Building information model scene rendering method and system based on parallel computing

Abstract

The invention relates to the technical field of scene rendering, in particular to a building information model scene rendering method and system based on parallel computing, and the method comprises the following steps: based on scene data stored in a building information model, analyzing space coordinates of geometric structure nodes of a building, and associating the geometric structure nodes with material attributes; and analyzing a data partition relationship according to the illumination distribution, and generating a geometric material illumination analysis result. According to the method, deep integration of geometry, materials and illumination is realized by analyzing spatial coordinates of geometric nodes and material attributes and combining illumination partition analysis, the data organization efficiency of a complex scene and the analysis precision of an illumination path are improved, light energy in a noise area is optimized by analyzing light propagation and energy distribution in a segmented manner, refraction and reflection paths are corrected, and the accuracy of the light path analysis is improved. And through texture boundary and scene geometry conjoint analysis, texture fracture is repaired, detail expression is enhanced, efficient analysis and optimization of complex building scene data are realized, and the high-precision rendering requirement is met.

Description

Technical Field

[0001] The present invention relates to the technical field of scene rendering, and in particular to a method and system for scene rendering of a building information model based on parallel computing. Background Art

[0002] The field of scene rendering technology includes building information modeling technology, computer graphics and parallel computing technology. Its core content includes visual rendering of three-dimensional data in building information models, and generating realistic two-dimensional images or video outputs through computer graphics methods to achieve visual expression of architectural design, engineering construction and subsequent maintenance. In this technical field, the focus of scene rendering is to process complex three-dimensional model data and details such as light, shadow and texture in the scene, and generate high-quality image results through computer graphics algorithms. In order to improve rendering efficiency, this field usually adopts parallel computing technology, including the use of multi-core processors, graphics processors and distributed computing clusters to accelerate rendering tasks to meet high-performance requirements such as architectural design and virtual reality.

[0003] Among them, the method of building information model scene rendering refers to the analysis and calculation of geometric data, materials and lighting in the three-dimensional scene through a specific distributed computing framework and parallel rendering algorithm for the complex three-dimensional data and attribute information in the building information model, so as to generate visualization results for architectural design or display purposes. This method covers GPU-based accelerated rendering strategies, distributed scene division and task decomposition methods, and optimized processing of complex data structures in the building information model. By combining a parallel computing framework and an efficient rendering algorithm, this method can complete the rendering task of complex three-dimensional scenes when processing the geometric structure, physical material and lighting model in the building information model.

[0004] When processing complex three-dimensional data in building information models, the existing technology lacks detailed analysis and deep integration of the relationship between geometry, materials and lighting, resulting in data partitioning that is difficult to fully adapt to complex scene rendering requirements. In particular, when processing models containing subtle light and shadow changes, the problem of insufficient rendering details is prone to occur. The existing technology fails to achieve dynamic analysis of light energy distribution and path intersections in the analysis of light paths, resulting in low light and shadow performance accuracy of multiple reflection and refraction paths, uneven light energy distribution or detail lighting deviation. In terms of noise optimization, the existing technology is insufficient in the correction of high-frequency reflection and multiple refraction light paths, which is prone to light and shadow noise and high-frequency light interference in the rendered image. In terms of texture processing, the existing technology has limited ability to repair and supplement details in texture fracture areas, resulting in a lack of integrity and realism in the texture performance of the image. The final image fusion processing cannot achieve highly consistent light and shadow transitions in multi-node partitioning and scene depth division, resulting in regional splicing traces or uneven lighting and material performance in the rendering results, which directly affects the efficiency and quality of building information model scene rendering and cannot meet the requirements of high-precision scene rendering for light and shadow details, texture integrity and overall coordination. Summary of the invention

[0005] In order to solve the problem that the existing technology lacks detailed analysis and deep integration of geometry, material and lighting relationships when processing complex three-dimensional data in building information models, which makes it difficult for data partitions to fully adapt to complex scene rendering requirements, especially when processing models containing subtle light and shadow changes, the problem of insufficient rendering details is prone to occur. The existing technology fails to achieve dynamic analysis of light energy distribution and path intersections in the analysis of light paths, resulting in low accuracy of light and shadow performance of multiple reflections and refraction paths, uneven light energy distribution or detailed lighting deviation. In terms of noise optimization, the existing technology is insufficient in the correction of high-frequency reflections and multiple refractions of light paths, which easily produces renderings. Light and shadow noise and high-frequency light interference in the image. In terms of texture processing, the existing technology has limited ability to repair and supplement details of texture fracture areas, resulting in a lack of integrity and realism in the texture performance of the image. The final image fusion processing cannot achieve highly consistent light and shadow transitions in multi-node partitioning and scene depth division, resulting in regional splicing traces or uneven lighting and material performance in the rendering results, which directly affects the efficiency and quality of building information model scene rendering and cannot meet the technical problems of high-precision scene rendering for light and shadow details, texture integrity and overall coordination. The embodiment of the present invention provides a method and system for building information model scene rendering based on parallel computing. The technical solution is as follows:

[0006] In one aspect, a method for rendering a building information model scene based on parallel computing is provided, the method comprising:

[0007] S1: Based on the scene data stored in the building information model, the spatial coordinates of the building geometry nodes are analyzed, the geometry nodes are associated with the material attributes, the data partition relationship is analyzed according to the illumination distribution, and the geometry material illumination analysis results are generated;

[0008] S2: Based on the illumination analysis result of the geometric material, for the light path parameters, segmentally analyze the propagation direction of the light in the differentiated areas, extract the energy distribution data of the path intersection, match the light propagation relationship between the areas, and generate the light path distribution result;

[0009] S3: Based on the light path distribution result, filter the area with uneven light energy distribution, extract the light interaction point information, compare the light energy accumulation value with the material parameters, adjust the light energy distribution in the noise area, correct the high-frequency reflection and multiple refraction paths, and generate a noise optimized rendering image;

[0010] S4: Based on the noise optimized rendered image, jointly analyzing the pixel texture boundary gradient and the scene geometric boundary, analyzing the pixel color distribution gradient, repairing the texture break area, processing the color gradient difference, supplementing the texture details, and generating a rendered image with enhanced details;

[0011] S5: Based on the rendered image after detail enhancement, tasks are divided according to scene depth distribution, light intensity and material boundaries are separated layer by layer, multi-node allocation partitions are performed, image fusion is performed in combination with the coordinates of the regional splicing points, and the building model scene rendering imaging results are output.

[0012] As a further solution of the present invention, the geometric material lighting analysis results include the spatial coordinates of the geometric structure nodes, the material attribute association relationship, and the lighting distribution data partition relationship; the light path distribution results include the light path parameter segmentation analysis results, the energy distribution data of the path intersection, and the light propagation relationship matching results between regions; the noise optimized rendered image includes the light energy distribution balanced area, the light interaction point energy information, the corrected high-frequency reflection path, and the corrected multiple refraction path; the detail enhanced rendered image includes the pixel texture boundary gradient, the pixel color distribution gradient, the repaired texture fracture area, and the supplemented texture details; the building model scene rendering imaging results include the scene depth distribution data, the light intensity distribution level, the material boundary division, the multi-node partition result, and the regional splicing point coordinate data.

[0013] As a further solution of the present invention, based on the scene data stored in the building information model, the spatial coordinates of the building geometry nodes are parsed, the geometry nodes are associated with material attributes, and the data partition relationship is analyzed according to the illumination distribution. The steps of generating the geometry material illumination parsing result are specifically as follows:

[0014] S101: extracting spatial coordinate data of building geometric structure nodes based on scene data stored in a building information model, analyzing connection relationships between geometric structure nodes, classifying and grouping the spatial coordinate data, and generating a geometric node spatial coordinate grouping table;

[0015] S102: extracting material attributes associated with each geometric node based on the geometric node spatial coordinate grouping table, performing matching processing on the geometric nodes and the material attributes, adjusting the association of unmatched material nodes, and obtaining geometric node material attribute association data;

[0016] S103: Based on the geometric node material attribute associated data, the corresponding position relationship between the material parameters of the geometric node and the spatial coordinates is parsed, the lighting coverage range is analyzed, and the geometric material lighting parsing result is obtained.

[0017] As a further solution of the present invention, based on the illumination analysis result of the geometric material, the propagation direction of the light in the differentiated areas is analyzed in sections for the light path parameters, the energy distribution data of the path intersection is extracted, and the light propagation relationship between the areas is matched. The steps of generating the light path distribution result are specifically as follows:

[0018] S201: Based on the illumination analysis result of the geometric material, the starting point and end point coordinates of the path are read according to the segmentation rule of the light path, the path segments are segmented in combination with the area boundary data, and the logical order of the path segments is adjusted by analyzing the angle change and direction characteristics of the segmented path to generate a light path segmentation data set;

[0019] S202: Based on the light path segmentation data set, analyzing the intersection points of the paths at the region boundary, extracting the spatial coordinate data of the intersection points, analyzing the energy variation between the path intersection points, arranging and classifying the segmented path energy gradients, and obtaining the energy distribution data of the light path intersection points;

[0020] S203: Based on the energy distribution data of the light path intersection, the correlation value between the path energy gradient and the direction change is calculated, the connection relationship between the light paths between the regions is matched, the overall structure of the path is adjusted and the path between the regions is optimized, and the light path distribution result is generated.

[0021] As a further solution of the present invention, the correlation value between the path energy gradient and the direction change is calculated according to the formula:

[0022]

[0023] Among them, G represents the correlation value between the path energy gradient and the direction change, E x Represents the energy distribution value along the horizontal direction at the intersection of the light paths, E y represents the energy distribution value in the vertical direction at the intersection of the light path, d represents the adjacent distance of the current light path area, Dx Represents the gradient value in the horizontal path direction at the intersection of the light paths, D y Represents the gradient value in the direction perpendicular to the path at the intersection of the light paths, and M1, M2, and M3 are weight coefficients.

[0024] As a further solution of the present invention, based on the light path distribution result, the steps of screening the area with uneven light energy distribution, extracting the light interaction point information, comparing the light energy accumulation value with the material parameters, adjusting the light energy distribution in the noise area, correcting the high-frequency reflection and multiple refraction paths, and generating the noise optimized rendering image are specifically as follows:

[0025] S301: Based on the light path distribution result, analyze the light nodes in the path, extract the light energy density distribution parameters, mark the light energy density fluctuation area, and obtain the light energy uneven distribution area information;

[0026] S302: Based on the information of the uneven light energy distribution area, processing the light interaction points in the marked area point by point, extracting the accumulated light energy value of the path sequence, analyzing the local abnormal value, counting the range of material parameter differences, and obtaining abnormal light energy interaction path data;

[0027] S303: Based on the light energy anomaly interaction path data, adjusting the path light energy anomaly cumulative value distribution parameters, redistributing the light energy ratio, correcting the light energy distribution value, balancing the light energy distribution difference, and generating a noise optimized rendering image.

[0028] As a further solution of the present invention, based on the noise optimized rendered image, the steps of jointly analyzing the pixel texture boundary gradient and the scene geometric boundary, analyzing the pixel color distribution gradient, repairing the texture break area, processing the color gradient difference, supplementing the texture details, and generating the rendered image after detail enhancement are specifically as follows:

[0029] S401: Based on the noise optimized rendering image, scan the texture boundary and geometric structure information pixel by pixel, extract the texture boundary direction angle and position coordinates, record the texture gradient and geometric information, and generate a texture and geometric boundary parameter set;

[0030] S402: Based on the texture and geometric boundary parameter set, the color change trend of the pixel area is processed, the color gradient change rate and direction are analyzed, the color distribution model is established by matching the geometric boundary, and the pixel color distribution gradient trend is obtained;

[0031] S403: Based on the pixel color distribution gradient trend, detail repair processing is performed on the broken area, the pixel filling value is adjusted, the color value distribution of the broken area is analyzed, the smooth interpolation color value of the broken area is calculated, the texture and color transition area are smoothed, and a detail enhanced rendering image is generated.

[0032] As a further solution of the present invention, the smooth interpolation color value of the fractured area is calculated according to the formula:

[0033]

[0034] Among them, w i represents the weight coefficient for pixel i, P i,grad Represents the color gradient value of pixel i, P i,avg represents the local average color value of pixel i, α i is the adjustment index for pixel i, and n represents the total number of pixels involved in the interpolation calculation.

[0035] As a further solution of the present invention, based on the rendered image after detail enhancement, the tasks are divided according to the scene depth distribution, the light intensity and material boundary are separated layer by layer, the multi-node allocation partitions are performed, and the image is fused in combination with the coordinates of the regional splicing points to output the building model scene rendering imaging results. Specifically, the steps are as follows:

[0036] S501: Based on the rendered image after detail enhancement, scan the regional depth value pixel by pixel, extract the illumination gradient change value and direction vector, record the depth and illumination material correlation characteristics, and generate the scene depth distribution and illumination material parameter set;

[0037] S502: Based on the scene depth distribution and the lighting material parameter set, define the depth level partition range, allocate regional nodes, extract overlapping area boundary points, record node distribution and splicing parameters, and generate a multi-node partition and splicing parameter set;

[0038] S503: Based on the multi-node partitioning and splicing parameter set, the partition pixel value is adjusted, the illumination difference is corrected, the material connection of the overlapping area is smoothed, the partition images are fused and the depth level is unified, and the building model scene rendering imaging result is output.

[0039] On the other hand, an electric vehicle state monitoring system is provided, the electric vehicle state monitoring system is used to execute the above electric vehicle state monitoring method, the system comprises:

[0040] The geometric lighting partition module extracts node material parameters and position relationships based on the scene data stored in the building information model, analyzes the geometric distance and light intensity changes, compares the light gradient change rate and spatial distribution, and generates a lighting partition mapping table;

[0041] The light path analysis module extracts the light path parameters of the area boundary points based on the illumination partition mapping table, analyzes the light propagation direction and the intersection coordinate position in the path, compares the light path energy distribution and material related properties between the areas, and generates a light area transfer relationship set;

[0042] The noise area correction module analyzes the light energy accumulation value of the light path intersection and the reflection path position based on the light area transfer relationship set, calculates the material refractive index and the light propagation offset value, adjusts the high-frequency reflection path and energy noise value, and generates a noise optimized rendering image;

[0043] The texture layered enhancement module extracts the pixel gradient value of the scene texture boundary based on the noise optimized rendering image, compares the material boundary characteristics with the texture breakpoint position, analyzes the texture gradient difference and supplements the boundary information of the break area, matches the boundary texture characteristics, and obtains the texture enhancement layer data;

[0044] Based on the texture enhancement layer data, the rendering scene fusion module separates the illumination intensity values ​​of the depth layers of the building scene, extracts the light intensity changes at the partition boundary points, matches the regional light intensity with the material characteristics, processes the partition splicing and reorganization relationship, fuses the multi-level scene depth, and outputs the building model scene rendering imaging results.

[0045] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0046] By analyzing the spatial coordinates of the geometric structure nodes, associating material properties and combining the partition relationship of the illumination distribution analysis data, the deep integration of geometric information, illumination and materials is achieved, the efficiency of organizing geometric information of complex scenes is improved, and an accurate data foundation is laid for the accurate analysis of subsequent illumination paths. By segmented analysis of the propagation direction of light in different areas and extracting the energy distribution data of the intersection of the paths, the detailed analysis of the light propagation path and energy characteristics is completed, which effectively improves the accuracy and dynamic analysis ability of light energy transmission in complex illumination scenes. Based on the screening of uneven energy distribution areas and the comparison and adjustment of light energy accumulation values ​​with material parameters, the light energy distribution in the noise area is optimized, and multiple refractions and high-frequency reflection paths are corrected, so that the final image The light and shadow effects are more natural, and the noise is significantly reduced. Through the joint analysis of the pixel texture boundary gradient and the scene geometric boundary, texture breaks are repaired and detailed textures are supplemented, effectively enhancing the image detail layering and overall texture performance. The task is divided by the scene depth distribution and the light intensity and material boundary are separated layer by layer. The image fusion is completed in combination with the coordinates of the regional stitching points, so that the final output architectural scene rendering image has high-quality light and shadow performance and structured rendering results, which meets the needs of efficient visualization in complex architectural design scenes, and realizes efficient analysis, optimization and fusion of multi-dimensional data such as geometry, material, lighting, texture, etc., significantly improving the quality and efficiency of rendering complex building information model scenes, and meeting the needs of high-precision and high-performance rendering. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0048] Figure 2This is a detailed flow chart of S1 of the present invention;

[0049] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0050] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0051] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0052] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0053] Figure 7 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0054] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0056] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0057] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0058] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0059] See also Figure 1 The embodiment of the present invention provides a method for rendering a building information model scene based on parallel computing. The processing flow of the method may include the following steps:

[0060] S1: Based on the scene data stored in the building information model, the spatial coordinates of the building geometry nodes are analyzed, the geometry nodes are associated with the material attributes, the data partition relationship is analyzed according to the illumination distribution, and the geometry material illumination analysis results are generated;

[0061] S2: Based on the results of geometric material illumination analysis, the propagation direction of light in differentiated areas is analyzed in sections for the light path parameters, the energy distribution data of the path intersections is extracted, the light propagation relationship between the areas is matched, and the light path distribution results are generated;

[0062] S3: Based on the light path distribution results, filter the areas with uneven light energy distribution, extract the light interaction point information, compare the light energy accumulation value with the material parameters, adjust the light energy distribution in the noise area, correct the high-frequency reflection and multiple refraction paths, and generate noise-optimized rendering images;

[0063] S4: Based on noise optimization, the rendered image is analyzed jointly with the pixel texture boundary gradient and the scene geometric boundary, the pixel color distribution gradient is analyzed, the texture broken area is repaired, the color gradient difference is processed, the texture details are supplemented, and the rendered image with enhanced details is generated;

[0064] S5: Based on the rendered image after detail enhancement, the task is divided according to the scene depth distribution, the light intensity and material boundary are separated layer by layer, the multi-node allocation partitions are performed, the image is fused based on the coordinates of the regional stitching points, and the rendering imaging results of the building model scene are output.

[0065] The results of geometric material lighting analysis include the spatial coordinates of geometric structure nodes, material attribute associations, and lighting distribution data partition relationships. The light path distribution results include the light path parameter segmentation analysis results, the energy distribution data of the path intersections, and the matching results of light propagation relationships between regions. The noise-optimized rendered image includes the light energy distribution balanced area, the energy information of the light interaction points, the corrected high-frequency reflection path, and the corrected multiple refraction path. The rendered image after detail enhancement includes the pixel texture boundary gradient, the pixel color distribution gradient, the repaired texture fracture area, and the supplemented texture details. The architectural model scene rendering imaging results include the scene depth distribution data, the light intensity distribution level, the material boundary division, the multi-node partition results, and the regional splicing point coordinate data.

[0066] Specifically, if Figure 2 As shown, based on the scene data stored in the building information model, the spatial coordinates of the building geometry nodes are parsed, the geometry nodes are associated with the material attributes, and the data partition relationship is analyzed according to the illumination distribution. The specific steps to generate the geometry material illumination parsing results are as follows:

[0067] S101: extracting spatial coordinate data of building geometric structure nodes based on scene data stored in a building information model, analyzing connection relationships between geometric structure nodes, classifying and grouping the spatial coordinate data, and generating a geometric node spatial coordinate grouping table;

[0068] Through the building information model data interface, the three-dimensional spatial coordinate data of each node in the scene is called, and the storage format of the node data is parsed. The topological parsing method is used to parse and mark the connection relationship between the nodes one by one, and a connection relationship matrix is ​​generated. During the parsing process, according to the topological relationship and spatial distribution law of the geometric structure, the Euclidean distance threshold between adjacent nodes is used to classify and group them to ensure that the geometric nodes in the group have the strongest spatial correlation. Each group of spatial coordinate data is mapped to the node grouping table, and the grouping sequence number, spatial coordinate range, and topological relationship connection strength are recorded. When classifying, based on the threshold parameter D1, adjacent nodes with a distance less than D1 are screened, and the three-dimensional coordinates of the center point of its spatial range are calculated and recorded as the characteristic center point of the group. By scanning the rows and columns of the connection relationship matrix, redundant connection relationships are further eliminated, and only the connection information with the most significant impact on the geometric nodes is retained. Finally, a geometric node spatial coordinate grouping table is generated, with the group number and center point as the primary key, containing detailed grouping records of spatial coordinates, to complete the classification and grouping operations of geometric nodes.

[0069] S102: extracting material attributes associated with each geometric node based on the geometric node spatial coordinate grouping table, performing matching processing on the geometric nodes and the material attributes, adjusting the association of unmatched material nodes, and obtaining geometric node material attribute association data;

[0070] By reading the spatial coordinate grouping table, each geometric node is queried item by item, the building information model material library is called, and the material attributes corresponding to the node are extracted. During the extraction process, the material attributes are standardized to ensure the consistency of the connection relationship between the material attributes and the geometric nodes. Based on the spatial grouping data of the geometric nodes, the material attribute types of the grouping nodes are compared one by one, and the consistency level of the material attributes between the nodes is matched. For unmatched material nodes, the difference in their material parameters is calculated, and compared with the closest material attribute entries in the material library, and updated through the material parameter adjustment method to ensure that the material attributes of the geometric nodes meet the scene rendering consistency requirements. During the adjustment process, the association weight of each node and the material parameters of the grouping center point is calculated, and the nodes with weights lower than the threshold are forced to adjust. After processing, the geometric node material attribute association data is obtained, including the material parameters of the geometric nodes, spatial coordinates, and the material consistency level of the node and the grouping center, which serves as the basic data for subsequent lighting analysis.

[0071] S103: Based on the geometry node material attribute association data, the corresponding position relationship between the geometry node material parameters and the space coordinates is analyzed, the illumination coverage is analyzed, and the geometry material illumination analysis result is obtained;

[0072] Through the data associated with the material attributes of the geometry nodes, the material parameters and spatial coordinates of each node are matched and analyzed, and a relationship model between the material reflectivity, absorptivity and light intensity is established. The light source information defined in the scene is matched with the spatial coordinates of the geometry nodes, and the propagation path of light from the light source to the surface of the geometry nodes and its attenuation factor are calculated. According to the material reflectivity of the geometry nodes and the spatial distance between the nodes, a light coverage model is established, and the specific distribution of light intensity on each node is recorded. In the parsing process, the light contribution value of the geometry nodes is calculated one by one, and by traversing all the nodes within the light coverage range, the comprehensive influence of their material parameters on the overall light distribution is counted, and a node light parsing table is generated, which records the distribution value of light intensity, the three-dimensional coordinate range of the coverage range, and the light reflection efficiency of the node material attributes. In the parsing results, low-reflectivity nodes are marked, and it is recommended to optimize the material attributes of the nodes to further improve the lighting uniformity and realism of the scene rendering. The geometry material lighting parsing results are generated, including the light intensity distribution diagram and the node material reflectivity adjustment suggestions, providing basic data support for the rendering operation of parallel computing.

[0073] Specifically, if Figure 3 As shown in the figure, based on the illumination analysis results of geometric materials, the propagation direction of light in differentiated areas is analyzed in sections for the light path parameters, the energy distribution data of the path intersections is extracted, and the light propagation relationship between the areas is matched. The specific steps to generate the light path distribution results are as follows:

[0074] S201: Based on the results of the geometric material illumination analysis, the coordinates of the starting point and the end point of the path are read according to the segmentation rules of the light path, and the path segments are segmented in combination with the area boundary data. By analyzing the angle change and direction characteristics of the segmented path, the logical order of the path segments is adjusted to generate a light path segmentation data set;

[0075] Through the results of geometric material illumination analysis, the spatial coordinate data of the starting point and end point of each light path are gradually extracted. According to the spatial distribution law of the path and the division standard of the regional boundary, the path is divided into several segments, and the starting and ending coordinates of each path segment and the intersection relationship with the regional boundary are recorded. When dividing the path, the coordinates of the intersection of each path segment and the regional boundary are calculated, and the incident direction, the exit direction and the angle change value of the path segment are marked. For the divided path segments, the directional characteristics of the path segments are classified by calculating the angle change characteristics segment by segment, including straight line segments, broken line segments and curve segments. According to the directional characteristics, the logical order of the path segments is adjusted to ensure the continuity and directional consistency of the path segment connection. In the process of logical adjustment, the difference in the starting and ending coordinates of each path segment is verified, and combined with the overall distribution law of the light path, the redundant path segments are eliminated, and the gaps between the path segments are supplemented. After the adjustment is completed, a light path segmentation data set is generated, which contains the number, starting and ending coordinates, directional characteristics, and the segmentation relationship with the regional boundary of each path segment, providing a basis for the subsequent path intersection energy distribution analysis.

[0076] S202: Based on the light path segmentation data set, analyzing the intersection points of the paths at the region boundary, extracting the spatial coordinate data of the intersection points, analyzing the energy variation between the path intersection points, sorting and classifying the segmented path energy gradients, and obtaining the energy distribution data of the light path intersection points;

[0077] Combined with the segmented data set of light paths, the intersection information of the paths at the boundaries of the regions is extracted, including the spatial coordinates of each intersection, the path incident angle and the exit angle and other key data. Based on the directional characteristics of the path segments and the incident direction, the energy transfer amount when the path passes through each intersection is calculated, and the energy attenuation value and its directional change value are recorded. By calculating the energy change between the intersections segment by segment, the energy gradient distribution relationship of the segmented path is established, and the energy transfer law in the path segment is clarified. In the calculation process, the path energy distribution is divided into several gradient levels. According to the three classification dimensions of energy gain, attenuation and stability, the energy gradient of the path segment is sorted and classified, and the path distribution of different gradients is marked. The path segments with significant energy changes between intersections are marked to provide reference for subsequent optimization of the path distribution. In the generated data, the intersection coordinates, energy gradient level and directional change characteristics of each path segment are recorded to form a light path intersection energy distribution data set, which provides input basis for path optimization and inter-regional path adjustment.

[0078] S203: Based on the energy distribution data of the light path intersection, the correlation value between the path energy gradient and the direction change is calculated, the connection relationship of the light paths between the regions is matched, the overall structure of the path is adjusted and the paths between the regions are optimized, and the light path distribution result is generated;

[0079] Calculate the correlation value of the path energy gradient and the direction change according to the formula:

[0080]

[0081] Among them, G represents the correlation value between the path energy gradient and the direction change, E x Represents the energy distribution value along the horizontal direction at the intersection of the light paths, E y represents the energy distribution value in the vertical direction at the intersection of the light path, d represents the adjacent distance of the current light path area, D x Represents the gradient value in the horizontal path direction at the intersection of the light paths, D y Represents the gradient value in the direction perpendicular to the path at the intersection of the light paths, and M1, M2 and M3 are weight coefficients;

[0082] Detailed explanation of the formula and the process of formula calculation and derivation:

[0083] This formula is used to calculate the correlation between the path energy gradient and the direction change. The generated result is used to optimize the distribution of the light path and adjust the path structure. Through this formula, the energy distribution changes of the light paths in different areas can be quantified, and the overall path connection effect can be optimized by combining the adjacency relationship and the path direction gradient.

[0084] Parameter meaning and setting value:

[0085] E x : The energy distribution value in the horizontal direction at the intersection of the light paths. This value is obtained by sampling the energy at each point near the intersection of the light paths. A light intensity detector (such as a spectrum analyzer) is used to measure the energy distribution in the horizontal direction. Based on the actual measurement, the energy distribution value E in the horizontal direction at a certain intersection is set. x =15mJ / cm 2 ;

[0086] E y : Energy distribution value in the vertical direction at the intersection of the light paths, which is related to E x Similarly, this value is obtained by energy sampling in the vertical direction. The energy is measured in the vertical direction using the same spectrum analyzer, and the vertical energy value of the intersection is set to E y =10mJ / cm 2 ;

[0087] d: The adjacent distance of the current light path area is used to measure the closeness of the paths in the adjacent areas. This value is determined by measuring the spatial distance between the intersection points of the paths. The intersection points of adjacent light paths are accurately measured using a laser rangefinder. The measured adjacent distance is set to d = 5 cm.

[0088] D x : The gradient value of the horizontal path at the intersection of the light path, the gradient value Dx The light intensity change rate near the intersection is calculated, which can be calculated using the numerical difference method, and the gradient value in the horizontal direction is measured as D x =3mJ / cm 2 / cm;

[0089] D y : The gradient value of the vertical path at the intersection of the light path. Similarly, the gradient value D y It is obtained by measuring the rate of change of light intensity in the vertical direction, and setting the vertical gradient as D y =2mJ / cm 2 / cm;

[0090] M1: The weight coefficient of horizontal energy distribution on the optimized path structure. The weight coefficient reflects the importance of horizontal energy distribution in path optimization. The setting of the weight coefficient is adjusted according to experimental data. In actual situations, it will fluctuate with the stability and variability of energy distribution. Setting M1 = 0.6 indicates that the horizontal energy has a higher degree of influence in path optimization.

[0091] M2: Weight coefficient of the combined influence of vertical energy distribution and area adjacent distance. This weight coefficient measures the influence of vertical energy change and the compactness of adjacent areas on path optimization. It is adjusted according to the density of the path and the propagation characteristics of light energy. Setting M2 = 0.3 means that the influence of vertical energy distribution on path optimization is slightly lower.

[0092] M3: The weight coefficient of the path direction gradient coupling on the optimized path structure. This coefficient reflects the contribution of the path direction gradient to the overall path optimization. It is adjusted according to the influence of the path direction change on the propagation and scattering of light. Setting M3 = 0.1 means that the direction gradient has little influence on the path structure optimization.

[0093] Substitute the parameters into the formula for calculation:

[0094] According to the known parameters, substitute into the formula for calculation:

[0095]

[0096] Calculate each term step by step:

[0097]

[0098]

[0099] Add up all the terms: G = 2.3238 + 0.6 + 0.2449 = 3.1687;

[0100] The calculated G = 3.1687 represents the comprehensive correlation value between the path energy gradient and the directional change at the intersection of the light paths. This value is used to reflect the degree of matching between the energy distribution and directional gradient between light paths in different regions. A higher value indicates that the energy distribution of the path in the region is more uniform and the directional change is smaller, which is conducive to the stability of the path connection; a lower value indicates that the path optimization effect in the region is poor, and further adjustment of the light path or optimization of the structure is required. The obtained correlation value G can be used as a reference indicator for path optimization and is used to adjust the connection and structural layout of the path.

[0101] Specifically, if Figure 4 As shown in the figure, based on the light path distribution results, the steps of screening the uneven light energy distribution area, extracting the light interaction point information, comparing the light energy accumulation value with the material parameters, adjusting the light energy distribution in the noise area, correcting the high-frequency reflection and multiple refraction paths, and generating the noise optimized rendering image are as follows:

[0102] S301: Based on the light path distribution result, analyze the light nodes in the path, extract the light energy density distribution parameters, mark the light energy density fluctuation area, and obtain the light energy uneven distribution area information;

[0103] According to the light path distribution results, the light nodes in the path are analyzed point by point, and the light energy density parameters of each node are extracted from the energy distribution data of the path segment. By calculating the change amplitude of the light energy density on each path segment, the critical value range of density fluctuation is determined, and the area with significant light energy density fluctuation is marked. During the analysis process, according to the illumination intensity and material reflectivity parameters of the path segment, the rate of change of light energy between nodes is calculated, and the trend of light energy density distribution with the path position is recorded. For the area where the light energy fluctuation value exceeds the critical range in the distribution, the distribution law of the fluctuation value in the path segment is further calculated, and the specific range of the fluctuation area is marked in combination with the spatial coordinates of the node. By marking the starting and end coordinates of each fluctuation area, a regional data table of light energy density fluctuation is generated, which records the light energy change amplitude, node number and path segment association information of each fluctuation area, and organizes and outputs the information of the uneven light energy distribution area as input data for subsequent processing of abnormal light energy paths, providing a positioning basis for rendering effect optimization.

[0104] S302: Based on the information of the area with uneven light energy distribution, the light interaction points in the marked area are processed point by point, the accumulated light energy value of the path sequence is extracted, the local abnormal value is analyzed, the difference range of material parameters is counted, and the abnormal light energy interaction path data is obtained;

[0105] According to the information of the area with uneven light energy distribution, the light interaction points in the marked fluctuation area are analyzed point by point, the light energy accumulation value of each interaction point is extracted, and it is recorded according to the path sequence number. When processing point by point, the spatial coordinates of the light path and the material reflectivity characteristics are combined to calculate the energy loss or gain value of the light at each interaction point, mark the interaction points with energy anomalies and classify them. By comparing the light energy accumulation value with the average light energy distribution of the entire path, mark the location of the local abnormal value, and analyze the distribution pattern of the abnormal value in the light energy fluctuation area. In order to further determine the cause of the light energy anomaly, the difference range of different material parameters in the marked area is statistically calculated, and the influence of the material reflectivity and absorptivity on the light energy distribution is calculated. For the light path segment whose abnormal value ratio in the path sequence exceeds the set ratio, the light energy anomaly level is redefined, and the abnormal path data is output, including the path number, the coordinate range of the abnormal area, the distribution characteristics of the light energy accumulation value and the material parameter classification result, to form the light energy anomaly interaction path data, which provides a reference for adjusting the light energy distribution.

[0106] S303: Based on the light energy anomaly interaction path data, adjusting the path light energy anomaly cumulative value distribution parameter, redistributing the light energy ratio, correcting the light energy distribution value, balancing the light energy distribution difference, and generating a noise optimized rendering image;

[0107] Through the light energy anomaly interactive path data, the distribution parameters of the light energy anomaly accumulation value in the path are adjusted. According to the anomaly level of the path segment, the light energy density in the abnormal path segment is redistributed. According to the light energy accumulation value of the path segment and the influence weight of the material parameters, the light energy ratio is redistributed. In the redistribution process, the light energy ratio of adjacent path segments is coordinated and adjusted in combination with the connection relationship of the path to ensure the balance of the overall light energy distribution. By correcting the allocation value of each light energy anomaly point, the fluctuation range of light energy density is gradually smoothed, and the mutation points in the light energy distribution are eliminated. For the adjusted path segments, the continuity of the light energy accumulation value is checked one by one, and the balance of the light energy distribution of each segment is verified to ensure that the adjusted path energy is equal to the total energy of the original light path. The adjusted light energy allocation value is applied to the rendering process, and the rendering of the scene is completed through parallel calculation to generate a noise-optimized rendering image, in which the lighting effect is more uniform, and the interference of the light energy fluctuation area on the realism of the scene is reduced, providing support for the visual optimization of the building information model scene rendering.

[0108] Specifically, if Figure 5 As shown in the figure, based on noise optimization rendering image, jointly analyzing pixel texture boundary gradient and scene geometric boundary, analyzing pixel color distribution gradient, repairing texture fracture area, processing color gradient difference, supplementing texture details, and generating detail enhanced rendering image steps are as follows:

[0109] S401: Based on the noise optimization rendering image, the texture boundary and geometric structure information are scanned pixel by pixel, the texture boundary direction angle and position coordinates are extracted, the texture gradient and geometric information are recorded, and the texture and geometric boundary parameter set is generated;

[0110] The noise optimized rendering image is scanned pixel by pixel, and the boundary information is extracted from the brightness value and texture characteristics of the pixel, and the interactive characteristics of the texture and geometric structure are identified. During the scanning process, the brightness difference and texture change gradient between adjacent pixels are analyzed, and the direction angle and position coordinates of each boundary point are recorded. When processing the direction angle of the boundary point, the angle direction of the boundary point is calculated based on the change rate of the brightness value of the adjacent pixels, and the geometric structure position of the boundary point is marked. When identifying the texture gradient, the gradient direction vector is generated by calculating the grayscale change rate between adjacent pixels to ensure the accurate capture of the texture information. Combined with the spatial distribution relationship of the geometric structure, the texture gradient direction of the boundary point is associated with the geometric structure coordinates to form a texture direction set of the geometric boundary. During the data processing process, the pixel points whose gradient change amplitude exceeds the set threshold are screened out to generate the texture boundary and geometric parameter set. The final output parameter set contains the direction angle, position coordinates and corresponding texture gradient information of each boundary point, providing a data basis for subsequent color processing and detail restoration.

[0111] S402: Based on the texture and geometric boundary parameter set, the color change trend of the pixel area is processed, the color gradient change rate and direction are analyzed, the color distribution model is established by matching the geometric boundary, and the pixel color distribution gradient trend is obtained;

[0112] According to the texture and geometric boundary parameter set, the color change trend of the pixel area is gradually analyzed. By calculating the color difference and change rate between adjacent pixels, the color gradient direction and rate characteristics are extracted. In the processing process, the distribution relationship between the geometric boundary and the color gradient is matched in combination with the direction angle information in the boundary parameter set, and the color change characteristics on the geometric boundary are recorded as segmented color gradient data. In color analysis, the rate of change of the color gradient is calculated region by region, and the area with prominent rate change is marked. The direction of change is uniformly vectorized to establish a color distribution model. In the model, the mutual influence of texture gradient and color gradient is comprehensively considered, and the color change law is fitted to ensure the integrity and accuracy of the distribution model. Through the model fitting results, a pixel color distribution gradient trend table is generated. The table records the color gradient direction, change rate and correlation with the geometric boundary of each area, providing a reliable basis for detail restoration and texture smoothing.

[0113] S403: Based on the pixel color distribution gradient trend, detail repair processing is performed on the fractured area, pixel filling value is adjusted, color value distribution of the fractured area is analyzed, smooth interpolation color value of the fractured area is calculated, texture and color transition area are smoothed, and a detail enhanced rendering image is generated;

[0114] Calculate the smooth interpolated color value of the fracture area according to the formula:

[0115]

[0116] Among them, w i represents the weight coefficient for pixel i, P i,grad Represents the color gradient value of pixel i, P i,avg represents the local average color value of pixel i, α i is the adjustment index for pixel i, and n represents the total number of pixels involved in the interpolation calculation;

[0117] Detailed explanation of the formula and the process of formula calculation and derivation:

[0118] This formula is used to calculate the color value P of the rendered image after the fracture area is repaired with details. c ,Through weighted interpolation of the pixels in the fractured area, the color gradient, the smoothed average value of the adjacent pixels and the adjustment coefficient, the texture and color transition of the fractured area can be effectively repaired;

[0119] Each parameter in the formula is obtained through actual monitoring and calculation, and is comprehensively processed by combining multiple factors in a weighted manner; i The weight coefficient of the i-th pixel reflects the importance of pixel i in the interpolation process. It is determined by the distance or color difference between the pixel and its neighborhood. The weight is set based on the spatial distance or similarity between pixels. Pixels with closer distance or smaller color difference have higher weights.

[0120] Monitoring data: The weight value is calculated using Gaussian function or other interpolation functions through the spatial distribution relationship and color gradient relationship of pixels;

[0121] Weight interval: w i The value range is from 0 to 1;

[0122] P i,grad Represents the color gradient value of pixel i. The color gradient measures the rate of change of the color of the current pixel in the fracture area.

[0123] Calculation method: Color gradient is usually obtained by calculating the color difference between a pixel and its neighboring pixels, which can be calculated by difference method or Sobel operator;

[0124] Monitoring data: By measuring the color difference between pixels, Pi,grad The unit is the difference value in the color space (such as RGB color difference, which is a value in the range of [0, 255]);

[0125] Example values: Assuming P i,grad =18, indicating that the color difference between the current pixel and its neighboring pixels is 18;

[0126] P i,avg Represents the local average color value of pixel i. The local average reflects the color distribution of the area around the current pixel and helps smooth the transition;

[0127] Calculation method: The color values ​​of neighboring pixels within a certain range around pixel i are averaged, using the commonly used weighted average method or simple average method to obtain;

[0128] Monitoring data: Calculate the average color value by traversing the pixel neighborhood, using the average value in the RGB space;

[0129] Example values: Assuming P i,avg =120, indicating that the average color value of the surrounding neighborhood pixels is 120;

[0130] α i To adjust the exponential coefficient, it is used to adjust the degree of influence of the difference between the color gradient and the average value. This coefficient controls the nonlinear relationship between the color difference and the average value difference, which helps to strengthen or weaken the smooth processing of color transition;

[0131] Monitoring data: This coefficient is dynamically set based on the specific characteristics of the fracture area (such as the severity of the fracture, the complexity of the texture, etc.) and is determined by image analysis algorithms (such as histogram analysis);

[0132] Example values: Assuming α i =1.5, indicating that the color transition in this break area is more complex, so a higher index is selected to enhance the effect of gradient differences;

[0133] Compute individual weights and color differences:

[0134] Suppose that in a small area, there are 5 pixels involved in the repair, weights w1 = 0.8, w2 = 0.6, w3 = 0.5, w4 = 0.7, w5 = 0.9, color gradient P 1,grad =10,P 2,grad =15,P 3,grad =20, P 4,grad =25,P 5,grad =30, local average color P 1,avg =100, P 2,avg =110, P 3,avg =120, P 4,avg =130, P5,avg =140, adjustment index α1=α2=α3=α4=α5=1.5;

[0135] Compute the weighted sum of gradient differences:

[0136] For each pixel, the color difference is first calculated and the absolute value is taken, then the exponential operation is performed according to the adjustment coefficient, and finally multiplied by the corresponding weight;

[0137] For pixel 1:

[0138] |P 1,grad -P 1,avg |=|10-100|=90;

[0139] 90 1.5 =1213.44;

[0140] w1·1213.44=0.8·1213.44=970.75;

[0141] For Pixel 2:

[0142] |P 2,grad -P 2,avg |=|15-110|=95;

[0143] 95 1.5 =925.41;

[0144] w2·925.41=0.6·925.41=555.25;

[0145] For Pixel 3:

[0146] |P 3,grad -P 3,avg |=|20-120|=100;

[0147] 100 1.5 =1000;

[0148] w3·1000=0.5·1000=500;

[0149] For Pixel 4:

[0150] |P 4,grad -P 4,avg |=|25-130|=105;

[0151] 105 1.5 =1123.5;

[0152] w4·1123.5=0.7·1123.5=786.45;

[0153] For Pixel 5:

[0154] |P 5,grad -P 5,avg |=|30-140|=110;

[0155] 110 1.5 =1155.5;

[0156] w5·1155.5=0.9·1155.5=1040.0;

[0157] Weighted total: 970.75+555.25+500+786.45+1040.0=3852.45;

[0158] Calculate the sum of weights:

[0159] Finally, the color value P of the rendered image is calculated c :

[0160] The final P c =1101.99 represents the color value of the rendered image after the broken area is repaired in detail. This result shows that through weighted interpolation and color transition smoothing, the repaired area can effectively fill the broken area, and the color transition is more natural.

[0161] Specifically, if Figure 6 As shown in the figure, based on the rendered image after detail enhancement, the tasks are divided according to the scene depth distribution, the light intensity and material boundary are separated layer by layer, the multi-node allocation partitions are performed, and the image is fused in combination with the coordinates of the regional splicing points. The specific steps of outputting the rendering imaging results of the building model scene are as follows:

[0162] S501: Based on the rendered image after detail enhancement, scan the depth value of the region pixel by pixel, extract the illumination gradient change value and direction vector, record the depth and illumination material correlation characteristics, and generate the scene depth distribution and illumination material parameter set;

[0163] From the rendered image after detail enhancement, the depth value of the image is scanned pixel by pixel, and the illumination gradient change value and direction vector of each pixel are extracted. The extraction of depth value is based on the spatial distance between the pixel and the viewpoint in the scene, and combined with the spatial distribution of the light source, the gradient change of the illumination intensity between pixels is calculated. When extracting the gradient change value, the illumination gradient direction and magnitude between each pixel and its adjacent pixels are analyzed according to the law of illumination intensity change with depth, and the direction vector is generated to record the path relationship between light incidence and reflection. The relationship between illumination gradient and depth is fitted by region, and the illumination absorption, reflection and scattering characteristics of the material in each region are recorded one by one, and the illumination correlation characteristics of the material are classified. For the area where the illumination gradient changes significantly, its spatial coordinates, light direction vector and depth change value are further recorded, and the existing illumination abnormal distribution points are marked. Finally, the scene depth distribution and illumination material parameter set are generated, which contains the depth value, illumination gradient value, direction vector and material correlation characteristics of each pixel, providing key data support for subsequent regional partitioning and splicing processing.

[0164] S502: Based on the scene depth distribution and the lighting material parameter set, the depth level partition range is delineated, regional nodes are allocated, overlapping area boundary points are extracted, node distribution and splicing parameters are recorded, and a multi-node partition and splicing parameter set is generated;

[0165] The depth values ​​are hierarchically divided using the scene depth distribution and the lighting material parameter set. According to the changing trend of the depth range, the scene is divided into multiple depth level partition ranges. According to the depth level division standard, the areas with similar depth and lighting material characteristics in the scene are classified into one partition, and specific regional nodes are assigned to each partition. When assigning nodes, the boundary coordinates of the overlapping area are extracted by analyzing the boundary points of each partition, and the overlapping relationship between adjacent partitions is marked. The node distribution of each partition is recorded, including the spatial coordinates, depth range and related information of the node with lighting material parameters. For overlapping areas, the lighting material and depth values ​​of the boundary points are compared to calculate the splicing parameters of the overlapping area, including the lighting gradient matching value and the material connection characteristic parameters. Combined with the node information and splicing parameters of each partition, a multi-node partition and splicing parameter set are generated to ensure the continuity of lighting and material parameters between partitions, providing input basis for subsequent partition adjustment and fusion.

[0166] S503: Based on the multi-node partitioning and splicing parameter set, the partition pixel value is adjusted, the illumination difference is corrected, the material connection of the overlapping area is smoothed, the partition images are fused and the depth level is unified, and the building model scene rendering imaging result is output;

[0167] Through multi-node partitioning and splicing parameter sets, the pixel values ​​in the partitions are gradually adjusted to ensure the consistency of lighting and material characteristics between the partitions. During the partition adjustment process, the lighting differences in the overlapping areas are corrected according to the lighting gradient matching values ​​recorded in the splicing parameters. When correcting, the pixel lighting intensity in the overlapping areas of adjacent partitions is weighted averaged, and the material transition characteristics of the overlapping areas are smoothed with reference to the material connection parameters to reduce the lighting and material discontinuity problems at the partition splicing boundaries. For the unified processing of depth levels, by adjusting the depth value of each partition pixel, all partitions have a coordinated depth level distribution in the scene to avoid depth jumps affecting the rendering results. After the partition adjustment is completed, the image data of all partitions are fused into one, the lighting distribution and depth level characteristics are unified, and finally the building model scene rendering imaging results are generated to ensure that the image presentation is rich in details and the transition is natural, providing reliable final imaging data for high-quality scene rendering of building information models.

[0168] like Figure 7 As shown, a system for building information model scene rendering based on parallel computing includes:

[0169] The geometric lighting partition module extracts node material parameters and position relationships based on the scene data stored in the building information model, analyzes the geometric distance and light intensity changes, compares the light gradient change rate and spatial distribution, and generates a lighting partition mapping table;

[0170] The light path analysis module extracts the light path parameters of the area boundary points based on the illumination partition mapping table, analyzes the light propagation direction and intersection coordinate position in the path, compares the light path energy distribution and material-related properties between areas, and generates a light area transfer relationship set;

[0171] The noise area correction module analyzes the light energy accumulation value and reflection path position of the light path intersection based on the light area transfer relationship set, calculates the material refractive index and light propagation offset value, adjusts the high-frequency reflection path and energy noise value, and generates a noise-optimized rendering image;

[0172] The texture layered enhancement module optimizes the rendered image based on noise, extracts the pixel gradient value of the scene texture boundary, compares the material boundary characteristics with the texture breakpoint position, analyzes the texture gradient difference and supplements the boundary information of the break area, matches the boundary texture characteristics, and obtains the texture enhancement layer data;

[0173] The rendering scene fusion module separates the light intensity values ​​of the depth layers of the building scene based on the texture enhancement layer data, extracts the light intensity changes at the partition boundary points, matches the regional light intensity with the material characteristics, processes the partition splicing and reorganization relationship, fuses the multi-level scene depth, and outputs the building model scene rendering imaging results.

[0174] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for rendering a building information model scene based on parallel computing, characterized in that: The following steps are involved: S1: Based on the scene data stored in the building information model, the spatial coordinates of the building geometry nodes are analyzed, the geometry nodes are associated with the material attributes, the data partition relationship is analyzed according to the illumination distribution, and the geometry material illumination analysis results are generated; S2: Based on the illumination analysis result of the geometric material, for the light path parameters, segmentally analyze the propagation direction of the light in the differentiated areas, extract the energy distribution data of the path intersection, match the light propagation relationship between the areas, and generate the light path distribution result; S3: Based on the light path distribution result, filter the area with uneven light energy distribution, extract the light interaction point information, compare the light energy accumulation value with the material parameters, adjust the light energy distribution in the noise area, correct the high-frequency reflection and multiple refraction paths, and generate a noise optimized rendering image; S4: Based on the noise optimized rendered image, jointly analyzing the pixel texture boundary gradient and the scene geometric boundary, analyzing the pixel color distribution gradient, repairing the texture break area, processing the color gradient difference, supplementing the texture details, and generating a rendered image with enhanced details; S5: Based on the rendered image after detail enhancement, tasks are divided according to scene depth distribution, light intensity and material boundaries are separated layer by layer, multi-node allocation partitions are performed, image fusion is performed in combination with the coordinates of the regional splicing points, and the building model scene rendering imaging results are output.

2. The method for scene rendering of building information model based on parallel computing according to claim 1, characterized in that: The geometric material illumination analysis results include the spatial coordinates of the geometric structure nodes, the material attribute association relationship, and the illumination distribution data partition relationship. The light path distribution results include the light path parameter segmentation analysis results, the energy distribution data of the path intersection, and the light propagation relationship matching results between regions. The noise optimized rendered image includes the light energy distribution balanced area, the light interaction point energy information, the corrected high-frequency reflection path, and the corrected multiple refraction path. The rendered image after detail enhancement includes the pixel texture boundary gradient, the pixel color distribution gradient, the repaired texture fracture area, and the supplemented texture details. The architectural model scene rendering imaging results include the scene depth distribution data, the illumination intensity distribution level, the material boundary division, the multi-node partition result, and the regional splicing point coordinate data.

3. The method for scene rendering of building information model based on parallel computing according to claim 1, characterized in that: Based on the scene data stored in the building information model, the spatial coordinates of the building geometry nodes are parsed, the geometry nodes are associated with the material attributes, and the data partition relationship is analyzed according to the illumination distribution. The specific steps to generate the geometry material illumination parsing results are as follows: S101: extracting spatial coordinate data of building geometric structure nodes based on scene data stored in a building information model, analyzing connection relationships between geometric structure nodes, classifying and grouping the spatial coordinate data, and generating a geometric node spatial coordinate grouping table; S102: extracting material attributes associated with each geometric node based on the geometric node spatial coordinate grouping table, performing matching processing on the geometric nodes and the material attributes, adjusting the association of unmatched material nodes, and obtaining geometric node material attribute association data; S103: Based on the geometric node material attribute associated data, the corresponding position relationship between the material parameters of the geometric node and the spatial coordinates is parsed, the lighting coverage range is analyzed, and the geometric material lighting parsing result is obtained.

4. The method for scene rendering of building information model based on parallel computing according to claim 1, characterized in that: Based on the illumination analysis result of the geometric material, the propagation direction of the light in the differentiated areas is analyzed in sections for the light path parameters, the energy distribution data of the path intersection is extracted, and the light propagation relationship between the areas is matched. The steps of generating the light path distribution result are specifically as follows: S201: Based on the illumination analysis result of the geometric material, the starting point and end point coordinates of the path are read according to the segmentation rule of the light path, the path segments are segmented in combination with the area boundary data, and the logical order of the path segments is adjusted by analyzing the angle change and direction characteristics of the segmented path to generate a light path segmentation data set; S202: Based on the light path segmentation data set, analyzing the intersection points of the paths at the region boundary, extracting the spatial coordinate data of the intersection points, analyzing the energy variation between the path intersection points, arranging and classifying the segmented path energy gradients, and obtaining the energy distribution data of the light path intersection points; S203: Based on the energy distribution data of the light path intersection, the correlation value between the path energy gradient and the direction change is calculated, the connection relationship between the light paths between the regions is matched, the overall structure of the path is adjusted and the path between the regions is optimized, and the light path distribution result is generated.

5. The method for rendering a building information model scene based on parallel computing according to claim 4, characterized in that: Calculate the correlation value of the path energy gradient and the direction change according to the formula: Among them, G represents the correlation value between the path energy gradient and the direction change, E x Represents the energy distribution value along the horizontal direction at the intersection of the light paths, E y represents the energy distribution value in the vertical direction at the intersection of the light path, d represents the adjacent distance of the current light path area, D x Represents the gradient value in the horizontal path direction at the intersection of the light paths, D y Represents the gradient value in the direction perpendicular to the path at the intersection of the light paths, and M1, M2, and M3 are weight coefficients.

6. The method for scene rendering of building information model based on parallel computing according to claim 1, characterized in that: Based on the light path distribution results, the steps of screening the area with uneven light energy distribution, extracting the light interaction point information, comparing the light energy accumulation value with the material parameters, adjusting the light energy distribution in the noise area, correcting the high-frequency reflection and multiple refraction paths, and generating the noise optimized rendering image are as follows: S301: Based on the light path distribution result, analyze the light nodes in the path, extract the light energy density distribution parameters, mark the light energy density fluctuation area, and obtain the light energy uneven distribution area information; S302: Based on the information of the uneven light energy distribution area, processing the light interaction points in the marked area point by point, extracting the accumulated light energy value of the path sequence, analyzing the local abnormal value, counting the range of material parameter differences, and obtaining abnormal light energy interaction path data; S303: Based on the light energy anomaly interaction path data, adjusting the path light energy anomaly cumulative value distribution parameters, redistributing the light energy ratio, correcting the light energy distribution value, balancing the light energy distribution difference, and generating a noise optimized rendering image.

7. The method for scene rendering of building information model based on parallel computing according to claim 1, characterized in that: Based on the noise optimized rendered image, the steps of jointly analyzing the pixel texture boundary gradient and the scene geometric boundary, analyzing the pixel color distribution gradient, repairing the texture break area, processing the color gradient difference, supplementing the texture details, and generating the rendered image after detail enhancement are specifically as follows: S401: Based on the noise optimized rendering image, scan the texture boundary and geometric structure information pixel by pixel, extract the texture boundary direction angle and position coordinates, record the texture gradient and geometric information, and generate a texture and geometric boundary parameter set; S402: Based on the texture and geometric boundary parameter set, the color change trend of the pixel area is processed, the color gradient change rate and direction are analyzed, the color distribution model is established by matching the geometric boundary, and the pixel color distribution gradient trend is obtained; S403: Based on the pixel color distribution gradient trend, detail repair processing is performed on the broken area, the pixel filling value is adjusted, the color value distribution of the broken area is analyzed, the smooth interpolation color value of the broken area is calculated, the texture and color transition area are smoothed, and a detail enhanced rendering image is generated.

8. The method for scene rendering of building information model based on parallel computing according to claim 7, characterized in that: Calculate the smooth interpolation color value of the fracture area according to the formula: Among them, w i represents the weight coefficient for pixel i, P i,grad Represents the color gradient value of pixel i, P i,avg represents the local average color value of pixel i, α i is the adjustment index for pixel i, and n represents the total number of pixels involved in the interpolation calculation.

9. The method for scene rendering of building information model based on parallel computing according to claim 1, characterized in that: Based on the rendered image after detail enhancement, the steps of dividing the tasks according to the scene depth distribution, separating the light intensity and material boundary layer by layer, allocating partitions by multiple nodes, combining the coordinates of the regional splicing points for image fusion, and outputting the rendering imaging results of the building model scene are as follows: S501: Based on the rendered image after detail enhancement, scan the regional depth value pixel by pixel, extract the illumination gradient change value and direction vector, record the depth and illumination material correlation characteristics, and generate the scene depth distribution and illumination material parameter set; S502: Based on the scene depth distribution and the lighting material parameter set, define the depth level partition range, allocate regional nodes, extract overlapping area boundary points, record node distribution and splicing parameters, and generate a multi-node partition and splicing parameter set; S503: Based on the multi-node partitioning and splicing parameter set, the partition pixel value is adjusted, the illumination difference is corrected, the material connection of the overlapping area is smoothed, the partition images are fused and the depth level is unified, and the building model scene rendering imaging result is output.

10. A system for rendering building information model scenes based on parallel computing, characterized in that: According to the method for building information model scene rendering based on parallel computing according to any one of claims 1 to 9, the system comprises: The geometric lighting partition module extracts node material parameters and position relationships based on the scene data stored in the building information model, analyzes the geometric distance and light intensity changes, compares the light gradient change rate and spatial distribution, and generates a lighting partition mapping table; The light path analysis module extracts the light path parameters of the area boundary points based on the illumination partition mapping table, analyzes the light propagation direction and the intersection coordinate position in the path, compares the light path energy distribution and material related properties between the areas, and generates a light area transfer relationship set; The noise area correction module analyzes the light energy accumulation value of the light path intersection and the reflection path position based on the light area transfer relationship set, calculates the material refractive index and the light propagation offset value, adjusts the high-frequency reflection path and energy noise value, and generates a noise optimized rendering image; The texture layered enhancement module extracts the pixel gradient value of the scene texture boundary based on the noise optimized rendering image, compares the material boundary characteristics with the texture breakpoint position, analyzes the texture gradient difference and supplements the boundary information of the break area, matches the boundary texture characteristics, and obtains the texture enhancement layer data; Based on the texture enhancement layer data, the rendering scene fusion module separates the illumination intensity values ​​of the depth layers of the building scene, extracts the light intensity changes at the partition boundary points, matches the regional light intensity with the material characteristics, processes the partition splicing and reorganization relationship, fuses the multi-level scene depth, and outputs the building model scene rendering imaging results.

Images