important area, and if it is small, it means that the corresponding area is a local small area. At this time, it will be processed in sequence according to the size of the ratio, and the corresponding deviation will be combined in the form of three or more consecutive mapping areas to complete the processing of the multiple groups of logical sub-areas in the current house model.

[0064] S42, the traversed mapping area is divided into data point pairs according to the ratio of the number of contour edge lines in the mapping area to the total number of contour edge lines, and the parameter offset comparison is performed on the adjacent mapping areas under the data point pairs to determine the output offset area; the data point pairs are sorted based on the ratio so that each mapping area corresponds to a ratio.

[0065] When the bias area is identified in step S42, the implementation method includes: responding to the input mapping area, saving the mapping area with the data point pairs corresponding to the mapping area, and determining the type of problem that occurs in the data point pairs.

[0066] If the problem type is boundary condition redundancy, the area with the maximum offset value after the difference between the current input mapping area and the adjacent mapping area is output as the bias area.

[0067] If the problem type is a boundary condition defect, the average offset value of the mapping area is calculated based on the current mapping area, and the central area corresponding to the average offset value is used as the output bias area.

[0068] If the problem type is a boundary condition conflict, the mapping area corresponding to the conflicting boundary condition is used as the output bias area.

[0069] At this time, the offset value is obtained by comparing multiple points corresponding to the contour edge line on the mapping area with the expected house model to determine the coordinate error, and then identify the errors in multiple positions; for example, boundary condition redundancy is to differentiate the current mapping area from the adjacent mapping area, find the different mapping positions, find the local area corresponding to the maximum offset value, and output the corresponding area as the offset area; when the boundary condition is missing, it is necessary to identify the central area where the average offset value is located when the defect is missing, that is, the center position of the currently input mapping area, based on the access to this center position, and finally readjust the mapping area related to this center position to achieve the adjustment of the house model on the offset area; as for boundary condition conflicts, it is only necessary to directly deal with the conflicting positions. This part of the position will have obvious coordinate point errors, that is, this part of the house model needs to be adjusted in real time according to the access situation.

[0070] In one embodiment of the present invention, during differential accumulation, differential accumulation needs to be performed based on the location of the biased area. When performing differential accumulation, the coordinate errors before and after the spatial solution on the biased area are used to form a differential accumulation path in the form of error points. The accessibility of the relevant area is identified based on the area passed by the differential accumulation path to determine whether there are accuracy setting problems at each location after the house is spliced ​​and reorganized.

[0071] like Figure 6 As shown, the implementation of step S5 includes: S51, performing spatial merging and matching according to the offset areas of adjacent housing units, obtaining a combined cumulative difference set, and sorting the data in the cumulative difference set to obtain a differential accumulation path.

[0072] S52 , connecting each house unit based on the single visit duration and cumulative number of times of each location in the differential cumulative path, and determining the traversal period of the differential path.

[0073] S53, updating the current house model according to the traversal cycle, and outputting the updated house model as a reference model.

[0074] The implementation method of determining the traversal period of the differential path includes: if the cumulative number of the differential cumulative path is less than a preset number value, determining the traversal period based on the position of the current house model and the duration of a single visit.

[0075] If the cumulative number of differential accumulation paths is greater than the preset number value, and the single access duration is greater than the preset access duration, the traversal period is determined by the difference between the current single access duration and the preset access duration.

[0076] The preset number of times set above is used to illustrate the number of times the current house model is required under normal user access. At this time, the preset number of times can be based on the number of user visits and views, and a confidence interval can be set. The lower limit of the confidence interval can be used as the preset number of times to illustrate the minimum number of times a user visits and views the house model. As for the preset access duration, the setting method is consistent with the preset number of times, and both use the lower limit of the confidence interval. Based on the user access data of the past three months, the lower limit of the 95% confidence interval can be taken as the preset number of times and the preset access duration. It represents the minimum time period required for a user to visit and view a house model. After that, the traversal cycle of the three-dimensional model is set by using this number of times and access duration to check in real time whether there are corresponding errors in the rendered house model, and update it in time.

[0077] It should be noted that the single visit duration and the cumulative number of times are used to illustrate the relative situation when the house model is generated. The single visit duration directly indicates the required display time of the current model under fast display. If the time is too short, there will be a problem of incomplete generation and display of the model. The quantified model deviation at this time is used to illustrate that under fast model generation, some unloaded deviations may appear within a specific time period. As for the cumulative number of times, it emphasizes the number of times the house model is visited. The deviation of the unloaded model is accumulated based on the number of visits. By accumulating and aggregating these deviations, we can know the differential relative position path that can appear when the current model is displayed in the scenario of fast house preview, which is convenient for subsequent model optimization to adjust its generation speed and the relative content filling time of model display.

[0078] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention, which are still covered by the scope of protection of the present invention.

Claims

1. A post-generation optimization method for a three-dimensional apartment model based on house surveying and mapping, characterized in that: include: S1, based on the connection properties of the 3D house model elements, extracts the edge point coordinates and topological relationships on multiple cross sections, and performs multi-level combination of the edge lines at different positions of the model through weight conversion to obtain the combined contour edge lines; S2: Analyze the outline edge lines to divide multiple housing units and calculate the three-dimensional overlap of the outline edge lines of adjacent housing units to obtain the corresponding overlapping area and broken area of ​​each housing unit. Combined with the projected area of ​​the outline edge lines, the logical sub-area corresponding to each housing unit is obtained. S3, based on the logical sub-areas of each housing unit, identifying the interval area of ​​each logical sub-area, and identifying the boundary conditions of the current logical sub-area based on the interval area to determine the target boundary conditions of the current logical sub-area; S4, based on the target boundary conditions of the current logical sub-region, performing spatial solution on the currently accessed three-dimensional house model, and determining the offset area of ​​each house unit based on the mapping area of ​​each target boundary condition after spatial solution; S5, performing differential accumulation on the offset areas of adjacent house units, and constructing a reference model of the current three-dimensional house model using the differential accumulation path.

2. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 1, characterized in that: The implementation of step S1 includes: S11, obtaining a curve at a connection position of the three-dimensional house model as a basic line segment, and determining whether the location of the basic line segment includes multiple endpoints and multiple corner points; S12, if any, using multiple endpoints and multiple corner points as the coordinates of the identified edge points, and recording the topological relationship of each edge point; S13, if it does not exist, project the basic line segment onto the center line of the house structure at the connection position, use the intersection of the projected center projection line and the current basic line segment as the edge point coordinates, and record the projection plane and the plane where the current basic line segment is located as the topological relationship of the edge point.

3. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 1, characterized in that: The implementation of step S2 includes: S21, based on the acquired contour edge line and the position of the contour edge line, generating a projection area of ​​the contour edge line; S22, using the density of the center points and the density of the vertices after the projection of the outline edge line, splicing the plane where the outline edge line is located according to the projected area to obtain multiple housing units; S23, distinguishing overlapping areas and disconnected areas of the housing units based on transition values ​​of the common contour edge lines of adjacent housing units, and calculating the three-dimensional overlap of the housing units; S24 , dividing the housing units into logical sub-areas of the housing units in a secondary combination of overlapping areas and disconnected areas according to the three-dimensional overlap of the housing units.

4. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 3, characterized in that: The implementation of step S24 includes: Determine the number of overlapping areas in the current housing unit. If the number of overlapping areas is greater than or equal to two, calculate the area ratio of the adjacent broken area to the overlapping area. If the area ratio of the adjacent broken area to the overlapping area is less than the preset area ratio, delete the corresponding broken area and use the remaining overlapping area and broken area as the output logical sub-area. If the area ratio of adjacent broken line areas to overlapping areas is greater than a preset area ratio, the corresponding overlapping areas are deleted, and the remaining overlapping areas and broken line areas are used as the output logical sub-areas; If the number of overlapping areas is less than two, the current overlapping area is expanded, and the broken area and the overlapping area are combined at the intersection of the expanded overlapping area and the broken area to obtain the logical sub-area of ​​the current housing unit.

5. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 1, characterized in that: The implementation of step S3 further includes: S31, based on the interval area of ​​each logical sub-region, the abnormal intervals between the logical sub-regions are checked, and each boundary condition is triggered by the abnormal interval to form an interval checking sequence; S32, determining the splicing relationship between the interval check sequences. When the splicing relationship of any group of logical sub-regions in the interval check sequence is consistent, the logical sub-regions are spliced ​​in a continuous hierarchical form, and the boundary conditions of the spliced ​​logical sub-regions are used as the target boundary conditions of the current logical sub-region. S33, when the splicing relationship is inconsistent, any group of logical sub-regions are spliced ​​in a cyclic splicing manner, the boundary conditions of the spliced ​​logical sub-regions are intersected, and the corresponding intersection is output as the target boundary condition of the current logical sub-region.

6. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 5, characterized in that: The implementation of the boundary condition check in step S31 includes: Based on the boundary conditions of each logical sub-region, the maximum difference of the logical sub-regions under the same boundary type is calculated based on the boundary type corresponding to the boundary conditions, and similarity calculation is performed on each logical sub-region based on the maximum difference under the same boundary type; The minimum difference value under the same boundary type is determined based on the calculated multiple logical sub-regions, and the logical sub-regions corresponding to the minimum difference value are combined to obtain an interval checking sequence.

7. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 1, characterized in that: The implementation of step S4 includes: S41, identifying the mapping area of ​​each target boundary condition, and performing traversal processing on the mapping area based on the ratio of the number of contour edge lines in the mapping area to the total number of contour edge lines; S42: The traversed mapping area is converted into data point pairs according to the ratio of the number of contour edge lines in the mapping area to the total number of contour edge lines. Parameter offset comparison is performed on adjacent mapping areas under the data point pairs to determine the output offset area.

8. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 7, characterized in that: The implementation of step S42 includes: In response to the input mapping area, the mapping area is saved with the data point pairs corresponding to the mapping area, and the type of problem that occurs in the data point pairs is determined; If the problem type is boundary condition redundancy, the area with the maximum offset value after the difference between the current input mapping area and the adjacent mapping area is output as the bias area; If the problem type is a boundary condition defect, the average offset value of the mapping area is calculated based on the current mapping area, and the central area corresponding to the average offset value is used as the output bias area; If the problem type is a boundary condition conflict, the mapping area corresponding to the conflicting boundary condition is used as the output bias area.

9. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 1, characterized in that: The implementation of step S5 includes: S51, performing spatial merging and matching based on the offset areas of adjacent housing units to obtain a combined cumulative difference set, and sorting the data in the cumulative difference set to obtain a differential accumulation path; S52, connecting each housing unit based on the single visit duration and cumulative number of times at each location on the differential cumulative path to determine the traversal period of the differential path; S53, updating the current house model according to the traversal cycle, and outputting the updated house model as a reference model.

10. The method for optimizing the generation of a three-dimensional apartment model based on house surveying according to claim 9, characterized in that: The implementation method of determining the traversal period of the differential path includes: if the cumulative number of the differential cumulative path is less than a preset number value, determining the traversal period based on the current position of the house model and the single visit duration; If the cumulative number of differential accumulation paths is greater than the preset number value, and the single access duration is greater than the preset access duration, the traversal period is determined by the difference between the current single access duration and the preset access duration.

Images