Lightweight processing method and system for PDMS three-dimensional design data

Abstract

The invention discloses a lightweight processing method and system for PDMS three-dimensional design data, and relates to the related field of data processing, and the method comprises the steps: receiving PDMS three-dimensional design data to be processed, and carrying out the preprocessing of attribute data; extracting a three-dimensional geometric model, performing geometric fidelity weight analysis, and establishing geometric fidelity weight distribution; determining a lightweight display scene; carrying out degradation processing through a polygon degradation module, and loading a visual distortion analyzer to carry out global visual distortion constraint; after the attribute information is rendered, target lightweight PDMS three-dimensional design data is generated; and transmitting the data to a three-dimensional display terminal for calling and displaying after hierarchical storage. The technical problem that it is difficult to guarantee the model precision, details and visual quality while improving the storage and transmission efficiency in existing lightweight processing is solved, and the technical effect of effectively guaranteeing the model precision, details and visual quality while improving the storage and transmission efficiency is achieved.

Description

Technical Field

[0001] The present application relates to data processing related fields, and in particular to a lightweight processing method and system for PDMS three-dimensional design data. Background Art

[0002] With the rapid development of technologies such as 3D scanning and photogrammetry, PDMS (Plant Design Management System) has been widely used in the field of 3D design. PDMS 3D design data usually contains a large amount of vertex, facet and texture data, which is huge in amount. This not only increases the cost of storage and transmission, but also affects the loading speed and rendering efficiency of the model. Therefore, lightweight processing of PDMS 3D design data has become an urgent problem to be solved. In the field of lightweight processing, traditional lightweight methods, such as geometric simplification and texture compression, often lead to significant loss of model accuracy and details, and cannot meet the needs of high-precision applications. In addition, in the process of reducing the complexity of the model, visual distortion is prone to occur, affecting the user experience.

[0003] In the current related technologies, the lightweight processing of PDMS three-dimensional design data has the technical problem of being difficult to ensure model accuracy, details and visual quality while improving storage and transmission efficiency. Summary of the invention

[0004] The present application provides a lightweight processing method and system for PDMS three-dimensional design data, which adopts technical means such as receiving the PDMS three-dimensional design data to be processed and preprocessing the attribute data, extracting the three-dimensional geometric model and performing geometric fidelity weight analysis, determining the lightweight display scene, performing polygon degradation processing with the geometric fidelity weight distribution as the degradation constraint, calling the lightweight display scene to load the visual distortion analyzer to perform global visual distortion constraints, rendering with attribute information and generating target lightweight PDMS three-dimensional design data, performing hierarchical storage and transmitting to the three-dimensional display terminal for calling and display, thereby achieving the technical effect of effectively ensuring the model accuracy, details and visual quality while improving the storage and transmission efficiency.

[0005] The present application provides a lightweight processing method for PDMS three-dimensional design data, comprising: receiving PDMS three-dimensional design data to be processed and preprocessing attribute data to generate target PDMS three-dimensional design data; extracting a three-dimensional geometric model in the target PDMS three-dimensional design data, performing 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, degrading the three-dimensional geometric model through a polygon degradation module, and calling the lightweight display scene to load a visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model; rendering the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data; and transmitting the target lightweight PDMS three-dimensional design data to a three-dimensional display terminal after storing it in layers for calling and displaying.

[0006] In a 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, and the following processing is performed: vertex curvature calculation is performed on the three-dimensional geometric model to generate a curvature distribution feature; geometric fidelity weight initialization assignment is performed based on the curvature distribution feature to generate an initialized geometric fidelity weight distribution; key node features are received, and the weights of key nodes in the initialized geometric fidelity weight distribution are enhanced with the key node features to generate the geometric fidelity weight distribution, wherein the key node features are obtained through the user end.

[0007] In a possible implementation, the geometric fidelity weights are initialized and assigned based on the curvature distribution characteristics to generate an initialized geometric fidelity weight distribution, and the following processing is performed: based on the curvature distribution characteristics, the three-dimensional geometric model is segmented into regions to generate multiple segmented regions, wherein the curvature fluctuation index within any segmented region is less than a preset curvature fluctuation stability index; weights are configured using the curvature mean values ​​corresponding to the multiple segmented regions to generate the initialized geometric fidelity weight distribution, wherein the initialized geometric fidelity weight distribution includes the initialized geometric fidelity weights of each segmented region, and the initialized geometric fidelity weights of each segmented region are proportional to the corresponding curvature mean value.

[0008] In a possible implementation, the geometric fidelity weight distribution is used as a degradation constraint, the three-dimensional geometric model is downgraded through a polygon degradation module, and the lightweight display scene is called to load a visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model, and the following processing is performed: step a: extract multiple model vertices in the three-dimensional geometric model, initialize the error metric matrix for the multiple model vertices, and generate multiple error metric matrices; step b: calculate the merged position error of any two vertices based on the multiple error metric matrices, and count the first-level vertex pair sets whose merged position errors are less than a preset error; step c: use the geometric fidelity weight distribution as a degradation constraint, merge the vertex pairs in the first-level vertex pair set, and generate a first-level lightweight three-dimensional geometric model; step d: configure 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; determine whether the first distortion evaluation index exceeds the preset visual distortion constraint, and perform multiple processing on the first-level lightweight three-dimensional geometric model according to the determination result to generate the target lightweight three-dimensional geometric model.

[0009] In a possible implementation, 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, generate a first distortion evaluation index, and perform the following processing: taking the lightweight display scene as a constraint, collecting original geometric model samples, lightweight geometric model samples, and visual error score samples; using the original geometric model samples, lightweight geometric model samples, and 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-level lightweight three-dimensional geometric model into the visual distortion analyzer for evaluation, and generating the first distortion evaluation index.

[0010] In a possible implementation, 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, and the following processing is performed: 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.

[0011] In a possible implementation, the following processing is also performed: if the first distortion evaluation index exceeds the preset visual distortion constraint, the vertex pairs with the largest merged position error are deleted from the first-level vertex pair set according to a preset ratio; vertices are merged according to the updated vertex pair set, and so on, and the analysis is repeated 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.

[0012] In a possible implementation, 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, and the following processing is performed: determining the model independent components of the target lightweight three-dimensional geometric model in the target lightweight PDMS three-dimensional design data; performing multi-level enclosing relationship division on the model independent components to generate multi-layer component division results; constructing a multi-layer R-tree node with the multi-layer component division results, and extracting the three-dimensional data corresponding to the multi-layer component division results from the target lightweight PDMS three-dimensional design data, storing them in the multi-layer R-tree nodes, and generating a multi-layer R-tree; transmitting the multi-layer R-tree to the three-dimensional display terminal for calling and displaying.

[0013] The present application also provides a lightweight processing system for PDMS three-dimensional design data, including: an attribute data preprocessing module, which 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, which is used to extract the three-dimensional geometric model in 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, which is used to determine the lightweight display scene of the target PDMS three-dimensional design data; a degradation processing module, which is used to use the geometric fidelity weight distribution as a degradation constraint, downgrade 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 constraints to generate a target lightweight three-dimensional geometric model; a rendering module, which is used to render the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data; a hierarchical storage module, which is used to store the target lightweight PDMS three-dimensional design data in a hierarchical manner and then transmit it 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 the present 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 extracts the three-dimensional geometric model in the target PDMS three-dimensional design data, performs geometric fidelity weight analysis, establishes geometric fidelity weight distribution, and then determines the lightweight display scene of the target PDMS three-dimensional design data, and then uses the geometric fidelity weight distribution as the degradation constraint to downgrade the three-dimensional geometric model through the polygon degradation module, and calls the lightweight display scene to load the visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model, and then renders the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data, and finally stores the target lightweight PDMS three-dimensional design data in layers and transmits it to a three-dimensional display terminal for calling and displaying, 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] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can 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 diagram of the structure 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 objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0021] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are 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 those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0022] The present application embodiment provides a lightweight processing method for PDMS three-dimensional design data, such as Figure 1 As shown, the method includes: Step S100, receiving PDMS three-dimensional design data to be processed and preprocessing the attribute data to generate target PDMS three-dimensional design data.

[0023] Specifically, the three-dimensional design data exported by the PDMS software is received through a dedicated data interface or file reading module. Among them, the PDMS three-dimensional design data is the three-dimensional model data generated by the PDMS software, which contains geometric information and attribute information. Use data processing algorithms or scripts to filter and compress the received data, that is, analyze the attribute items in the data, distinguish which are core engineering attributes (such as pipe diameter, material, pressure level), which are non-core attributes such as appearance texture and secondary attributes, and only retain the core attributes to obtain the target PDMS three-dimensional design data.

[0024] Step S200, extracting the three-dimensional geometric model in the target PDMS three-dimensional design data, performing geometric fidelity weight analysis, and establishing geometric fidelity weight distribution.

[0025] Specifically, the geometric fidelity refers to the ability of a 3D model to maintain its original shape and size during simplification or compression. A 3D geometric model is extracted from the preprocessed data. Using an algorithm, weights are assigned to different vertices or regions of the model. The weights reflect the importance of these regions in maintaining geometric accuracy, that is, the geometric details of key parts should be retained first. For example, in industrial equipment design, the geometric fidelity weight of pipeline interfaces or protruding parts is higher, while flat areas can be greatly simplified.

[0026] In a 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, performing 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 curvature of a curve or surface. For a three-dimensional model, the curvature of a vertex reflects the degree of curvature of the surface near the point. After calculating the curvature of all vertices, these curvature values ​​are used as feature information and correspond one-to-one with the vertices of the model to form a curvature distribution feature. This feature map or feature vector is used for subsequent weight allocation.

[0027] Step S220, based on the curvature distribution characteristics, the geometric fidelity weights are initialized and assigned, and the initialized geometric fidelity weight distribution is generated. Specifically, according to the curvature distribution characteristics, an initial weight is assigned to each vertex of the model. That is, vertices with larger curvatures (i.e., points with higher surface curvature) are assigned 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 model vertices is obtained, i.e., the initialized geometric fidelity weight distribution. Among them, the geometric fidelity weight is a quantity that measures the importance of the model vertex in maintaining its original geometric shape during the lightweight process.

[0028] Step S230, receiving key node features, and enhancing the weights of key nodes in the initial geometric fidelity weight distribution with the key node features to generate the geometric fidelity weight distribution, wherein the key node features are obtained through the user end. Specifically, the feature information of the key nodes is obtained through the user end or other data sources. These key nodes are points with important engineering significance or visual importance in the model, such as pipeline nodes, weld positions, 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 weights of the corresponding nodes in the initial geometric fidelity weight distribution are enhanced, that is, the weights of the key nodes in the initial weight distribution are adjusted to enhance the importance of these nodes in the lightweight processing. The enhancement processing can be achieved by directly increasing the weight value, applying a weight multiplier, or using other weight adjustment algorithms. After completing the weight enhancement processing, 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 method calculates vertex curvature and initializes weights based on curvature distribution features, ensuring that the model prioritizes areas with higher curvature (i.e. vertices with larger curvature) during lightweighting, thereby maintaining the geometric accuracy and details of the model. By receiving key node features and enhancing the weights of the corresponding nodes in the initial weight distribution, these key nodes are ensured to receive additional protection during lightweighting, avoiding the loss of important information or visual distortion during simplification.

[0029] In a possible implementation, the geometric fidelity weights are initialized and assigned based on the curvature distribution characteristics to generate the initialized geometric fidelity weight distribution. Step S220 further includes step S221, based on the curvature distribution characteristics, the three-dimensional geometric model is segmented to generate multiple segmented regions, wherein the curvature fluctuation index in any segmented region is less than the preset curvature fluctuation stability index. Specifically, a region segmentation algorithm based on curvature distribution is adopted, and the algorithm first analyzes the curvature distribution characteristics of the three-dimensional geometric model to identify the boundaries of the regions where the curvature changes significantly. These boundaries are locations with large curvature gradients, that is, points or lines where the curvature values ​​change significantly. According to the identified boundaries, the algorithm divides the three-dimensional geometric model into multiple relatively independent regions. The curvature fluctuation index in each region (a quantity that measures the degree of fluctuation of the curvature value in the region, such as standard deviation, coefficient of variation, etc.) is less than the preset curvature fluctuation stability index (a threshold used to determine whether the region segmentation is completed. When the curvature fluctuation index in the region is less than this value, the region segmentation is considered to be completed), so as to ensure that the curvature change in the region is relatively stable. After the segmentation is completed, a unique identifier is assigned to each region, and the region boundary information, vertex index, etc. are stored for subsequent processing.

[0030] Step S222, weight configuration is performed with the curvature mean values ​​corresponding to the plurality of segmented regions, and the initialization geometric fidelity weight distribution is generated, wherein the initialization geometric fidelity weight distribution includes the initialization geometric fidelity weights of each segmented region, and the initialization geometric fidelity weights of each segmented region are proportional to the corresponding curvature mean value. Specifically, for each segmented region, the curvature mean of all its vertices is calculated. The curvature mean reflects the overall curvature degree of the region. According to the curvature mean value, an initialization geometric fidelity weight is assigned to each segmented region. The weight is proportional to the curvature mean value, that is, the larger the curvature mean value of the region, the higher its weight, indicating that more geometric details need to be retained during the lightweight process. After completing the weight configuration, the weights of all segmented regions are combined to form the initialization geometric fidelity weight distribution. This implementation method can more accurately control the information retention and discarding in the lightweight process by dividing the three-dimensional geometric model into multiple relatively independent regions and configuring the weights according to the curvature mean value, which helps to reduce unnecessary calculations and improve lightweight efficiency.

[0031] Step S300, determining a lightweight display scene of the target PDMS three-dimensional design data.

[0032] Specifically, the lightweight display scene refers to a scene in which the model is simplified or compressed to different degrees according to the display requirements and performance requirements of the model. That is, the display requirements of the model in different scenes are determined according to the needs of the user or application. The display requirements are divided into long-range display scenes and close-up detailed viewing scenes, and the corresponding polygon count or visual fidelity requirements are set for each scene.

[0033] Step S400, using the geometric fidelity weight distribution as a degradation constraint, downgrading the three-dimensional geometric model through a polygon degradation module, and calling the lightweight display scene to load the visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model.

[0034] Specifically, a polygon downscaling algorithm is used to simplify the model to varying degrees based on the geometric fidelity weight distribution. Areas with higher weights maintain higher polygon density, while areas with lower weights are simplified more. During the downscaling process, a visual distortion analyzer is used to constrain global visual distortion to ensure that the simplified model is still visually acceptable. Polygon downscaling refers to the process of simplifying a model by reducing the number of polygons in the model. Visual distortion analysis is the process of evaluating the visual errors introduced during the model simplification process.

[0035] In a possible implementation, the three-dimensional geometric model is degraded by a polygon degradation module with the geometric fidelity weight distribution as a degradation constraint, and the lightweight display scene is called to load the visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model. Step S400 further includes step S410, extracting multiple model vertices in the three-dimensional geometric model, initializing the error metric matrix for the multiple model vertices, and generating multiple error metric matrices. Specifically, all vertices in the three-dimensional geometric model are first extracted. Subsequently, the error metric matrix of each vertex is initialized using the Quadric Error Metrics (QEM) algorithm. The QEM algorithm is a commonly used polygon simplification algorithm, which evaluates the importance of a vertex by expressing the error of the vertex as a quaternary quadratic form (i.e., an error metric matrix). Among them, 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 the identity matrix. For each vertex, its error metric matrix is ​​calculated and stored in a data structure for subsequent use.

[0036] Step S420, based on the multiple error metric matrices, the merged position error of any two vertices is calculated, and a set of first-level vertex pairs whose merged position error is less than a preset error is counted. Specifically, all vertex pairs are traversed, and according to their error metric matrices, the error (i.e., merged position error) that may be introduced when merging the two vertices is calculated by matrix addition (or multiplication, etc.). Then, all vertex pairs whose merged position error is less than the preset error are counted to form a set of first-level vertex pairs. Among them, the merged position error represents the error value of the position change after the two vertices are merged. The smaller this error value is, the closer the position of the merged vertex is to the original position, and the smaller the impact on the model is.

[0037] Step S430, using the geometric fidelity weight distribution as a degradation constraint, merge the vertex pairs in the first-level vertex pair set to generate a first-level lightweight three-dimensional geometric model. Specifically, the vertex pairs in the first-level vertex pair set are merged according to the geometric fidelity weight distribution. When merging, regions with smaller weights (i.e., regions with lower fidelity requirements) are given priority for vertex 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 first-level vertex pair set, it is decided whether to merge based on the geometric fidelity weight distribution. If it is decided to merge, the graph data structure is updated and a first-level lightweight three-dimensional geometric model is generated.

[0038] Step S440, based on the configuration of the visual distortion analyzer of the lightweight display scene, performs global visual distortion analysis on the first-level lightweight three-dimensional geometric model to 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 when a lightweight three-dimensional geometric model is displayed. According to the configuration information of the lightweight display scene, the visual distortion analyzer is called to perform a global visual distortion analysis on the first-level lightweight three-dimensional geometric model. The visual distortion analyzer generates an evaluation index (i.e., a first distortion evaluation index) reflecting global visual distortion through image-based difference analysis, geometric shape-based comparison, and the like. The first distortion evaluation index is a quantitative index reflecting the degree of global visual distortion of the lightweight three-dimensional geometric model.

[0039] Step S450, judging whether the first distortion evaluation index exceeds the preset visual distortion constraint, and processing the first-level lightweight three-dimensional geometric model multiple times according to the judgment result 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 first-level lightweight three-dimensional geometric model (such as increasing the number of vertex merging times, adjusting the error metric matrix, etc.), and re-perform visual distortion analysis. This process will be repeated until the first distortion evaluation index meets the preset visual distortion constraint. Finally, a target lightweight three-dimensional geometric model that meets the requirements is generated. This implementation method can efficiently simplify the three-dimensional geometric model while maintaining the key geometric features of the model through the constraints of the QEM algorithm and the geometric fidelity weight distribution. Through the visual distortion analyzer and the preset visual distortion constraints, it can be ensured that the lightweight three-dimensional geometric model still has acceptable visual quality when displayed.

[0040] In a possible implementation, 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. Step S440 further includes step S441, taking the lightweight display scene as a constraint, 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, sample data for training the visual distortion analyzer is collected using three-dimensional modeling software, image processing software, automated testing tools, etc. 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), and visual error score samples (the visual errors of lightweight geometric models relative to the original geometric models are scored by the automatic evaluation tool, and these scores reflect the impact of lightweight processing on the visual quality of the model).

[0041] Step S442, using the original geometric model samples, lightweight geometric model samples and visual error score samples as sample data, the visual distortion analyzer is trained based on the convolutional neural network. Specifically, the original geometric model samples, lightweight geometric model samples and 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 errors of these models relative to the original geometric model. During the training process, the network output is calculated through forward propagation, and the network parameters are adjusted through back propagation to minimize the prediction error until the network can accurately predict the visual error of the lightweight geometric model.

[0042] Step S443, input the three-dimensional geometric model and the first-level lightweight three-dimensional geometric model into the visual distortion analyzer for evaluation, and generate the first distortion evaluation index. Specifically, the three-dimensional geometric model and the first-level lightweight three-dimensional geometric model are input into the trained visual distortion analyzer, and 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 degree of visual distortion of the first-level lightweight three-dimensional geometric model relative to the three-dimensional geometric model. This implementation method trains the visual distortion analyzer through a convolutional neural network, which can more accurately evaluate the visual error of the lightweight three-dimensional geometric model and improve the accuracy of the evaluation.

[0043] In a possible implementation, 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, 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 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.

[0044] Specifically, if the first distortion evaluation index of the first-level lightweight three-dimensional geometric model evaluated by the visual distortion analyzer does not exceed the preset visual distortion constraint, the model will continue to be further lightweighted to try to achieve a higher lightweight level while ensuring that the visual distortion remains within an acceptable range. Specifically, it is an iterative process, that is, the entire process from vertex extraction (S410) to visual distortion analysis (S440) is repeatedly executed, and the vertices of the current lightweight three-dimensional geometric model (i.e., the first-level 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 merged position error of any two vertices is calculated, and those vertex pairs whose errors are less than the 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 continues; if it exceeds, the iteration stops, and the lightweight three-dimensional geometric model generated by the last iteration is used as the target lightweight three-dimensional geometric model. This implementation method can continuously attempt to further lightweight the model through iterative processing, achieving a higher level of lightweighting while maintaining visual quality.

[0045] In a possible implementation, step S450 further includes step S452, if the first distortion evaluation index exceeds the preset visual distortion constraint, deleting the vertex pair with the largest merge position error in the first-level vertex pair set according to a preset ratio. Specifically, if the first distortion evaluation index generated by the visual distortion analyzer exceeds the preset visual distortion constraint, it means that the current lightweight three-dimensional geometric model is unacceptable in visual quality. In order to improve this situation, first sort the first-level vertex pair set in order from large to small according to the merge position error. Then, according to a preset ratio (a ratio value used to determine the number of vertex pairs to be deleted, for example, deleting a certain ratio of vertex pairs with the largest error), select the vertex pairs with the largest merge position error for deletion, that is, no longer merge these vertex pairs. After deleting the specified vertex pairs, update the vertex pair set for subsequent vertex merging operations.

[0046] Step S453, merging vertices according to the updated vertex pair set, and so on, repeating the analysis until a lightweight three-dimensional geometric model whose distortion evaluation index satisfies 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, and 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 satisfies the preset visual distortion constraint. If not, repeating steps S452 and S453 until a lightweight three-dimensional geometric model that satisfies the constraint is generated. This implementation method 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, thereby achieving the technical effect of ensuring visual quality.

[0047] Step S500 : Rendering the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data to generate target lightweight PDMS three-dimensional design data.

[0048] Specifically, a 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 obtain the target lightweight PDMS three-dimensional design data.

[0049] Step S600: 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.

[0050] Specifically, the target lightweight PDMS three-dimensional design data is stored according to different layers 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 encrypted transmission, incremental transmission, etc. The embodiment of the present application adopts the following technical means: receiving the PDMS three-dimensional design data to be processed and preprocessing the attribute data, extracting the three-dimensional geometric model and performing geometric fidelity weight analysis, determining the lightweight display scene, performing polygon degradation processing with the geometric fidelity weight distribution as the degradation constraint, calling the lightweight display scene to load the visual distortion analyzer for global visual distortion constraints, rendering and generating the target lightweight PDMS three-dimensional design data with attribute information, performing hierarchical storage and transmitting it to the three-dimensional display terminal for calling and displaying, etc., so as to achieve the technical effect of effectively ensuring the accuracy and details of the model and the visual quality while improving the storage and transmission efficiency.

[0051] In a possible implementation, 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. Step S600 further includes step S610, determining the model independent components of the target lightweight three-dimensional geometric model in the target lightweight PDMS three-dimensional design data. Specifically, the independent components in the target lightweight PDMS three-dimensional design data are identified and distinguished by component recognition algorithms or attribute matching (matching using the attribute information of the component, such as name, type, size, etc.). These components can be any basic units constituting a three-dimensional model, such as equipment, pipelines, and support structures.

[0052] Step S620, the model independent components are divided into multiple levels of enclosing relationships to generate a multi-layer component division result. Specifically, according to the spatial relationship between components (such as adjacent, contained, etc.), a hierarchical division algorithm or bounding box technology is used to further divide the identified independent components to form a multi-level enclosing relationship, so as to more effectively manage and access these components in the subsequent storage and transmission process.

[0053] Step S630, construct a multi-layer R-tree node with the multi-layer component division result, extract the three-dimensional data corresponding to the multi-layer component division result in the target lightweight PDMS three-dimensional design data, store it in the multi-layer R-tree node, and generate a multi-layer R-tree. Specifically, according to the multi-layer component division result, create an R-tree node for each level, extract the three-dimensional data corresponding to each node from the target lightweight PDMS three-dimensional design data, and store it in the corresponding R-tree node. Among them, the R-tree is a data structure for storing multi-dimensional spatial data, which can efficiently manage and query spatial data.

[0054] Step S640, the multi-layer R-tree is transmitted to the three-dimensional display terminal for calling and display. Specifically, the multi-layer R-tree is transmitted to the three-dimensional display terminal through an appropriate communication protocol. After receiving the multi-layer R-tree, the three-dimensional display terminal uses the R-tree structure for rendering and display, thereby realizing the rapid display of lightweight PDMS three-dimensional design data. This implementation method can more effectively manage and access three-dimensional data through hierarchical storage and R-tree structure, thereby improving storage and transmission efficiency.

[0055] 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.

[0056] A lightweight processing system for PDMS three-dimensional design data according to an embodiment of the present invention is used to solve the technical problem that it is difficult to ensure model accuracy, details and visual quality while improving storage and transmission efficiency in the existing PDMS three-dimensional design data lightweight processing, so as to achieve the technical effect of effectively ensuring model accuracy, details and visual quality while improving storage and transmission efficiency. A lightweight processing system for PDMS three-dimensional design data includes: 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.

[0057] The attribute data preprocessing module 10 is used to receive the PDMS three-dimensional design data to be processed and preprocess the attribute data to generate the target PDMS three-dimensional design data; the geometric fidelity weight analysis module 20 is used to extract the three-dimensional geometric model in 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 used to determine the lightweight display scene of the target PDMS three-dimensional design data; the degradation processing module 40 is used to use the geometric fidelity weight distribution as the degradation constraint, degrade the three-dimensional geometric model through the polygon degradation module, and call the lightweight display scene to load the visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model; the rendering module 50 is used to render the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data to generate the target lightweight PDMS three-dimensional design data; the hierarchical storage module 60 is used to store the target lightweight PDMS three-dimensional design data in layers and then transmit it to the three-dimensional display terminal for calling and display.

[0058] The specific configuration of the geometric fidelity weight analysis module 20 will be described in detail below. As described above, the three-dimensional geometric model in the target PDMS three-dimensional design data is extracted, and the geometric fidelity weight analysis is performed to establish the geometric fidelity weight distribution. The geometric fidelity weight analysis module 20 may further include: a vertex curvature calculation unit for performing vertex curvature calculation on the three-dimensional geometric model to generate curvature distribution features; a weight initialization assignment unit for performing geometric fidelity weight initialization assignment based on the curvature distribution features to generate an initialized geometric fidelity weight distribution; an enhancement processing unit for receiving key node features, and enhancing the weights of key nodes in the initialized geometric fidelity weight distribution with the key node features to generate the geometric fidelity weight distribution, wherein the key node features are obtained through the user end.

[0059] Among them, the geometric fidelity weight is initialized and assigned based on the curvature distribution characteristics to generate the initialized geometric fidelity weight distribution, and the weight initialization assignment unit may further include: a region segmentation subunit for performing region segmentation on the three-dimensional geometric model based on the curvature distribution characteristics to generate multiple segmented regions, wherein the curvature fluctuation index within any segmented region is less than a preset curvature fluctuation stability index; a weight configuration subunit for performing weight configuration based on the curvature mean values ​​corresponding to the multiple segmented regions to generate the initialized geometric fidelity weight distribution, wherein the initialized geometric fidelity weight distribution includes the initialized geometric fidelity weights of each segmented region, and the initialized geometric fidelity weights of each segmented region are proportional to the corresponding curvature mean value.

[0060] Below, the specific configuration of the degradation processing module 40 will be described in detail. As described above, taking the geometric fidelity weight distribution as the degradation constraint, the three-dimensional geometric model is downgraded through the polygon degradation module, and the lightweight display scene is called to load the visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model. The degradation processing module 40 may further include: an error metric matrix initialization unit for step a: extracting multiple model vertices in the three-dimensional geometric model, initializing the error metric matrix for the multiple model vertices, and generating multiple error metric matrices; a merged position error calculation unit for step b: performing merged position error calculation of any two vertices based on the multiple error metric matrices, and statistically merging A set of first-level vertex pairs whose position errors are less than a preset error; a merging unit is used for step c: merging the vertex pairs in the first-level vertex pair set with the geometric fidelity weight distribution as a degradation constraint to generate a first-level lightweight three-dimensional geometric model; a global visual distortion analysis unit is used for step d: configuring the visual distortion analyzer based on the lightweight display scene to perform a global visual distortion analysis on the first-level lightweight three-dimensional geometric model to generate a first distortion evaluation index; a judgment processing unit is used to judge whether the first distortion evaluation index exceeds the preset visual distortion constraint, and according to the judgment result, the first-level lightweight three-dimensional geometric model is processed multiple times to generate the target lightweight three-dimensional geometric model.

[0061] Among them, based on the lightweight display scene configuration, the visual distortion analyzer performs 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 may further include: a sample collection subunit is used to collect original geometric model samples, lightweight geometric model samples and visual error score samples with the lightweight display scene as a constraint; a visual distortion analyzer training subunit is used to use the original geometric model samples, lightweight geometric model samples and visual error score samples as sample data to train the visual distortion analyzer based on a convolutional neural network; a first distortion evaluation index generation subunit is used 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.

[0062] Among them, 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, and the judgment processing unit may further include: a multiple processing sub-unit is used to repeat steps a to d if the first distortion evaluation index does not exceed the preset visual distortion constraint, and continue to process the first-level lightweight three-dimensional geometric model multiple times to 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.

[0063] Among them, the judgment processing unit can further include: a deletion and merging sub-unit is used to delete the vertex pairs with the largest merging position error in the first-level vertex pair set according to a preset ratio if the first distortion evaluation index exceeds the preset visual distortion constraint; a repeated analysis sub-unit is used to merge vertices according to the updated vertex pair set, and so on, repeating the analysis until a lightweight three-dimensional geometric model is generated whose distortion evaluation index meets the preset visual distortion constraint as the target lightweight three-dimensional geometric model.

[0064] The specific configuration of the hierarchical storage module 60 will be described in detail below. As described above, the target lightweight PDMS three-dimensional design data is hierarchically stored and then transmitted to the three-dimensional display terminal for calling and displaying. The hierarchical storage module 60 may further include: a model independent component determination unit for determining the model independent component of the target lightweight three-dimensional geometric model in the target lightweight PDMS three-dimensional design data; an enclosing relationship division unit for performing multi-level enclosing relationship division on the model independent component to generate a multi-layer component division result; a multi-layer R-tree generation unit for constructing a multi-layer R-tree node with the multi-layer component division result, and extracting the three-dimensional data corresponding to the multi-layer component division result in the target lightweight PDMS three-dimensional design data, storing it in the multi-layer R-tree node, and generating a multi-layer R-tree; a calling and displaying unit for transmitting the multi-layer R-tree to the three-dimensional display terminal for calling and displaying.

[0065] A lightweight processing system for PDMS three-dimensional design data provided by an embodiment of the present invention can execute a lightweight processing method for PDMS three-dimensional design data provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0066] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0067] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements 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 performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A lightweight processing method for PDMS three-dimensional design data, characterized in that: include: Receiving the PDMS three-dimensional design data to be processed and preprocessing 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 of the target PDMS three-dimensional design data; Using the geometric fidelity weight distribution as a degradation constraint, the three-dimensional geometric model is downgraded 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; After rendering the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data, the target lightweight PDMS three-dimensional design data is generated; 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.

2. A lightweight processing method for PDMS three-dimensional design data according to claim 1, characterized in that: Extracting the three-dimensional geometric model in the target PDMS three-dimensional design data, and performing geometric fidelity weight analysis to establish 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; Key node features are received, and weights of key nodes in the initialized geometric fidelity weight distribution are enhanced using the key node features to generate the geometric fidelity weight distribution, wherein the key node features are obtained through a user terminal.

3. A lightweight processing method for PDMS three-dimensional design data according to claim 2, characterized in that: Initializing and assigning geometric fidelity weights based on the curvature distribution characteristics 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 weights are configured 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.

4. A lightweight processing method for PDMS three-dimensional design data according to claim 1, characterized in that: The three-dimensional geometric model is downgraded by a polygon downgrading module using the geometric fidelity weight distribution as a degradation constraint, and the lightweight display scene is called to load the visual distortion analyzer for global visual distortion constraints to generate a target lightweight three-dimensional geometric model, including: Step a: 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; Step b: calculating the merged position error of any two vertices based on the multiple error metric matrices, and counting a set of first-level vertex pairs whose merged 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 primary 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.

5. A lightweight processing method for PDMS three-dimensional design data according to claim 4, characterized in that: Configuring the visual distortion analyzer based on the lightweight display scene to perform global visual distortion analysis on the primary 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, and training the visual distortion analyzer 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.

6. A lightweight processing method for PDMS three-dimensional design data according to claim 5, 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.

7. A lightweight processing method for PDMS three-dimensional design data according to claim 6, 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 vertices, and the 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.

8. 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 with the multi-layer component division result, and extracting three-dimensional data corresponding to the multi-layer component division result in the target lightweight PDMS three-dimensional design data, storing them in the multi-layer R-tree node, and generating a multi-layer R-tree; The multi-layer R-tree is transmitted to a three-dimensional display terminal for calling and displaying.

9. A lightweight processing system for PDMS three-dimensional design data, characterized in that: The system is used to implement a lightweight processing method for PDMS three-dimensional design data according to any one of claims 1 to 8, and the system comprises: The 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, 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, used to determine a lightweight display scene for the target PDMS three-dimensional design data; A degradation processing module, used 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 to load the visual distortion analyzer to perform global visual distortion constraints to generate a target lightweight three-dimensional geometric model; A rendering module, used for rendering the target lightweight three-dimensional geometric model with the attribute information in the target PDMS three-dimensional design data to generate the target lightweight PDMS three-dimensional design data; The hierarchical storage module is used to store the target lightweight PDMS three-dimensional design data in layers and then transmit it to the three-dimensional display terminal for calling and displaying.

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