An importance ranking-based digital twin model feature simplification method

Abstract

The application discloses a digital twin model feature simplification method based on importance ranking, establishes a model feature simplification criterion, and simplifies features with low importance according to the complexity of a model and different requirements by taking additive features as a simplification basis and introducing a feature importance parameter for four features of a round corner, a chamfer, a hole and a cavity.

Description

Technical Field

[0001] This invention relates to the field of model feature simplification, and more specifically to a model feature simplification method based on importance ranking. Background Technology

[0002] With the maturation of digital twin technology, the quality of virtual workshops is further improving. To achieve better visualization effects, the complexity of the models is also gradually increasing. The main component of a virtual workshop is the model, and features, as a major element of the model, involve a huge amount of computation during the operation of the virtual workshop and are also a key factor in ensuring the appearance quality of the model. Simplifying model features can reduce the amount of computation involved in the model, but it can also lead to distortion of the model's appearance. To solve the above problems, a model feature simplification method based on importance ranking is proposed, which simplifies model features while maintaining the high-fidelity appearance quality of the model.

[0003] Current research on model feature simplification largely focuses on reducing computational cost, neglecting model fidelity. Simplification can damage important model characteristics and topology, significantly reducing the model's visualization in virtual workshops and consequently lowering the overall quality. Furthermore, most simplification methods are range-based, batch-selecting and simplifying model features without a fixed simplification criterion. Many model feature simplification studies aim to minimize model data volume, but the amount of shadow data generated by lighting in a virtual workshop far exceeds the feature's actual data volume. To address these issues, this paper proposes a model feature simplification method based on importance ranking, simplifying features such as rounded corners, chamfers, holes, and cavities.

[0004] The innovation of this invention lies in establishing a model feature simplification criterion. For four major features—rounded corners, chamfers, holes, and cavities—additive features are used as the basis for simplification. The importance of model features is ranked, and feature importance parameters are introduced according to the complexity of the model and different needs. Features with lower importance are selected and simplified. The appearance of the simplified model will not change significantly, but the amount of computation involved will be greatly reduced. Summary of the Invention

[0005] The purpose of this invention is to address the model calculation problems in virtual workshops and the shortcomings of existing technologies by proposing a model feature simplification method based on importance ranking. This method defines importance ranking criteria, ranks model features by importance, and filters out unimportant features, simplifying four main features of the model: fillets, chamfers, holes, and cavities. The resulting model will involve a significant reduction in computational load.

[0006] This invention provides a method for simplifying model features based on importance ranking, specifically including the following steps:

[0007] Step 1: Based on the analysis of the computational load generated in rendering different types of features, we select four major features—rounded corners, chamfers, holes, and cavities—for simplified processing.

[0008] Step 2: Define the feature importance ranking criteria. For rounded corner features and chamfer features, the additive feature volume is used as the standard for measuring importance. For hole and cavity features, the additive feature cross-sectional surface area is used as the standard for measuring feature importance.

[0009] Step 3: Input the model to be simplified and extract the model's planar parameters and contour parameters;

[0010] Step 4: Traverse and test the plane and contour parameters of the model, identify the four major features of the model: holes, cavities, rounded corners, and chamfers, determine the feature type, and record them into the corresponding set;

[0011] Step 5: Calculate the feature importance index of the model. Traverse the set of hole features, mark the hole features, calculate the feature area Ks1, and at the same time identify the reference surface where the feature is located and calculate its area Ks2. Use the ratio method to obtain the feature importance index Ku, and record the data in the set Hs.

[0012] Step 6: Traverse the set Hs and sort the model features in descending order of feature importance index Ku. The larger the feature importance index Ku is, the more important the feature is, and vice versa. Introduce the feature importance parameter Kub. If the feature importance index Ku of a certain feature is less than the introduced feature importance parameter Kub, then this feature is included in the features to be simplified.

[0013] Step 7: Simplify other features. Following the hole feature simplification process, simplify the features according to the four types of features. After the features to be simplified are selected, the simplified model is finally output to complete the simplification.

[0014] The present invention is characterized by simplifying model features through a model feature simplification method based on importance ranking. The simplified model data volume is reduced to the maximum extent, the amount of shadow data caused by lighting in the virtual workshop is also effectively reduced, the amount of computation involved in the model features is also greatly reduced, and the simplified model still has high-fidelity appearance quality. Attached Figure Description

[0015] Figure 1 This is the overall flowchart of the present invention. Detailed Implementation

[0016] The present invention will now be described in detail through specific embodiments.

[0017] This embodiment is a method for simplifying model features based on importance ranking.

[0018] Step 1: Based on the analysis of the computational load generated in rendering different types of features, we select four major features—rounded corners, chamfers, holes, and cavities—for simplified processing.

[0019] The simplification of fillets and chamfers aims to reduce shadow calculations and the interaction area between lighting and the model. This simplification is primarily achieved through an additive approach. Specifically, fillet simplification involves generating a new model based on the two adjacent edges of the fillet, attaching it to the original model, and thus simplifying the fillet feature into a right angle. The simplification process for chamfers is the same as for fillets.

[0020] When a model contains holes, the resulting shadows require more computational resources to calculate compared to a model without holes. Furthermore, light entering the holes undergoes multiple reflections, significantly increasing rendering time and complexity. Simplifying hole features primarily involves simplifying rounded corners using an additive approach. Specifically, based on the existing model, a new model is generated according to the height of the top and bottom planes and the size of the hole, and then attached to the existing model, thus filling the hole. The simplification process for cavities is similar to that for holes; reducing multiple reflections of light on the inner surfaces of the cavity achieves lightweight optimization, and this feature is also simplified using the additive approach.

[0021] Step 2: Define the feature importance ranking criteria. For rounded corner features and chamfer features, the additive feature volume is used as the standard for measuring importance. For hole and cavity features, the additive feature cross-sectional surface area is used as the standard for measuring feature importance.

[0022] To prevent excessive differences in the model before and after simplification, the importance of rounded corner and chamfer features is measured using additive feature volume. Specifically, a volume threshold 'a' is set, and the feature volume is checked to see if it exceeds threshold 'a'. If it exceeds threshold 'a', the feature will not be simplified; if it does not exceed threshold 'a', the feature will be included in the simplification list. For hole and cavity features, the importance of feature importance is measured using additive feature cross-sectional surface area. This avoids simplifying hole and cavity features with large cross-sections and shallow depths. Specifically, a surface area threshold 'b' is set, and the feature cross-sectional surface area is checked to see if it exceeds threshold 'b'. If it exceeds threshold 'b', the feature will not be simplified; if it does not exceed threshold 'b', the feature will be included in the simplification list.

[0023] Step 3: Input the model to be simplified and extract the model's planar parameters and contour parameters;

[0024] The system identifies the input model and extracts its planar and contour parameters. Planar parameters record the size, position, and normal vector of the plane, while contour parameters record the appearance of the model part. Planar parameters are used to identify hole and cavity features in the model, while contour parameters are used to identify fillet and chamfer features.

[0025] Step 4: Traverse and test the plane and contour parameters of the model, identify the four major features of the model: holes, cavities, rounded corners, and chamfers, determine the feature type, and record them into the corresponding set;

[0026] By traversing the planar parameters of the model, features are identified and classified into hole features and cavity features. By traversing the contour parameters of the model, features are identified and classified into rounded corner features and chamfer features. Then, four feature sets are established: holes, cavities, rounded corners, and chamfers, and the identified features are stored in them.

[0027] Step 5: Calculate the feature importance index of the model. Traverse the set of hole features, mark the hole features, calculate the feature area Ks1, and at the same time identify the reference surface where the feature is located and calculate its area Ks2. Use the ratio method to obtain the feature importance index Ku, and record the data in the set Hs.

[0028] By using ratios, the relative influence factor of a feature in the original model can be obtained. This allows features with relatively minor impact on the original model to be identified and simplified without affecting its overall appearance. Specifically, the cross-sectional area Ks1 of the hole feature is calculated, the reference plane where the feature is located is identified, and the area Ks2 of the reference plane is calculated. The importance index V = Ks1 / Ks2 is then calculated, and this formula is used to calculate the importance index of all features, which is then included in the set Hs.

[0029] Step 6: Traverse the set Hs, sort the model features according to the feature importance index Ku, introduce the feature importance parameter Kub, extract the unimportant features from the sorted list, and add them to the simplification list for simplification.

[0030] Traverse the set Hs and sort the model features in descending order of feature importance index Ku. The larger the feature importance index Ku is, the more important the feature is, and vice versa. Then, based on the simplification depth, introduce the feature importance parameter Kub. If the feature importance index Ku of a certain feature is less than the introduced feature importance parameter Kub, then this feature is included in the features to be simplified.

[0031] Step 7: Simplify other features. Following the hole feature simplification process, simplify the features according to the four types of features. After the features to be simplified are selected, the simplified model is finally output to complete the simplification.

[0032] Repeat steps five and six, simplifying the other three features in turn according to the simplification process of the hole feature. Note that the hole and cavity are simplified by relative area ratio, and the fillets and chamfers are simplified by relative volume ratio. After all features are simplified, save and output the simplified model.

Claims

1. A feature simplification method for digital twin models based on importance ranking, characterized in that, Includes the following steps: Step 1: Based on the analysis of the computational load generated in rendering different types of features, we select four major features—rounded corners, chamfers, holes, and cavities—for simplified processing. Step 2: Define the feature importance ranking criteria. For rounded corner features and chamfer features, the additive feature volume is used as the standard for measuring importance. For hole and cavity features, the additive feature cross-sectional surface area is used as the standard for measuring feature importance. Step 3: Input the model to be simplified and extract the model's planar parameters and contour parameters; Step 4: Traverse and test the plane and contour parameters of the model, identify the four major features of the model: holes, cavities, rounded corners, and chamfers, determine the feature type, and record them into the corresponding set; Step 5: Calculate the feature importance index of the model. Traverse the set of hole features, mark the hole features, calculate the feature area Ks1, and at the same time identify the reference surface where the feature is located and calculate its area Ks2. Use the ratio method to obtain the feature importance index Ku, and record the data in the set Hs. Step 6: Traverse the set Hs, sort the model features according to the feature importance index Ku, introduce the feature importance parameter Kub, extract the unimportant features from the sorted list, and add them to the simplification list for simplification; Step 7: Simplify other features. Simplify according to the hole feature simplification process. After filtering out the features to be simplified from the four types of features, finally output the simplified model to complete the simplification. The simplification of the four major features—rounded corners, chamfers, holes, and cavities—in step one includes: Based on the original model, a model to be added is generated according to the two adjacent sides of the rounded corner, and then attached to the original model, thus simplifying the rounded corner feature into a right angle feature; the simplification process for chamfers is the same as for rounded corners; the simplification of hole features mainly uses the additive principle to simplify the rounded corner feature; based on the original model, a model to be added is generated according to the height of the upper and lower planes of the model and the size of the hole diameter, and then attached to the original model, thus filling the hole feature; the simplification process for cavities is the same as for hole features, reducing multiple reflections of light on the inner surface of the cavity to achieve lightweight optimization, and simplifying this feature using the additive principle.

2. The feature simplification method for digital twin models based on importance ranking according to claim 1, characterized in that: The feature importance ranking criteria defined in step two include: for rounded corner and chamfer features, the additive feature volume is used as the standard for measuring importance. Specifically, a certain volume threshold 'a' is set, and it is checked whether the additive feature volume exceeds threshold 'a'. If it exceeds threshold 'a', the feature will not be simplified; if it does not exceed threshold 'a', the feature will be included in the simplification list. For hole and cavity features, the additive feature cross-sectional surface area is used as the standard for measuring feature importance. This can avoid simplifying hole and cavity features with large cross-sections and small depths. A certain surface area threshold 'b' is set, and it is checked whether the additive feature cross-sectional surface area exceeds threshold 'b'. If it exceeds threshold 'b', the feature will not be simplified; if it does not exceed threshold 'b', the feature will be included in the simplification list.

3. The feature simplification method for digital twin models based on importance ranking according to claim 1, characterized in that: The extraction of model plane parameters and contour parameters in step three includes: plane parameters record the size, position, and normal vector of the plane, and contour parameters record the appearance of the model parts; plane parameters are used to identify the hole features and cavity features of the model, and contour parameters are used to identify the rounded corner features and chamfer features of the model.

4. The feature simplification method for digital twin models based on importance ranking according to claim 1, characterized in that: The step five of calculating the importance index of model features includes: calculating the cross-sectional area Ks1 of the hole feature, then identifying the reference plane where the feature is located and calculating the area Ks2 of the reference plane, calculating the importance index V=Ks1 / Ks2, using this formula to calculate the importance index of all features and adding it to the set Hs.

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