WO2020155049A1 - 模型简化特征的识别方法、装置和设备 - Google Patents

模型简化特征的识别方法、装置和设备 Download PDF

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Publication number
WO2020155049A1
WO2020155049A1 PCT/CN2019/074259 CN2019074259W WO2020155049A1 WO 2020155049 A1 WO2020155049 A1 WO 2020155049A1 CN 2019074259 W CN2019074259 W CN 2019074259W WO 2020155049 A1 WO2020155049 A1 WO 2020155049A1
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model
simplified
point cloud
geometric
feature
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PCT/CN2019/074259
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English (en)
French (fr)
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闵作兴
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西门子股份公司
西门子（中国）有限公司
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Priority to CN201980078804.4A priority Critical patent/CN113168730A/zh
Priority to PCT/CN2019/074259 priority patent/WO2020155049A1/zh
Publication of WO2020155049A1 publication Critical patent/WO2020155049A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects

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  • the present invention relates to computer-aided engineering, in particular to a method, device and system for identifying simplified features of a model.
  • CAE Computer Aided Engineering
  • the first aspect of the present invention provides a method for identifying simplified features of a model, which includes the following steps: S1, converting a three-dimensional geometric model and its simplified simplified model into point cloud models, respectively; S2, using points of the three-dimensional geometric model The point cloud model of the simplified model is subtracted from the cloud model, and the result is a point cloud model with simplified geometric features; S3, identifying a plurality of feature parameters of the point cloud model with simplified geometric features, and digitizing the plurality of feature parameters, A plurality of feature parameter sets are used to express the simplified geometric feature.
  • step S2 further includes the following steps: comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and using a Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the simplified model.
  • comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model and using a Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the simplified model.
  • the feature parameters include the coordinate positions and geometric structure features of the point cloud model of the simplified geometric feature
  • the step S3 further includes the following step: projecting the point cloud model of the simplified geometric feature on x, y , Z three coordinate directions, and identify the coordinate positions and geometric structure features of the simplified geometric feature point cloud model in the x, y, z three coordinate directions, and pack the coordinate positions and geometric structure features into The feature parameter set of the point cloud model of the simplified geometric feature, and a plurality of feature parameter sets are used to express the simplified geometric feature.
  • geometric structure features include centroid and area.
  • the second aspect of the present invention provides a recognition device with simplified features of a model, including: a processor; and a memory coupled with the processor, the memory having instructions stored therein, and when the instructions are executed by the processor, the The electronic device performs actions, and the actions include: S1, respectively transforming a three-dimensional geometric model and its simplified simplified model into a point cloud model; S2, subtracting the value of the simplified model from the point cloud model of the three-dimensional geometric model Point cloud model, the result is both a point cloud model with simplified geometric features; S3, identify multiple feature parameters of the point cloud model with simplified geometric features, and digitize the multiple feature parameters, and use multiple feature parameter sets to express all Describe simplified geometric features.
  • the action S2 further includes: comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and using a Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model and using a Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • the feature parameters include the coordinate positions and geometric structure features of the point cloud model of the simplified geometric feature
  • the action S3 further includes: projecting the point cloud model of the simplified geometric feature on x, y, z In the three coordinate directions, and identify the coordinate positions and geometric structure features of the simplified geometric feature point cloud model in the x, y, and z coordinate directions, and package the coordinate positions and geometric structure features as the The feature parameter set of the point cloud model of the simplified geometric feature, and a plurality of feature parameter sets are used to express the simplified geometric feature.
  • geometric structure features include centroid and area.
  • a third aspect of the present invention provides a recognition device for simplified features of a model, including: a conversion device that converts a three-dimensional geometric model and its simplified simplified model into point cloud models, respectively; and an acquisition device that uses the points of the three-dimensional geometric model The point cloud model of the simplified model is subtracted from the cloud model, and the result is both a point cloud model with simplified geometric features; a recognition device that recognizes a plurality of feature parameters of the point cloud model with simplified geometric features, and compares the plurality of feature parameters Digitize, and use a plurality of feature parameter sets to express the simplified geometric features.
  • the acquisition device is also used to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and use the Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • the same points in the point cloud model are omitted, and different points are retained as a point cloud model with simplified geometric features.
  • the feature parameters include the coordinate positions and geometric structure features of the point cloud model of the simplified geometric feature
  • the recognition device is further used for: projecting the point cloud model of the simplified geometric feature on x, y, z three coordinate directions, and identify the coordinate positions and geometric structure features of the simplified geometric feature point cloud model in the x, y, z three coordinate directions, and package the coordinate positions and geometric structure features as all Describe the feature parameter set of the point cloud model of the simplified geometric feature, and use a plurality of feature parameter sets to express the simplified geometric feature.
  • geometric structure features include centroid and area.
  • the fourth aspect of the present invention provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to execute the present invention.
  • the method of the first aspect of the invention is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to execute the present invention.
  • the fifth aspect of the present invention provides a computer-readable medium on which computer-executable instructions are stored, and when executed, the computer-executable instructions cause at least one processor to perform the method according to the first aspect of the present invention.
  • the recognition mechanism of simplified features of the model provided by the present invention is applicable to the original model of any structure, and therefore has a very wide application range, as long as the model can be converted into a point cloud model, it can be applied to the present invention.
  • most geometric structures can be output as intermediate formats in the point cloud model, such as *.stl.
  • the present invention saves calculation workload, and the present invention only needs to compare limited points in the point cloud model.
  • the recognition mechanism of model simplified features provided by the present invention is automatic and fast, and the result is more accurate, and does not rely on the experience of senior engineers.
  • the present invention uses a point cloud model, which is not sensitive to the modeling method.
  • the identification method of the present invention is very simple and does not need to perform collection or calculation.
  • the present invention is based on a computer algorithm, so it can be executed automatically.
  • the entire recognition process of the present invention does not require any human judgment.
  • the accuracy of the recognition result of the present invention is only determined by the density of points representing the entire three-dimensional collection model.
  • the simplified features of the model identified by the present invention are digitized and quantitative, rather than designation, and therefore can be used for further analysis.
  • the present invention saves the model simplified feature recognition time and improves the accuracy of the recognition result.
  • the invention can be applied to the further design process in the product development stage.
  • Fig. 1 is a simplified schematic diagram of a three-dimensional geometric model according to a specific embodiment of the present invention
  • FIG. 2 is a schematic diagram of a simplified point cloud model of a three-dimensional geometric model according to a specific embodiment of the present invention
  • FIG. 3 is a simplified schematic diagram of a three-dimensional geometric model according to another specific embodiment of the present invention.
  • FIG. 4 is a schematic diagram of a simplified point cloud model of a three-dimensional geometric model according to another specific embodiment of the present invention.
  • FIG. 5 is a schematic diagram of a point cloud model for identifying simplified geometric features according to another specific embodiment of the present invention.
  • Fig. 6 is a simplified schematic diagram of a three-dimensional geometric model according to a variation of the present invention.
  • Fig. 7 is a schematic diagram of a simplified point cloud model of a three-dimensional geometric model according to a variation of the present invention.
  • the model simplified feature recognition mechanism provided by the present invention converts the three-dimensional geometric model and its simplified simplified model into point cloud models respectively, compares the two to obtain simplified geometric features, and recognizes them for further analysis.
  • the first aspect of the present invention provides a method for identifying simplified features of a model.
  • step S1 is performed to convert the three-dimensional geometric model and its simplified simplified model into point cloud models.
  • the three-dimensional geometric model (3D geometric data) is data based on the combination of points, lines and other information based on the shape of the object itself.
  • Three-dimensional geometric models usually rely on computer-aided engineering, and special computer software generates files in special formats according to certain algorithms.
  • the point cloud model is a three-dimensional model grid that forms a cloud structure through discrete points based on the shape of the object itself, and usually includes three-dimensional coordinates, laser reflection intensity, or color information.
  • the first model 100 is a three-dimensional geometric model, which is an irregular object, which is roughly composed of two modules and a rounded corner.
  • the first module B 1 and the second module B 2 are rectangular parallelepipeds.
  • the fillet B 3 is a quasi-polygon, which has three sides, one of which is a circular arc.
  • the first model 100 has 9 faces.
  • the first simplified model 100 is the second model 200, showing its simplified geometric out hereinabove wherein said rounded portion B 3, simplified geometric features in the FIG. 1 by the reference numeral 300 denotes.
  • the geometric feature omitted in the model simplification process is rounded corner B3.
  • the purpose of the present invention is to recognize that the model is simplified during the model analysis process. Geometric features.
  • step S1 transforms the first model 100 and the second model 200 into a first point cloud model 100' and a second point cloud model 200', respectively.
  • the conversion of the three-dimensional geometric model into the point cloud model is usually performed by special software, and the existing technology is supported by mature technology, and the present invention will not be repeated.
  • step S2 is executed to subtract the point cloud model of the simplified model from the point cloud model of the three-dimensional geometric model, and the result is a point cloud model of simplified geometric features.
  • the first point cloud model 100' and the second point cloud model 200' are parallel, and the first point cloud model 100' and the second point cloud model 200' are based on the object itself.
  • the shape forms a cloud structure through discrete points.
  • the simplified geometric structure is the third point cloud model 300' must belong to the first One point cloud model 100', and the second point cloud model 200' does not have the third point cloud model 300' which has been simplified.
  • the step S2 further includes the following steps: comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and using the Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • Boolean calculation is a logical deduction mechanism based on digital symbols, and the Boolean algorithm includes content such as union, intersection, and subtraction.
  • the Boolean algorithm is used to make simple basic graphics combinations produce new shapes.
  • the Boolean algorithm is not only suitable for three-dimensional geometric models, but also for two-dimensional geometric models. It should be noted that the Boolean algorithm has mature technical support in the prior art, and for the sake of brevity, it will not be repeated.
  • the first point cloud model 100' and the second point cloud model 200' are compared, and the same points in the first point cloud model 100' and the second point cloud model 200' are omitted using the Boolean algorithm. And keep the two different points as a simplified geometric feature, that is, the third point cloud model 300'. 2, should be omitted portion of the first module and the second module B 1 B 2 based on the point cloud model, was retained fillet B 3 shown in Figure 1.
  • the invention is suitable for three-dimensional geometric models of different shapes.
  • the first model 500 is a three-dimensional geometric model, which is an irregular object, which is generally composed of two modules and a ring.
  • the first module D 1 and the second module D 2 are cylindrical, and the cross-sectional area of the first module D 1 is smaller than the cross-sectional area of the second module D 2 .
  • the first model 100 has 6 faces.
  • the simplified first model 500 is the second model 600, and it can be seen that the simplified geometric feature is the rounded corner D 3 described above.
  • the simplified geometric feature is denoted by reference numeral 700.
  • the geometric feature omitted in the model simplification process is the rounded corner D 3.
  • the purpose of the present invention is to recognize that the geometric feature that was omitted in the model simplification process is Simplified geometric features.
  • step S1 transforms the first model 500 and the second model 600 into a first point cloud model 500' and a second point cloud model 600', respectively.
  • the conversion of the three-dimensional geometric model into the point cloud model is usually performed by special software, and the existing technology is supported by mature technology, and the present invention will not be repeated.
  • step S2 is executed to subtract the point cloud model of the simplified model from the point cloud model of the three-dimensional geometric model, and the result is a point cloud model of simplified geometric features.
  • the first point cloud model 500' and the second point cloud model 600' are parallel, and the first point cloud model 500' and the second point cloud model 600' are based on the object itself.
  • the shape forms a cloud structure through discrete points.
  • the simplified geometric structure is the third point cloud model 700' must belong to the first One point cloud model 500', and the second point cloud model 600' does not have the third point cloud model 700' which has been simplified.
  • the step S2 further includes the following steps: comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and using the Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model
  • the first point cloud model 500' and the second point cloud model 600' are compared, and the same points in the first point cloud model 500' and the second point cloud model 600' are omitted using the Boolean algorithm.
  • the two different points as a simplified geometric feature, that is, the third point cloud model 700'.
  • the omitted part should be the point cloud model based on the first module D 1 and the second module D 2 as shown in Fig. 1, and the remaining is the rounded corner D 3 .
  • the model simplification process mentioned in the present invention not only refers to removing features from the original three-dimensional geometric model, but also includes adding features to the original three-dimensional geometric model.
  • the first model 800 is a three-dimensional geometric model, which is an irregular object, which is generally a hollow cylinder.
  • the simplified first model 800 is the second model 900.
  • an irregular ring E 3 is added .
  • the geometric feature to be simplified in FIG. 6 is denoted by the reference symbol E 3 .
  • the purpose of the present invention is to identify the model simplification process in the model analysis process. Geometric features that are simplified in the process.
  • step S1 transforms the first model 800 and the second model 900 into a first point cloud model 800' and a second point cloud model 900', respectively.
  • the conversion of the three-dimensional geometric model into the point cloud model is usually performed by special software, and the existing technology is supported by mature technology, and the present invention will not be repeated.
  • step S2 is executed to subtract the point cloud model of the simplified model from the point cloud model of the three-dimensional geometric model, and the result is a point cloud model of simplified geometric features.
  • the first point cloud model 800' and the second point cloud model 900' are parallel, and the first point cloud model 800' and the second point cloud model 900' are based on the object itself.
  • the shape forms a cloud structure through discrete points.
  • the structures of the first point cloud model 800' and the second point cloud model 900' are mostly the same, they have the same set of points, and the simplified geometric structure is the third point cloud model E 3 '
  • the second point cloud model 900', and the first point cloud model 800' does not have the third point cloud model E 3 'which has been simplified.
  • the step S2 further includes the following steps: comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and using the Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model
  • the same points in the point cloud model are omitted, and different points are retained as a point cloud model with simplified geometric features. Therefore, in this embodiment, the first point cloud model 800' and the second point cloud model 900' are compared, and the same points in the first point cloud model 800' and the second point cloud model 900' are omitted using the Boolean algorithm. And keep the two different points as a simplified geometric feature, which is the third point cloud model E 3 ′.
  • step S3 is performed to identify a plurality of feature parameters of the point cloud model of simplified geometric features, and digitize the plurality of feature parameters, and use a plurality of feature parameter sets to express the simplified geometric features.
  • the purpose of step S3 is to digitize the simplified geometric features, which can be applied in subsequent related model operations.
  • the characteristic parameter includes the coordinate position and geometric structure characteristic of the point cloud model of the simplified geometric characteristic.
  • the coordinate position is the coordinate position of any point or any number of points of the point cloud model.
  • the coordinate position includes both the horizontal X axis
  • the coordinate position also includes the coordinate position of the Y axis in the vertical direction, and also includes the coordinate position of the Z axis perpendicular to the X axis and the Y axis.
  • the omitted geometric features usually include chamfer, fillet, and small arc; design features and their array features (pattern features) further include blender ), chamfer, hole, boss, rib, groove, thread, dart, slot, and square boss (pad); pattern features further include blender, chamfer, hole, boss, rib, groove, and thread (thread), bevel (dart), keyway (slot), square boss (pad); pattern features (pattern features) further include fillet (blender), groove (chamfer), hole (Hole), round platform (boss) ), ribs, grooves, threads, darts, slots, pads; array features (pattern features) further include rounded corners ( blender), groove (chamfer), hole (Hole), round table (boss), rib reinforcement (rib), groove (groove), thread (thread), bevel (dart), slot (slot), lining Pad. But regardless of the omitted simplified geometric feature shape, all geometric features have an area in the X-axis direction, all geometric
  • the step S3 further includes the following steps: project the point cloud model of the simplified geometric feature on the three coordinate directions of x, y, and z, and identify the simplified geometric feature
  • the coordinate position and geometric structure feature of the point cloud model in the x, y, and z coordinate directions, and the coordinate position and geometric structure feature are packaged into the feature parameter set of the simplified geometric feature point cloud model, and used
  • a plurality of feature parameter sets are used to express the simplified geometric feature.
  • step S3 is to identify the plurality of feature parameters of the point cloud model of the simplified geometric feature 410, thereby converting Simplify the digitization of geometric features 410.
  • the feature parameters of the point cloud model of the simplified geometric feature 410 that need to be recognized are the centroid and its coordinates of the projection in the three coordinate directions of x, y, and z. position.
  • the simplified geometric feature 410 is projected into the first shape 412 on the x coordinate
  • the simplified geometric feature 410 is projected into the first shape 414 on the y coordinate
  • the simplified geometric feature 410 is projected into the first shape 416 on the z coordinate.
  • the first centroid of the first shape 412 is C 1
  • the second centroid of the second shape 414 is C 2
  • the third centroid of the third shape 416 is C 3 .
  • the coordinate position of the first centroid C 1 is (x 1 , x 2 ), the coordinate position of the first centroid C 2 is (y 1 , y 2 ), and the coordinate position of the first centroid C 3 is ( z 1 , z 2 ).
  • the area of the first shape 412 is S 1
  • the area of the second shape 414 is S 2
  • the area of the third area 416 is S 3 . Therefore, in this embodiment, the simplified geometric feature 410 is represented by the centroid set of its shape projected in the three coordinate directions x, y, and z as:
  • Feature f 410 ⁇ S 1 , x 1 , x 2 , S 2 , y 1 , y 2 , S 3 , z 1 , z 2 ⁇
  • the second aspect of the present invention provides a recognition device with simplified features of a model, including: a processor; and a memory coupled with the processor, the memory having instructions stored therein, and when the instructions are executed by the processor, the The electronic device performs actions, and the actions include: S1, respectively transforming a three-dimensional geometric model and its simplified simplified model into a point cloud model; S2, subtracting the value of the simplified model from the point cloud model of the three-dimensional geometric model Point cloud model, the result is both a point cloud model with simplified geometric features; S3, identify multiple feature parameters of the point cloud model with simplified geometric features, and digitize the multiple feature parameters, and use multiple feature parameter sets to express all Describe simplified geometric features.
  • the action S2 further includes: comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and using a Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • comparing the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model and using a Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • the feature parameters include the coordinate positions and geometric structure features of the point cloud model of the simplified geometric feature
  • the action S3 further includes: projecting the point cloud model of the simplified geometric feature on x, y, z In the three coordinate directions, and identify the coordinate positions and geometric structure features of the simplified geometric feature point cloud model in the x, y, and z coordinate directions, and package the coordinate positions and geometric structure features as the The feature parameter set of the point cloud model of the simplified geometric feature, and a plurality of feature parameter sets are used to express the simplified geometric feature.
  • geometric structure features include centroid and area.
  • a third aspect of the present invention provides a recognition device for simplified features of a model, including: a conversion device that converts a three-dimensional geometric model and its simplified simplified model into point cloud models respectively; and an acquisition device that uses the points of the three-dimensional geometric model The point cloud model of the simplified model is subtracted from the cloud model, and the result is both a point cloud model with simplified geometric features; a recognition device that recognizes a plurality of feature parameters of the point cloud model with simplified geometric features, and compares the plurality of feature parameters Digitize, and use a plurality of feature parameter sets to express the simplified geometric features.
  • the acquisition device is also used to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model, and use the Boolean algorithm to compare the point cloud model of the three-dimensional geometric model with the point cloud model of the simplified model.
  • the same points in the point cloud model are omitted, and different points are retained as a point cloud model with simplified geometric features.
  • the feature parameters include the coordinate positions and geometric structure features of the point cloud model of the simplified geometric feature
  • the recognition device is further used for: projecting the point cloud model of the simplified geometric feature on x, y, z three coordinate directions, and identify the coordinate positions and geometric structure features of the simplified geometric feature point cloud model in the x, y, z three coordinate directions, and package the coordinate positions and geometric structure features as all Describe the feature parameter set of the point cloud model of the simplified geometric feature, and use a plurality of feature parameter sets to express the simplified geometric feature.
  • geometric structure features include centroid and area.
  • the fourth aspect of the present invention provides a computer program product, which is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to execute the present invention.
  • the method of the first aspect of the invention is tangibly stored on a computer-readable medium and includes computer-executable instructions that, when executed, cause at least one processor to execute the present invention.
  • the fifth aspect of the present invention provides a computer-readable medium on which computer-executable instructions are stored, and when executed, the computer-executable instructions cause at least one processor to perform the method according to the first aspect of the present invention.
  • the recognition mechanism of simplified features of the model provided by the present invention is applicable to the original model of any structure, and therefore has a very wide application range, as long as the model can be converted into a point cloud model, it can be applied to the present invention.
  • most geometric structures can be output as intermediate formats in the point cloud model, such as *.stl.
  • the present invention saves calculation workload, and the present invention only needs to compare limited points in the point cloud model.
  • the recognition mechanism of model simplified features provided by the present invention is automatic and fast, and the result is more accurate, and does not rely on the experience of senior engineers.
  • the present invention uses a point cloud model, which is not sensitive to the modeling method.
  • the identification method of the present invention is very simple and does not need to perform collection or calculation.
  • the present invention is based on a computer algorithm, so it can be executed automatically.
  • the entire recognition process of the present invention does not require any human judgment.
  • the accuracy of the recognition result of the present invention is only determined by the density of points representing the entire three-dimensional collection model.
  • the simplified features of the model identified by the present invention are digitized and quantitative, rather than designation, and therefore can be used for further analysis.
  • the present invention saves the model simplified feature recognition time and improves the accuracy of the recognition result.
  • the invention can be applied to the further design process in the product development stage.

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Abstract

本发明提供了模型简化特征的识别方法、装置和设备，其中，包括如下步骤：S1，将三维几何模型及其简化后的简化模型分别转化为点云模型；S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。本发明自动快速，并且精确度更高，节省了大量计算时间，并且不依赖于人力。

Description

模型简化特征的识别方法、装置和设备 技术领域
本发明涉及计算机辅助工程，尤其涉及模型简化特征的识别方法、装置和系统。
背景技术
基于三维几何数据(3D geometric data)的计算机辅助工程(CAE，Computer Aided Engineering)模型具有许多小几何特征，几何特征会增加在模型分析过程中的有限单元(finite elements)，因此增加计算量(computation cost)。因此，为了减少模型分析过程中的计算量，几何特征会被分析器谨慎简化，或者用CAE预处理软件自动简化，甚至执行人为简化。
因此，设计者建模完成后所交出的模型和模型分析时利用的模型具有差距，需要进行模型简化。然而，模型简化也会丢失数据。在一些情况下，被简化掉的几何特征需要在模型中识别并进行进一步地分析。然而，被简化的几何特征并不会自动在简化过程中保存并直接发送。恢复简化特征过程的执行高度依赖于设计者和分析者的经验，并且，对于复杂的三维几何模型，恢复简化特征需要非常精确，因此识别过程需要非常多时间。
发明内容
本发明第一方面提供了模型简化特征的识别方法，其中，包括如下步骤：S1，将三维几何模型及其简化后的简化模型分别转化为点云模型；S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述步骤S2还包括如下步骤：比较所述三维几何模型的 点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
进一步地，所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，其中，所述步骤S3还包括如下步骤：将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述几何结构特征包括形心、面积。
本发明第二方面提供了模型简化特征的识别设备，包括：处理器；以及与所述处理器耦合的存储器，所述存储器具有存储于其中的指令，所述指令在被处理器执行时使所述电子设备执行动作，所述动作包括：S1，将三维几何模型及其简化后的简化模型分别转化为点云模型；S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述动作S2还包括：比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
进一步地，所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，其中，所述动作S3还包括：将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述几何结构特征包括形心、面积。
本发明第三方面提供了模型简化特征的识别装置，包括：转化装置， 其将三维几何模型及其简化后的简化模型分别转化为点云模型；获取装置，其用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；识别装置，其识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述获取装置还用于：比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
进一步地，所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，其中，所述识别装置还用于：将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述几何结构特征包括形心、面积。
本发明第四方面提供了计算机程序产品，所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明第五方面提供了计算机可读介质，其上存储有计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据本发明第一方面所述的方法。
本发明提供的模型简化特征的识别机制适用于任何结构的原始模型，因此具有非常宽的应用范围，只要模型能够给转化为点云模型就能应用于本发明。其中，大部分几何结构都能输出为点云模型中的中间格式，例如*.stl。
本发明和复杂的三维体积集合或面积计算方法相比，节省了计算工作量，本发明仅仅需要对比点云模型中有限的点。
本发明提供的模型简化特征的识别机制是自动和快速的，并且结果精确度更高，并不依赖于资深工程师的经验。
通过执行本发明识别的模型简化特征能够方便用于进一步的相关分析，例如用于结果分析等。
本发明利用了点云模型，点云模型对建模方法并不敏感。本发明的识别方法很简单，并不需要执行集合或者计算。本发明基于计算机算法，因此能够自动执行，本发明的整个识别过程不需要任何人力的判断，本发明的识别结果精度仅仅由代表整个三维集合模型的点的密度来决定。本发明识别的模型简化特征是数字化的和定量的，而并非是指定的(designation)，因此能够用于进一步的分析。
此外，本发明节省了模型简化特征识别的时间，提高了识别结果的精确度。本发明能够再产品研发阶段适用于进一步的设计过程。
附图说明
图1是根据本发明一个具体实施例的三维几何模型简化的示意图；
图2是根据本发明一个具体实施例的三维几何模型简化的点云模型示意图；
图3是根据本发明又一具体实施例的三维几何模型简化的示意图；
图4是根据本发明又一具体实施例的三维几何模型简化的点云模型示意图；
图5是根据本发明另一具体实施例的识别简化几何特征的点云模型示意图；
图6是根据本发明一个变化例的三维几何模型简化的示意图；
图7是根据本发明一个变化例的三维几何模型简化的点云模型示意图。
具体实施方式
以下结合附图，对本发明的具体实施方式进行说明。
本发明提供的模型简化特征的识别机制将三维几何模型及其简化后的简化模型分别转化为点云模型，比较两者从而获得被简化的几何特征，从而识别以便进一步分析。
本发明第一方面提供了一种模型简化特征的识别方法。首先执行步骤S1，将三维几何模型及其简化后的简化模型分别转化为点云模型。其 中，三维几何模型(3D geometric data)是基于物本身的形状所做的点、线和其他信息结合的数据。三维几何模型通常借助计算机辅助工程，由专门的计算机软件按照一定的算法生成专门格式的文件。点云模型是基于物体本身的形状通过离散的点形成云结构的三维模型网格，通常包括三维坐标、激光反射强度或者颜色信息等。
如图1所示，在本实施例中，第一模型100是三维几何模型，其为一个不规则物体，大体是由两个模块和一个圆角。其中，第一模块B 1和第二模块B 2是长方体。圆角B 3是类多边体，其具有三条边，其中一个边是圆弧。所述第一模型100具有9个面。简化后的第一模型100为第二模型200，可见其简化掉的几何特征是上文所述的圆角B 3部分，在图1中将被简化的几何特征用附图标记300表示。然而，需要说明的是，在模型分析过程中并不能得知模型简化过程中被省略掉的几何特征为圆角B3，本发明的目的就在于在模型分析过程中识别在模型简化过程中被简化的几何特征。
具体地，如图2所示，步骤S1将第一模型100和第二模型200分别转化为第一点云模型100’和第二点云模型200’。优选地，将三维几何模型转化为点云模型通常由专门的软件来执行，现有技术由成熟的技术支持，本发明不再赘述。
然后执行步骤S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型。具体地，如图2所示，第一点云模型100’和第二点云模型200’并无线条，所述第一点云模型100’和第二点云模型200’是基于物体本身的形状通过离散的点形成云结构。其中，既然第一点云模型100’和第二点云模型200’的结构大部分都相同，因此具有相同的点的集合，而被简化的几何结构为第三点云模型300’必然属于第一点云模型100’，而第二点云模型200’并不具有已经被简化掉的第三点云模型300’。
因此，所述步骤S2还包括如下步骤：比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
其中，布尔算法(Boolean calculation)是基于数字符号的逻辑推演 机制，所述布尔算法包括联合、相交、相减等内容。在模型简化和模型分析过程中利用布尔算法使简单的基本图形组合产生新的形体，布尔算法不仅适用于三维几何模型，也适用于二维几何模型。需要说明的是，布尔算法在现有技术中已有成熟的技术支持，为简明起见，不再赘述。
因此，在本实施例中，比较第一点云模型100’和第二点云模型200’，利用布尔算法将第一点云模型100’和第二点云模型200’中相同的点省略，并将两者不同的点保留下来作为简化几何特征也就是第三点云模型300’。如图2所示，省略的部分应当为如图1所示的基于第一模块B 1和第二模块B 2的点云模型，保留的则为圆角B 3。
本发明适用于不同形状的三维几何模型。
根据本发明另一实施例，如图3所示，第一模型500是三维几何模型，其为一个不规则物体，大体是由两个模块和一个环状物。其中，第一模块D 1和第二模块D 2是圆柱形，其中，第一模块D 1的横截面面积小于第二模块D 2的横截面面积。所述第一模型100具有6个面。简化后的第一模型500为第二模型600，可见其简化掉的几何特征是上文所述的圆角D 3部分，在图1中将被简化的几何特征用附图标记700表示。然而，需要说明的是，在模型分析过程中并不能得知模型简化过程中被省略掉的几何特征为圆角D 3，本发明的目的就在于在模型分析过程中识别在模型简化过程中被简化的几何特征。
具体地，如图4所示，步骤S1将第一模型500和第二模型600分别转化为第一点云模型500’和第二点云模型600’。优选地，将三维几何模型转化为点云模型通常由专门的软件来执行，现有技术由成熟的技术支持，本发明不再赘述。
然后执行步骤S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型。具体地，如图2所示，第一点云模型500’和第二点云模型600’并无线条，所述第一点云模型500’和第二点云模型600’是基于物体本身的形状通过离散的点形成云结构。其中，既然第一点云模型500’和第二点云模型600’的结构大部分都相同，因此具有相同的点的集合，而被简化的几何结构为第三点云模型700’必然属于第一点云模型500’，而第二点云模型600’并不具有已经被简化掉的第三点云模型700’。
因此，所述步骤S2还包括如下步骤：比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。因此，在本实施例中，比较第一点云模型500’和第二点云模型600’，利用布尔算法将第一点云模型500’和第二点云模型600’中相同的点省略，并将两者不同的点保留下来作为简化几何特征也就是第三点云模型700’。如图2所示，省略的部分应当为如图1所示的基于第一模块D 1和第二模块D 2的点云模型，保留的则为圆角D 3。
本发明所提及的模型简化过程不仅仅是指在原始三维几何模型上去除特征，还包括在原始三维几何模型上添加特征。
根据本发明的一个变化例，如图6所示，第一模型800是三维几何模型，其为一个不规则物体，大体为一个中空的圆柱体。简化后的第一模型800为第二模型900，第一模型800简化以后增加了不规则环状物E 3。在图6中将被简化的几何特征用附图标记E 3表示。然而，需要说明的是，在模型分析过程中并不能得知模型简化过程中被省略掉的几何特征为不规则环状物E 3，本发明的目的就在于在模型分析过程中识别在模型简化过程中被简化的几何特征。
具体地，如图7所示，步骤S1将第一模型800和第二模型900分别转化为第一点云模型800’和第二点云模型900’。优选地，将三维几何模型转化为点云模型通常由专门的软件来执行，现有技术由成熟的技术支持，本发明不再赘述。
然后执行步骤S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型。具体地，如图7所示，第一点云模型800’和第二点云模型900’并无线条，所述第一点云模型800’和第二点云模型900’是基于物体本身的形状通过离散的点形成云结构。其中，既然第一点云模型800’和第二点云模型900’的结构大部分都相同，因此具有相同的点的集合，而被简化的几何结构为第三点云模型E 3’必然属于第二点云模型900’，而第一点云模型800’并不具有已经被简化掉的第三点云模型E 3’。
因此，所述步骤S2还包括如下步骤：比较所述三维几何模型的点云 模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。因此，在本实施例中，比较第一点云模型800’和第二点云模型900’，利用布尔算法将第一点云模型800’和第二点云模型900’中相同的点省略，并将两者不同的点保留下来作为简化几何特征也就是第三点云模型E 3’。
最后执行步骤S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。步骤S3的目的是为了将被简化的几何特征数字化，能够在后续相关模型操作中被应用。具体地，所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征。
特别地，所述坐标位置即为点云模型的任一点或者任多点的坐标位置，在本实施例中，既然是针对三维几何模型，可选地，既坐标位置既包括水平方向X轴的坐标位置，又包括垂直方向Y轴的坐标位置，还包括分别垂直于X轴和Y轴的Z轴的坐标位置。
进一步地，被省略的几何特征通常包括斜角(chamfer)、圆角(fillet)、圆弧(small arc)；设计特征(design feature)及其阵列特征(pattern features)进一步地包括圆角(blender)、凹槽(chamfer)、孔洞(Hole)、圆台(boss)、筋板加强筋(rib)、开槽(groove)、螺纹(thread)、斜面(dart)、键槽(slot)、方形凸台(pad)；阵列特征(pattern features)进一步地包括圆角(blender)、斜角(chamfer)、孔洞(Hole)、圆台(boss)、筋板加强筋(rib)、环形槽(groove)、螺纹(thread)、斜面(dart)、键槽(slot)、方形凸台(pad)；阵列特征(pattern features)进一步地包括圆角(blender)、凹槽(chamfer)、孔洞(Hole)、圆台(boss)、筋板加强筋(rib)、开槽(groove)、螺纹(thread)、斜面(dart)、狭槽(slot)、衬垫(pad)；阵列特征(pattern features)进一步地包括圆角(blender)、凹槽(chamfer)、孔洞(Hole)、圆台(boss)、筋板加强筋(rib)、环槽(groove)、螺纹(thread)、斜面(dart)、狭槽(slot)、衬垫(pad)。但是不论被省略的简化几何特征形状如何，所有的几何特征都具有在X轴方向、Y轴方向或者Z轴方向的面积和所有形状的形心等。
为了将被简化的简化几何特征数字化，其中，所述步骤S3还包括如 下步骤：将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
具体地，如图2所示，根据本发明另一个具体实施例，三维几何模型400中在简化过程中被简化的是简化几何特征410。根据上文所述，在执行步骤S1和步骤S2以后已经识别了简化几何特征410的点云模型，步骤S3的目的是为了通过识别简化几何特征410的点云模型的复数个特征参数，从而将简化几何特征410数字化。
特别地，如图5所示，在本实施例中，需要识别的简化几何特征410的点云模型的特征参数为其在x、y、z三个坐标方向上的投影的形心及其坐标位置。具体地，简化几何特征410投影在x坐标上为第一形状412，简化几何特征410投影在y坐标上为第一形状414，简化几何特征410投影在z坐标上为第一形状416。其中，第一形状412的第一形心为C 1，第二形状414的第二形心为C 2，第三形状416的第三形心为C 3。具体地，第一形心C 1的坐标位置为(x 1，x 2)，第一形心C 2的坐标位置为(y 1，y 2)，第一形心C 3的坐标位置为(z 1，z 2)。其中，第一形状412的面积为S 1，第二形状414的面积为S 2，第三面积416的面积为S 3。因此，在本实施例中，简化几何特征410通过其投影在x、y、z三个坐标方向上的形状的形心集合来表示为：
Feature f 410＝{S 1，x 1，x 2，S 2，y 1，y 2，S 3，z 1，z 2}
这样，简化几何特征410则被数字化了，以便后续进行模型分析等待用。
本发明第二方面提供了模型简化特征的识别设备，包括：处理器；以及与所述处理器耦合的存储器，所述存储器具有存储于其中的指令，所述指令在被处理器执行时使所述电子设备执行动作，所述动作包括：S1，将三维几何模型及其简化后的简化模型分别转化为点云模型；S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集 合来表述所述简化几何特征。
进一步地，所述动作S2还包括：比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
进一步地，所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，其中，所述动作S3还包括：将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述几何结构特征包括形心、面积。
本发明第三方面提供了模型简化特征的识别装置，包括：转化装置，其将三维几何模型及其简化后的简化模型分别转化为点云模型；获取装置，其用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；识别装置，其识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述获取装置还用于：比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
进一步地，所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，其中，所述识别装置还用于：将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
进一步地，所述几何结构特征包括形心、面积。
本发明第四方面提供了计算机程序产品，所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令，所述计算机 可执行指令在被执行时使至少一个处理器执行本发明第一方面所述的方法。
本发明第五方面提供了计算机可读介质，其上存储有计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据本发明第一方面所述的方法。
本发明提供的模型简化特征的识别机制适用于任何结构的原始模型，因此具有非常宽的应用范围，只要模型能够给转化为点云模型就能应用于本发明。其中，大部分几何结构都能输出为点云模型中的中间格式，例如*.stl。
本发明和复杂的三维体积集合或面积计算方法相比，节省了计算工作量，本发明仅仅需要对比点云模型中有限的点。
本发明提供的模型简化特征的识别机制是自动和快速的，并且结果精确度更高，并不依赖于资深工程师的经验。
通过执行本发明识别的模型简化特征能够方便用于进一步的相关分析，例如用于结果分析等。
本发明利用了点云模型，点云模型对建模方法并不敏感。本发明的识别方法很简单，并不需要执行集合或者计算。本发明基于计算机算法，因此能够自动执行，本发明的整个识别过程不需要任何人力的判断，本发明的识别结果精度仅仅由代表整个三维集合模型的点的密度来决定。本发明识别的模型简化特征是数字化的和定量的，而并非是指定的(designation)，因此能够用于进一步的分析。
此外，本发明节省了模型简化特征识别的时间，提高了识别结果的精确度。本发明能够再产品研发阶段适用于进一步的设计过程。
尽管本发明的内容已经通过上述优选实施例作了详细介绍，但应当认识到上述的描述不应被认为是对本发明的限制。在本领域技术人员阅读了上述内容后，对于本发明的多种修改和替代都将是显而易见的。因此，本发明的保护范围应由所附的权利要求来限定。此外，不应将权利要求中的任何附图标记视为限制所涉及的权利要求；“包括”一词不排除其它权利要求或说明书中未列出的装置或步骤；“第一”、“第二”等词语仅用来表示名称，而并不表示任何特定的顺序。

Claims (14)

  1. 模型简化特征的识别方法，其中，包括如下步骤：
    S1，将三维几何模型及其简化后的简化模型分别转化为点云模型；
    S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；
    S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
  2. 根据权利要求1所述的模型简化特征的识别方法，其特征在于，所述步骤S2还包括如下步骤：
    比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
  3. 根据权利要求1所述的模型简化特征的识别方法，其特征在于，
    所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，
    其中，所述步骤S3还包括如下步骤：
    将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
  4. 根据权利要求3所述的模型简化特征的识别方法，其特征在于，所述几何结构特征包括形心、面积。
  5. 模型简化特征的识别设备，包括：
    处理器；以及
    与所述处理器耦合的存储器，所述存储器具有存储于其中的指令，所述指令在被处理器执行时使所述电子设备执行动作，所述动作包括：
    S1，将三维几何模型及其简化后的简化模型分别转化为点云模型；
    S2，用所述三维几何模型的点云模型减去所述简化模型的点云模型， 结果既为简化几何特征的点云模型；
    S3，识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
  6. 根据权利要求5所述的模型简化特征的识别设备，其特征在于，所述动作S2还包括：
    比较所述三维几何模型的点云模型和所述简化模型的点云模型，利用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
  7. 根据权利要求5所述的模型简化特征的识别设备，其特征在于，
    所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，
    其中，所述动作S3还包括：
    将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
  8. 根据权利要求7所述的模型简化特征的识别设备，其特征在于，所述几何结构特征包括形心、面积。
  9. 模型简化特征的识别装置，包括：
    转化装置，其将三维几何模型及其简化后的简化模型分别转化为点云模型；
    获取装置，其用所述三维几何模型的点云模型减去所述简化模型的点云模型，结果既为简化几何特征的点云模型；
    识别装置，其识别简化几何特征的点云模型的复数个特征参数，并将所述复数个特征参数数字化，并用复数个特征参数集合来表述所述简化几何特征。
  10. 根据权利要求1所述的模型简化特征的识别装置，其特征在于，所述获取装置还用于：
    比较所述三维几何模型的点云模型和所述简化模型的点云模型，利 用布尔算法将所述三维几何模型的点云模型和所述简化模型的点云模型中相同的点省略，并将不同的点保留下来作为简化几何特征的点云模型。
  11. 根据权利要求1所述的模型简化特征的识别装置，其特征在于，
    所述特征参数包括所述简化几何特征的点云模型的坐标位置和几何结构特征，
    其中，所述识别装置还用于：
    将所述简化几何特征得点云模型投影在x、y、z三个坐标方向上，并识别所述简化几何特征的点云模型在x、y、z三个坐标方向上的坐标位置和几何结构特征，并将所述坐标位置和几何结构特征打包为所述简化几何特征的点云模型的特征参数集合，并用复数个特征参数集合来表述所述简化几何特征。
  12. 根据权利要求11所述的模型简化特征的识别装置，其特征在于，所述几何结构特征包括形心、面积。
  13. 计算机程序产品，所述计算机程序产品被有形地存储在计算机可读介质上并且包括计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1至4中任一项所述的方法。
  14. 计算机可读介质，其上存储有计算机可执行指令，所述计算机可执行指令在被执行时使至少一个处理器执行根据权利要求1至4中任一项所述的方法。
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