A lightweight processing method and system for PDMS three-dimensional design data

Abstract

The application discloses a kind of PDMS three-dimensional design data's lightweight processing method and system, related field is related to data processing, this method includes: receiving the PDMS three-dimensional design data to be handled and carrying out the pre-processing of attribute data;Extract three-dimensional geometric model, carry out geometry fidelity weight analysis, establish geometry fidelity weight distribution;Determine lightweight display scene;It is degraded by polygon degradation module and is loaded visual distortion analyzer to carry out global visual distortion constraint;After rendering with attribute information, generate target lightweight PDMS three-dimensional design data;After layering storage, transmission is called display to three-dimensional display terminal.It solves the technical problem that existing lightweight processing exists, cannot improve storage and transmission efficiency while ensuring model accuracy and details and visual quality, reaches the technical effect that in improving storage and transmission efficiency, effectively guarantee model accuracy and details and visual quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data processing, and particularly relates to a lightweight processing method and system for PDMS three-dimensional design data. BACKGROUND

[0002] With the rapid development of three-dimensional scanning, photogrammetry and other technologies, PDMS (Plant Design Management System) has been widely applied in the field of three-dimensional design. PDMS three-dimensional design data usually contains a large amount of vertex, face and texture data, and the data volume is huge, which not only increases the storage and transmission cost, but also affects the loading speed and rendering efficiency of the model. Therefore, lightweight processing of PDMS three-dimensional design data has become a problem to be solved. In the field of lightweight processing, traditional lightweight methods such as geometry simplification and texture compression often result in significant loss of model precision and details, which cannot meet the needs of high-precision applications. And in the process of reducing the complexity of the model, visual distortion phenomenon is easy to appear, which affects the user experience.

[0003] In the related art, the lightweight processing of PDMS three-dimensional design data has the technical problem that it is difficult to improve the storage and transmission efficiency while ensuring the model precision and details and visual quality. SUMMARY

[0004] The present application provides a lightweight processing method and system for PDMS three-dimensional design data, which receives the PDMS three-dimensional design data to be processed and pre-processes the attribute data, extracts the three-dimensional geometric model and analyzes the geometry fidelity weight, determines the lightweight display scene, performs polygon degradation processing with the geometry fidelity weight distribution as the degradation constraint, calls the lightweight display scene to load the visual distortion analyzer for global visual distortion constraint, renders with attribute information and generates target lightweight PDMS three-dimensional design data, performs layered storage and transmits to a three-dimensional display terminal for calling and display. Technical means such as, achieve the technical effect of improving the storage and transmission efficiency while effectively ensuring the model precision and details and visual quality.

[0005] The application provides a lightweight processing method for PDMS three-dimensional design data, comprising: receiving PDMS three-dimensional design data to be processed and performing attribute data preprocessing to generate target PDMS three-dimensional design data; extracting a three-dimensional geometric model in the target PDMS three-dimensional design data and performing geometric fidelity weight analysis to establish a geometric fidelity weight distribution; determining a lightweight display scene of the target PDMS three-dimensional design data; performing downgrade processing on the three-dimensional geometric model through a polygon downgrade module with the geometric fidelity weight distribution as a downgrade constraint, and calling the lightweight display scene to load a visual distortion analyzer to perform global visual distortion constraint to generate a target lightweight three-dimensional geometric model; after rendering the target lightweight three-dimensional geometric model with attribute information in the target PDMS three-dimensional design data, generating target lightweight PDMS three-dimensional design data; and performing layered storage on the target lightweight PDMS three-dimensional design data and then transmitting the target lightweight PDMS three-dimensional design data to a three-dimensional display terminal for calling and display.

[0006] In a possible implementation, the three-dimensional geometric model in the target PDMS three-dimensional design data is extracted, and geometric fidelity weight analysis is performed to establish a geometric fidelity weight distribution, and the following processing is performed: vertex curvature calculation is performed on the three-dimensional geometric model to generate a curvature distribution feature; initialization and assignment of geometric fidelity weight are performed based on the curvature distribution feature to generate an initialization geometric fidelity weight distribution; key node features are received, and the key node features are used to enhance the weight of key nodes in the initialization geometric fidelity weight distribution to generate the geometric fidelity weight distribution, wherein the key node features are obtained through a user terminal.

[0007] In a possible implementation, initialization and assignment of geometric fidelity weight are performed based on the curvature distribution feature to generate an initialization geometric fidelity weight distribution, and the following processing is performed: based on the curvature distribution feature, region segmentation is performed on the three-dimensional geometric model to generate a plurality of segmentation regions, wherein the curvature fluctuation index in any segmentation region is less than a preset curvature fluctuation stability index; weight configuration is performed on the curvature mean values corresponding to the plurality of segmentation regions to generate the initialization geometric fidelity weight distribution, wherein the initialization geometric fidelity weight distribution includes initialization geometric fidelity weights of each segmentation region, and the initialization geometric fidelity weight of each segmentation region is proportional to the corresponding curvature mean value.

[0008] In a possible implementation, the three-dimensional geometric model is subjected to a degradation process by a polygon degradation module with the geometric fidelity weight distribution as a degradation constraint, and a global visual distortion constraint is called by the lightweight display scene loading visual distortion analyzer to generate a target lightweight three-dimensional geometric model, and the following processing is performed: step a: extracting a plurality of model vertices in the three-dimensional geometric model, initializing an error measurement matrix for the plurality of model vertices, and generating a plurality of error measurement matrices; step b: performing a merging position error calculation of any two vertices based on the plurality of error measurement matrices, and counting a first vertex pair set with a merging position error less than a preset error; step c: merging vertex pairs in the first vertex pair set with the geometric fidelity weight distribution as a degradation constraint to generate a first lightweight three-dimensional geometric model; and step d: configuring the visual distortion analyzer based on the lightweight display scene to perform a global visual distortion analysis on the first lightweight three-dimensional geometric model to generate a first distortion evaluation index; determining whether the first distortion evaluation index exceeds a preset visual distortion constraint, and performing a plurality of processes on the first lightweight three-dimensional geometric model according to a determination result to generate the target lightweight three-dimensional geometric model.

[0009] In a possible implementation, the global visual distortion analysis of the first lightweight three-dimensional geometric model is performed by the visual distortion analyzer based on the lightweight display scene to generate a first distortion evaluation index, and the following processing is performed: collecting original geometric model samples, lightweight geometric model samples, and visual error score samples as constraint samples; using the original geometric model samples, the lightweight geometric model samples, and the visual error score samples as sample data, training the visual distortion analyzer based on a convolutional neural network; inputting the three-dimensional geometric model and the first lightweight three-dimensional geometric model into the visual distortion analyzer for evaluation to generate the first distortion evaluation index.

[0010] In a possible implementation, the target lightweight three-dimensional geometric model is generated by performing a plurality of processes on the first lightweight three-dimensional geometric model according to a determination result, and the following processing is performed: if the first distortion evaluation index does not exceed the preset visual distortion constraint, repeating steps a to d to continue to perform a plurality of processes on the first lightweight three-dimensional geometric model, and determining a lightweight three-dimensional geometric model corresponding to a previous vertex merging node whose distortion evaluation index exceeds the preset visual distortion constraint to generate the target lightweight three-dimensional geometric model.

[0011] In a possible implementation, the following processing is further performed: if the first distortion evaluation index exceeds the preset visual distortion constraint, the vertex pair with the largest position error is deleted from the set of primary vertex pairs according to a preset proportion; vertex merging is performed according to the updated set of vertex pairs, and the analysis is repeated in this way until a lightweight three-dimensional geometric model with a distortion evaluation index satisfying the preset visual distortion constraint is generated as the target lightweight three-dimensional geometric model.

[0012] In a possible implementation, after the target lightweight PDMS three-dimensional design data is stored in layers and transmitted to a three-dimensional display terminal for calling and display, the following processing is performed: model independent components of the target lightweight three-dimensional geometric model in the target lightweight PDMS three-dimensional design data are determined; the model independent components are divided in multiple levels according to the surrounding relationship to generate a multi-level component division result; the multi-level component division result is used to construct a multi-level R-tree node, and three-dimensional data corresponding to the multi-level component division result is extracted from the target lightweight PDMS three-dimensional design data and stored in the multi-level R-tree node to generate a multi-level R-tree; and the multi-level R-tree is transmitted to the three-dimensional display terminal for calling and display.

[0013] The application further provides a lightweight processing system for PDMS three-dimensional design data, which comprises: an attribute data preprocessing module, configured to receive PDMS three-dimensional design data to be processed and perform preprocessing on attribute data to generate target PDMS three-dimensional design data; a geometric fidelity weight analysis module, configured to extract a three-dimensional geometric model from the target PDMS three-dimensional design data and perform geometric fidelity weight analysis to establish a geometric fidelity weight distribution; a lightweight display scene determination module, configured to determine a lightweight display scene of the target PDMS three-dimensional design data; a degradation processing module, configured to take the geometric fidelity weight distribution as a degradation constraint, perform degradation processing on the three-dimensional geometric model through a polygon degradation module, and call the lightweight display scene to load a visual distortion analyzer to perform global visual distortion constraint to generate a target lightweight three-dimensional geometric model; a rendering module, configured to perform rendering on the target lightweight three-dimensional geometric model according to attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data; and a hierarchical storage module, configured to store the target lightweight PDMS three-dimensional design data in layers and transmit the target lightweight PDMS three-dimensional design data to a three-dimensional display terminal for calling and display.

[0014] A lightweight processing method and system for PDMS three-dimensional design data proposed in this application first receives the PDMS three-dimensional design data to be processed and preprocesses the attribute data to generate target PDMS three-dimensional design data. Then, the three-dimensional geometric model in the target PDMS three-dimensional design data is extracted, and a geometric fidelity weight analysis is performed to establish a geometric fidelity weight distribution. Then, a lightweight display scene of the target PDMS three-dimensional design data is determined. Then, the three-dimensional geometric model is downgraded using the geometric fidelity weight distribution as a degradation constraint through a polygon degradation module, and a visual distortion analyzer is called to load the lightweight display scene for global visual distortion constraints to generate a target lightweight three-dimensional geometric model. The target lightweight three-dimensional geometric model is then rendered using the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data. Finally, the target lightweight PDMS three-dimensional design data is hierarchically stored and transmitted to a three-dimensional display terminal for display, thereby achieving the technical effect of effectively ensuring model accuracy, details and visual quality while improving storage and transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0016] Figure 1 A schematic flow chart of a lightweight processing method for PDMS three-dimensional design data provided in an embodiment of the present application.

[0017] Figure 2 A schematic structural diagram of a lightweight processing system for PDMS three-dimensional design data provided in an embodiment of the present application.

[0018] Explanation of reference numerals: attribute data preprocessing module 10 , geometric fidelity weight analysis module 20 , lightweight display scene determination module 30 , degradation processing module 40 , rendering module 50 , hierarchical storage module 60 . DETAILED DESCRIPTION

[0019] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0020] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limitations to the present application. All other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0021] In the following description, "some embodiments" are involved, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The term "first\second" involved only distinguishes similar objects and does not represent a specific order of the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0022] The embodiments of the present application provide a lightweight processing method for PDMS three-dimensional design data, as shown in Figure 1 The method comprises the following steps:

[0023] In step S100, the PDMS three-dimensional design data to be processed is received and the attribute data is preprocessed to generate target PDMS three-dimensional design data.

[0024] Specifically, the three-dimensional design data exported by the PDMS software is received through a dedicated data interface or a file reading module. The PDMS three-dimensional design data is three-dimensional model data generated by the PDMS software, which contains geometric information and attribute information. Using a data processing algorithm or script, the received data is filtered and compressed, that is, the attribute items in the data are analyzed to distinguish which are core engineering attributes (such as pipe diameter, material, pressure rating), and which are appearance texture, secondary attributes and other non-core attributes, and only the core attributes are retained to obtain the target PDMS three-dimensional design data.

[0025] In step S200, the three-dimensional geometric model in the target PDMS three-dimensional design data is extracted, and geometric fidelity weight analysis is performed to establish a geometric fidelity weight distribution.

[0026] Specifically, the geometric fidelity refers to the ability of a three-dimensional model to maintain its original shape and size during simplification or compression. A three-dimensional geometric model is extracted from the pre-processed data. An algorithm is used to assign weights to different vertices or regions of the model. The weights reflect the importance of these regions in maintaining geometric accuracy, i.e. to prioritize the geometric details of key parts. For example, in industrial equipment design, the geometric fidelity weight of pipe interfaces or protruding parts is higher, while flat areas can be greatly simplified.

[0027] In one possible implementation, the three-dimensional geometric model in the target PDMS three-dimensional design data is extracted, and a geometric fidelity weight analysis is performed to establish a geometric fidelity weight distribution. Step S200 further includes step S210, which performs vertex curvature calculation on the three-dimensional geometric model to generate a curvature distribution feature. Specifically, the curvature of each vertex of the three-dimensional geometric model is calculated using the principles of computational geometry and differential geometry. Curvature is a quantity that describes the degree of bending of a curve or surface. For a three-dimensional model, the curvature of a vertex reflects the degree of bending of the surface near that point. After calculating the curvature of all vertices, these curvature values are used as feature information, corresponding to the vertices of the model, to form a curvature distribution feature. This feature map or feature vector is used for subsequent weight assignment.

[0028] Step S220, based on the curvature distribution feature, performs geometric fidelity weight initialization assignment to generate an initialization geometric fidelity weight distribution. Specifically, according to the curvature distribution feature, an initial weight is assigned to each vertex of the model. That is, vertices with higher curvature (i.e. points with higher surface bending degree) are given higher weights, because these points are crucial to maintaining the geometric shape and details of the model. After completing the weight initialization assignment, a weight distribution corresponding to the vertices of the model is obtained, i.e. the initialization geometric fidelity weight distribution. The geometric fidelity weight is a quantity that measures the importance of a model vertex in maintaining its original geometric shape during lightweighting.

[0029] Step S230, receiving key node features to enhance the weight of the key nodes in the initial geometric fidelity weight distribution, and generating the geometric fidelity weight distribution, wherein the key node features are obtained by the user end. Specifically, the feature information of the key nodes is obtained by the user end or other data sources. These key nodes are points in the model that have important engineering significance or visual importance, such as pipeline nodes, weld locations, etc. The key node features describe the information of the key node attributes in the model, such as location, type, importance, etc. According to the key node features, the weight of the corresponding node in the initial geometric fidelity weight distribution is enhanced, that is, the weight of the key node in the initial weight distribution is adjusted to enhance the importance of these nodes in the lightweight processing. The enhancement can be realized by directly increasing the weight value, applying a weight multiplier, or using other weight adjustment algorithms. After the weight enhancement processing is completed, the final geometric fidelity weight distribution is obtained. This distribution will be used as a degradation constraint for the polygon degradation module to guide the lightweight processing process. This implementation ensures that the model prioritizes preserving areas with higher curvature (i.e., vertices with larger curvature) during the lightweight process, thereby maintaining the geometric accuracy and details of the model. By receiving key node features and enhancing the weight of the corresponding nodes in the initial weight distribution, these key nodes are given additional protection during the lightweight process, avoiding the loss of important information or visual distortion during simplification.

[0030] In one possible implementation, the geometric fidelity weight initialization is assigned based on the curvature distribution characteristics to generate an initial geometric fidelity weight distribution, and step S220 further includes step S221, based on the curvature distribution characteristics, the three-dimensional geometric model is segmented into multiple segmented regions, and the curvature fluctuation index in any segmented region is less than a preset curvature fluctuation stability index. Specifically, a region segmentation algorithm based on the curvature distribution is used. The algorithm first analyzes the curvature distribution characteristics of the three-dimensional geometric model and identifies the boundaries of regions with significant curvature changes. These boundaries are positions with large curvature gradients, i.e., points or lines where the curvature value changes significantly. Based on the identified boundaries, the algorithm segments the three-dimensional geometric model into multiple relatively independent regions. The curvature fluctuation index (a measure of the degree of curvature value fluctuation in a region, such as standard deviation, coefficient of variation, etc.) in each region is less than a preset curvature fluctuation stability index (a threshold value for determining whether the region segmentation is complete. When the curvature fluctuation index in a region is less than this value, it is considered that the region segmentation is complete) to ensure that the curvature change in the region is relatively stable. After segmentation, each region is assigned a unique identifier, and the region boundary information, vertex index, etc. are stored for subsequent processing.

[0031] Step S222, the curvature mean value corresponding to each of the plurality of segmentation regions is configured with a weight, and an initialization geometric fidelity weight distribution is generated, wherein the initialization geometric fidelity weight distribution includes an initialization geometric fidelity weight of each segmentation region, and the initialization geometric fidelity weight of each segmentation region is proportional to the corresponding curvature mean value. Specifically, for each segmentation region, the curvature mean value of all vertices thereof is calculated. The curvature mean value reflects the overall bending degree of the region. According to the curvature mean value, an initialization geometric fidelity weight is assigned to each segmentation region. The weight is proportional to the curvature mean value, that is, the greater the curvature mean value of a region, the higher the weight thereof, indicating that more geometric details need to be retained in the lightweight process. After the weight configuration is completed, the weights of all segmentation regions are combined to form the initialization geometric fidelity weight distribution. This implementation can more accurately control information retention and discard in the lightweight processing process by segmenting the three-dimensional geometric model into a plurality of relatively independent regions and configuring the weights according to the curvature mean value, which helps to reduce unnecessary calculation amount and improve lightweight efficiency.

[0032] Step S300, determining a lightweight display scenario of the target PDMS three-dimensional design data.

[0033] Specifically, the lightweight display scenario refers to a scenario in which the model is simplified or compressed to different degrees according to display requirements and performance requirements of the model. That is, according to the requirements of the user or the application program, the display requirements of the model in different scenarios are determined. The display requirements are divided into a long-distance display scenario and a close-range fine viewing scenario, and a corresponding polygon number or visual fidelity requirement is set for each scenario.

[0034] Step S400, using the geometric fidelity weight distribution as a degradation constraint, performing degradation processing on the three-dimensional geometric model through a polygon degradation module, and calling the lightweight display scenario to load a visual distortion analyzer to perform global visual distortion constraint, and generating a target lightweight three-dimensional geometric model.

[0035] Specifically, using a polygon degradation algorithm, the model is simplified to different degrees according to the geometric fidelity weight distribution. Regions with higher weights maintain higher polygon density, and regions with lower weights are simplified more. In the degradation process, the visual distortion analyzer is used to constrain the global visual distortion, ensuring that the simplified model is still visually acceptable. Among them, polygon degradation refers to the process of simplifying the model by reducing the number of polygons in the model. Visual distortion analysis is a process of evaluating the visual error introduced in the model simplification process.

[0036] In a possible implementation, the three-dimensional geometric model is subjected to a degradation process by a polygon degradation module with the geometric fidelity weight distribution as a degradation constraint, and a global visual distortion constraint is called by the lightweight display scene loading visual distortion analyzer to generate a target lightweight three-dimensional geometric model, and step S400 further includes step S410 of extracting a plurality of model vertices in the three-dimensional geometric model, initializing an error metric matrix for the plurality of model vertices, and generating a plurality of error metric matrices. Specifically, all vertices in the three-dimensional geometric model are first extracted. Subsequently, a Quadric Error Metrics (QEM) algorithm is used to initialize an error metric matrix for each vertex. The QEM algorithm is a commonly used polygon simplification algorithm that evaluates the importance of a vertex by representing the error of the vertex as a quadric form (i.e., an error metric matrix). The error metric matrix describes the error that may be introduced by the vertex during the simplification process. During initialization, the error metric matrix of each vertex is set to an identity matrix. For each vertex, its error metric matrix is calculated and stored in a data structure for subsequent use.

[0037] Step S420, based on the plurality of error metric matrices, a merged position error calculation of any two vertices is performed, and a set of primary vertex pairs with a merged position error less than a preset error is counted. Specifically, all vertex pairs are traversed, and the merged position error that may be introduced when the two vertices are merged is calculated according to their error metric matrices through matrix addition (or multiplication, etc.). Then, all vertex pairs with a merged position error less than a preset error are counted to form a set of primary vertex pairs. The merged position error represents the error value of the position change after the two vertices are merged. The smaller the error value, the closer the position of the merged vertex to the original position, and the smaller the impact on the model.

[0038] Step S430, with the geometric fidelity weight distribution as a degradation constraint, the vertex pairs in the set of primary vertex pairs are merged to generate a primary lightweight three-dimensional geometric model. Specifically, the vertex pairs in the set of primary vertex pairs are merged according to the geometric fidelity weight distribution. When merging, the vertices in regions with smaller weights (i.e., regions with lower fidelity requirements) are preferentially considered for merging to reduce the impact on the overall geometric shape. When merging vertices, the connection relationship and error metric matrix of adjacent vertices are updated by modifying the graph data structure. For each vertex pair in the set of primary vertex pairs, it is determined whether to merge according to the geometric fidelity weight distribution. If it is determined to merge, the graph data structure is updated, and a primary lightweight three-dimensional geometric model is generated.

[0039] Step S440, based on the lightweight display scene configuration, the visual distortion analyzer is configured to perform global visual distortion analysis on the primary lightweight three-dimensional geometric model, and generate a first distortion evaluation index. Specifically, the visual distortion analyzer is a tool or algorithm for evaluating the degree of visual distortion that may be introduced by the lightweight three-dimensional geometric model when displayed. According to the configuration information of the lightweight display scene, the visual distortion analyzer is called to perform global visual distortion analysis on the primary lightweight three-dimensional geometric model. The visual distortion analyzer generates an evaluation index (i.e., the first distortion evaluation index) reflecting the global visual distortion by means of image-based difference analysis, geometry-based comparison, etc. The first distortion evaluation index is a quantitative index reflecting the degree of global visual distortion of the lightweight three-dimensional geometric model.

[0040] Step S450, judging whether the first distortion evaluation index exceeds the preset visual distortion constraint, and according to the judgment result, performing multiple processing on the primary lightweight three-dimensional geometric model to generate the target lightweight three-dimensional geometric model. Specifically, the first distortion evaluation index is compared with the preset visual distortion constraint. If the first distortion evaluation index exceeds the constraint range, the system will further simplify the primary lightweight three-dimensional geometric model (such as increasing the number of vertex merging, adjusting the error metric matrix, etc.), and re-perform visual distortion analysis. This process is repeated until the first distortion evaluation index meets the preset visual distortion constraint. Finally, the target lightweight three-dimensional geometric model that meets the requirements is generated. This implementation can efficiently simplify the three-dimensional geometric model while maintaining the key geometric features of the model through the QEM algorithm and the constraint of the geometric fidelity weight distribution. Through the visual distortion analyzer and the preset visual distortion constraint, it can ensure that the three-dimensional geometric model after lightweight display still has acceptable visual quality.

[0041] In a possible implementation, based on the lightweight display scene, the visual distortion analyzer is configured to perform global visual distortion analysis on the primary lightweight three-dimensional geometric model, and generate a first distortion evaluation index, step S440 further includes step S441, under the constraint of the lightweight display scene, collecting original geometric model samples, lightweight geometric model samples and visual error score samples. Specifically, according to the specific requirements of the lightweight display scene, three-dimensional modeling software, image processing software, automated testing tools and the like are used to collect sample data for training the visual distortion analyzer. These data include: original geometric model samples (unsimplified, high-fidelity three-dimensional geometric models, representing the initial state of the design data), lightweight geometric model samples (three-dimensional geometric models after simplification processing, these models are the result of lightweight processing, with different simplification degrees and fidelity), visual error score samples (visual error scores of lightweight geometric models relative to original geometric models evaluated by automatic evaluation tools, which reflect the impact of lightweight processing on model visual quality).

[0042] Step S442, using the original geometric model samples, the lightweight geometric model samples and the visual error score samples as sample data, training the visual distortion analyzer based on the convolutional neural network. Specifically, the original geometric model samples, the lightweight geometric model samples and the visual error score samples are input into the convolutional neural network for training. The convolutional neural network can extract features from the lightweight geometric model and predict the visual error of the model relative to the original geometric model. During the training process, the network output is calculated by forward propagation, and the network parameters are adjusted by back propagation to minimize the prediction error, until the network can accurately predict the visual error of the lightweight geometric model.

[0043] Step S443, inputting the three-dimensional geometric model and the primary lightweight three-dimensional geometric model into the visual distortion analyzer for evaluation, and generating the first distortion evaluation index. Specifically, the three-dimensional geometric model and the primary lightweight three-dimensional geometric model are input into the trained visual distortion analyzer, the analyzer extracts the features of the two models, and calculates the visual error between them. Finally, a quantitative index (i.e. the first distortion evaluation index) is generated to measure the visual distortion degree of the primary lightweight three-dimensional geometric model relative to the three-dimensional geometric model. This implementation can more accurately evaluate the visual error of the lightweight three-dimensional geometric model by training the visual distortion analyzer through the convolutional neural network, and improves the accuracy of the evaluation.

[0044] In a possible implementation, the first lightweight three-dimensional geometric model is processed multiple times according to the judgment result to generate the target lightweight three-dimensional geometric model, and step S450 further includes step S451. If the first distortion evaluation index does not exceed the preset visual distortion constraint, steps S410 to S440 are repeated to continue processing the first lightweight three-dimensional geometric model multiple times, and a lightweight three-dimensional geometric model corresponding to a previous vertex merging node whose distortion evaluation index exceeds the preset visual distortion constraint is determined as the target lightweight three-dimensional geometric model.

[0045] Specifically, if the first distortion evaluation index of the first lightweight three-dimensional geometric model evaluated by the visual distortion analyzer does not exceed the preset visual distortion constraint, the model is further processed to attempt to achieve a higher level of lightweight while ensuring that the visual distortion remains within an acceptable range. Specifically, iterative processing is performed, that is, the entire process from vertex extraction (S410) to visual distortion analysis (S440) is repeatedly executed. The vertices of the current lightweight three-dimensional geometric model (i.e., the first lightweight three-dimensional geometric model after the last iteration) are extracted again, and the error metric matrix is initialized. Based on the error metric matrix, the merging position error of any two vertices is calculated, and those vertex pairs with an error less than a preset value are merged to generate a new lightweight three-dimensional geometric model. The new lightweight three-dimensional geometric model is input into the visual distortion analyzer for evaluation to generate a new distortion evaluation index. It is determined whether the new distortion evaluation index exceeds the preset visual distortion constraint. If not, the iteration is continued; if yes, the iteration is stopped, and the lightweight three-dimensional geometric model generated in the last iteration is taken as the target lightweight three-dimensional geometric model. This implementation can continuously attempt to further lightweight the model through iterative processing, and achieve a higher level of lightweight while maintaining visual quality.

[0046] In a possible implementation, step S450 further includes step S452. If the first distortion evaluation index exceeds the preset visual distortion constraint, vertex pairs with the largest merging position error are deleted from the first vertex pair set according to a preset proportion. Specifically, if the first distortion evaluation index generated by the visual distortion analyzer exceeds the preset visual distortion constraint, it indicates that the current lightweight three-dimensional geometric model is unacceptable in terms of visual quality. To improve this situation, first, the vertex pairs in the first vertex pair set are sorted in descending order of merging position error. Then, vertex pairs with the largest merging position error are selected for deletion according to a preset proportion (a proportion value used to determine the number of vertex pairs to be deleted, for example, a certain proportion of vertex pairs with the largest error are deleted), that is, these vertex pairs are not merged. After the specified vertex pairs are deleted, the vertex pair set is updated for subsequent vertex merging operations.

[0047] Step S453, vertex merging is performed on the updated vertex pair set, and the analysis is repeated in this way until a lightweight three-dimensional geometric model whose distortion evaluation index meets the preset visual distortion constraint is generated as the target lightweight three-dimensional geometric model. Specifically, after the vertex pair set is updated, the vertex merging operation is performed again to generate a new lightweight three-dimensional geometric model, the new lightweight three-dimensional geometric model is input into the visual distortion analyzer, the evaluation is performed, and a new distortion evaluation index is generated. It is judged whether the new distortion evaluation index meets the preset visual distortion constraint. If not, steps S452 and S453 are repeated until a lightweight three-dimensional geometric model meeting the constraint is generated. This implementation ensures that the generated lightweight three-dimensional geometric model is acceptable in visual quality through the visual distortion analyzer and the preset visual distortion constraint, and achieves the technical effect of guaranteeing the visual quality.

[0048] Step S500, after rendering the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data, target lightweight PDMS three-dimensional design data is generated.

[0049] Specifically, the rendering engine is used to apply attribute information (such as color, texture, material, etc.) to the target lightweight three-dimensional geometric model to generate a model with visual effects, and the target lightweight PDMS three-dimensional design data is obtained.

[0050] Step S600, after the target lightweight PDMS three-dimensional design data is stored in layers, it is transmitted to a three-dimensional display terminal for calling and display.

[0051] Specifically, the target lightweight PDMS three-dimensional design data is stored according to different levels or levels for quick access and transmission. The stored data is transmitted to a three-dimensional display terminal (a device capable of displaying three-dimensional models or images, such as a computer, a virtual reality helmet, etc.) for display through existing encryption transmission, incremental transmission, etc. The embodiments of the present application adopt technical means such as receiving PDMS three-dimensional design data to be processed and preprocessing attribute data, extracting three-dimensional geometric models and performing geometric fidelity weight analysis, determining a lightweight display scene, performing polygon degradation processing with geometric fidelity weight distribution as degradation constraint, calling a lightweight display scene to load a visual distortion analyzer for global visual distortion constraint, rendering with attribute information and generating target lightweight PDMS three-dimensional design data, storing in layers and transmitting to a three-dimensional display terminal for calling and display, which achieves the technical effect of improving storage and transmission efficiency while effectively guaranteeing model precision and details and visual quality.

[0052] In one possible implementation, the target lightweight PDMS 3D design data is stored hierarchically and then transmitted to a 3D display terminal for display. Step S600 further includes step S610, which identifies individual model components of the target lightweight 3D geometric model within the target lightweight PDMS 3D design data. Specifically, the individual components within the target lightweight PDMS 3D design data are identified and distinguished using methods such as component recognition algorithms or attribute matching (using component attribute information such as name, type, and size). These components can be any basic units that constitute the 3D model, such as equipment, piping, and support structures.

[0053] Step S620 : The model's independent components are partitioned into multiple levels based on their enclosing relationships, generating a multi-layered component partitioning result. Specifically, based on the spatial relationships between components (e.g., adjacency, inclusion, etc.), a hierarchical partitioning algorithm or bounding box technology is used to further partition the identified independent components into multiple levels of enclosing relationships, enabling more efficient management and access of these components during subsequent storage and transmission.

[0054] Step S630 constructs a multi-layer R-tree node based on the multi-layer component partitioning results. The three-dimensional data corresponding to each of the multi-layer component partitioning results is extracted from the target lightweight PDMS three-dimensional design data and stored in the multi-layer R-tree nodes, thereby generating a multi-layer R-tree. Specifically, based on the multi-layer component partitioning results, an R-tree node is created for each level. The three-dimensional data corresponding to each node is extracted from the target lightweight PDMS three-dimensional design data and stored in the corresponding R-tree node. The R-tree is a data structure for storing multidimensional spatial data, enabling efficient management and querying of spatial data.

[0055] Step S640: Transmit the multi-layer R-tree to a 3D display terminal for display. Specifically, the multi-layer R-tree is transmitted to the 3D display terminal via an appropriate communication protocol. After receiving the multi-layer R-tree, the 3D display terminal uses the R-tree structure for rendering and display, enabling rapid display of lightweight PDMS 3D design data. This implementation, through hierarchical storage and the R-tree structure, enables more efficient management and access of 3D data, thereby improving storage and transmission efficiency.

[0056] In the above, refer to Figure 1 A lightweight processing method for PDMS three-dimensional design data according to an embodiment of the present invention is described in detail. Figure 2 A lightweight processing system for PDMS three-dimensional design data according to an embodiment of the present invention is described.

[0057] The PDMS three-dimensional design data lightweight processing system according to the embodiment of the present application is used to solve the technical problem that the existing PDMS three-dimensional design data lightweight processing is difficult to improve the storage and transmission efficiency while ensuring the model precision and details and visual quality, and achieve the technical effect of effectively ensuring the model precision and details and visual quality while improving the storage and transmission efficiency. The PDMS three-dimensional design data lightweight processing system comprises an attribute data preprocessing module 10, a geometric fidelity weight analysis module 20, a lightweight display scene determination module 30, a degradation processing module 40, a rendering module 50, and a hierarchical storage module 60.

[0058] The attribute data preprocessing module 10 is configured to receive the PDMS three-dimensional design data to be processed and perform preprocessing on attribute data to generate target PDMS three-dimensional design data. The geometric fidelity weight analysis module 20 is configured to extract a three-dimensional geometric model from the target PDMS three-dimensional design data and perform geometric fidelity weight analysis to establish a geometric fidelity weight distribution. The lightweight display scene determination module 30 is configured to determine a lightweight display scene of the target PDMS three-dimensional design data. The degradation processing module 40 is configured to perform degradation processing on the three-dimensional geometric model through a polygon degradation module with the geometric fidelity weight distribution as a degradation constraint, and call a visual distortion analyzer of the lightweight display scene to perform global visual distortion constraint to generate a target lightweight three-dimensional geometric model. The rendering module 50 is configured to perform rendering on the target lightweight three-dimensional geometric model with attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data. The hierarchical storage module 60 is configured to perform hierarchical storage on the target lightweight PDMS three-dimensional design data and transmit the target lightweight PDMS three-dimensional design data to a three-dimensional display terminal for calling and display.

[0059] In the following, the specific configuration of the geometric fidelity weight analysis module 20 will be described in detail. As described above, the three-dimensional geometric model in the target PDMS three-dimensional design data is extracted and geometric fidelity weight analysis is performed to establish a geometric fidelity weight distribution. The geometric fidelity weight analysis module 20 can further comprise a vertex curvature calculation unit configured to perform vertex curvature calculation on the three-dimensional geometric model to generate a curvature distribution feature; a weight initialization assignment unit configured to perform geometric fidelity weight initialization assignment based on the curvature distribution feature to generate an initialization geometric fidelity weight distribution; and an enhancement processing unit configured to receive a key node feature, perform enhancement processing on the weight of a key node in the initialization geometric fidelity weight distribution based on the key node feature, and generate the geometric fidelity weight distribution, wherein the key node feature is obtained through a user terminal.

[0060] The weight initialization assignment unit can further include: a region segmentation sub-unit configured to perform region segmentation on the three-dimensional geometric model based on the curvature distribution characteristics to generate a plurality of segmentation regions, wherein the curvature fluctuation index in any segmentation region is less than a preset curvature fluctuation stability index; and a weight configuration sub-unit configured to perform weight configuration on the plurality of segmentation regions based on the curvature mean values corresponding to the plurality of segmentation regions, respectively, to generate the initialization geometric fidelity weight distribution, wherein the initialization geometric fidelity weight distribution includes initialization geometric fidelity weights of each segmentation region, and the initialization geometric fidelity weight of each segmentation region is proportional to the corresponding curvature mean value.

[0061] In the following, the specific configuration of the degradation processing module 40 will be described in detail. As described above, the three-dimensional geometric model is degraded by the polygon degradation module based on the geometric fidelity weight distribution as a degradation constraint, and the lightweight display scene loading visual distortion analyzer is called to perform global visual distortion constraint to generate a target lightweight three-dimensional geometric model. The degradation processing module 40 can further include: an error metric matrix initialization unit configured to perform step a: extracting a plurality of model vertices in the three-dimensional geometric model, and initializing error metric matrices for the plurality of model vertices to generate a plurality of error metric matrices; a merged position error calculation unit configured to perform step b: based on the plurality of error metric matrices, calculating the merged position error of any two vertices, and counting a set of first vertex pairs with a merged position error less than a preset error; a merging unit configured to perform step c: based on the geometric fidelity weight distribution as a degradation constraint, merging the vertex pairs in the set of first vertex pairs to generate a first lightweight three-dimensional geometric model; a global visual distortion analysis unit configured to perform step d: based on the lightweight display scene, the visual distortion analyzer is configured to perform global visual distortion analysis on the first lightweight three-dimensional geometric model to generate a first distortion evaluation index; a judgment processing unit configured to judge whether the first distortion evaluation index exceeds a preset visual distortion constraint, and according to the judgment result, the first lightweight three-dimensional geometric model is processed multiple times to generate the target lightweight three-dimensional geometric model.

[0062] The visual distortion analyzer is configured based on the lightweight display scene to perform global visual distortion analysis on the first-level lightweight three-dimensional geometric model to generate a first distortion evaluation index. The global visual distortion analysis unit can further include: a sample collection subunit configured to collect original geometric model samples, lightweight geometric model samples, and visual error score samples under the constraint of the lightweight display scene; a visual distortion analyzer training subunit configured to use the original geometric model samples, the lightweight geometric model samples, and the visual error score samples as sample data, and train the visual distortion analyzer based on a convolutional neural network; and a first distortion evaluation index generation subunit configured to input the three-dimensional geometric model and the first-level lightweight three-dimensional geometric model into the visual distortion analyzer for evaluation to generate the first distortion evaluation index.

[0063] The first-level lightweight three-dimensional geometric model is processed multiple times according to the judgment result to generate the target lightweight three-dimensional geometric model. The judgment processing unit can further include: a multiple processing subunit configured to, if the first distortion evaluation index does not exceed the preset visual distortion constraint, repeat steps a to d to continue processing the first-level lightweight three-dimensional geometric model multiple times, and determine a lightweight three-dimensional geometric model corresponding to a previous vertex merging node whose distortion evaluation index exceeds the preset visual distortion constraint, as the target lightweight three-dimensional geometric model.

[0064] The judgment processing unit can further include: a deletion merging subunit configured to, if the first distortion evaluation index exceeds the preset visual distortion constraint, delete vertex pairs with the largest merging position error from the first vertex pair set according to a preset proportion; and a repeated analysis subunit configured to perform vertex merging according to an updated vertex pair set, and repeatedly analyze in this way until a lightweight three-dimensional geometric model whose distortion evaluation index meets the preset visual distortion constraint is generated as the target lightweight three-dimensional geometric model.

[0065] The specific configuration of the hierarchical storage module 60 will be described in detail below. As described above, after the target lightweight PDMS three-dimensional design data is stored hierarchically, it is transmitted to a three-dimensional display terminal for calling and display. The hierarchical storage module 60 can further include: a model independent component determination unit configured to determine model independent components of the target lightweight three-dimensional geometric model in the target lightweight PDMS three-dimensional design data; an enclosing relationship division unit configured to perform multi-level enclosing relationship division on the model independent components to generate a multi-level component division result; a multi-level R-tree generation unit configured to construct a multi-level R-tree node based on the multi-level component division result, and extract three-dimensional data corresponding to the multi-level component division result in the target lightweight PDMS three-dimensional design data respectively and store the three-dimensional data in the multi-level R-tree node to generate a multi-level R-tree; and a calling and display unit configured to transmit the multi-level R-tree to a three-dimensional display terminal for calling and display.

[0066] The PDMS three-dimensional design data lightweight processing system provided by the embodiment of the application can execute the PDMS three-dimensional design data lightweight processing method provided by any embodiment of the application, and has the function modules and beneficial effects corresponding to the execution method.

[0067] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific name of each functional unit is only for the convenience of mutual differentiation, and does not limit the protection scope of the present application.

[0068] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent replacement and improvement made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be executed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

Claims

1. A lightweight processing method for PDMS three-dimensional design data, characterized in that: include: Receive the PDMS three-dimensional design data to be processed and pre-process the attribute data to generate target PDMS three-dimensional design data; Extracting a three-dimensional geometric model from the target PDMS three-dimensional design data, performing a geometric fidelity weight analysis, and establishing a geometric fidelity weight distribution; Determining a lightweight display scene for the target PDMS three-dimensional design data; Using the geometric fidelity weight distribution as a degradation constraint, the three-dimensional geometric model is degraded by a polygon degradation module, and the lightweight display scene loading visual distortion analyzer is called to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model; Rendering the target lightweight three-dimensional geometric model using the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data; The target lightweight PDMS three-dimensional design data is stored in layers and then transmitted to a three-dimensional display terminal for calling and displaying; The method comprises extracting a three-dimensional geometric model from the target PDMS three-dimensional design data, performing a geometric fidelity weight analysis, and establishing a geometric fidelity weight distribution, including: Calculating vertex curvature of the three-dimensional geometric model to generate curvature distribution features; Performing geometric fidelity weight initialization assignment based on the curvature distribution characteristics to generate an initialized geometric fidelity weight distribution; Receiving key node features, and enhancing the weights of key nodes in the initialized geometric fidelity weight distribution using the key node features to generate the geometric fidelity weight distribution, wherein the key node features are obtained by a user terminal; The method includes: using the geometric fidelity weight distribution as a degradation constraint, performing degradation processing on the three-dimensional geometric model through a polygon degradation module, calling the lightweight display scene loading visual distortion analyzer to perform global visual distortion constraints, and generating a target lightweight three-dimensional geometric model. Step a: extracting multiple model vertices from the three-dimensional geometric model, initializing error metric matrices for the multiple model vertices, and generating multiple error metric matrices; Step b: calculating the merge position error of any two vertices based on the multiple error metric matrices, and counting a set of first-level vertex pairs whose merge position error is less than a preset error; Step c: using the geometric fidelity weight distribution as a degradation constraint, merging the vertex pairs in the first-level vertex pair set to generate a first-level lightweight three-dimensional geometric model; Step d: configuring the visual distortion analyzer based on the lightweight display scene to perform global visual distortion analysis on the first-level lightweight three-dimensional geometric model to generate a first distortion evaluation index; It is determined whether the first distortion evaluation index exceeds a preset visual distortion constraint, and the first-level lightweight three-dimensional geometric model is processed multiple times according to the determination result to generate the target lightweight three-dimensional geometric model.

2. A lightweight processing method for PDMS three-dimensional design data according to claim 1, characterized in that: Performing geometric fidelity weight initialization assignment based on the curvature distribution feature to generate an initialized geometric fidelity weight distribution includes: Based on the curvature distribution characteristics, the three-dimensional geometric model is segmented to generate a plurality of segmented regions, wherein the curvature fluctuation index in any segmented region is less than a preset curvature fluctuation stability index; The weight configuration is performed using the curvature mean values ​​corresponding to the multiple segmented areas to generate the initialized geometric fidelity weight distribution, wherein the initialized geometric fidelity weight distribution includes the initialized geometric fidelity weights of each segmented area, and the initialized geometric fidelity weights of each segmented area are proportional to the corresponding curvature mean value.

3. The lightweight processing method of PDMS three-dimensional design data according to claim 1, characterized in that: Configuring the visual distortion analyzer based on the lightweight display scene to perform global visual distortion analysis on the first-level lightweight three-dimensional geometric model to generate a first distortion evaluation index, including: Taking the lightweight display scene as a constraint, collecting original geometric model samples, lightweight geometric model samples and visual error score samples; Using original geometric model samples, lightweight geometric model samples and visual error score samples as sample data, the visual distortion analyzer is trained based on a convolutional neural network; The three-dimensional geometric model and the first-level lightweight three-dimensional geometric model are input into the visual distortion analyzer for evaluation to generate the first distortion evaluation index.

4. A lightweight processing method for PDMS three-dimensional design data according to claim 1, characterized in that: The first-level lightweight three-dimensional geometric model is processed multiple times according to the judgment result to generate the target lightweight three-dimensional geometric model, including: If the first distortion evaluation index does not exceed the preset visual distortion constraint, repeat steps a to d, continue to process the first-level lightweight three-dimensional geometric model multiple times, determine the lightweight three-dimensional geometric model corresponding to the previous vertex merging node whose distortion evaluation index exceeds the preset visual distortion constraint, and generate the target lightweight three-dimensional geometric model.

5. A lightweight processing method for PDMS three-dimensional design data according to claim 4, characterized in that: Also includes: If the first distortion evaluation index exceeds the preset visual distortion constraint, deleting the vertex pair with the largest merge position error from the first-level vertex pair set according to a preset ratio; Vertex merging is performed on the set according to the updated vertex pair, and analysis is repeated in this way until a lightweight three-dimensional geometric model is generated whose distortion evaluation index satisfies the preset visual distortion constraint, as the target lightweight three-dimensional geometric model.

6. A lightweight processing method for PDMS three-dimensional design data according to claim 1, characterized in that: The target lightweight PDMS three-dimensional design data is stored in layers and then transmitted to a three-dimensional display terminal for calling and displaying, including: Determining a model-independent component of a target lightweight three-dimensional geometric model in the target lightweight PDMS three-dimensional design data; Performing multi-level enclosing relationship division on the independent components of the model to generate a multi-level component division result; Constructing a multi-layer R-tree node based on the multi-layer component partitioning result, extracting three-dimensional data corresponding to each of the multi-layer component partitioning results from the target lightweight PDMS three-dimensional design data, and storing the data in the multi-layer R-tree node to generate a multi-layer R-tree; The multi-layer R-tree is transmitted to a three-dimensional display terminal for calling and displaying.

7. A lightweight processing system for PDMS three-dimensional design data, characterized in that: The system is used to implement the lightweight processing method of PDMS three-dimensional design data according to any one of claims 1 to 6, and the system includes: An attribute data preprocessing module is used to receive the PDMS three-dimensional design data to be processed and preprocess the attribute data to generate target PDMS three-dimensional design data; A geometric fidelity weight analysis module is used to extract the three-dimensional geometric model in the target PDMS three-dimensional design data, perform geometric fidelity weight analysis, and establish a geometric fidelity weight distribution; A lightweight display scene determination module, configured to determine a lightweight display scene for the target PDMS three-dimensional design data; a degradation processing module, configured to use the geometric fidelity weight distribution as a degradation constraint, perform degradation processing on the three-dimensional geometric model through a polygon degradation module, and call the lightweight display scene loading visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model; a rendering module, configured to render the target lightweight three-dimensional geometric model using the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data; The hierarchical storage module is used to store the target lightweight PDMS three-dimensional design data in a hierarchical manner and then transmit the data to a three-dimensional display terminal for calling and displaying.

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