Model cavity detection method and system

Abstract

The invention discloses a model cavity detection method and system. The method comprises the following steps: acquiring target model data to be detected; based on a voxel grid division algorithm, dividing the target model data to obtain a plurality of model parts; based on a seed filling algorithm, determining cavity information corresponding to each model part; and according to the cavity information corresponding to each model part and preset area communication information, identifying isolated cavity data corresponding to the target model data. Therefore, accurate recognition and positioning of the isolated cavity in the complex model can be achieved, the accuracy and efficiency of subsequent geometric repair and structural analysis are improved, printing errors in subsequent 3D printing are reduced, and the printing quality is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a model cavity detection method and system. Background Art

[0002] In the technologies related to 3D modeling and printing, model data usually contains a large number of complex geometric structures, which may contain closed but unconnected cavity areas. If these cavities are not accurately identified during the model processing stage, it is very easy to cause residual uncured resin or cracking or deformation of the model during the subsequent printing process, affecting the integrity of the model structure and printing quality. The existing technology usually relies on global geometric judgment or simple connectivity analysis for cavity detection, which is difficult to adapt to model data with complex structures and dense details. It leads to incomplete identification of isolated cavities, which in turn affects the accuracy of geometric repair and the effectiveness of structural analysis, reducing the stability of the overall modeling and printing process and the quality of the finished product. It can be seen that the existing technology has defects that need to be solved urgently. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a model cavity detection method and system, which can realize the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce the uncured resin residue in subsequent printing and improve the printing quality.

[0004] In order to solve the above technical problems, the first aspect of the present invention discloses a model cavity detection method, which includes: Obtain target model data to be detected; Based on a voxel grid partitioning algorithm, the target model data is divided into multiple model parts; Determining cavity information corresponding to each of the model parts based on a seed filling algorithm; According to the cavity information corresponding to each model part and the preset region connectivity information, the isolated cavity data corresponding to the target model data is identified.

[0005] As an optional embodiment, in the first aspect of the present invention, the target model data is divided into multiple model parts based on the voxel grid partitioning algorithm, including: determining a model usage of the target model data; Determining voxel partitioning parameters according to the model purpose and the correspondence between the preset purpose and the partitioning parameters; Based on a voxel grid partitioning algorithm, regular voxel partitioning is performed on the three-dimensional space where the target model data is located according to the voxel partitioning parameters and the direction parameters to obtain a plurality of voxel space units; Determining the voxel space unit to which each geometric unit belongs according to the spatial coordinates of each geometric unit in the target model data; The geometric units belonging to the same voxel space unit are grouped as one model part to obtain multiple model parts.

[0006] As an optional embodiment, in the first aspect of the present invention, the voxel partitioning parameters include voxel partitioning granularity, partitioning direction and partitioning efficiency.

[0007] As an optional embodiment, in the first aspect of the present invention, the model uses include one or more of product design uses, architectural planning uses, medical diagnosis uses, film and television production uses, game development uses, virtual reality environment modeling uses, urban planning uses, cultural relics protection uses, education and training uses, and engineering analysis uses.

[0008] As an optional embodiment, in the first aspect of the present invention, determining the cavity information corresponding to each of the model parts based on a seed filling algorithm includes: For each of the model parts, identifying a plurality of non-solid regions of the model part; Determining the cavity area and cavity parameters corresponding to each of the non-solid areas based on a seed filling algorithm; All the cavity regions and the corresponding cavity parameters corresponding to the model part are determined as the cavity information corresponding to the model part.

[0009] As an optional embodiment, in the first aspect of the present invention, determining the cavity area and cavity parameters corresponding to each of the non-solid areas based on a seed filling algorithm includes: In each of the non-solid regions, an initial non-solid voxel is selected as a seed voxel based on a preset boundary condition and a cavity connectivity rule; Starting from the seed voxel, a seed filling algorithm is used to recursively traverse all non-solid voxel sets connected to the seed voxel in the voxel space of the non-solid region to form a cavity region corresponding to each non-solid region; Determine cavity parameters corresponding to the cavity area; the cavity parameters include area boundary range, area volume and area center position.

[0010] As an optional embodiment, in the first aspect of the present invention, identifying the isolated cavity data corresponding to the target model data based on the cavity information corresponding to each model part and the preset region connectivity information includes: screening out a plurality of high-risk model parts from all the model parts according to the cavity information; For any two high-risk model parts, determining the connectivity parameters between the two model parts based on the preset model region connectivity data; the connectivity parameters include the region connectivity position and the connectivity cavity diameter; Inputting the cavity information and the connectivity parameters in the two model parts into a preset cavity connectivity judgment model to determine connectivity judgment results corresponding to the cavity areas in the two model parts; Determining the cavity region whose connectivity judgment result is that it is not connected as an isolated cavity region; All the isolated cavity regions and the corresponding cavity parameters are determined as the isolated cavity data corresponding to the target model data.

[0011] As an optional embodiment, in the first aspect of the present invention, screening out a plurality of high-risk model parts from all the model parts based on the cavity information includes: For each of the model parts, inputting each of the cavity regions and the corresponding cavity parameters in the model part into a trained isolation judgment model to obtain the isolation possibility corresponding to each of the cavity regions; Calculate the weighted sum average of the isolation possibilities corresponding to all the cavity areas corresponding to the model part to obtain the isolation risk parameter corresponding to the model part; wherein the calculation weight of the isolation possibility corresponding to each cavity area includes a first weight and a second weight; the first weight and the cavity area The model parts whose isolated risk parameters are greater than the parameter threshold are screened out to obtain multiple high-risk model parts.

[0012] A second aspect of an embodiment of the present invention discloses a model cavity detection system, the system comprising: An acquisition module is used to obtain target model data to be detected; A partitioning module, configured to partition the target model data into a plurality of model parts based on a voxel grid partitioning algorithm; a determination module, configured to determine cavity information corresponding to each of the model parts based on a seed filling algorithm; The identification module is used to identify the isolated cavity data corresponding to the target model data according to the cavity information corresponding to each model part and the preset regional connectivity information.

[0013] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the partitioning module partitions the target model data to obtain multiple model parts based on a voxel grid partitioning algorithm includes: determining a model usage of the target model data; Determining voxel partitioning parameters according to the model purpose and the correspondence between the preset purpose and the partitioning parameters; Based on a voxel grid partitioning algorithm, regular voxel partitioning is performed on the three-dimensional space where the target model data is located according to the voxel partitioning parameters and the direction parameters to obtain a plurality of voxel space units; Determining the voxel space unit to which each geometric unit belongs according to the spatial coordinates of each geometric unit in the target model data; The geometric units belonging to the same voxel space unit are grouped as one model part to obtain multiple model parts.

[0014] As an optional implementation, in the second aspect of the present invention, the voxel partitioning parameters include voxel partitioning granularity, partitioning direction, and partitioning efficiency.

[0015] As an optional embodiment, in the second aspect of the present invention, the model uses include one or more of product design uses, architectural planning uses, medical diagnosis uses, film and television production uses, game development uses, virtual reality environment modeling uses, urban planning uses, cultural relics protection uses, education and training uses, and engineering analysis uses.

[0016] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the cavity information corresponding to each of the model parts based on a seed filling algorithm includes: For each of the model parts, identifying a plurality of non-solid regions of the model part; Determining the cavity area and cavity parameters corresponding to each of the non-solid areas based on a seed filling algorithm; All the cavity regions and the corresponding cavity parameters corresponding to the model part are determined as the cavity information corresponding to the model part.

[0017] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the determination module determines the cavity area and cavity parameters corresponding to each of the non-solid areas based on a seed filling algorithm includes: In each of the non-solid regions, an initial non-solid voxel is selected as a seed voxel based on a preset boundary condition and a cavity connectivity rule; Starting from the seed voxel, a seed filling algorithm is used to recursively traverse all non-solid voxel sets connected to the seed voxel in the voxel space of the non-solid region to form a cavity region corresponding to each non-solid region; Determine cavity parameters corresponding to the cavity area; the cavity parameters include area boundary range, area volume and area center position.

[0018] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the recognition module identifies the isolated cavity data corresponding to the target model data based on the cavity information corresponding to each model part and the preset regional connectivity information includes: screening out a plurality of high-risk model parts from all the model parts according to the cavity information; For any two high-risk model parts, determining the connectivity parameters between the two model parts based on the preset model region connectivity data; the connectivity parameters include the region connectivity position and the connectivity cavity diameter; Inputting the cavity information and the connectivity parameters in the two model parts into a preset cavity connectivity judgment model to determine connectivity judgment results corresponding to the cavity areas in the two model parts; Determining the cavity region whose connectivity judgment result is that it is not connected as an isolated cavity region; All the isolated cavity regions and the corresponding cavity parameters are determined as the isolated cavity data corresponding to the target model data.

[0019] As an optional embodiment, in the second aspect of the present invention, the specific manner in which the identification module selects a plurality of high-risk model parts from all the model parts based on the cavity information includes: For each of the model parts, inputting each of the cavity regions and the corresponding cavity parameters in the model part into a trained isolation judgment model to obtain the isolation possibility corresponding to each of the cavity regions; Calculate the weighted sum average of the isolation possibilities corresponding to all the cavity areas corresponding to the model part to obtain the isolation risk parameter corresponding to the model part; wherein the calculation weight of the isolation possibility corresponding to each cavity area includes a first weight and a second weight; the first weight and the cavity area The model parts whose isolated risk parameters are greater than the parameter threshold are screened out to obtain multiple high-risk model parts.

[0020] A third aspect of the present invention discloses another model cavity detection system, the system comprising: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute part or all of the steps in the model cavity detection method disclosed in the first aspect of the present invention.

[0021] A fourth aspect of the present invention discloses a computer storage medium storing computer instructions. When the computer instructions are called, they are used to execute some or all of the steps in the model cavity detection method disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The present invention divides the model data into multiple model parts through a voxel grid division algorithm, combines the seed filling algorithm to identify the cavity information within each model part, and uses the preset regional connectivity information to identify isolated cavity data, thereby realizing the precise identification and positioning of isolated cavities in complex models, improving the accuracy and efficiency of subsequent geometric repair and structural analysis, reducing the uncured resin residue in subsequent printing and improving the printing quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 It is a flow chart of a model cavity detection method disclosed in an embodiment of the present invention.

[0025] Figure 2 It is a structural schematic diagram of a model cavity detection system disclosed in an embodiment of the present invention.

[0026] Figure 3 It is a structural schematic diagram of another model cavity detection system disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] The terms "first," "second," and so on, in the description and claims of the present invention and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or device.

[0029] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present invention. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute a separate or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] This invention discloses a model cavity detection method and system. This method uses a voxel gridding algorithm to divide model data into multiple model sections, combines it with a seed-filling algorithm to identify cavity information within each model section, and uses preset regional connectivity information to identify isolated cavity data. This method enables precise identification and location of isolated cavities in complex models, improving the accuracy and efficiency of subsequent geometric restoration and structural analysis, reducing uncured resin residue during subsequent printing, and improving print quality. Each of these methods is described in detail below.

[0031] Example 1 See also Figure 1 , Figure 1 This is a flow chart of a model cavity detection method disclosed in an embodiment of the present invention. Figure 1 The described model cavity detection method can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 1 As shown, the model cavity detection method may include the following operations: 101. Obtain target model data to be detected.

[0032] 102. Based on the voxel grid partitioning algorithm, the target model data is divided into multiple model parts. 103. Based on the seed filling algorithm, determine the cavity information corresponding to each model part. 104. Identify isolated cavity data corresponding to the target model data based on the cavity information corresponding to each model part and the preset region connectivity information.

[0033] It can be seen that the above-mentioned embodiment of the invention divides the model data into multiple model parts through the voxel grid division algorithm, combines the seed filling algorithm to identify the cavity information within each model part, and uses the preset regional connectivity information to identify isolated cavity data, thereby realizing the accurate identification and positioning of isolated cavities in complex models, improving the accuracy and efficiency of subsequent geometric repair and structural analysis, reducing the uncured resin residue in subsequent printing and improving the printing quality.

[0034] As an optional embodiment, in the above steps, the target model data is divided into multiple model parts based on a voxel grid division algorithm, including: Determine the model usage of the target model data; Determine voxel partitioning parameters based on the purpose of the model and the correspondence between the preset purpose and the partitioning parameters; Based on the voxel grid partitioning algorithm, the three-dimensional space where the target model data is located is divided into regular voxels according to the voxel partitioning parameters and direction parameters to obtain multiple voxel space units; Determine the voxel space unit to which each geometric unit belongs according to the spatial coordinates of each geometric unit in the target model data; The geometric units belonging to the same voxel space unit are grouped as one model part to obtain multiple model parts.

[0035] It can be seen that through the above optional embodiments, the model purpose of the target model data is first determined, and the voxel division parameters are determined based on the correspondence between the preset purpose and the division parameters, and then the three-dimensional space is regularly voxel-divided according to the voxel division parameters based on the voxel grid division algorithm, and then the geometric units are classified into corresponding voxel space units according to their spatial coordinates. Finally, the geometric units in each voxel space unit are classified as the same model part, which can perform adaptive space division according to the model application requirements, assist in realizing the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce the uncured resin residue in subsequent printing and improve the printing quality.

[0036] As an optional embodiment, in the above steps, the voxel partitioning parameters include voxel partitioning granularity, partitioning direction and partitioning efficiency.

[0037] It can be seen that through the above optional embodiments, the content of the voxel partitioning parameters is limited to comprehensively characterize the associated features of the voxel partitioning operation, so as to facilitate subsequent accurate cavity detection, assist in the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce the uncured resin residue in subsequent printing and improve the printing quality.

[0038] As an optional embodiment, in the above steps, the model use includes one or more of product design use, architectural planning use, medical diagnosis use, film and television production use, game development use, virtual reality environment modeling use, urban planning use, cultural relics protection use, education and training use, and engineering analysis use.

[0039] It can be seen that through the above optional embodiments, the content of the model's use is limited to comprehensively characterize the model's use characteristics, so as to facilitate subsequent accurate voxel division and cavity detection, assist in the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce uncured resin residues in subsequent printing and improve printing quality.

[0040] As an optional embodiment, in the above step, determining the cavity information corresponding to each model part based on the seed filling algorithm includes: For each model portion, identifying a plurality of non-solid regions of the model portion; Based on the seed filling algorithm, the cavity area and cavity parameters corresponding to each non-solid area are determined; All cavity regions and corresponding cavity parameters corresponding to the model part are determined as cavity information corresponding to the model part.

[0041] It can be seen that through the above optional embodiments, for each model part, the multiple non-solid areas it contains are first identified, and then the cavity areas and cavity parameters corresponding to the non-solid areas are determined one by one based on the seed filling algorithm, and finally all the cavity areas and cavity parameters are used as the cavity information of the model part. This can achieve accurate identification and parameter extraction of potential cavity features in complex structures, assist in the precise identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce uncured resin residues in subsequent printing and improve printing quality.

[0042] As an optional embodiment, in the above step, determining the cavity area and cavity parameters corresponding to each non-solid area based on a seed filling algorithm includes: In each non-solid region, an initial non-solid voxel is selected as a seed voxel based on the preset boundary conditions and cavity connectivity rules; Starting from the seed voxel, a seed filling algorithm is used to recursively traverse all non-solid voxel sets connected to the seed voxel in the voxel space of the non-solid area to form the cavity area corresponding to each non-solid area; Determine the cavity parameters corresponding to the cavity area; the cavity parameters include the area boundary range, area volume and area center position.

[0043] It can be seen that through the above optional embodiments, in each non-solid area, based on the preset boundary conditions and cavity connectivity rules, an initial non-solid voxel is preferably selected as the seed voxel, and with the seed voxel as the starting point, the seed filling algorithm is used to recursively traverse all non-solid voxels connected to it to construct a complete cavity area, and then the cavity parameters such as the boundary range, volume and center position corresponding to each cavity area are extracted. High-precision cavity extraction and parametric expression can be achieved in complex model structures, providing basic support for isolated cavity identification and geometric analysis, and significantly enhancing the accuracy and automation level of subsequent processing.

[0044] As an optional embodiment, in the above step, identifying isolated cavity data corresponding to the target model data according to the cavity information corresponding to each model part and the preset region connectivity information includes: According to the cavity information, multiple high-risk model parts are screened out from all model parts; For any two high-risk model parts, based on the preset model region connectivity data, the connectivity parameters between the two model parts are determined; optionally, the connectivity parameters include the regional connectivity position and the connectivity cavity diameter; Inputting the cavity information and connectivity parameters of the two model parts into a preset cavity connectivity judgment model to determine connectivity judgment results corresponding to the cavity areas in the two model parts; The cavity region whose connectivity judgment result is that it is not connected is determined as an isolated cavity region; All isolated cavity regions and corresponding cavity parameters are determined as isolated cavity data corresponding to the target model data.

[0045] It can be seen that through the above optional embodiments, based on the extracted cavity information, the high-risk model parts with potential isolated structural risks are first screened out from all model parts, and then combined with the preset model area connectivity data, the connectivity parameters such as the regional connectivity position and the connected cavity diameter between any two high-risk model parts are obtained, and the corresponding cavity information and connectivity parameters are input into the cavity connectivity judgment model to accurately judge whether there is an effective connectivity relationship between the cavity areas, and then the cavity areas with the judgment result of being disconnected are identified as isolated cavity areas, and their parameter data are output. This can achieve efficient and accurate isolated cavity identification when the model structure is complex and there are intersecting cavity paths, provide data support for subsequent repair, support design and resin residue control, and improve printing quality and structural reliability.

[0046] As an optional embodiment, in the above step, multiple high-risk model parts are screened out from all model parts based on the cavity information, including: For each model part, each cavity region and corresponding cavity parameters in the model part are input into the trained isolation judgment model to obtain the isolation probability corresponding to each cavity region; Calculate the weighted sum average of the isolation possibilities corresponding to all cavity areas corresponding to the model part to obtain the isolation risk parameter corresponding to the model part; optionally, the calculation weight of the isolation possibility corresponding to each cavity area includes a first weight and a second weight; the first weight and the cavity area The model parts with isolated risk parameters greater than the parameter threshold are screened out to obtain multiple high-risk model parts.

[0047] It can be seen that through the above optional embodiments, by inputting the cavity area and cavity parameters in each model part into the trained isolation judgment model, the isolation possibility corresponding to each cavity area is calculated, and the isolation possibility is weighted and averaged by combining the first weight and the second weight to obtain the isolation risk parameter that reflects the cavity isolation risk of the model part as a whole, and then the high-risk model part whose isolation risk parameter exceeds the set threshold is screened out, which can realize quantitative evaluation and efficient screening of cavity isolation risk under complex structures, provide a basis for subsequent connectivity analysis and cavity processing optimization, and effectively improve the three-dimensional model quality and printing reliability.

[0048] Example 2 See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a model cavity detection system disclosed in an embodiment of the present invention. Figure 2 The model cavity detection system described can be applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 2 As shown, the model cavity detection system may include: The acquisition module 201 is used to acquire target model data to be detected.

[0049] The partitioning module 202 is configured to partition the target model data into a plurality of model parts based on a voxel grid partitioning algorithm. The determination module 203 is configured to determine the cavity information corresponding to each model part based on a seed filling algorithm. The identification module 204 is configured to identify isolated cavity data corresponding to the target model data based on the cavity information corresponding to each model part and the preset region connectivity information.

[0050] It can be seen that the above-mentioned embodiment of the invention divides the model data into multiple model parts through the voxel grid division algorithm, combines the seed filling algorithm to identify the cavity information within each model part, and uses the preset regional connectivity information to identify isolated cavity data, thereby realizing the accurate identification and positioning of isolated cavities in complex models, improving the accuracy and efficiency of subsequent geometric repair and structural analysis, reducing the uncured resin residue in subsequent printing and improving the printing quality.

[0051] As an optional embodiment, the specific manner in which the partitioning module partitions the target model data to obtain multiple model parts based on a voxel grid partitioning algorithm includes: Determine the model usage of the target model data; Determine voxel partitioning parameters based on the purpose of the model and the correspondence between the preset purpose and the partitioning parameters; Based on the voxel grid partitioning algorithm, the three-dimensional space where the target model data is located is divided into regular voxels according to the voxel partitioning parameters and direction parameters to obtain multiple voxel space units; Determine the voxel space unit to which each geometric unit belongs according to the spatial coordinates of each geometric unit in the target model data; The geometric units belonging to the same voxel space unit are grouped as one model part to obtain multiple model parts.

[0052] It can be seen that through the above optional embodiments, the model purpose of the target model data is first determined, and the voxel division parameters are determined based on the correspondence between the preset purpose and the division parameters, and then the three-dimensional space is regularly voxel-divided according to the voxel division parameters based on the voxel grid division algorithm, and then the geometric units are classified into corresponding voxel space units according to their spatial coordinates. Finally, the geometric units in each voxel space unit are classified as the same model part, which can perform adaptive space division according to the model application requirements, assist in realizing the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce the uncured resin residue in subsequent printing and improve the printing quality.

[0053] As an optional embodiment, the voxel partitioning parameters include voxel partitioning granularity, partitioning direction, and partitioning efficiency.

[0054] It can be seen that through the above optional embodiments, the content of the voxel partitioning parameters is limited to comprehensively characterize the associated features of the voxel partitioning operation, so as to facilitate subsequent accurate cavity detection, assist in the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce the uncured resin residue in subsequent printing and improve the printing quality.

[0055] As an optional embodiment, the model uses include one or more of product design uses, architectural planning uses, medical diagnosis uses, film and television production uses, game development uses, virtual reality environment modeling uses, urban planning uses, cultural relics protection uses, education and training uses, and engineering analysis uses.

[0056] It can be seen that through the above optional embodiments, the content of the model's use is limited to comprehensively characterize the model's use characteristics, so as to facilitate subsequent accurate voxel division and cavity detection, assist in the accurate identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce uncured resin residues in subsequent printing and improve printing quality.

[0057] As an optional embodiment, the specific manner in which the determination module determines the cavity information corresponding to each model part based on the seed filling algorithm includes: For each model portion, identifying a plurality of non-solid regions of the model portion; Based on the seed filling algorithm, the cavity area and cavity parameters corresponding to each non-solid area are determined; All cavity regions and corresponding cavity parameters corresponding to the model part are determined as cavity information corresponding to the model part.

[0058] It can be seen that through the above optional embodiments, for each model part, the multiple non-solid areas it contains are first identified, and then the cavity areas and cavity parameters corresponding to the non-solid areas are determined one by one based on the seed filling algorithm, and finally all the cavity areas and cavity parameters are used as the cavity information of the model part. This can achieve accurate identification and parameter extraction of potential cavity features in complex structures, assist in the precise identification and positioning of isolated cavities in complex models, improve the accuracy and efficiency of subsequent geometric repair and structural analysis, reduce uncured resin residues in subsequent printing and improve printing quality.

[0059] As an optional embodiment, the specific manner in which the determination module determines the cavity area and cavity parameters corresponding to each non-solid area based on a seed filling algorithm includes: In each non-solid region, an initial non-solid voxel is selected as a seed voxel based on the preset boundary conditions and cavity connectivity rules; Starting from the seed voxel, a seed filling algorithm is used to recursively traverse all non-solid voxel sets connected to the seed voxel in the voxel space of the non-solid area to form the cavity area corresponding to each non-solid area; Determine the cavity parameters corresponding to the cavity area; the cavity parameters include the area boundary range, area volume and area center position.

[0060] It can be seen that through the above optional embodiments, in each non-solid area, based on the preset boundary conditions and cavity connectivity rules, an initial non-solid voxel is preferably selected as the seed voxel, and with the seed voxel as the starting point, the seed filling algorithm is used to recursively traverse all non-solid voxels connected to it to construct a complete cavity area, and then the cavity parameters such as the boundary range, volume and center position corresponding to each cavity area are extracted. High-precision cavity extraction and parametric expression can be achieved in complex model structures, providing basic support for isolated cavity identification and geometric analysis, and significantly enhancing the accuracy and automation level of subsequent processing.

[0061] As an optional embodiment, the specific manner in which the recognition module identifies isolated cavity data corresponding to the target model data based on the cavity information corresponding to each model part and the preset regional connectivity information includes: According to the cavity information, multiple high-risk model parts are screened out from all model parts; For any two high-risk model parts, based on the preset model region connectivity data, the connectivity parameters between the two model parts are determined; optionally, the connectivity parameters include the regional connectivity position and the connectivity cavity diameter; Inputting the cavity information and connectivity parameters of the two model parts into a preset cavity connectivity judgment model to determine connectivity judgment results corresponding to the cavity areas in the two model parts; The cavity region whose connectivity judgment result is that it is not connected is determined as an isolated cavity region; All isolated cavity regions and corresponding cavity parameters are determined as isolated cavity data corresponding to the target model data.

[0062] It can be seen that through the above optional embodiments, based on the extracted cavity information, the high-risk model parts with potential isolated structural risks are first screened out from all model parts, and then combined with the preset model area connectivity data, the connectivity parameters such as the regional connectivity position and the connected cavity diameter between any two high-risk model parts are obtained, and the corresponding cavity information and connectivity parameters are input into the cavity connectivity judgment model to accurately judge whether there is an effective connectivity relationship between the cavity areas, and then the cavity areas with the judgment result of being disconnected are identified as isolated cavity areas, and their parameter data are output. This can achieve efficient and accurate isolated cavity identification when the model structure is complex and there are intersecting cavity paths, provide data support for subsequent repair, support design and resin residue control, and improve printing quality and structural reliability.

[0063] As an optional embodiment, the specific manner in which the identification module selects multiple high-risk model parts from all model parts based on the cavity information includes: For each model part, each cavity region and corresponding cavity parameters in the model part are input into the trained isolation judgment model to obtain the isolation probability corresponding to each cavity region; Calculate the weighted sum average of the isolation possibilities corresponding to all cavity areas corresponding to the model part to obtain the isolation risk parameter corresponding to the model part; optionally, the calculation weight of the isolation possibility corresponding to each cavity area includes a first weight and a second weight; the first weight and the cavity area The model parts with isolated risk parameters greater than the parameter threshold are screened out to obtain multiple high-risk model parts.

[0064] It can be seen that through the above optional embodiments, by inputting the cavity area and cavity parameters in each model part into the trained isolation judgment model, the isolation possibility corresponding to each cavity area is calculated, and the isolation possibility is weighted and averaged by combining the first weight and the second weight to obtain the isolation risk parameter that reflects the cavity isolation risk of the model part as a whole, and then the high-risk model part whose isolation risk parameter exceeds the set threshold is screened out, which can realize quantitative evaluation and efficient screening of cavity isolation risk under complex structures, provide a basis for subsequent connectivity analysis and cavity processing optimization, and effectively improve the three-dimensional model quality and printing reliability.

[0065] Example 3 See also Figure 3 , Figure 3 This is another model cavity detection system disclosed in an embodiment of the present invention. Figure 3 The model cavity detection system described is applied to a data processing system / data processing device / data processing server (wherein the server includes a local processing server or a cloud processing server). Figure 3 As shown, the model cavity detection system may include: A memory 301 storing executable program code; a processor 302 coupled to the memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the model cavity detection method described in the first embodiment.

[0066] Example 4 An embodiment of the present invention discloses a computer-readable storage medium storing a computer program for electronic data exchange, wherein the computer program enables a computer to execute the steps of the model cavity detection method described in the first embodiment.

[0067] Example 5 An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps of the model cavity detection method described in the first embodiment.

[0068] The foregoing description of specific embodiments of the present disclosure is intended to illustrate a method for performing a multi-tasking process. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or the sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0070] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0071] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0076] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0077] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0078] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0079] This specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0080] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0081] Finally, it should be noted that the model cavity detection method and system disclosed in the embodiments of the present invention are only preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A model cavity detection method, characterized in that: The method comprises: Obtain target model data to be detected; Based on a voxel grid partitioning algorithm, the target model data is divided into multiple model parts; Determining cavity information corresponding to each of the model parts based on a seed filling algorithm; According to the cavity information corresponding to each model part and the preset region connectivity information, the isolated cavity data corresponding to the target model data is identified.

2. The model cavity detection method according to claim 1, characterized in that: The voxel-based grid partitioning algorithm is used to partition the target model data to obtain multiple model parts, including: determining a model usage of the target model data; Determining voxel partitioning parameters according to the model purpose and the correspondence between the preset purpose and the partitioning parameters; Based on a voxel grid partitioning algorithm, regular voxel partitioning is performed on the three-dimensional space where the target model data is located according to the voxel partitioning parameters and the direction parameters to obtain a plurality of voxel space units; Determining the voxel space unit to which each geometric unit belongs according to the spatial coordinates of each geometric unit in the target model data; The geometric units belonging to the same voxel space unit are grouped as one model part to obtain multiple model parts.

3. The model cavity detection method according to claim 2, characterized in that: The voxel partitioning parameters include voxel partitioning granularity, partitioning direction and partitioning efficiency.

4. The model cavity detection method according to claim 2, characterized in that: The model uses include one or more of product design, architectural planning, medical diagnosis, film and television production, game development, virtual reality environment modeling, urban planning, cultural relics protection, education and training, and engineering analysis.

5. The model cavity detection method according to claim 1, characterized in that: The step of determining the cavity information corresponding to each of the model parts based on a seed filling algorithm includes: For each of the model parts, identifying a plurality of non-solid regions of the model part; Determining the cavity area and cavity parameters corresponding to each of the non-solid areas based on a seed filling algorithm; All the cavity regions and the corresponding cavity parameters corresponding to the model part are determined as the cavity information corresponding to the model part.

6. The model cavity detection method according to claim 5, characterized in that: The step of determining the cavity area and cavity parameters corresponding to each of the non-solid areas based on a seed filling algorithm includes: In each of the non-solid regions, based on preset boundary conditions and cavity connectivity rules, an initial non-solid voxel is selected as a seed voxel; Starting from the seed voxel, a seed filling algorithm is used to recursively traverse all non-solid voxel sets connected to the seed voxel in the voxel space of the non-solid region to form a cavity region corresponding to each non-solid region; Determine cavity parameters corresponding to the cavity area; the cavity parameters include area boundary range, area volume and area center position.

7. The model cavity detection method according to claim 5, characterized in that: The identifying of isolated cavity data corresponding to the target model data according to the cavity information corresponding to each model part and the preset regional connectivity information includes: screening out a plurality of high-risk model parts from all the model parts according to the cavity information; For any two high-risk model parts, determining the connectivity parameters between the two model parts based on the preset model region connectivity data; the connectivity parameters include the region connectivity position and the connectivity cavity diameter; Inputting the cavity information and the connectivity parameters in the two model parts into a preset cavity connectivity judgment model to determine connectivity judgment results corresponding to the cavity areas in the two model parts; Determining the cavity region whose connectivity judgment result is that it is not connected as an isolated cavity region; All the isolated cavity regions and the corresponding cavity parameters are determined as the isolated cavity data corresponding to the target model data.

8. The model cavity detection method according to claim 7, characterized in that: The step of screening out a plurality of high-risk model parts from all the model parts according to the cavity information includes: For each of the model parts, inputting each of the cavity regions and the corresponding cavity parameters in the model part into a trained isolation judgment model to obtain the isolation possibility corresponding to each of the cavity regions; Calculate the weighted sum average of the isolation possibilities corresponding to all the cavity areas corresponding to the model part to obtain the isolation risk parameter corresponding to the model part; wherein the calculation weight of the isolation possibility corresponding to each cavity area includes a first weight and a second weight; the first weight and the cavity area The model parts whose isolated risk parameters are greater than the parameter threshold are screened out to obtain multiple high-risk model parts.

9. A model cavity detection system, characterized in that: The system comprises: An acquisition module is used to obtain target model data to be detected; A partitioning module, configured to partition the target model data into a plurality of model parts based on a voxel grid partitioning algorithm; a determination module, configured to determine cavity information corresponding to each of the model parts based on a seed filling algorithm; The identification module is used to identify the isolated cavity data corresponding to the target model data according to the cavity information corresponding to each model part and the preset regional connectivity information.

10. A model cavity detection system, characterized in that: The system comprises: a memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the model cavity detection method according to any one of claims 1 to 8.

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