Three-dimensional digital-analog file generation method and device, equipment, medium and program product

Abstract

The embodiment of the invention provides a three-dimensional digital-analog file generation method and device, equipment, a medium and a program product. The method comprises the steps of determining a target three-dimensional digital-analog file based on at least one of the following modes: text description information, a first three-dimensional digital-analog file and an edited three-dimensional CAD modeling language data sequence; the text description information is description information of the target three-dimensional digital-analog file or modification parameter description information of the first three-dimensional digital-analog file; the edited three-dimensional CAD modeling language data sequence is obtained by combining editing parameters with a three-dimensional CAD modeling language data sequence generated by a CAD sequence modeling language generation model which is finely adjusted in advance. According to the embodiment of the invention, the target digital-analog file can be automatically generated, the target three-dimensional digital-analog file can be more quickly and accurately determined, the quality and efficiency of generating the three-dimensional digital-analog file are improved, meanwhile, the labor cost can be reduced, and the dependence on computer aided design software is reduced.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence technology, and in particular to a method, device, equipment, medium and program product for generating a three-dimensional digital model file. Background Art

[0002] Computer Aided Design (CAD) is a basic technology for industrial design. In the CAD field, even with computer-aided design software, industrial design still requires a lot of manual labor from skilled designers, resulting in high labor costs, low design efficiency, and poor quality of the generated three-dimensional digital model files. Summary of the invention

[0003] The present application provides a method, device, equipment, medium and program product for generating a three-dimensional digital model file, which can improve the quality and efficiency of generating a three-dimensional digital model file and reduce labor costs.

[0004] In a first aspect, an embodiment of the present application provides a method for generating a three-dimensional digital model file, comprising: determining a target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, combined with editing parameters.

[0005] In the second aspect, an embodiment of the present application also provides a device for generating a three-dimensional digital model file, including: a target three-dimensional digital model file determination module, used to determine the target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, combined with editing parameters.

[0006] In a third aspect, an embodiment of the present application further provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating a three-dimensional digital model file as described in the embodiment of the present application.

[0007] In a fourth aspect, an embodiment of the present application further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the method for generating a three-dimensional digital model file as described in the embodiment of the present application.

[0008] In a fifth aspect, an embodiment of the present application further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for generating a three-dimensional digital model file as described in the embodiment of the present application.

[0009] The technical solution of the embodiment of the present application determines the target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, obtained in combination with editing parameters. The disclosed embodiment determines the target three-dimensional digital model file by at least one of the text description information, the first three-dimensional digital model file, and the edited three-dimensional CAD modeling language data sequence, so that the target digital model file can be automatically generated, and the target three-dimensional digital model file can be determined faster and more accurately, thereby improving the quality and efficiency of the generation of the three-dimensional digital model file, while reducing labor costs and reducing dependence on computer-aided design software. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0011] Figure 1 A schematic diagram of a flow chart of a method for generating a three-dimensional digital model file provided in an embodiment of the present application;

[0012] Figure 2 A schematic diagram of the display effect of a first three-dimensional digital model file provided in an embodiment of the present invention;

[0013] Figure 3 A schematic diagram of the display effect of a target three-dimensional digital model file provided by an embodiment of the present invention;

[0014] Figure 4 is a schematic diagram of the display effect of a second three-dimensional digital model file provided by an embodiment of the present invention;

[0015] Figure 5A schematic diagram of the effect of a 3D CAD modeling language data sequence provided by an embodiment of the present invention;

[0016] Figure 6 A schematic diagram of the structure of a device for generating a three-dimensional digital model file provided in an embodiment of the present application;

[0017] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0019] It should be understood that the various steps recorded in the method implementation of the present disclosure can be performed in different orders and / or in parallel. In addition, the method implementation may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect. The term "including" and its variations used herein are open inclusions, that is, "including but not limited to". It should be noted that the concepts of "first", "second", etc. mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units. It should be noted that the modifications of "one" and "multiple" mentioned in the present disclosure are illustrative and not restrictive. Those skilled in the art should understand that unless otherwise clearly stated in the context, it should be understood as "one or more". It is understandable that the data involved in the present technical solution (including but not limited to the data itself, the acquisition or use of the data) shall comply with the requirements of the relevant laws, regulations and relevant provisions.

[0020] The technical solution provided in this embodiment can generate three-dimensional digital model files in the field of three-dimensional digital modeling in the industry.

[0021] Figure 1 The present invention provides a flow chart of a method for generating a three-dimensional digital model file. The method can be executed by a device for generating a three-dimensional digital model file, which can be implemented in the form of software and / or hardware, and optionally, by an electronic device, which can be a mobile terminal, a PC, or a server. Figure 1 As shown, the method includes:

[0022] S110, determining a target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence.

[0023] The text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file.

[0024] In this embodiment, there is no restriction on the description information of the target three-dimensional digital model file. In different scenarios, the content included in the text description information may be different. Exemplarily, if the text description information is used alone (not combined with the first three-dimensional digital model file) and is not used as the input of the three-dimensional CAD large model intelligent body, the text description information is the detailed description information of the target three-dimensional digital model file, and the text description information may include parameter information (such as thickness, length, etc.) of the target three-dimensional digital model file, meta information (such as the name of the target three-dimensional digital model file, its use in industry, etc.) and prompt words (including format regulations and regulations related to the target three-dimensional digital model file). If the text description information is used in combination with the first three-dimensional digital model file, the text description information is the modified parameter description information of the first three-dimensional digital model file, such as "change the length 5 to 10".

[0025] It should be noted that if used as input to the 3D CAD large model agent in the subsequent embodiments, the text description information can be a simple description information of the target 3D digital model file (including key content and requirements), that is, the content of the simple description information is relatively less than the content of the detailed description information, and the 3D CAD large model agent can autonomously understand and improve the user's intention through the planning module based on the text description information. Exemplarily, in this scenario, the text description information can be "help me generate a specific component" or "help me modify the following digital model file" or "help me generate a 3D CAD modeling language data sequence", etc.

[0026] The edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, combined with editing parameters.

[0027] The 3D CAD modeling language data sequence can be understood as a series of commands and parameters used to describe the process of building a 3D digital model file, which can be understood and executed by computer-aided design software. In this embodiment, the 3D CAD modeling language data sequence or the edited 3D CAD modeling language data sequence can be directly converted into a corresponding 3D digital model file using computer-aided design software.

[0028] In this embodiment, the editing parameters are not limited, and may be edited length, edited thickness parameters, etc. The editing parameters may be directly applied to the 3D CAD modeling language data sequence, that is, the editing parameters may be understood as the edited parameters in the 3D CAD modeling language data sequence.

[0029] The target three-dimensional digital model file can be understood as a three-dimensional digital model file that meets the user's business needs or is finally obtained. The first three-dimensional digital model file can be understood as an existing, arbitrary, three-dimensional digital model file that needs to be modified or updated.

[0030] In this embodiment, the target three-dimensional digital model file can be determined according to the text description information. For example, any large language model can be directly used to output the target three-dimensional digital model file corresponding to the text description information based on the text description information.

[0031] In this embodiment, the target three-dimensional digital model file can be determined according to the edited three-dimensional CAD modeling language data sequence, that is, the edited three-dimensional CAD modeling language data sequence can be directly converted into the target three-dimensional digital model file using computer-aided design software.

[0032] In this embodiment, the target three-dimensional digital model file can be determined according to the text description information and the first three-dimensional digital model file. For example, any large language model can be directly used to output a new first three-dimensional digital model file as the target three-dimensional digital model file based on the text description information and the first three-dimensional digital model file. The new first three-dimensional digital model file can be understood as a three-dimensional digital model file modified or updated according to the text description information.

[0033] In this embodiment, the target three-dimensional digital model file can be determined according to the first three-dimensional digital model file and the edited three-dimensional CAD modeling language data sequence. For example, any large language model can be used to generate a corresponding three-dimensional CAD modeling language data sequence based on the first three-dimensional digital model file, and then the corresponding three-dimensional CAD modeling language data sequence can be edited in combination with the editing parameters to obtain the edited three-dimensional CAD modeling language data sequence, and then the edited three-dimensional CAD modeling language data sequence can be directly converted into a new first three-dimensional digital model file using computer-aided design software as the target three-dimensional digital model file.

[0034] In this embodiment, the target three-dimensional digital model file can be determined according to the text description information, the first three-dimensional digital model file and the edited three-dimensional CAD modeling language data sequence. For example, any large language model can be used to output the second three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file. Among them, the second three-dimensional digital model file can be a digital model file obtained according to the text description information, or a new first three-dimensional digital model file obtained according to the text description information and the first three-dimensional digital model file. Then, any large language model is used to generate a corresponding three-dimensional CAD modeling language data sequence based on the second three-dimensional digital model file, and then the corresponding three-dimensional CAD modeling language data sequence is edited in combination with the editing parameters to obtain the edited three-dimensional CAD modeling language data sequence, and then the edited three-dimensional CAD modeling language data sequence is directly converted into a new second three-dimensional digital model file using computer-aided design software as the target three-dimensional digital model file.

[0035] The technical solution of the embodiment of the present application determines the target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, obtained in combination with editing parameters. The disclosed embodiment determines the target three-dimensional digital model file by at least one of the text description information, the first three-dimensional digital model file, and the edited three-dimensional CAD modeling language data sequence, so that the target digital model file can be automatically generated, and the target three-dimensional digital model file can be determined faster and more accurately, thereby improving the quality and efficiency of the generation of the three-dimensional digital model file, while reducing labor costs and reducing dependence on computer-aided design software.

[0036] Optionally, determining a target three-dimensional digital model file based on text description information and / or a first three-dimensional digital model file includes: digitally encoding the text description information to obtain a text description code; digitally encoding the first three-dimensional digital model file to obtain a first digital model code; inputting the text description code and / or the first digital model code into a pre-fine-tuned first large language model, and outputting a second digital model code; decoding the second digital model code to obtain the target three-dimensional digital model file.

[0037] In this embodiment, there is no limitation on the numerical encoding of the text description information. For example, the text description information can be numerically encoded by the following encoding modules: Bit-Pair encoding module, WordPiece encoding module, Unigram LM encoding module, SentencePiece encoding module. The Bit-Pair encoding module can be a Byte Pair Encoding (BPE) module, which is an algorithm for text processing.

[0038] Exemplary, the method of numerically encoding text description information is: using a tokenizer to break the text description information into discrete sub-words (tokens), and then looking up the corresponding serial numbers from a pre-set vocabulary based on the sub-words, and converting a whole paragraph of text description information into serial numbers. For the numerical expression converted from the above text description information, use an embedding network (Embedding) to project it into a text description code. The specific process is as follows:

[0039]

[0040] in, , They represent the corresponding sequence number and word segmenter in the vocabulary respectively. and They are the subwords in the vocabulary and the numerical text expressions. Represents text description information.

[0041]

[0042] Among them, Embedding means embedding network. Indicates the text description encoding.

[0043] In this embodiment, there is no restriction on the method of digitally encoding the first three-dimensional digital model file. For example, the first three-dimensional digital model file can be decomposed into scattered faces and edges, and the faces and edges in the first three-dimensional digital model file are encoded by a variational autoencoder and a residual vector quantization network to obtain a first digital model code.

[0044] For example, Figure 2 The first three-dimensional digital model file is a schematic diagram of the display effect provided by the embodiment of the present invention. The first three-dimensional digital model file takes a rounded L-shaped bracket with a single-sided hole as an example. Figure 3 A schematic diagram of the display effect of a target three-dimensional digital model file provided in an embodiment of the present invention. Figure 3 is Figure 2 On the basis of the above, a schematic diagram showing the length and height of the un-dug surface is added.

[0045] Optionally, the first three-dimensional digital model file is digitally encoded to obtain a first digital model code, including: using a first variational autoencoder to extract multiple surface features and multiple surface position features of the first three-dimensional digital model file; using a second variational autoencoder to extract multiple edge features and multiple edge position features of the first three-dimensional digital model file; fusing the multiple surface features, the multiple surface position features, the multiple edge features and the multiple edge position features to obtain fused features; encoding the fused features through a residual vector quantization network to obtain a first digital model code.

[0046] The first variational autoencoder may be a variational autoencoder of a surface. The second variational autoencoder may be a variational autoencoder of an edge. The position feature of a surface may be the center point of the corresponding surface. The position feature of an edge may be the center point of the corresponding edge.

[0047] In this embodiment, a first variational autoencoder is used to extract multiple surface features and multiple surface position features of the first three-dimensional digital model file; a second variational autoencoder is used to extract multiple edge features and multiple edge position features of the first three-dimensional digital model file; the multiple surface features, the multiple surface position features, the multiple edge features and the multiple edge position features are fused (i.e., combined) to obtain fused features; in order to obtain a more efficient coding expression, the fused features are quantized and encoded through a residual vector quantization network to obtain a first digital model code.

[0048] Exemplarily, the formula of the first digital-analog encoding may be:

[0049]

[0050] in, represents the first digital-analog encoding, Indicates the first to last face feature code or edge feature code or face position feature code or edge position feature code, such as represents the first face feature encoding after the residual vector quantization network, Represents the position feature encoding of the first face after passing through the residual vector quantization network. Represents the encoding of the fused features, i.e., the first digital-analog encoding. Represents fusion features. N represents the total number of face feature codes, edge feature codes, face position feature codes, or edge position feature codes.

[0051] In this embodiment, the text description code and / or the first digital model code are input into the first large language model that has been fine-tuned in advance. After the second digital model code is output, that is, all face feature codes, all edge feature codes, all face position feature codes, and all edge position feature codes are output, the residual vector quantization network can be used in sequence to decode all face feature codes, all edge feature codes, all face position feature codes, and all edge position feature codes to obtain all face features, all edge features, all face position features, and all edge position features, and then the decoder corresponding to the first variational self-encoder is used to decode all face features and all face position features, and the decoder corresponding to the second variational self-encoder is used to decode all edge features and all edge position features, and then all faces and all edges are spliced ​​according to the positions of all faces and all edges, so that the target three-dimensional digital model file represented by the boundary can be obtained. Among them, the boundary representation can be understood as the representation of faces and edges. The target three-dimensional digital model file represented by the boundary has no precision loss and can be directly used in industrial production and manufacturing.

[0052] In this embodiment, by digitally encoding the text description information and the first three-dimensional digital model file, determining the second digital model code based on the text description code and the first digital model code, and decoding the second digital model code, the generation of the target three-dimensional digital model file can be controlled in a finer granularity, thereby improving the efficiency and quality of the generation of the target three-dimensional digital model file.

[0053] Optionally, the text description code and the first digital-to-analog code are input into a pre-fine-tuned first large language model to output a second digital-to-analog code, including: the first large language model, based on the text description code, uses an autoregressive method to start from a starting identifier, and gradually infers the next face feature code or the next edge feature code or the next face position feature code or the next edge position feature code until an end identifier is output and the output stops.

[0054] In this embodiment, the first large language model, when reasoning based on the text description code, adopts an autoregressive method to reason: starting from the starting mark, the next face feature code or the next edge feature code or the next face position feature code or the next edge position feature code corresponding to the target three-dimensional digital model file is gradually reasoned until the end mark is output, and the output is stopped, so that the inferred second digital model code can be obtained. That is, each reasoning only outputs one face feature code or one edge feature code or one face position feature code or one edge position feature code (starting reasoning from the first face feature code or the first edge feature code or the first face position feature code or the first edge position feature code until the last face feature code or the last edge feature code or the last face position feature code or the last edge position feature code is inferred), but each reasoning will consider all the output content. For example, the first face feature code is output for the first time, and when it is output for the second time, the first face position feature code will be inferred based on the first face feature code. When it is output for the third time, the first edge feature code will be inferred based on the first face feature code and the first face position feature code, and so on, until the last face feature code or the last edge feature code or the last face position feature code or the last edge position feature code is inferred. The end mark is the generated end mark output by the first language model, indicating that all faces, edges, edge positions, and face position reasoning are complete. The start mark can be understood as the content in the text description information, which is used to remind the first language model that it can start reasoning.

[0055] In this embodiment, an autoregressive method is used to infer each face feature code, each edge feature code, each face position feature code, and each edge position feature code corresponding to the target three-dimensional digital model file, which can ensure the consistency and logic of the generation process, so that the final output target three-dimensional digital model file can accurately reflect the input text description information.

[0056] Optionally, the fine-tuning data set of the first large language model is composed of multiple first text-mathematical-model pairs, and each of the first text-mathematical-model pairs is generated by: parsing the first fine-tuning three-dimensional mathematic-model file to obtain three-dimensional mathematic-model parameter information; inputting the three-dimensional mathematic-model parameter information, three-dimensional mathematic-model element information and first setting prompt word corresponding to the first fine-tuning three-dimensional mathematic-model file into the second large language model, and outputting the first fine-tuning text description information; and combining the first fine-tuning text description information and the first fine-tuning three-dimensional mathematic-model file into the first text-mathematic-model pair.

[0057] The first text-digital model pair may be expressed as (<first fine-tuning text description information>, <first fine-tuning three-dimensional digital model file>). The first fine-tuning three-dimensional digital model file may be understood as an arbitrary three-dimensional digital model file used for fine-tuning the first large language model.

[0058] In this embodiment, the first fine-tuning three-dimensional digital model file can be parsed by using any existing three-dimensional digital model parsing tool (such as OpenCASCADE, CATIA, SolidWorks, AutoDesk, NX and other CAD software, or a digital model parsing module based on deep learning, such as a neural network, a Transformer network, etc.), and the three-dimensional digital model parameter information (such as the thickness and length of the first fine-tuning three-dimensional digital model file) is obtained. The three-dimensional digital model element information corresponding to the first fine-tuning three-dimensional digital model file (such as the name of the first fine-tuning three-dimensional digital model file, its use in industry, etc.) and the first setting prompt word corresponding to the first fine-tuning three-dimensional digital model file (including format regulations and first fine-tuning three-dimensional digital model file related content regulations) are obtained, and the three-dimensional digital model parameter information, three-dimensional digital model element information and first setting prompt word corresponding to the first fine-tuning three-dimensional digital model file are input into the second large language model together, and the complete text description information of the first fine-tuning three-dimensional digital model file is output as the first fine-tuning text description information. In this embodiment, there is no restriction on the second large language model and the first large language model, and they can be any general large language model. The second largest language model is used to expand the fine-tuning data set of the first largest language model, and the fine-tuned first largest language model is used to determine the target three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file.

[0059] In this embodiment, the fine-tuning dataset of the first large language model is generated by using the second large language model, so that the generation speed and quality of the fine-tuning dataset of the first large language model can be improved.

[0060] Optionally, the fine-tuning data set of the first large language model consists of multiple second text-mathematical-model pairs, and each of the second text-mathematical-model pairs is generated by: determining a third fine-tuning three-dimensional digital-model file based on a second fine-tuning three-dimensional digital-model file and modification parameter description information of the second fine-tuning three-dimensional digital-model file; and combining the modification parameter description information of the second fine-tuning three-dimensional digital-model file, the second fine-tuning three-dimensional digital-model file, and the third fine-tuning three-dimensional digital-model file into the second text-mathematical-model pair.

[0061] The second text digital model pair can be expressed in the form of (<modification parameter description information of the second fine-tuning three-dimensional digital model file, the second fine-tuning three-dimensional digital model file>-<third fine-tuning three-dimensional digital model file>). The second fine-tuning three-dimensional digital model file can be understood as an arbitrary three-dimensional digital model file used to fine-tune the first language model.

[0062] In this embodiment, there is no restriction on the modification parameter description information of the second fine-tuning three-dimensional digital model file, for example

[0063] "Change the length from 5 to 10".

[0064] In this embodiment, by calling an industrial-grade three-dimensional digital model design software interface (which may be the same as the three-dimensional digital model analysis tool, such as CAD software such as CATIA, Solidworks, etc.), based on the second fine-tuning three-dimensional digital model file and the modified parameter description information of the second fine-tuning three-dimensional digital model file (such as "changing the length 5 to 10"), a new second fine-tuning three-dimensional digital model file can be automatically generated as the third fine-tuning three-dimensional digital model file, so that the fine-tuning data set of the first large language model is more accurate.

[0065] Optionally, determining a target three-dimensional digital model file based on text description information and / or a first three-dimensional digital model file and an edited three-dimensional CAD modeling language data sequence includes: determining a second three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file; generating a model through the pre-fine-tuned CAD sequence modeling language, and determining the three-dimensional CAD modeling language data sequence based on the second three-dimensional digital model file or the first three-dimensional digital model file; generating an edited three-dimensional CAD modeling language data sequence based on the three-dimensional CAD modeling language data sequence in combination with the editing parameters; and converting the edited three-dimensional CAD modeling language data sequence into the target three-dimensional digital model file.

[0066] In this embodiment, the second three-dimensional digital model file is determined based on the text description information and / or the first three-dimensional digital model file. This is the same as the method for determining the target three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file, and will not be described in detail in this embodiment.

[0067] In this embodiment, the pre-adjusted CAD sequence modeling language generation model is used to generate a 3D CAD modeling language data sequence based on the second 3D digital model file or the first 3D digital model file (or any other 3D digital model file). Based on the 3D CAD modeling language data sequence combined with the editing parameters, an edited 3D CAD modeling language data sequence is generated; and the edited 3D CAD modeling language data sequence is converted into the target 3D digital model file using computer-aided design software.

[0068] For example, Figure 4 It is a schematic diagram of the display effect of a second three-dimensional digital model file provided by an embodiment of the present invention. Figure 5 A schematic diagram of the effect of a 3D CAD modeling language data sequence provided by an embodiment of the present invention. Figure 5 is based on Figure 4 The second three-dimensional digital model file generates a three-dimensional CAD modeling language data sequence.

[0069] In this embodiment, according to the pre-fine-tuned CAD sequence modeling language generation model, a three-dimensional CAD modeling language data sequence is generated based on the second three-dimensional digital model file or the first three-dimensional digital model file, so that a more accurate three-dimensional CAD modeling language data sequence can be obtained and the interpretability of the target three-dimensional digital model file can be improved.

[0070] The CAD sequence modeling language generation model includes a graph neural network, a target generation model and a decoder. In this embodiment, there is no restriction on the target generation model, which is used to generate a three-dimensional CAD sequence modeling language code corresponding to the decoder, that is, as long as the generated three-dimensional CAD sequence modeling language code can be decoded by the decoder, for example, the target generation model can be a generative variational autoencoder model, a generative Transformer model or an autoregressive generative diffusion model; it can also be a generative adversarial network, a flow-based generative model, etc.

[0071] Optionally, the three-dimensional CAD modeling language data sequence is determined based on the second three-dimensional digital model file or the first three-dimensional digital model file, including: inputting the second three-dimensional digital model file or the first three-dimensional digital model file into the graph neural network, and outputting connection matrix information and geometric features; inputting the connection matrix information and the geometric features into the target generation model, and outputting the three-dimensional CAD sequence modeling language code; decoding the three-dimensional CAD sequence modeling language code by the decoder to obtain the three-dimensional CAD modeling language data sequence.

[0072] The connection matrix information is used to describe the topological structure of faces and edges, that is, the connection relationship between each edge and each face. The connection matrix information can be obtained through the graph topology model. Characterization, where Representative surface, Represents an edge. Geometric features can include surfaces for each face and curves for each edge. Surfaces and curves can be represented as regular coordinate grids and stored as node attributes (faces are equivalent to nodes) and edge attributes in the graph. Each surface can be understood as a mapping from a two-dimensional interval to a three-dimensional geometric domain.

[0073] In this embodiment, for each surface, the surface can be discretized into a regular two-dimensional sample grid, with the number of steps in the two dimensions being M and N', thereby generating M N' grid points, each of which is identified by a pair of indices [k, l], where k and l are coordinates in two dimensions (for example, grid point [0, 0] is the first point in the upper left corner, and [M-1, N'-1] is the last point in the lower right corner). At each grid point indexed by [k, l], the following local features are attached: the absolute position of the 3D sampling point (i.e., the specific coordinates of each grid point in 3D space), which is normalized to a cube of size 2 centered at the origin for ease of processing.

[0074] The curve of each edge can define the actual geometric shape, which can be understood as a mapping from a one-dimensional interval to a three-dimensional geometric domain. The curve can be a straight line, an arc, or a B-spline curve. In this embodiment, the curve can be discretized into a regular one-dimensional grid with a step number of M, that is, a one-dimensional grid including M network points. At each grid point, the absolute position information of the additional sampling point is used as a feature, that is, each grid point also includes the specific coordinates of the point in three-dimensional space.

[0075] In this embodiment, a three-dimensional CAD sequence modeling language encoding is obtained based on the connection matrix information and geometric features through the target generation model. The three-dimensional CAD sequence modeling language encoding is decoded by the decoder to obtain the three-dimensional CAD modeling language data sequence. Among them, the three-dimensional CAD modeling language data sequence includes the CAD command type, CAD command parameters and the position of the CAD command in the three-dimensional CAD modeling language data sequence. In this embodiment, there is no restriction on the CAD command type and CAD command parameters. Exemplarily, the CAD command type can be "draw edge", and the CAD command parameters can be "starting point parameter and end point parameter of the edge".

[0076] Exemplarily, the target generation model takes an autoregressive generation diffusion model as an example. Optionally, the connection matrix information and the geometric features are input into the target generation model, and the three-dimensional CAD sequence modeling language code is output, including: the target generation model, based on the connection matrix information and the geometric features, uses an autoregressive method to gradually generate the code of the next CAD command; each of the CAD commands includes a CAD command type, CAD command parameters and the position of the CAD command in the three-dimensional CAD modeling language data sequence; the three-dimensional CAD modeling language data sequence consists of multiple CAD commands.

[0077] In this embodiment, the target generation model uses an autoregressive method to perform reasoning based on the connection matrix information and the geometric features: the code of the next CAD command corresponding to the three-dimensional CAD sequence modeling language code is gradually inferred. Each reasoning only outputs the code of one CAD command (the reasoning starts from the code of the first CAD command until the reasoning of the code of the last CAD command is completed), but each reasoning will consider all the output content. For example, the code of the first CAD command is output for the first time. When it is output for the second time, the code of the second CAD command will be inferred based on the code of the first CAD command, and so on, until the reasoning of the code of the last CAD command is completed. It should be noted that when reasoning the code of the CAD command, the target generation model starts reasoning when it encounters a start identifier and stops reasoning when it encounters an end identifier, indicating that the reasoning is completed.

[0078] Exemplarily, the coding reasoning process of multiple CAD commands can be expressed as:

[0079]

[0080] in, Indicates the code of the first CAD command, Indicates the code of the second CAD command. Indicates the code of the last CAD command. t indicates the total number of CAD command codes. Represents 3D CAD sequential modeling language encoding.

[0081] In this embodiment, the target generation model is used to infer the encoding of each CAD command corresponding to the three-dimensional CAD sequence modeling language encoding in an autoregressive manner, so as to improve the accuracy of the three-dimensional CAD sequence modeling language encoding.

[0082] Optionally, the fine-tuning data set of the CAD sequence modeling language generation model is composed of multiple digital-to-model sequence pairs; each of the digital-to-model sequence pairs is generated by: using a third language model, based on the first fine-tuning three-dimensional CAD modeling language data sequence, fine-tuning editing parameters and a second setting prompt word, generating a second fine-tuning three-dimensional CAD modeling language data sequence; and combining the digital-to-model file corresponding to the first fine-tuning three-dimensional CAD modeling language data sequence and the second fine-tuning three-dimensional CAD modeling language data sequence into the digital-to-model sequence pair.

[0083] Among them, the form of the digital-model sequence pair can be expressed as (<digital-model file corresponding to the first fine-tuning three-dimensional CAD modeling language data sequence>-<second fine-tuning three-dimensional CAD modeling language data sequence>). The first fine-tuning three-dimensional CAD modeling language data sequence can be an existing, arbitrary industrial three-dimensional CAD modeling language data sequence, which is used to generate more second fine-tuning three-dimensional CAD modeling language data sequences to fine-tune the CAD sequence modeling language generation model. In this embodiment, there is no restriction on the fine-tuning editing parameters, for example, "change the length of 5 to 10". The second setting prompt word may include relevant format regulations and relevant content regulations of the second fine-tuning three-dimensional CAD modeling language data sequence. In this embodiment, there is no restriction on the third large language model, which can be any general large language model. The third large language model is used to expand the fine-tuning data set of the CAD sequence modeling language generation model.

[0084] In this embodiment, the method of determining the fine-tuning dataset of the CAD sequence modeling language generation model by the third language model can improve the generation speed and quality of the fine-tuning dataset of the CAD sequence modeling language generation model.

[0085] Optionally, determining the target three-dimensional digital model file based on the text description information includes: determining the target three-dimensional digital model file based on the text description information through a pre-fine-tuned three-dimensional CAD large model intelligent body, wherein the three-dimensional CAD large model intelligent body includes a fourth language model, a memory module and a tool module; the fourth language model includes a planning module and an execution module; wherein the memory module is used to store historical text description information; the planning module is used to determine planning instructions based on the text description information and the historical text description information; the execution module is used to call the tool module based on the planning instructions to execute the planning instructions; the tool module includes a three-dimensional digital model file generation interface, a three-dimensional digital model file editing interface and a three-dimensional CAD modeling language data sequence generation interface.

[0086] Among them, the fourth language model can be any existing large model. The historical text description information can be understood as the input of the history of the 3D CAD large model agent and is successfully executed. The tool module provides various tool interfaces required by the execution module, such as API calls, database access, etc. The tool module is a bridge for the interaction between the 3D CAD large model agent and the external system or data, supporting the operation implementation and data processing of the 3D CAD large model agent.

[0087] Exemplarily, if the text description information is about the content of generating a three-dimensional digital model file, the planning module in the three-dimensional CAD large model intelligent body determines the planning instructions based on the text description information, and the execution module queries the target function in the setting data table based on the planning instructions, and calls the target function, that is, calling the three-dimensional digital model file generation interface in the tool module. The three-dimensional digital model file generation interface, the specific process of generating the target three-dimensional digital model file is similar to the method of "determining the target three-dimensional digital model file based on text description information" in the aforementioned embodiment, and will not be specifically described. The setting data table includes the functions of each interface in the tool module.

[0088] Exemplarily, if the text description information is about editing the content of the three-dimensional digital model file, the planning module in the three-dimensional CAD large model intelligent body determines the planning instructions based on the text description information, and the execution module queries the objective function in the setting data table based on the planning instructions, and calls the objective function, that is, calls the three-dimensional digital model file editing interface in the tool module. The three-dimensional digital model file editing interface specifically generates the target three-dimensional digital model file. The method of "determining the target three-dimensional digital model file based on text description information and the first three-dimensional digital model file" in the aforementioned embodiment is similar, and will not be elaborated in detail.

[0089] Exemplarily, if the text description information is about generating a 3D CAD modeling language data sequence, the planning module in the 3D CAD large model intelligent body determines the planning instructions based on the text description information, and the execution module queries the objective function in the set data table based on the planning instructions, and calls the objective function, that is, calls the 3D CAD modeling language data sequence generation interface in the tool module. The 3D CAD modeling language data sequence generation interface specifically generates the target 3D digital model file. The method of "determining the target 3D digital model file based on text description information and the edited 3D CAD modeling language data sequence" in the aforementioned embodiment is similar and will not be elaborated in detail.

[0090] Optionally, the memory module includes a short-term memory unit and a long-term memory unit; the short-term memory unit is used to temporarily store recent historical text description information; the long-term memory unit is used to long-term store historical text description information selected from the recent historical text description information.

[0091] Among them, the short-term memory unit is used to temporarily store recent historical text description information. Among them, recent can be understood as historical text description information in the recent time period, such as all historical text description information input into the three-dimensional CAD large model intelligent body within the previous day. The short-term memory unit can also store planning instructions corresponding to the historical text description information. In this embodiment, there is no limit on the temporary duration, for example, it can be one month. The short-term memory unit can be read quickly to respond to continuous user queries and maintain continuous cognition of the current session. The long-term memory unit is used for long-term storage, and the more important or representative historical text description information (such as the historical text description information related to the parts with a large number of occurrences) selected from the recent historical text description information is stored persistently through the retrieval-augmented generation (RAG) vector library and database structure. The long-term memory unit is also used to store planning instructions corresponding to the historical text description information. In this embodiment, there is no limit on the specific duration of the long term, for example, it can be several years, that is, the long term is greater than the temporary. In this embodiment, the RAG technology can be replaced by Graph Retrieval-augmented generation (GraphRAG), Knowledge Graph and other technologies. The long-term memory unit can ensure that the 3D CAD large model agent can continue to evolve and adapt to the ever-changing user environment over a long period of time. In this embodiment, the database structure can be replaced by a file system, a distributed file system, an object storage, a cache system and a log to store data.

[0092] Optionally, the planning module includes a first planning unit, a second planning unit, a third planning unit and a fourth planning unit: the first planning unit is used to determine the planning instructions based on the text description information; the second planning unit is used to determine the planning instructions based on the text description information and a third set prompt word if the execution result corresponding to the planning instructions of the first planning unit is execution failure; the third planning unit is used to determine the planning instructions based on the text description information and the historical text description information and a fourth set prompt word if the execution result corresponding to the planning instructions of the second planning unit is execution failure; the fourth planning unit is used to fine-tune the three-dimensional CAD large model intelligent body again if the execution result corresponding to the planning instructions of the third planning unit is execution failure.

[0093] Among them, the third setting prompt word, the fourth setting prompt word, and the historical text description information are used to improve the text description information to ensure the correct execution of the execution module. The planning instruction includes specific execution steps and plans.

[0094] In this embodiment, the first planning unit determines the planning instruction based on the text description information, and the execution module executes the planning instruction. During the execution process, if the execution module cannot find the objective function in the setting data table, the execution result corresponding to the planning instruction of the first planning unit is execution failure.

[0095] The second planning unit is used to determine the planning instruction based on the text description information and in combination with the third setting prompt word if the execution result corresponding to the planning instruction of the first planning unit is execution failure; the execution module executes the planning instruction, and during the execution process of the execution module, if the objective function cannot be found in the setting data table, the execution result corresponding to the planning instruction of the second planning unit is execution failure.

[0096] The third planning unit is used to, if the execution result corresponding to the planning instruction of the second planning unit is execution failure, then based on the text description information and the historical text description information, combined with the fourth setting prompt word and determine the planning instruction; the execution module executes the planning instruction, and during the execution process of the execution module, if the objective function cannot be found in the setting data table, the execution result corresponding to the planning instruction of the third planning unit is execution failure.

[0097] The fourth planning unit is used to call the execution module again to fine-tune the three-dimensional CAD large model intelligent body if the execution result corresponding to the planning instruction of the third planning unit is execution failure.

[0098] In this embodiment, if the text description information is a description of a simple generation task, such as a simple mathematical calculation, the execution module can be directly executed according to the planning module without calling the tool module.

[0099] It should be noted that the tool module can be embedded in the computer-aided design software as a function of the computer-aided design software.

[0100] The fine-tuning dataset of the 3D CAD large model agent consists of multiple text planning instruction pairs, and each text planning instruction pair can be in the form of (<fine-tuning text>, <planning instruction>). The fine-tuning dataset of the 3D CAD large model agent is used to fine-tune the 3D CAD large model agent, learn the planning ability based on the fine-tuning text and operation sequence, and improve the planning ability of the planning module, so that the generated target 3D digital model file is more accurate.

[0101] In this embodiment, the three-dimensional CAD large model intelligent body can receive any text description information from the user, and can autonomously call the computer-aided design software for manual operation and autonomous editing to generate the required industrial-grade target three-dimensional digital model files.

[0102] Figure 6A schematic diagram of a device for generating a three-dimensional digital model file provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the device includes: a target three-dimensional digital model file determination module 610;

[0103] The target three-dimensional digital model file determination module 610 is used to determine the target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, combined with editing parameters.

[0104] The technical solution of the embodiment of the present application determines the target three-dimensional digital model file through the target three-dimensional digital model file determination module based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, obtained in combination with editing parameters. In the disclosed embodiment, the method of determining the target three-dimensional digital model file through at least one of the text description information, the first three-dimensional digital model file, and the edited three-dimensional CAD modeling language data sequence can automatically generate the target digital model file, and can determine the target three-dimensional digital model file faster and more accurately, thereby improving the quality and efficiency of the generation of the three-dimensional digital model file, and at the same time can reduce labor costs and reduce dependence on computer-aided design software.

[0105] Optionally, the target three-dimensional digital model file determination module is specifically used to: digitally encode the text description information to obtain a text description code; digitally encode the first three-dimensional digital model file to obtain a first digital model code; input the text description code and / or the first digital model code into a pre-fine-tuned first large language model, and output a second digital model code; decode the second digital model code to obtain the target three-dimensional digital model file.

[0106] Optionally, the target three-dimensional digital model file determination module is also used to: use a first variational autoencoder to extract multiple surface features and multiple surface position features of the first three-dimensional digital model file; use a second variational autoencoder to extract multiple edge features and multiple edge position features of the first three-dimensional digital model file; fuse the multiple surface features, the multiple surface position features, the multiple edge features and the multiple edge position features to obtain fused features; encode the fused features through a residual vector quantization network to obtain a first digital model code.

[0107] Optionally, the target three-dimensional digital model file determination module is specifically used for: the first largest language model, based on the text description code, adopts an autoregressive method starting from a starting identifier, and gradually infers the next face feature code or the next edge feature code or the next face position feature code or the next edge position feature code until the output end identifier is output and the output is stopped.

[0108] The fine-tuning data set of the first largest language model is composed of multiple first text-mathematical-model pairs. Optionally, the above-mentioned device also includes a data set generation module, which is used to parse the first fine-tuning three-dimensional mathematical-model file to obtain three-dimensional mathematical-model parameter information; input the three-dimensional mathematical-model parameter information, three-dimensional mathematical-model element information and first setting prompt word corresponding to the first fine-tuning three-dimensional mathematical-model file into the second largest language model, and output the first fine-tuning text description information; the first fine-tuning text description information and the first fine-tuning three-dimensional mathematical-model file are combined into the first text-mathematical-model pair.

[0109] The fine-tuning data set of the first large language model is composed of multiple second text-mathematical-model pairs. Optionally, the above-mentioned device also includes a data set generation module, which is also used to determine the third fine-tuning three-dimensional digital-model file based on the second fine-tuning three-dimensional digital-model file and the modification parameter description information of the second fine-tuning three-dimensional digital-model file; the modification parameter description information of the second fine-tuning three-dimensional digital-model file, the second fine-tuning three-dimensional digital-model file and the third fine-tuning three-dimensional digital-model file are combined into the second text-mathematical-model pair.

[0110] Optionally, the target three-dimensional digital model file determination module is also used to: determine a second three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file; generate a model through the pre-fine-tuned CAD sequence modeling language, and determine the three-dimensional CAD modeling language data sequence based on the second three-dimensional digital model file or the first three-dimensional digital model file; generate an edited three-dimensional CAD modeling language data sequence based on the three-dimensional CAD modeling language data sequence in combination with the editing parameters; and convert the edited three-dimensional CAD modeling language data sequence into the target three-dimensional digital model file.

[0111] The CAD sequence modeling language generation model includes a graph neural network, a target generation model and a decoder. The target generation model is used to generate a three-dimensional CAD sequence modeling language code corresponding to the decoder.

[0112] Optionally, the target three-dimensional digital model file determination module is also used to: input the second three-dimensional digital model file or the first three-dimensional digital model file into the graph neural network, and output connection matrix information and geometric features; input the connection matrix information and the geometric features into the target generation model, and output the three-dimensional CAD sequence modeling language encoding; decode the three-dimensional CAD sequence modeling language encoding through the decoder to obtain the three-dimensional CAD modeling language data sequence.

[0113] Wherein, the target generation model is an autoregressive generation diffusion model.

[0114] Optionally, the target three-dimensional digital model file determination module is also used for: the target generation model, based on the connection matrix information and the geometric features, adopts an autoregressive method to gradually generate the encoding of the next CAD command; each of the CAD commands includes a CAD command type, CAD command parameters and the position of the CAD command in the three-dimensional CAD modeling language data sequence; the three-dimensional CAD modeling language data sequence is composed of multiple CAD commands.

[0115] The fine-tuning data set of the CAD sequence modeling language generation model is composed of multiple digital-analog sequence pairs. Optionally, the above-mentioned device also includes a data set generation module, which is also used to generate a second fine-tuning three-dimensional CAD modeling language data sequence based on the first fine-tuning three-dimensional CAD modeling language data sequence, the fine-tuning editing parameters and the second setting prompt word through a third language model; the digital-analog file corresponding to the first fine-tuning three-dimensional CAD modeling language data sequence and the second fine-tuning three-dimensional CAD modeling language data sequence are combined into the digital-analog sequence pair.

[0116] Optionally, the target three-dimensional digital model file determination module is also used to: determine the target three-dimensional digital model file based on the text description information through a pre-fine-tuned three-dimensional CAD large model intelligent body, wherein the three-dimensional CAD large model intelligent body includes a fourth language model, a memory module and a tool module; the fourth language model includes a planning module and an execution module; wherein the memory module is used to store historical text description information; the planning module is used to determine planning instructions based on the text description information and the historical text description information; the execution module is used to call the tool module based on the planning instructions to execute the planning instructions; the tool module includes a three-dimensional digital model file generation interface, a three-dimensional digital model file editing interface and a three-dimensional CAD modeling language data sequence generation interface.

[0117] Optionally, the memory module includes a short-term memory unit and a long-term memory unit; the short-term memory unit is used to temporarily store recent historical text description information; the long-term memory unit is used to long-term store historical text description information selected from the recent historical text description information.

[0118] Optionally, the planning module includes a first planning unit, a second planning unit, a third planning unit and a fourth planning unit: the first planning unit is used to determine the planning instructions based on the text description information; the second planning unit is used to determine the planning instructions based on the text description information and a third set prompt word if the execution result corresponding to the planning instructions of the first planning unit is execution failure; the third planning unit is used to determine the planning instructions based on the text description information and the historical text description information and a fourth set prompt word if the execution result corresponding to the planning instructions of the second planning unit is execution failure; the fourth planning unit is used to fine-tune the three-dimensional CAD large model intelligent body again if the execution result corresponding to the planning instructions of the third planning unit is execution failure.

[0119] The device for generating a three-dimensional digital model file provided in the embodiment of the present application can execute the method for generating a three-dimensional digital model file provided in any embodiment of the present disclosure, and has functional modules and beneficial effects corresponding to the execution method.

[0120] Figure 7 A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0121] like Figure 7As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11 in communication, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the random access memory (RAM) 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the read-only memory (ROM) 12, and the random access memory (RAM) 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0122] A number of components in the electronic device 10 are connected to an input / output (I / O) interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, speakers, etc., a storage unit 18, such as a disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0123] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the generation of a three-dimensional digital model file.

[0124] In some embodiments, the generation of the method three-dimensional digital model file can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via a read-only memory (ROM) 12 and / or a communication unit 19. When the computer program is loaded into a random access memory (RAM) 13 and executed by the processor 11, one or more steps of the generation of the method three-dimensional digital model file described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the generation of the method three-dimensional digital model file by any other appropriate means (for example, by means of firmware).

[0125] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0127] In the context of the present application, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0130] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0131] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed by a processor, implements a method for generating a three-dimensional digital model file as provided in any embodiment of the present application.

[0132] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present application, and the programming language includes an object-oriented programming language, such as Java, Smalltalk, C++, and also includes a conventional procedural programming language, such as "C" language or similar programming language. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0133] Note that the above are only preferred embodiments of the present application and the technical principles used. Those skilled in the art will understand that the present application is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present application. Therefore, although the present application is described in more detail through the above embodiments, the present application is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present application, and the scope of the present application is determined by the scope of the appended claims.

Claims

1. A method for generating a three-dimensional digital model file, characterized in that: include: The target three-dimensional digital model file is determined based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, combined with editing parameters.

2. The method according to claim 1, characterized in that Determining a target three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file includes: Numerically encoding the text description information to obtain a text description code; Digitally encode the first three-dimensional digital model file to obtain a first digital model code; Input the text description code and / or the first digital-analog code into a pre-fine-tuned first large language model, and output a second digital-analog code; The second digital model code is decoded to obtain the target three-dimensional digital model file.

3. The method according to claim 2, characterized in that The first three-dimensional digital model file is digitally encoded to obtain a first digital model code, including: Extracting multiple surface features and multiple surface position features of the first three-dimensional digital model file using a first variational autoencoder; Extracting a plurality of edge features and position features of a plurality of edges of the first three-dimensional digital model file using a second variational autoencoder; fusing the plurality of surface features, the plurality of surface position features, the plurality of edge features, and the plurality of edge position features to obtain a fused feature; The fused features are encoded through a residual vector quantization network to obtain a first digital-analog code.

4. The method according to claim 2, characterized in that: Inputting the text description code and the first digital-analog code into a pre-fine-tuned first large language model, and outputting a second digital-analog code, comprises: The first language model, based on the text description code, uses an autoregressive method to start from a start identifier, and gradually infers the next face feature code or the next edge feature code or the next face position feature code or the next edge position feature code until an end identifier is output and the output stops.

5. The method according to claim 2, characterized in that: The fine-tuning dataset of the first language model consists of a plurality of first text-digital-model pairs, and each of the first text-digital-model pairs is generated as follows: Parsing the first fine-tuning three-dimensional digital model file to obtain three-dimensional digital model parameter information; Input the three-dimensional digital model parameter information, three-dimensional digital model element information and first setting prompt word corresponding to the first fine-tuning three-dimensional digital model file into the second language model, and output first fine-tuning text description information; The first fine-tuning text description information and the first fine-tuning three-dimensional digital model file are combined into the first text-digital model pair.

6. The method according to claim 2, characterized in that The fine-tuning dataset of the first language model consists of a plurality of second text-digital-model pairs, and each of the second text-digital-model pairs is generated as follows: Determine a third fine-tuning three-dimensional digital model file based on the second fine-tuning three-dimensional digital model file and the modification parameter description information of the second fine-tuning three-dimensional digital model file; The modification parameter description information of the second fine-tuning three-dimensional digital model file, the second fine-tuning three-dimensional digital model file and the third fine-tuning three-dimensional digital model file are combined into the second text digital model pair.

7. The method according to claim 1, characterized in that Determining a target three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file and the edited three-dimensional CAD modeling language data sequence includes: Determine a second three-dimensional digital model file based on the text description information and / or the first three-dimensional digital model file; Generate a model by the pre-adjusted CAD sequence modeling language, and determine the 3D CAD modeling language data sequence based on the second 3D digital model file or the first 3D digital model file; generating an edited 3D CAD modeling language data sequence based on the 3D CAD modeling language data sequence and the editing parameters; The edited three-dimensional CAD modeling language data sequence is converted into the target three-dimensional digital model file.

8. The method according to claim 7, characterized in that in, The CAD sequence modeling language generation model includes a graph neural network, a target generation model and a decoder; wherein the target generation model is used to generate a three-dimensional CAD sequence modeling language code corresponding to the decoder; and determining the three-dimensional CAD modeling language data sequence based on the second three-dimensional digital model file or the first three-dimensional digital model file includes: Inputting the second three-dimensional digital model file or the first three-dimensional digital model file into the graph neural network, and outputting connection matrix information and geometric features; Inputting the connection matrix information and the geometric features into a target generation model, and outputting the three-dimensional CAD sequential modeling language code; The three-dimensional CAD sequence modeling language code is decoded by the decoder to obtain the three-dimensional CAD modeling language data sequence.

9. The method according to claim 8, characterized in that in, The target generation model is an autoregressive generation diffusion model; the connection matrix information and the geometric features are input into the target generation model, and the three-dimensional CAD sequence modeling language code is output, including: The target generation model, based on the connection matrix information and the geometric features, uses an autoregressive method to gradually generate the encoding of the next CAD command; each of the CAD commands includes a CAD command type, CAD command parameters and the position of the CAD command in the three-dimensional CAD modeling language data sequence; the three-dimensional CAD modeling language data sequence is composed of multiple CAD commands.

10. The method according to claim 1, 7 or 8, characterized in that: The fine-tuning data set of the CAD sequential modeling language generation model is composed of a plurality of digital-analog sequence pairs; each of the digital-analog sequence pairs is generated as follows: Generate a second fine-tuning 3D CAD modeling language data sequence based on the first fine-tuning 3D CAD modeling language data sequence, the fine-tuning editing parameters and the second setting prompt words through the third language model; The digital-model file corresponding to the first fine-tuning three-dimensional CAD modeling language data sequence and the second fine-tuning three-dimensional CAD modeling language data sequence are combined into the digital-model sequence pair.

11. The method according to claim 1, characterized in that Determining a target three-dimensional digital model file based on the text description information includes: Through the pre-fine-tuned 3D CAD large model intelligent body, the target 3D digital model file is determined based on the text description information, wherein the 3D CAD large model intelligent body includes a fourth language model, a memory module and a tool module; the fourth language model includes a planning module and an execution module; wherein the memory module is used to store historical text description information; the planning module is used to determine planning instructions based on the text description information and the historical text description information; the execution module is used to call the tool module based on the planning instructions to execute the planning instructions; the tool module includes a 3D digital model file generation interface, a 3D digital model file editing interface and a 3D CAD modeling language data sequence generation interface.

12. The method according to claim 11, characterized in that The memory module includes a short-term memory unit and a long-term memory unit; the short-term memory unit is used to temporarily store recent historical text description information; the long-term memory unit is used to long-term store historical text description information selected from the recent historical text description information.

13. The method according to claim 11, characterized in that The planning module includes a first planning unit, a second planning unit, a third planning unit and a fourth planning unit: the first planning unit is used to determine the planning instruction based on the text description information; the second planning unit is used to determine the planning instruction based on the text description information and a third setting prompt word if the execution result corresponding to the planning instruction of the first planning unit is execution failure; the third planning unit is used to determine the planning instruction based on the text description information and the historical text description information and a fourth setting prompt word if the execution result corresponding to the planning instruction of the second planning unit is execution failure; the fourth planning unit is used to fine-tune the three-dimensional CAD large model intelligent body again if the execution result corresponding to the planning instruction of the third planning unit is execution failure.

14. A device for generating a three-dimensional digital model file, characterized in that: include: A target three-dimensional digital model file determination module is used to determine the target three-dimensional digital model file based on at least one of the following methods: text description information, a first three-dimensional digital model file, and an edited three-dimensional CAD modeling language data sequence; wherein the text description information is the description information of the target three-dimensional digital model file or the modification parameter description information of the first three-dimensional digital model file; the edited three-dimensional CAD modeling language data sequence is a three-dimensional CAD modeling language data sequence generated by a pre-fine-tuned CAD sequence modeling language generation model, combined with editing parameters.

15. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method for generating a three-dimensional digital model file as described in any one of claims 1-13.

16. A storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute the method for generating a three-dimensional digital model file as described in any one of claims 1 to 13.

17. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the computer program implements the method for generating a three-dimensional digital model file according to any one of claims 1 to 13.

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