New energy station rendering method based on virtual micro-polygon geometry technology

Abstract

The invention discloses a new energy station rendering method based on a virtual micro-polygon geometry technology, and relates to the technical field of new energy station three-dimensional rendering, and the method comprises the following steps: combining new energy station model triangular patches into a micro-polygon data set, carrying out the model clustering through a graph data structure, and obtaining a micro-polygon data set; forming virtual geometric grid bodies with different granularity refining grades; and splicing the micro-polygon data sets into polygon groups, carrying out surface reduction operation by taking the polygon groups as units, and locking boundaries among the groups. According to the method, the triangular patches of the new energy station model are combined into the micro-polygon data set, rendering is performed at the pixel-level precision, the detail performance of the new energy station model can be remarkably improved, and compared with a traditional granularity refining technology, the detail level can be dynamically adjusted at different sight distances and visual angles, high-precision visualization of the model is ensured, and the method is suitable for large-scale popularization and application. Technicians can be helped to understand and evaluate the structure and layout of the station more visually.

Description

Technical Field

[0001] The present invention relates to the technical field of three-dimensional rendering of new energy stations, and in particular to a new energy station rendering method based on virtual micro-polygon geometry technology. Background Art

[0002] Three-dimensional rendering of new energy stations is an important technical requirement in the current new energy field. With the rapid development of the new energy industry, the design, construction, operation and maintenance of new energy stations all require high-precision and high-efficiency three-dimensional rendering technology to provide intuitive and accurate visualization support. Traditional new energy station rendering methods mainly use granularity refinement technology, which dynamically adjusts the detail level of objects or scenes to adapt to different rendering requirements.

[0003] However, the prior art has the following disadvantages: 1. When rendering new energy stations, traditional granularity refinement technology has insufficient details due to data redundancy and refinement level limitations, and cannot meet the high-precision visualization requirements for new energy facilities. 2. In order to achieve better rendering effects, traditional technologies often require high-performance hardware configuration, which not only increases costs but also limits the popularity and application scope of rendering technology; 3. Due to data redundancy and algorithm limitations, traditional technologies take a long time to render new energy stations, are inefficient, and cannot meet the needs of real-time rendering, especially in large-scale, high-complexity scenes. Summary of the invention

[0004] The purpose of the present invention is to provide a new energy station rendering method based on virtual micro-polygon geometry technology to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A new energy station rendering method based on virtual micropolygon geometry technology comprises the following steps: Step 1: Combine the triangular facets of the new energy station model into a micro-polygon data set, cluster the model through the graph data structure, and form virtual geometric meshes with different granularity levels; Step 2: The micropolygon dataset is spliced ​​into polygon groups, and the polygon reduction operation is performed on the polygon groups as units to lock the boundaries between groups to ensure the consistency of the simplified model boundaries and avoid boundary mismatch or abnormal triangle density; Step 3: construct a directed acyclic graph (DAG) for micropolygon datasets of different granularity levels and mark the flow direction to achieve efficient management of the rendering process; Step 4: Combine the camera parameters and the pre-stored geometric errors of the micropolygon data set to convert and obtain the screen space pixel error value, analyze the actual rendering accuracy of the micropolygon on the screen, and provide a basis for rendering decisions; Step 5: Based on the constructed directed acyclic graph (DAG), start from the root of the directed acyclic graph (DAG), advance layer by layer, and render nodes that meet the requirements according to the screen space pixel error to achieve pixel-level efficient rendering; Step 6: Compare the rendering effects based on the directed acyclic graph and the traditional granularity refinement technology, obtain the rendering difference coefficient, analyze the difference in rendering effects, and then adjust the parameters to optimize the rendering effect.

[0006] A further improvement of the technical solution of the present invention is that: the step 1 specifically includes: Obtain the three-dimensional model of the new energy station, analyze it to identify all the triangular facets, and assign a unique index identifier to each triangular facet, wherein the geometric data of the three-dimensional model is traversed, the vertex coordinates and normal vector information of each triangular facet are extracted, and the information is stored in a data structure. At the same time, an index table is established to record the position and attributes of each triangular facet in the model; The adjacency relationship between triangles is abstracted into a graph data structure, and two arrays are constructed to store the information of the graph data structure. Each triangle is regarded as a node, and its adjacent triangles are connected to the node as edges. The two arrays are Adjacency array and AdjacencyOffset array. The Adjacency array is used to store the adjacency relationship of all nodes, and the AdjacencyOffset array is used to store the starting position of the adjacency relationship of each node. Use the graph partitioning library (Metis) to recursively divide the constructed graph data structure into multiple subgraphs, and store the partition results in two arrays, namely the Indexes array and the Ranges array. The Indexes array is used to record the triangle numbers belonging to each subgraph, and the Ranges array is used to record the start and end ranges of each subgraph. According to the partitioning result, the triangular facets of each sub-image are combined into a virtual geometric mesh, and a granularity refinement level is assigned to each virtual geometric mesh, so that the triangular facets of the 3D model are organized into multiple virtual geometric meshes, each of which represents a local area of ​​the 3D model and has different granularity refinement potentials; According to the rendering requirements of the 3D model, different granularity refinement levels are assigned to each virtual geometric mesh based on the distance and field of view parameters, ensuring that the detail level of each mesh can be dynamically adjusted during rendering, thereby forming a hierarchical virtual geometric mesh structure, which is the micropolygon dataset.

[0007] A further improvement of the technical solution of the present invention is that: the step 2 specifically includes: The micro-polygon data sets obtained by division are spliced ​​according to geometric continuity and attribute similarity to form polygon groups, and each spliced ​​polygon group is assigned a unique identifier, and its initial triangle number and boundary information are recorded; Formulate face reduction rules. When reducing faces, lock the shared boundaries between polygon groups to prevent misalignment caused by inconsistent numbers of triangles at the boundaries. Set a mark for each boundary to ensure that the number and position of boundary triangles remain unchanged during the face reduction process. Combined with the established surface reduction rules, the quadratic error metric (QEM) algorithm is used to reduce the polygon group. The optimal fusion point with the adjacent vertices is calculated for each triangle vertex, and the distance from the fused vertex to each surface of the original 3D model is quantified as an error value to ensure that the distance from the fused vertex to the relevant plane of the original 3D model is minimized. Combine the quantized error value and the preset error threshold to gradually reduce the number of triangles in each group while keeping the boundaries between groups locked. In each round of surface reduction, the number of triangles in the group is halved, and the remaining triangles are reassembled into a new micropolygon data set. In the case of an odd number of triangles, the excess triangles are evenly distributed to adjacent groups to ensure the quality of the 3D model after surface reduction. After the surface reduction operation is completed, the surface reduction results are verified and optimized. The verification content includes checking the integrity, correctness and boundary consistency of the 3D model. By analyzing the error between the 3D model after surface reduction and the original 3D model, the error trend index is calculated to evaluate whether the surface reduction effect meets the preset accuracy requirements.

[0008] A further improvement of the technical solution of the present invention is that the error value of the distance quantization from the fused vertex to each surface of the original three-dimensional model is calculated as follows: ; In the formula, is the distance from the point to the plane, i.e. the quadratic surface error, represents a plane, represents the fusion point, is the unit normal vector of the plane, Is a constant.

[0009] A further improvement of the technical solution of the present invention is that: the step 3 specifically includes: Define a corresponding node for each micropolygon dataset with different granularity refinement levels as the basic element of the directed acyclic graph (DAG), clarify the granularity level of each node, ensure the hierarchical relationship between nodes is clear, assign a unique identifier to each node, and record the attributes of each node, including granularity refinement level, pre-stored geometric error, number of triangles and boundary information, to ensure that the node contains enough information to support rendering decisions; Based on the defined nodes and granularity levels, combined with the logical order of the rendering process, determine the dependencies between nodes, identify the preconditions between nodes, and the flow direction between nodes, to ensure that during the rendering process, the nodes with preconditions are processed first, and then the nodes that depend on the preconditions are processed; According to the nodes and dependencies, a directed acyclic graph is constructed, and the nodes are connected according to the dependencies to form directed edges. At the same time, the flow direction is marked on each edge to clearly indicate the order in which rendering tasks are transferred between nodes.

[0010] A further improvement of the technical solution of the present invention is that: the step 4 specifically includes: Get the current camera parameters from the rendering system, including position, direction, field of view (FOV), near clipping plane, far clipping plane, and screen pixel resolution, and calculate the view transformation matrix and projection matrix based on the camera parameters to convert the 3D world coordinates into screen space coordinates to ensure that the transformation matrix can accurately reflect the current rendering perspective and projection method; For each micropolygon dataset, calculate the position of its geometric center point in the world space, and apply the view transformation matrix and projection matrix to transform the geometric center point from the world space to the screen space, where the screen space coordinates are expressed as normalized device coordinates (NDC) in the range of [-1, 1], which are further converted to screen pixel coordinates; The distance from the micropolygon dataset to the camera, the field of view angle, and the screen pixel resolution are comprehensively analyzed. Combined with the pre-stored geometric error of each micropolygon dataset, the screen space pixel error value is calculated to analyze the actual rendering accuracy of the micropolygon on the screen.

[0011] A further improvement of the technical solution of the present invention is that the screen space pixel error value is calculated by the following formula: ; In the formula, is the screen space pixel error value, is the pre-stored geometric error, is the distance from the micropolygon dataset to the camera, is the screen pixel resolution, is the field of view.

[0012] A further improvement of the technical solution of the present invention is that the step 5 specifically includes: Starting from the root node of the directed acyclic graph, an empty rendering queue is initialized and the root node is placed in the queue as the starting point. The root node represents the coarsest granularity refinement level and sets the rendering parameters, including the screen space pixel error standard value, camera position, field of view, and screen resolution. At the same time, a marker list of visited nodes is initialized to avoid repeated processing of the same node. Take the current node from the rendering queue, and traverse the nodes layer by layer starting from the root node according to the hierarchical structure of the directed acyclic graph. For each node, calculate its screen space pixel error value and compare it with the preset screen space pixel error standard value. If the screen space pixel error value of the node is less than or equal to the screen space pixel error standard value, it means that the rendering accuracy of the node on the screen meets the requirements, and add it to the rendering queue for further processing. If the screen space pixel error value is greater than the screen space pixel error standard value, it is necessary to decide whether to perform detail enhancement or use a finer granularity level node for replacement according to the rendering strategy; According to the order of nodes in the rendering queue, the renderable nodes are processed one by one. For each renderable node, the rendering operation is performed and marked as visited. At the same time, the granularity refinement level and screen space pixel error value of the rendering node are recorded.

[0013] A further improvement of the technical solution of the present invention is that the step 6 specifically includes: Select the traditional granularity refinement technology as the benchmark rendering solution, set its rendering parameters and configuration, and use the directed acyclic graph rendering technology and the traditional granularity refinement technology to perform rendering tests on the same scene to ensure that the test conditions are consistent; From the results of the rendering test, we extract the rendering effect indicators of the two rendering technologies, namely frame rate, rendering time, detail performance, data redundancy, and visual consistency, and convert detail performance, data redundancy, and visual consistency into quantifiable scores; Compare the rendering effect indicators of the two rendering technologies extracted, calculate the rendering difference coefficient, quantify the effect difference between the two rendering technologies, and then analyze the value of the rendering difference coefficient to judge the advantages and disadvantages of the two rendering technologies; By comparing the differences in texture clarity and edge smoothness between the two rendering technologies in the performance of 3D model details, the frame rate stability of the two rendering technologies in different scenarios is analyzed to determine their performance in large-scale high-precision model rendering. Based on the differences in rendering effects and analysis results, the parameters and directions that need to be optimized are determined, including adjusting the hierarchical structure of the directed acyclic graph and optimizing the granularity refinement strategy. According to the optimization direction, adjust the relevant parameters of the directed acyclic graph rendering technology, including the granularity refinement level and the standard value of the screen space pixel error, and use the adjusted parameters to re-perform the rendering test, collect new rendering data, compare the rendering difference coefficient and rendering effect before and after optimization, and evaluate whether the optimization effect meets expectations. If not, continue to adjust the parameters and repeat the test until a satisfactory effect is achieved.

[0014] A further improvement of the technical solution of the present invention is that the calculation formula of the rendering difference coefficient is as follows: ; In the formula, is the rendering difference coefficient, which indicates the difference between the two rendering techniques. It is the total number of rendering performance indicators, including frame rate, rendering time, detail performance, data redundancy and visual consistency. The rendering technology based on directed acyclic graph (DAG) is The value of the rendering effect indicator, Based on the traditional LOD rendering technology The value of the rendering effect indicator, is the absolute value function, which is used to calculate the difference between two values. The value range is .

[0015] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art: 1. The present invention provides a new energy station rendering method based on virtual micropolygon geometry technology. By combining the triangular facets of the new energy station model into a micropolygon data set and rendering it with pixel-level accuracy, the detail performance of the new energy station model can be significantly improved. Compared with traditional granularity refinement technology, the detail level can be dynamically adjusted at different viewing distances and viewing angles to ensure high-precision visualization of the model, which can help technicians understand and evaluate the structure and layout of the station more intuitively.

[0016] 2. The present invention provides a new energy station rendering method based on virtual micropolygon geometry technology, which uses a directed acyclic graph to manage micropolygon data sets of different granularity refinement levels, thereby realizing an efficient rendering process. By advancing the rendering task layer by layer starting from the root of the directed acyclic graph and dynamically selecting the appropriate granularity refinement level according to the screen space pixel error, it can significantly reduce unnecessary calculations and data processing while ensuring the rendering quality, thereby improving the rendering efficiency and reducing the requirements for hardware performance, so that high-precision rendering can be achieved on a wider range of devices, reducing costs and improving the popularity of the technology.

[0017] 3. The present invention provides a new energy station rendering method based on virtual micro-polygon geometry technology. By locking the boundaries between polygon groups and maintaining boundary consistency during surface reduction operations, the problems of boundary mismatch or abnormal triangle density common in traditional technologies are avoided, making the presentation of new energy stations at different viewing distances and viewing angles more stable and natural, providing users with a consistent visual experience, which is especially important for real-time roaming and interactive display of new energy stations, and can effectively improve user satisfaction and usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0019] Figure 1 is a flow chart of the present invention; Figure 2 It is a schematic diagram of the method flow of the present invention; Figure 3 It is a schematic diagram of vertex merging loss of the present invention; Figure 4 It is a comparison diagram of two rendering modes of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Embodiment 1, as Figures 1 to 3 As shown, the present invention provides a new energy station rendering method based on virtual micro-polygon geometry technology, comprising the following steps: Step 1: Combine the triangular facets of the new energy station model into a micro-polygon data set, cluster the model through the graph data structure, form virtual geometric meshes with different granularity levels, obtain the three-dimensional model of the new energy station, analyze and identify all the triangular facets, and assign a unique index identifier to each triangular facet. The geometric data of the three-dimensional model is traversed, the vertex coordinates and normal vector information of each triangular facet are extracted, and stored in the data structure. At the same time, an index table is established to record the position and attributes of each triangular facet in the model, and the adjacency relationship between the triangular facets is abstracted into a graph data structure, and two arrays are constructed to store the information of the graph data structure. Each triangular facet is used as a node, and its adjacent triangular facets are connected to the node as edges. The two arrays are Adjacency array and AdjacencyOffset array. The Adjacency array is used to store the adjacency relationship of all nodes, and the AdjacencyOffset array is used to store the starting position of the adjacency relationship of each node. The constructed graph data structure is recursively binary partitioned using the graph partitioning library (Metis). The graph data structure is divided into multiple sub-graphs, and the division results are stored in two arrays, namely, the Indexes array and the Ranges array. In the division process, it is ensured that the triangles in each sub-graph are geometrically continuous and the boundaries between sub-graphs are as few as possible. The Indexes array is used to record the triangle numbers belonging to each sub-graph, and the Ranges array is used to record the start and end ranges of each sub-graph. According to the division results, the triangles of each sub-graph are combined into a virtual geometric mesh, and a granularity refinement level is assigned to each virtual geometric mesh. Initially, the granularity refinement levels of all virtual geometric meshes are set to the highest (finest), and then the triangles of the three-dimensional model are organized into multiple virtual geometric meshes. Each virtual geometric mesh represents a local area of ​​the three-dimensional model and has different granularity refinement potentials. According to the rendering requirements of the three-dimensional model, different granularity refinement levels are assigned to each virtual geometric mesh based on the distance and field of view parameters to ensure that the detail level of each mesh can be dynamically adjusted during rendering, thereby forming a hierarchical virtual geometric mesh structure, which is a micropolygon dataset. Step 2, stitch the micropolygon dataset into polygon groups, perform face reduction operation on polygon groups, lock the boundaries between groups, ensure the consistency of the simplified model boundaries, avoid boundary mismatch or abnormal triangle density, stitch the divided micropolygon dataset according to geometric continuity and attribute similarity to form polygon groups, and assign a unique identifier to each stitched polygon group, record its initial number of triangles and boundary information, wherein, during the stitching process, ensure that the triangles in each group are geometrically continuous and the boundaries between groups are as few as possible, initialize the granularity refinement level of the polygon group to the highest (finest), formulate face reduction rules, lock the shared boundaries between polygon groups when reducing the surface, prevent the mismatch caused by the inconsistent number of triangles at the boundary, and set a mark for each boundary to ensure that the number and position of boundary triangles remain unchanged during the face reduction process, so that the boundaries of adjacent polygon groups remain consistent during the face reduction process, and ensure the visual consistency and geometric continuity of the three-dimensional model after the face reduction, and combine the formulated face reduction rules, use the quadratic error metric (QEM) algorithm to perform face reduction operation on the polygon group, for each triangle vertex Calculate the optimal fusion point with adjacent vertices, quantify the distance from the fused vertex to each face of the original 3D model as an error value, ensure that the distance from the fused vertex to the relevant plane of the original 3D model is minimized, combine the quantized error value and the preset error threshold, gradually reduce the number of triangles in each group, and keep the boundary between groups locked. In each round of face reduction operation, the number of triangles in the group is halved, and the remaining triangles are recombined into a new micropolygon data set. In the case of an odd number of triangles, the excess triangles are evenly distributed to adjacent groups to ensure the quality of the 3D model after face reduction. After the face reduction operation is completed, the face reduction result is verified and optimized. The verification content includes checking the integrity, correctness and boundary consistency of the 3D model. By analyzing the error between the 3D model after face reduction and the original 3D model, the error trend index is calculated to evaluate whether the face reduction effect meets the preset accuracy requirements. If the error exceeds the allowable range, adjust the face reduction parameters (error threshold, boundary locking strategy) and re-perform the face reduction operation. At the same time, check whether there is abnormal triangle density or non-fitting phenomenon at the boundary of the 3D model to ensure the rendering effect of the 3D model at different granularity refinement levels; Furthermore, the distance quantization error value from the fused vertex to each surface of the original 3D model is calculated as follows: ; In the formula, is the distance from the point to the plane, i.e. the quadratic surface error, represents a plane, represents the fusion point, is the unit normal vector of the plane, is a constant; The error trend index is calculated as follows: ; In the formula, is the error trend index, which indicates the overall error between the three-dimensional model after surface reduction and the original three-dimensional model. is the total number of vertices in the model, The original 3D model The coordinates of the vertices, The first The coordinates of the vertices, is the reference value used to standardize the error. It is the average side length of the original 3D model. The reference value is calculated by calculating the length of all sides in the original 3D model and then taking the average of the lengths to ensure Matches the geometric features of the model to more accurately reflect the model's errors, is the error threshold, indicating the maximum allowed error, is the Euclidean norm, which is used to calculate the Euclidean distance of vertex coordinates. The value range is ,when When it is close to 0, it means that the 3D model after surface reduction is very close to the original 3D model, and the error is very small. When it increases, it means that the error increases, and the difference between the three-dimensional model after surface reduction and the original three-dimensional model becomes larger; In addition, using the quadratic error metric (QEM) algorithm, when the loss function is minimized, the appearance change after face reduction is also minimized. Since the maximum or minimum point of the quadratic function is where its derivative is 0, as long as the minimum value is found by calculation, the vertex position with the smallest appearance change after face reduction can be determined. The goal of the quadratic error metric (QEM) algorithm is as follows: Figure 3 As shown, the distance between the new vertex and several related planes of the original three-dimensional model is minimized; Step 3, construct a directed acyclic graph (DAG) for micropolygon datasets of different granularity refinement levels, and mark the flow direction to achieve efficient management of the rendering process. Define a corresponding node for each micropolygon dataset of different granularity refinement level as the basic element of the directed acyclic graph (DAG), clarify the granularity level of each node, ensure that the hierarchical relationship between nodes is clear, and assign a unique identifier to each node. Record the attributes of each node, including granularity refinement level, pre-stored geometric error, number of triangles and boundary information, to ensure that the node contains enough information to support rendering decisions. Based on the defined nodes and granularity levels, combined with the rendering process Determine the dependencies between nodes in a logical order, identify the preconditions between nodes, and the flow direction between nodes, ensure that in the rendering process, the nodes with preconditions are processed first, and then the nodes that depend on the preconditions are processed. According to the nodes and dependencies, a directed acyclic graph is constructed, and the nodes are connected according to the dependencies to form directed edges. At the same time, the flow direction is marked on each edge to clearly indicate the order in which rendering tasks are transferred between nodes. By marking the flow direction, the execution path of the rendering task can be clearly tracked. The flow direction points from the coarser nodes to the finer nodes, ensuring that the transfer order of rendering tasks between nodes is logical. Step 4: Combine the camera parameters and the pre-stored geometric errors of the micropolygon dataset to obtain the screen space pixel error value, analyze the actual rendering accuracy of the micropolygon on the screen, and provide a basis for rendering decisions. Obtain the current camera parameters from the rendering system, including position, direction, field of view (FOV), near clipping plane, far clipping plane, and screen pixel resolution, and calculate the view transformation matrix and projection matrix according to the camera parameters to convert the three-dimensional world coordinates into screen space coordinates to ensure that the transformation matrix can accurately reflect the current rendering perspective and projection mode. For each micropolygon dataset, calculate the position of its geometric center point in the world space, and apply the view transformation matrix and projection matrix to convert the geometric center point from the world space to the screen space, where the screen space coordinates are expressed as normalized device coordinates (NDC) ranging from [-1,1], and further converted to screen pixel coordinates. Comprehensively analyze the distance from the micropolygon dataset to the camera, the field of view, and the screen pixel resolution, combine the pre-stored geometric errors of each micropolygon dataset, calculate the screen space pixel error value, and analyze the actual rendering accuracy of the micropolygon on the screen; Furthermore, the screen space pixel error value is calculated as follows: ; In the formula, is the screen space pixel error value, is the pre-stored geometric error, is the distance from the micropolygon dataset to the camera, is the screen pixel resolution, is the field of view; Step 5, based on the constructed directed acyclic graph (DAG), starting from the root of the directed acyclic graph (DAG), advance layer by layer, and render the nodes that meet the requirements according to the screen space pixel error to achieve pixel-level efficient rendering. Starting from the root node of the directed acyclic graph, initialize an empty rendering queue, put the root node as the starting point into the queue, the root node represents the coarsest granularity refinement level, and set the rendering parameters, including the standard value of the screen space pixel error, camera position, field of view and screen resolution. At the same time, initialize a marker list of visited nodes to avoid repeated processing of the same node, take the current node out of the rendering queue, and traverse the nodes layer by layer starting from the root node according to the hierarchical structure of the directed acyclic graph. For each node, calculate its screen space pixel error value and compare it with the preset screen space pixel error. If the screen space pixel error value of the node is less than or equal to the standard value of the screen space pixel error, it means that the rendering accuracy of the node on the screen meets the requirements, and the node is added to the rendering queue for further processing. If the screen space pixel error value is greater than the standard value of the screen space pixel error, it is necessary to decide whether to perform detail enhancement or replace it with a node of finer granularity level according to the rendering strategy. According to the order of nodes in the rendering queue, the renderable nodes are processed one by one. For each renderable node, the rendering operation is performed and marked as visited. During the rendering process, it is ensured that the dependencies between nodes are met, that is, the nodes with preconditions are rendered first, and then the nodes that depend on the preconditions are rendered, so as to ensure the logic and hierarchy of the rendering process. At the same time, the granularity refinement level and screen space pixel error value of the rendering node are recorded. Step 6: Compare the rendering effects based on directed acyclic graph and traditional granularity refinement technology, obtain the rendering difference coefficient, analyze the difference in rendering effects, and then adjust the parameters to optimize the rendering effect to improve the frame rate and detail performance, ensuring high efficiency and stability in large-scale high-precision model rendering.

[0022] Embodiment 2, as Figures 1 to 3 As shown, based on Example 1, the present invention provides a technical solution: preferably, the step 6 specifically includes: The traditional granularity refinement technology was selected as the benchmark rendering scheme, and its rendering parameters and configuration were set. The directed acyclic graph rendering technology and the traditional granularity refinement technology were used to perform rendering tests on the same scene to ensure that the test conditions were consistent. From the results of the rendering test, the rendering effect indicators of the two rendering technologies were extracted, which were frame rate, rendering time, detail performance, data redundancy, and visual consistency. The detail performance, data redundancy, and visual consistency were converted into quantifiable scores, among which the detail performance was quantified by the texture clarity and edge smoothness indicators, the data redundancy was quantified by comparing the redundant data generated during the rendering process, and the visual consistency was quantified by comparing the similarity between the rendering results and the expected results. The rendering effect indicators of the two rendering technologies were compared, and the rendering difference coefficient was calculated to quantify the effect difference between the two rendering technologies, and then the rendering effect was analyzed. The value of the rendering difference coefficient is used to judge the advantages and disadvantages of the two rendering technologies. By comparing the differences in texture clarity and edge smoothness of the two rendering technologies in the performance of 3D model details, the frame rate stability of the two rendering technologies in different scenarios is analyzed to judge their performance in large-scale high-precision model rendering. According to the rendering effect differences and analysis results, the parameters and directions to be optimized are determined, including adjusting the hierarchical structure of the directed acyclic graph and optimizing the granularity refinement strategy. According to the optimization direction, the relevant parameters of the directed acyclic graph rendering technology are adjusted, including the granularity refinement level and the standard value of the screen space pixel error. The adjusted parameters are used to re-perform rendering tests, new rendering data is collected, the rendering difference coefficients and rendering effects before and after optimization are compared, and whether the optimization effect meets expectations is evaluated. If not, the parameters are adjusted and the test is repeated until a satisfactory effect is achieved. In addition, the calculation formula of the rendering difference coefficient is as follows: ; In the formula, is the rendering difference coefficient, which indicates the difference between the two rendering techniques. It is the total number of rendering performance indicators, including frame rate, rendering time, detail performance, data redundancy and visual consistency. The rendering technology based on directed acyclic graph (DAG) is The value of the rendering effect indicator, Based on the traditional LOD rendering technology The value of the rendering effect indicator, is the absolute value function, which is used to calculate the difference between two values. The value range is ,when When it is close to 0, it means that the effects of the two rendering techniques are very close and the difference is small. When it increases, it means that the difference between the two rendering technologies is increasing. When the performance of the two rendering technologies in each indicator is close, will be smaller, indicating that the effects of the two technologies are similar. When the performance of the two rendering technologies on some indicators is very different, It will increase, indicating that the effects of the two technologies are significantly different.

[0023] Embodiment 3, as Figure 4 As shown, based on embodiments 1 and 2, the present invention provides an example as follows: a new energy station model is rendered using traditional granularity refinement technology and virtual micro-polygon geometry technology, and the model image quality and corresponding frame rate and rendering time are compared. Figure 4 For comparison, we can see that with the traditional granularity refinement technology, the screen FPS is 42.30 and the rendering time is 23.64ms, while with the virtual micropolygon geometry technology, the screen FPS is 93.17 and the rendering time is 10.73ms; It can be seen that the virtual micropolygon geometry technology has a higher rendering frame rate and efficiency than the traditional granularity refinement technology, and the images rendered using the virtual micropolygon geometry technology have more details.

[0024] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A new energy station rendering method based on virtual micropolygon geometry technology, characterized in that: The following steps are involved: Step 1: Combine the triangular facets of the new energy station model into a micro-polygon data set, cluster the model through the graph data structure, and form virtual geometric meshes with different granularity levels; Step 2, stitching the micro-polygon data set into polygon groups, performing face reduction operations on polygon groups as units, and locking the boundaries between groups; Step 3, construct a directed acyclic graph for micropolygon datasets of different granularity refinement levels and mark the flow direction; Step 4, combining the camera parameters and the pre-stored geometric errors of the micropolygon data set, converting to obtain the screen space pixel error value, and analyzing the actual rendering accuracy of the micropolygon on the screen; Step 5: Based on the constructed directed acyclic graph, starting from the root of the directed acyclic graph, advancing layer by layer, and rendering nodes that meet the requirements according to the screen space pixel error; Step 6: Compare the rendering effects based on the directed acyclic graph and the traditional granularity refinement technology, obtain the rendering difference coefficient, analyze the difference in rendering effects, and then adjust the parameters to optimize the rendering effect.

2. According to claim 1, a new energy station rendering method based on virtual micro-polygon geometry technology is characterized in that: The step 1 specifically includes: Obtain the three-dimensional model of the new energy station, analyze it to identify all the triangular facets, and assign a unique index identifier to each triangular facet, wherein the geometric data of the three-dimensional model is traversed, the vertex coordinates and normal vector information of each triangular facet are extracted, and the information is stored in a data structure. At the same time, an index table is established to record the position and attributes of each triangular facet in the model; The adjacency relationship between triangles is abstracted into a graph data structure, and two arrays are constructed to store the information of the graph data structure. Each triangle is regarded as a node, and its adjacent triangles are connected to the node as edges. The two arrays are Adjacency array and AdjacencyOffset array. The Adjacency array is used to store the adjacency relationship of all nodes, and the AdjacencyOffset array is used to store the starting position of the adjacency relationship of each node. Use the graph partitioning library to recursively divide the constructed graph data structure into multiple subgraphs, and store the partitioning results in two arrays, namely the Indexes array and the Ranges array. The Indexes array is used to record the triangle numbers belonging to each subgraph, and the Ranges array is used to record the start and end ranges of each subgraph. According to the division result, the triangular facets of each sub-image are combined into a virtual geometric mesh, and a granularity refinement level is assigned to each virtual geometric mesh, thereby organizing the triangular facets of the 3D model into multiple virtual geometric meshes; According to the rendering requirements of the 3D model, different granularity refinement levels are assigned to each virtual geometric mesh based on the distance and field of view parameters, thereby forming a hierarchical virtual geometric mesh structure, which is the micropolygon dataset.

3. The method for rendering a new energy station based on virtual micropolygon geometry technology according to claim 1, characterized in that: The step 2 specifically includes: The micro-polygon data sets obtained by division are spliced ​​according to geometric continuity and attribute similarity to form polygon groups, and each spliced ​​polygon group is assigned a unique identifier, and its initial triangle number and boundary information are recorded; Formulate face reduction rules. When reducing faces, lock the shared boundaries between polygon groups and set markers for each boundary. Combined with the formulated surface reduction rules, the quadratic surface error measurement algorithm is used to perform surface reduction operations on the polygon group, and the optimal fusion point with the adjacent vertices is calculated for each triangle vertex, and the distance from the fused vertex to each surface of the original 3D model is quantified as an error value; Combine the quantized error value with the preset error threshold to gradually reduce the number of triangles in each group while keeping the boundaries between groups locked. In each round of face reduction, the number of triangles in the group is halved, and the remaining triangles are reassembled into a new micropolygon data set. After the surface reduction operation is completed, the surface reduction results are verified and optimized. The verification content includes checking the integrity, correctness and boundary consistency of the 3D model. By analyzing the error between the 3D model after surface reduction and the original 3D model, the error trend index is calculated to evaluate whether the surface reduction effect meets the preset accuracy requirements.

4. The method for rendering a new energy station based on virtual micropolygon geometry technology according to claim 3 is characterized in that: The error value of the distance quantification from the fused vertex to each face of the original 3D model is calculated as follows: ； In the formula, is the distance from the point to the plane, i.e. the quadratic surface error, represents a plane, represents the fusion point, is the unit normal vector of the plane, Is a constant.

5. The new energy station rendering method based on virtual micropolygon geometry technology according to claim 1 is characterized in that: The step 3 specifically includes: Define a corresponding node for each micropolygon dataset of different granularity refinement level as the basic element of the directed acyclic graph, clarify the granularity level of each node, assign a unique identifier to each node, and record the attributes of each node, including granularity refinement level, pre-stored geometric error, number of triangles and boundary information; Based on the defined nodes and granularity levels, the dependencies between nodes are determined in combination with the logical order of the rendering process, and the preconditions between nodes and the flow direction between nodes are identified; According to the nodes and dependencies, a directed acyclic graph is constructed, and the nodes are connected according to the dependencies to form directed edges. At the same time, the flow direction is marked on each edge to clearly indicate the order in which rendering tasks are transferred between nodes.

6. The new energy station rendering method based on virtual micropolygon geometry technology according to claim 1 is characterized in that: The step 4 specifically includes: Get the current camera parameters from the rendering system, including position, direction, field of view, near clipping plane, far clipping plane, and screen pixel resolution, and calculate the view transformation matrix and projection matrix based on the camera parameters to convert the 3D world coordinates into screen space coordinates; For each micropolygon data set, calculate the position of its geometric center point in the world space, and apply the view transformation matrix and the projection matrix to transform the geometric center point from the world space to the screen space, where the screen space coordinates are expressed as normalized device coordinates and further converted to screen pixel coordinates; The distance from the micropolygon dataset to the camera, the field of view angle, and the screen pixel resolution are comprehensively analyzed. Combined with the pre-stored geometric error of each micropolygon dataset, the screen space pixel error value is calculated to analyze the actual rendering accuracy of the micropolygon on the screen.

7. The method for rendering a new energy station based on virtual micropolygon geometry technology according to claim 6 is characterized in that: The screen space pixel error value is calculated as follows: ； In the formula, is the screen space pixel error value, is the pre-stored geometric error, is the distance from the micropolygon dataset to the camera, is the screen pixel resolution, is the field of view.

8. The method for rendering a new energy station based on virtual micropolygon geometry technology according to claim 7 is characterized in that: The step 5 specifically includes: Starting from the root node of the directed acyclic graph, initialize an empty rendering queue, put the root node into the queue as the starting point, and set the rendering parameters, including the standard value of screen space pixel error, camera position, field of view, and screen resolution. At the same time, initialize a marker list of visited nodes; Take the current node from the rendering queue, and traverse the nodes layer by layer starting from the root node according to the hierarchical structure of the directed acyclic graph. For each node, calculate its screen space pixel error value and compare it with the preset screen space pixel error standard value. If the screen space pixel error value of the node is less than or equal to the screen space pixel error standard value, it means that the rendering accuracy of the node on the screen meets the requirements, and add it to the rendering queue for further processing; According to the order of nodes in the rendering queue, the renderable nodes are processed one by one. For each renderable node, the rendering operation is performed and marked as visited. At the same time, the granularity refinement level and screen space pixel error value of the rendering node are recorded.

9. The method for rendering a new energy station based on virtual micropolygon geometry technology according to claim 1, characterized in that: The step 6 specifically includes: Select the traditional granularity refinement technology as the benchmark rendering solution, set its rendering parameters and configuration, and use the directed acyclic graph rendering technology and the traditional granularity refinement technology to perform rendering tests on the same scene; From the results of the rendering test, we extract the rendering effect indicators of the two rendering technologies, namely frame rate, rendering time, detail performance, data redundancy, and visual consistency, and convert detail performance, data redundancy, and visual consistency into quantifiable scores; Compare the rendering effect indicators of the two rendering technologies extracted, calculate the rendering difference coefficient, quantify the effect difference between the two rendering technologies, and then analyze the value of the rendering difference coefficient to judge the advantages and disadvantages of the two rendering technologies; By comparing the differences in texture clarity and edge smoothness between the two rendering technologies in the performance of 3D model details, the frame rate stability of the two rendering technologies in different scenarios is analyzed to determine their performance in large-scale high-precision model rendering. Based on the differences in rendering effects and analysis results, the parameters and directions that need to be optimized are determined, including adjusting the hierarchical structure of the directed acyclic graph and optimizing the granularity refinement strategy. According to the optimization direction, adjust the relevant parameters of the directed acyclic graph rendering technology, including the granularity refinement level and the standard value of the screen space pixel error, and use the adjusted parameters to re-perform the rendering test, collect new rendering data, compare the rendering difference coefficient and rendering effect before and after optimization, and evaluate whether the optimization effect meets expectations. If not, continue to adjust the parameters and repeat the test until a satisfactory effect is achieved.

10. The new energy station rendering method based on virtual micro-polygon geometry technology according to claim 9 is characterized in that: The calculation formula of the rendering difference coefficient is as follows: ； In the formula, is the rendering difference coefficient, It is the total number of rendering performance indicators, including frame rate, rendering time, detail performance, data redundancy and visual consistency. The rendering technology based on directed acyclic graph is The value of the rendering effect indicator, Based on the traditional granularity refinement rendering technology The value of the rendering effect indicator, is the absolute value function, which is used to calculate the difference between two values. The value range is .

Images