Data processing method and device, equipment, storage medium and program product

Abstract

The invention discloses a data processing method and device, equipment, a storage medium and a program product, and is applied to the technical field of computers. The data processing method comprises the steps of obtaining a two-dimensional engineering drawing of a product, wherein the two-dimensional engineering drawing comprises object feature information of a design object in the product; according to feature deviation information of the object feature information of the design object and reference object feature information of the design object, determining a deviation correction strategy matched with the feature deviation information; adjusting object feature information of the design object according to the deviation correction strategy to obtain adjusted object feature information; and constructing a three-dimensional model of the product according to the two-dimensional engineering drawing and the adjusted object feature information. According to the method, the three-dimensional model meeting the product design requirements can be efficiently and accurately obtained, the conversion period from the two-dimensional engineering drawing to the three-dimensional model is shortened, the overall design period of the product is further shortened, and the product design efficiency is improved.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and in particular relates to a data processing method, apparatus, device, storage medium and program product. Background Technology

[0002] A 3D model is a digital representation of an object in three-dimensional space, typically described by a mesh or surface composed of geometric elements such as points, lines, and faces. 3D models are used to digitally define and fully describe physical products. During product development, based on the constructed 3D model, subsequent work such as product simulation analysis, performance evaluation, and manufacturing process planning can be carried out sequentially.

[0003] In related technologies, the construction of 3D models can be achieved through computer systems. Specifically, the computer system receives 2D engineering drawings input by the user, extracts product design parameters from the drawings, and then uses 3D modeling software tools to convert the extracted product design parameters into a 3D model with spatial dimensions. However, since 2D engineering drawings are drawn manually, they are limited by human experience and may contain errors in dimensioning or incomplete representations. Incorrect 2D engineering drawings will generate incorrect 3D models, requiring the user to revise the 2D engineering drawings, increasing manual operation and extending the overall product design cycle. Summary of the Invention

[0004] This application provides a data processing method, apparatus, device, storage medium, and program product that can solve the problem of cumbersome user operations during product design, which leads to an extended overall product design cycle.

[0005] In a first aspect, embodiments of this application provide a data processing method, which includes: acquiring a two-dimensional engineering drawing of a product, the two-dimensional engineering drawing including object feature information of a design object in the product; determining a deviation correction strategy matching the feature deviation information based on the object feature information of the design object and feature deviation information of a reference object of the design object; adjusting the object feature information of the design object according to the deviation correction strategy to obtain adjusted object feature information; and constructing a three-dimensional model of the product based on the two-dimensional engineering drawing and the adjusted object feature information.

[0006] Secondly, embodiments of this application provide a data processing apparatus, comprising: a first acquisition module for acquiring a two-dimensional engineering drawing of a product, the two-dimensional engineering drawing including object feature information of a design object in the product; a first determination module for determining a deviation correction strategy matching the feature deviation information based on the object feature information of the design object and feature deviation information of a reference object of the design object; an adjustment module for adjusting the object feature information of the design object according to the deviation correction strategy to obtain adjusted object feature information; and a model construction module for constructing a three-dimensional model of the product based on the two-dimensional engineering drawing and the adjusted object feature information.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions; the processor executes the computer program instructions to implement the data processing method as described in any of the first aspects.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the data processing method as described in any of the first aspects.

[0009] Fifthly, embodiments of this application provide a computer program product, which includes a computer program or instructions that, when executed by a processor, implement the data processing method as described in any of the first aspects.

[0010] The data processing method, apparatus, device, storage medium, and program product of this application embodiment can automatically and accurately determine feature deviation information by comparing the object feature information of the design object in the two-dimensional engineering drawing of the product with standard and correct reference feature information. Subsequently, based on the feature deviation information, a deviation correction strategy corresponding to the feature deviation information is specifically matched. Then, by executing the matched deviation correction strategy, erroneous or incomplete object feature information in the two-dimensional engineering drawing can be automatically adjusted to obtain object feature information that conforms to the standard. This eliminates the need for manual modification of the two-dimensional engineering drawing, directly reducing the manual operation steps required by users to re-modify the two-dimensional engineering drawing in related technologies, and lowering manual operation costs. At the same time, this automated correction is not only more efficient than manual modification, but also avoids secondary errors that may occur during manual modification, further ensuring the accuracy of the two-dimensional engineering drawing. Finally, based on the accurate two-dimensional engineering drawing and the adjusted object feature information, a three-dimensional model that meets the product design requirements can be obtained efficiently and accurately. This automated processing flow shortens the conversion cycle from two-dimensional engineering drawing to three-dimensional model, thereby shortening the overall product design cycle and improving product design efficiency. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating a data processing method provided in some embodiments of this application is shown; Figure 2 A flowchart illustrating a specific implementation of step 120 provided in some embodiments of this application is shown; Figure 3 A flowchart illustrating a specific implementation of step 140 provided in some embodiments of this application is shown; Figure 4 A flowchart illustrating a method for generating a reference two-dimensional engineering drawing using a data processing method provided in some embodiments of this application is shown. Figure 5 The diagram illustrates a specific implementation of step 180 provided in some embodiments of this application; Figure 6 A flowchart illustrating a specific implementation of step 190 provided in some embodiments of this application is shown; Figure 7 The present application provides a schematic diagram of the structure of a data processing apparatus according to some embodiments; Figure 8 The diagram shows a schematic representation of the structure of an electronic device provided in some embodiments of this application. Detailed Implementation

[0013] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0014] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0015] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0016] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0017] Before describing the technical solutions provided in the embodiments of this application, in order to facilitate understanding of the embodiments of this application, this application first provides a detailed description of the relevant technologies involved: The field of automated modeling technology focuses on automating the creation of 3D models using computer systems. Its goal is to efficiently convert various design data into 3D models through algorithm optimization and software tools, thereby reducing the time and resources required for 3D model production. Automated modeling methods based on Computer-Aided Design (CAD) drawings are a significant direction in this field. These methods aim to automatically convert 2D drawings output by CAD software into 3D models. Specifically, they utilize the geometric information, dimensions, and other relevant data contained in the CAD 2D drawings, using specific algorithms to analyze and reconstruct this data, ultimately forming a solid object in 3D space. This automated modeling method can effectively accelerate the product design process, improve design accuracy, and make product visualization and simulation easier. The specific process is as follows: the computer system receives the 2D engineering drawings input by the user, extracts the product's design parameters from the drawings, and then uses 3D modeling software tools to convert the extracted design parameters into a 3D model with spatial dimensions. However, since 2D engineering drawings are mostly drawn manually, they are easily prone to errors in dimensioning or incomplete representation due to the limited experience of the illustrators. Incorrect 2D engineering drawings will directly result in the generated 3D model not meeting the design requirements. In this case, the user needs to modify the 2D engineering drawing and remodel, which not only increases the additional manual operation cost, but also significantly extends the overall design cycle of the product.

[0018] To address the problems in the aforementioned related technologies, embodiments of this application provide a data processing method, apparatus, device, storage medium, and program product. The following description, in conjunction with the appendix... Figure 1 To be continued Figure 6 The data processing method provided in this application will be described in detail through specific embodiments and application scenarios.

[0019] Figure 1 A flowchart illustrating some embodiments of the data processing methods provided in this application is shown. Figure 1 As shown, the data processing method may include steps 110 to 140.

[0020] Step 110: Obtain the two-dimensional engineering drawing of the product, which includes the object feature information of the design object in the product; Step 120: Determine the deviation correction strategy that matches the feature deviation information based on the object feature information of the design object and the feature deviation information of the reference object of the design object; Step 130: Adjust the object feature information of the design object according to the deviation correction strategy to obtain the adjusted object feature information; Step 140: Construct the three-dimensional model of the product based on the two-dimensional engineering drawing and the adjusted object feature information.

[0021] Therefore, by comparing the object feature information of the design object in the product's 2D engineering drawing with standard and correct reference feature information, feature deviation information can be automatically and accurately determined. Subsequently, based on the feature deviation information, a deviation correction strategy corresponding to the feature deviation information is specifically matched. Then, by executing the matched deviation correction strategy, erroneous or incomplete object feature information in the 2D engineering drawing can be automatically adjusted to obtain object feature information that conforms to the standard. This eliminates the need for manual modification of the 2D engineering drawing, directly reducing the manual operation steps required by users to re-edit the 2D engineering drawing in related technologies, and lowering labor costs. At the same time, this automated correction is not only more efficient than manual modification, but also avoids secondary errors that may occur during manual modification, further ensuring the accuracy of the 2D engineering drawing. Finally, based on the accurate 2D engineering drawing and the adjusted object feature information, a 3D model that meets the product design requirements can be obtained efficiently and accurately. This automated processing flow shortens the conversion cycle from 2D engineering drawings to 3D models, thereby shortening the overall product design cycle and improving product design efficiency.

[0022] The steps described above are explained in detail below.

[0023] First, regarding step 110, the two-dimensional engineering drawing involved in this embodiment refers to an engineering drawing that expresses the design information of a product, such as its shape, size, structure, and technical requirements, using a two-dimensional planar projection method. For example, it can be a CAD drawing. The design object refers to the specific parts or components in the product that need to be designed, analyzed, and processed. Object characteristic information refers to information that can uniquely characterize the attributes of the design object, and may include geometric characteristic information and non-geometric characteristic information. For example, geometric characteristic information may include, but is not limited to: shape, size, positional relationship, and tolerance; non-geometric characteristic information may include, but is not limited to: material type, processing requirements, and weight.

[0024] In some embodiments of this application, to further improve the accuracy of extracting the feature information of design objects in two-dimensional engineering drawings, the following uses CAD drawings as an example to illustrate the extraction of object feature information of design objects in step 110 above. Step 110 above may specifically include steps 1101 to 1103.

[0025] Step 1101: Scan the CAD drawing using a drawing scanning program, extract the graphic and structural data from the CAD drawing, and generate the basic scan data of the design object.

[0026] For example, a drawing scanning program is used to scan CAD drawings to capture the geometric shapes and structural data of various design objects on the CAD drawings; then, the obtained geometric shapes and structural data are initially extracted, and an edge detection algorithm is used to locate and determine the boundaries of the geometric shapes by calculating the gradient values ​​of pixels in the image. After marking the boundary points, a mathematical model is used to smoothly connect the boundary points to form a complete geometric figure; finally, the geometric figures are encoded and classified according to their structural characteristics, and the processed graphic and structural data are stored as basic scan data in a standard vector format.

[0027] Step 1102: Based on the basic scan data, analyze the geometry and structure, determine the size information and material type of each design object through the image recognition algorithm, and obtain the recognition data record; For example, based on the basic scan data, the extracted geometric shapes and structures are analyzed. Various design objects, such as lines, curves, and polygons, are identified and classified using image recognition algorithms. The dimensional information and geometric parameters of each design object are analyzed. Dimensional information can be automatically calculated using graphic measurement tools or determined by recognizing annotation information, specifically including but not limited to: length, width, diameter, thickness, angle, and area. Material type can be determined by recognizing annotation information. Finally, the dimensional information and material type of each design object are comprehensively recorded in the recognition data record.

[0028] Step 1103: Based on the identification data records, establish corresponding attribute labels for each design object. The attribute labels include size information labels and material type labels, thus obtaining the parsed dataset. The parsed design set includes the object feature information of the design object.

[0029] For example, for each design object, a unique identifier is assigned based on its dimensions and material type, and a data index is built based on this identifier. Simultaneously, detailed attribute labels, including dimension information labels and material type labels, are generated and attached to the corresponding design object. For instance, a geometric element is labeled with precise attribute information such as "length: 200mm, width: 50mm, material: aluminum alloy". Finally, the full dataset with indexes and attribute labels is integrated into a parsed dataset and stored in a database. This allows the attribute information of each design element to be quickly retrieved and referenced, providing accurate and reliable data support for subsequent design modifications, material selection, and manufacturing process planning.

[0030] Secondly, regarding step 120, the object feature information involved in this embodiment includes size information and material type. Size information refers to the dimension-related parameter data in the geometric features of the design object, which may include, but is not limited to, length, width, diameter, thickness, angle, and area. Material type refers to the material category of the design object, such as aluminum alloy, stainless steel, and plastic. Reference object feature information can be determined based on product design specifications and product design standards, and serves as the benchmark for judging whether there are deviations in the design object feature information. Reference object feature information includes reference size information and reference material type. Reference size information refers to the standard and compliant values ​​of the corresponding size parameters of the design object, and serves as the benchmark for judging whether the size information of the object is qualified. Reference material type refers to the standard material category that the design object should adopt, determined based on the functional requirements, usage environment, and design specifications of the design object in the product.

[0031] Feature deviation information refers to the difference between the actual feature information of the design object and the feature information of the reference object, which may include, but is not limited to, at least one of the following: dimensional deviation information and material deviation information. Deviation correction strategy refers to a pre-set specific plan for correcting the deviation and making the feature information of the design object conform to the reference standard, based on the identified feature deviation information. Different types and degrees of feature deviation information correspond to different deviation correction strategies, such as dimensional deviation correction strategies and feature missing supplementation strategies.

[0032] In some embodiments of this application, in order to accurately obtain feature deviation information, the above data processing method may further include: determining dimensional deviation information based on the reference dimensional information and the dimensional information when the dimensional information and the reference dimensional information do not match; and determining material deviation information based on the reference material type and the material type when the material type and the reference material type do not match.

[0033] For example, to determine the size deviation information, the size information and the reference size information are first matched and compared to determine whether they meet the preset matching threshold (e.g., ±0.05mm). If the preset matching threshold is not met, that is, the size information and the reference size information do not match, the deviation calculation process is automatically triggered to calculate the deviation value between the actual size and the reference size to obtain the size deviation information.

[0034] To determine material deviation information, the material type is first matched with the reference material type to determine if they are consistent. If they are inconsistent, i.e., the material type and the reference material type do not match, the attributes of the actual material type and the reference material type are automatically extracted, the differences between them are analyzed, and material deviation information is generated.

[0035] For example, if the actual size information of a part is "circle diameter 50.2mm", and the corresponding reference size information is "circle diameter 50mm", and the preset matching threshold is ±0.08mm, then the difference between the actual size and the reference size is 0.2mm, which exceeds the matching threshold. Therefore, the size deviation information is determined to be "circle diameter is too large by 0.2mm". If the actual material type of the part is "ordinary carbon steel", and the reference material type is "stainless steel", the material property comparison reveals that the corrosion resistance, strength and other properties of the two are not matched. Therefore, the material deviation information is generated as "actual material: ordinary carbon steel, reference material: stainless steel 304, mismatch reason: corrosion resistance does not meet the part's usage requirements".

[0036] Therefore, by accurately and automatically determining the dimensional and material deviation information, the subjectivity and error of manual judgment are avoided. This clear deviation information provides a precise basis for matching subsequent deviation correction strategies, effectively reducing the problem of subsequent correction failure caused by incomplete or inaccurate deviation identification, thus helping to build an accurate 3D model.

[0037] In some embodiments of this application, such as Figure 2 As shown, step 120 above may specifically include steps 1201 and 1202.

[0038] Step 1201: Determine the feature deviation type based on the feature deviation information of the design object and the reference object feature information of the design object.

[0039] Among them, the feature deviation type refers to the category into which feature deviation information is divided according to preset rules. The classification criteria may include, but are not limited to, the feature dimension corresponding to the deviation, the deviation direction, and the deviation level. Specifically, the feature dimension may include size dimension, material dimension, and structural dimension; the deviation direction may include size being too large, size being too small, positional deviation, and material performance not meeting standards; the deviation level may include slight deviation and severe deviation.

[0040] For example, if the feature deviation information is that the circle diameter is 0.2 mm larger than the tolerance, exceeding the tolerance by 0.15 mm, then the feature dimension is determined to be the size dimension, the deviation direction is marked as the size being larger, and the deviation level is determined to be a severe deviation based on the tolerance requirements. After integration and comparison, the feature deviation type can be determined to be either the size being larger or the severe deviation.

[0041] Step 1202: Based on the correlation between the reference feature deviation type and the reference deviation correction strategy, the reference deviation correction strategy associated with the feature deviation type is determined as the deviation correction strategy.

[0042] For example, a pre-built database of associations between reference feature deviation types and reference deviation correction strategies is invoked. This database stores all associated records of preset reference feature deviation types and their corresponding correction strategies. Then, using the feature deviation type as the search keyword, a precise search is performed in the database to locate the reference feature deviation type that perfectly matches it. Next, the reference deviation correction strategy associated with the matched feature deviation type is determined as the deviation correction strategy corresponding to the current feature deviation information. If a corresponding reference deviation correction strategy is found, it is directly determined as the deviation correction strategy to correct the current deviation. For example, when the feature deviation type is "size too large," the database is searched, and the corresponding reference deviation correction strategy is located as a size reduction correction strategy, which is then determined as the deviation correction strategy for the current deviation.

[0043] The reference deviation correction strategy is a user-pre-configured strategy corresponding to the characteristic deviation type, based on experience. After the strategy configuration is completed, the adaptability of each reference deviation correction strategy is evaluated, its compatibility with industry standards is analyzed, and it is determined whether each strategy can handle the corresponding characteristic deviation type, resulting in a deviation correction strategy verification record. Next, each reference deviation correction strategy is compared and analyzed with industry standards to ensure that the adopted strategy not only solves specific design errors but also meets current industrial application standards and safety requirements. Furthermore, for each reference deviation correction strategy, simulation applications are performed to evaluate potential new problems or additional impacts after implementation, ensuring that each strategy does not cause other problems while solving one type of error. Finally, based on the reference deviation correction strategy verification record, the verified strategies are integrated to obtain the correlation between reference characteristic deviation types and reference deviation correction strategies.

[0044] Therefore, by invoking the pre-built association between reference feature deviation types and reference deviation correction strategies, the reference deviation correction strategy associated with the feature deviation type can be determined as the deviation correction strategy. This enables efficient matching of deviation correction strategies, reduces manual intervention, and lowers the risk of incorrect deviation correction strategy selection due to insufficient human experience. This differentiated strategy for matching different levels of deviation can ensure the correction effect while avoiding resource waste caused by over-correction, further improving the stability and efficiency of overall data processing.

[0045] In some embodiments of this application, the aforementioned feature deviation information may further include the feature deviation location. The actual coordinate data of each design object in the 2D engineering drawing is extracted using a drawing analysis tool and matched with the recorded feature deviation information to determine the specific design object corresponding to each deviation, thereby determining the specific type and location of the deviation. The coordinate data of the deviation location is obtained through a precise graphic positioning algorithm to ensure that each deviation can be accurately marked at its corresponding position in the 2D engineering drawing. Based on this, the influence of the feature deviation on the subsequent 3D model design can be evaluated by combining the feature deviation type, feature deviation level, and feature deviation location, and the deviation location coordinates and impact evaluation results are simultaneously incorporated into the feature deviation information. In this way, the specific deviation location and corresponding drawing coordinates of the feature deviation information in the 2D engineering drawing can be determined.

[0046] Furthermore, in step 130, the deviation correction strategy is analyzed to obtain the deviation correction method (such as size adjustment, feature supplementation, and error correction) and deviation correction parameters (such as adjustment values ​​and supplementary content). Then, based on the deviation correction method and deviation correction parameters, corresponding feature information adjustment instructions are generated. Following the generated feature information adjustment instructions, the object feature information of the design object is automatically adjusted.

[0047] For example, for dimensional deviations, the dimensional values ​​of the corresponding design elements in the two-dimensional engineering drawing are directly increased, decreased, or corrected according to the preset correction parameters to ensure that the dimensions of the corrected design object match the reference dimensional information; for material deviations, the erroneous material information corresponding to the feature deviation is directly replaced with the material parameters corresponding to the reference material type required by the product design.

[0048] Then, in step 140, the three-dimensional model refers to the use of three-dimensional modeling technology to transform the two-dimensional design information of the product into a three-dimensional digital model with three dimensions: length, width, and height. It can intuitively and comprehensively show the spatial structure, dimensional relationships, and assembly relationships of the product, and is an important foundation for subsequent simulation analysis, processing and manufacturing, assembly and debugging of the product.

[0049] In some embodiments of this application, such as Figure 3 As shown, step 140 above may specifically include steps 1401 to 1403.

[0050] Step 1401: Obtain the geometric constraints of the design object based on the two-dimensional engineering drawing.

[0051] Geometric constraints refer to the constraint rules used to limit the relative position, shape, and size relationships between design objects. Specifically, these constraint rules may include, but are not limited to: coincidence constraints that limit two design objects to be at the same point, collinear, or coplanar; parallel constraints that limit two design objects to be parallel; angular constraints that limit the angle between two design objects; symmetry constraints that limit two design objects to be symmetrical about a certain axis or plane; and dimensional constraints that limit fixed values ​​such as length, angle, radius, and distance.

[0052] For example, the input 2D engineering drawing is first preprocessed to eliminate interference generated during scanning or drawing. Then, text recognition technology (such as Optical Character Recognition, OCR) is used to convert the text information on the 2D engineering drawing into a recognizable digital format. Next, text analysis algorithms (such as keyword matching algorithms and semantic parsing algorithms) are used to identify and extract text information containing size proportions and structural positional relationships. Simultaneously, pre-defined text deviation correction rules are used to perform preliminary verification and correction of the extracted text information (such as correcting deviations like blurred annotations and unit confusion). The processed information is stored in a text analysis dataset, which includes various dimensional data, relative positional relationships, and corresponding correction instructions. Subsequently, computer vision algorithms or CAD software interfaces are called to identify and extract various geometric elements of the design object from the preprocessed 2D engineering drawing, including lines, arcs, circles, polygons, and annotation lines. Finally, by analyzing the spatial positional relationships of the geometric elements and the dimensions, tolerances, and datum symbols in the engineering drawing, the constraint relationships between the geometric elements are parsed to obtain the geometric constraint conditions.

[0053] Step 1402: Generate model design parameters for the design object based on the adjusted object feature information and geometric constraints.

[0054] Here, model design parameters refer to the set of quantified parameters used to directly drive the construction of the 3D model. They are the fusion information of adjusted object feature information and geometric constraints. Specifically, they may include, but are not limited to: the length, width, height, wall thickness, radius of curvature, hole coordinates, and assembly clearance of each design object.

[0055] For example, the adjusted object feature information and geometric constraints are transformed into specific model design parameters. Specifically, the dimensional ratios in the geometric constraint adjustments can be converted into actual numerical ratios. For instance, the text annotation "1:100" can be converted into model scaling parameters, and the structural positional relationships in the text annotations can be converted into accurate coordinate information. For instance, the text annotation "10cm to the left" can be converted into relative coordinate values. The final model design parameters include the key dimensions, structural positions, and scaling ratios of all design objects in the 3D model.

[0056] Step 1403: Construct a 3D model of the product based on the model design parameters of the design object.

[0057] For example, the API interface of parametric 3D modeling software is called to construct a 3D model of the product using the model design parameters of the design object. Specifically, this may include: first, using a coordinate transformation algorithm to convert the 2D coordinate data in the 2D engineering drawing into corresponding 3D spatial points based on the scale data in the model design parameters; second, constructing a 3D model entity based on the identified model structural features and the converted 3D spatial points to obtain the basic 3D model.

[0058] The coordinate transformation algorithm can calculate the coordinates of a point in three-dimensional space using the following formula (1): ...(1) in, , and These represent the two-dimensional x-plane coordinates, two-dimensional y-plane coordinates, and the preset z-axis height coordinates for constructing the three-dimensional shape in the two-dimensional engineering drawing, respectively. , and It is a size scaling factor, used to scale the planar dimensions and preset z-axis dimensions of a 2D engineering drawing to a scale suitable for 3D modeling software. It is a unit conversion factor determined based on the standard conversion relationship between units, used to convert the units of two-dimensional engineering drawings into the units of three-dimensional modeling software, ensuring the compatibility and accuracy of data between different software.

[0059] In a specific example, the following parameter values ​​can be set: mm, mm, mm, , , This indicates that the size of the two-dimensional engineering drawing needs to be reduced to one-tenth of its original size. : represents the unit conversion factor used to convert millimeters to meters. Based on this, the coordinates of a point in three-dimensional space can be calculated using the following formulas (2) to (4): Rice... (2) Rice... (3) Rice... (4) Based on this, the coordinates of a point in three-dimensional space Meters represent the actual spatial location in a 3D model, ensuring that the scale and spatial layout of the 3D model remain consistent with the original design, while also adjusting it to a scale suitable for further processing in 3D modeling software.

[0060] Therefore, by obtaining the geometric constraints of the design object and generating model design parameters for the design object based on the adjusted object feature information and geometric constraints, the accuracy and efficiency of building the product's 3D model are improved.

[0061] In some embodiments of this application, after performing step 140 above, as Figure 4 As shown, the above data processing method may further include steps 150 to 170.

[0062] Step 150: Using a structural analysis algorithm, perform mechanical performance simulation and structural rationality analysis on the product's three-dimensional model to obtain structural analysis information, which includes abnormal areas and abnormal types.

[0063] Structural analysis algorithms refer to algorithms that analyze the mechanical properties and structural stability of a product's 3D model by simulating different loading conditions and product usage scenarios. Examples include, but are not limited to, finite element analysis algorithms, multibody dynamics simulation algorithms, and topology optimization algorithms. Specifically, mechanical performance simulation refers to the process of calculating mechanical parameters such as stress, strain, displacement, and fatigue life of various parts of the 3D model based on preset mechanical load conditions and constraints using structural analysis algorithms. Structural rationality analysis refers to comparing the parameters obtained from the mechanical performance simulation with preset product design standard thresholds to determine whether the 3D model has structural defects. Anomaly regions refer to specific spatial locations in the 3D model where mechanical parameters do not match the product design standard thresholds. Anomaly types refer to the categories of structural defects corresponding to the anomaly regions, which may include, but are not limited to, stress concentration, insufficient stiffness, and excessive deformation.

[0064] Step 160: Adjust the structure of the abnormal region in the 3D model according to the structural adjustment strategy corresponding to the abnormal type to generate a reference 3D model.

[0065] Among them, structural adjustment strategy refers to pre-defined structural optimization schemes for different anomaly types.

[0066] For example, a mapping table between anomaly types and structural adjustment strategies can be pre-established. Based on the anomaly type, the corresponding target adjustment strategy is matched from the mapping table. Subsequently, the 3D model editing interface is called to perform adjustment operations on the geometric parameters, material parameters, or constraint conditions of the anomaly region in the 3D model, generating a reference 3D model. Specifically, the mapping table can include the following: for anomaly types of stress concentration, the corresponding structural adjustment strategies can include adding fillet transitions, adding stiffeners, and optimizing the cross-sectional shape; for anomaly types of insufficient stiffness, the corresponding structural adjustment strategies can include increasing the wall thickness of key parts, replacing high-strength material parameters, and optimizing the layout of the support structure; for anomaly types of excessive deformation, the corresponding structural adjustment strategies can include adding constraint nodes.

[0067] Step 170: Generate reference 2D engineering drawings of the product based on the reference 3D model.

[0068] For example, after obtaining the reference 3D model, step 150 can be performed to simulate the mechanical properties and analyze the structural rationality of the reference 3D model. If the structural analysis results do not include abnormal areas, the reference 3D model can be orthogonally projected according to preset projection perspectives such as front view, top view, left view, and sectional view using a 3D model projection algorithm to automatically generate a reference 2D engineering drawing of the product; or, the interface of computer-aided design software can be called to import the geometric data of the reference 3D model into the computer-aided design software, and the engineering drawing generation function built into the computer-aided design software can be used to automatically generate a reference 2D engineering drawing including complete annotation information.

[0069] Therefore, by simulating the mechanical properties and analyzing the structural rationality of the product's 3D model, it is possible to accurately locate the structural anomalies and their types. Based on targeted structural adjustment strategies, a reference 3D model is obtained through structural optimization, significantly improving its mechanical stability and structural reliability, effectively mitigating the risk of structural failure in practical applications. Subsequently, a reference 2D engineering drawing is generated based on the optimized reference 3D model, achieving a precise mapping from the 3D model to the 2D engineering drawing. This reduces the manual intervention costs in the conversion process from 3D model design to 2D engineering drawing production, shortens the product design cycle, and improves overall product development efficiency.

[0070] In some embodiments of this application, model optimization can be further carried out based on the reference 3D model to optimize the geometric accuracy and structural stability of the reference 3D model. Specifically, new reference coordinate points can be set, and the installation position and interface docking position of the design objects in the reference 3D model can be adjusted to ensure accurate alignment and seamless connection between the design objects, thereby obtaining an optimized 3D model. Subsequently, based on the optimized 3D model, a 2D engineering drawing of the product can be generated.

[0071] In some embodiments of this application, after performing step 160 above, as Figure 5 As shown, the above data processing method may further include steps 1801 to 1805.

[0072] Step 1801: The reference 3D model is segmented into structural regions using a structural region segmentation algorithm to obtain structural region segmentation results. The structural region segmentation results include N structural regions, where N is an integer greater than 1. The structural region segmentation algorithm is an algorithm that segments the reference 3D model into structural regions based on the product's structural design requirements and functional design requirements.

[0073] Among them, structural region segmentation algorithms may include, but are not limited to: region segmentation algorithms based on functional clustering, region segmentation algorithms based on geometric feature similarity, and region segmentation algorithms based on load distribution.

[0074] For example, a structural region segmentation algorithm can be established based on the characteristic parameters corresponding to the product's structural and functional design requirements. Specifically, a function-based clustering algorithm segments the product's structural regions by grouping components with the same function into the same region; a geometric feature similarity algorithm segments the product's structural regions by grouping parts with geometric feature similarity higher than a preset threshold into the same region; and a load distribution-based algorithm segments the product's three-dimensional model's structural regions based on the product's load-bearing characteristics and load distribution patterns. Subsequently, any of the aforementioned structural region segmentation algorithms is called to segment the reference three-dimensional model's structural regions, outputting a segmentation result comprising N independent structural regions.

[0075] Step 1802: Based on the criticality evaluation index value of each structural region in the N structural regions, determine the structural region type of each structural region. The structural region type includes either the first structural region type or the second structural region type. The structural regions of the first structural region type are more critical in the product design than the structural regions of the second structural region type.

[0076] The critical assessment index value is used to quantify the impact of a structural region on the overall product performance. The critical assessment index value can be determined based on the stress information, functional importance information, and material density information of the structural region. It can be understood that a higher critical assessment index value for a structural region indicates its greater importance in the overall product design; conversely, a lower critical assessment index value indicates its lower importance in the overall product design.

[0077] For example, the key evaluation index value is compared with a preset key scoring threshold. If the key evaluation index value is greater than or equal to the preset key scoring threshold, the first structural region type is determined as the structural region type of the structural region; if the key evaluation index value is less than the preset key scoring threshold, the second structural region type is determined as the structural region type of the structural region.

[0078] Step 1803: Determine the resolution of each structural region based on its structural region type.

[0079] For example, when the structure region type is a first structure region type, the first preset resolution is determined as the resolution of the structure region; when the structure region type is a second structure region type, the second preset resolution is determined as the resolution of the structure region; the first preset resolution is greater than the second preset resolution.

[0080] Resolution refers to the fineness of geometric details of structural regions in a 3D model, specifically manifested in at least one of the following: mesh density, texture refinement, and surface fitting accuracy of the 3D model; the first preset resolution refers to the fine resolution that meets the high-precision analysis of key regions, and the second preset resolution refers to the conventional resolution that meets the basic display and rough analysis of non-key regions.

[0081] For example, a mapping table between two types of structural regions and preset resolutions can be stored in advance; then, all structural regions and their structural region types are matched with a first preset resolution for each first structural region type and a second preset resolution for each second structural region type according to the mapping table.

[0082] Therefore, by configuring differentiated resolutions for the 3D model, the model's data volume in non-critical areas is reduced while maintaining detail accuracy in key areas, thus balancing the accuracy of the 3D model with computational and storage costs. This targeted resolution adjustment not only optimizes the visual effects and functional performance of the 3D model but also improves the processing efficiency of subsequent 3D model loading and analysis.

[0083] Step 1804: Generate the target 3D model according to the resolution of each structural region and the reference 3D model.

[0084] For example, a 3D model editing tool is invoked to perform resolution adjustment operations on each structural region. Specifically, for the key regions of the first structural region type that are identified, the resolution of the key regions can be increased to a first preset resolution by increasing the mesh density, refining the texture processing, and improving the surface fitting accuracy. For the non-key regions of the second structural region type that are identified, the resolution of the non-key regions can be adjusted to a second preset resolution by simplifying the mesh and compressing the features. Subsequently, the model data of all structural regions after adjustment are stitched and merged to ensure that the connection surfaces between structural regions are geometrically continuous and without gaps, thereby generating the final target 3D model.

[0085] During step 1804, the allocation of computing resources for the 3D model can be adjusted based on the differentiated resolution configuration of the 3D model. The computing resources are reallocated according to the resolution of each structural region. Specifically, more rendering and computing resources are allocated to the critical regions of the first structural region type to ensure their performance, while the allocation of rendering and computing resources is reduced accordingly for the non-critical regions of the second structural region type to optimize the overall resource utilization of the 3D model. This differentiated resource allocation strategy not only improves the smoothness of the 3D model display, but also ensures that the performance and efficiency of the 3D model can be maximized under limited resources, generating the adjusted 3D model.

[0086] Step 1805: Display the target 3D model in the display area.

[0087] The display area refers to the visual interface within a display device used to present the 3D model. This display device may include, but is not limited to: computer monitors, mobile device touchscreens, and 3D projection devices specifically designed for industrial design.

[0088] For example, the geometric data of the target 3D model is first converted into a format supported by the display device; then a 3D visualization engine (such as OpenGL or Unity 3D engine) is called to load the converted 3D model data and perform rendering; subsequently, the rendered 3D model image is output to a preset display area.

[0089] Therefore, by segmenting structural regions, the 3D model is decomposed into structural regions with clearly defined structures. Then, by combining the key evaluation index values ​​of each structural region, the criticality classification of the 3D model's regions is achieved. High resolution is configured for critical regions to ensure the integrity of structural details, while low resolution is configured for non-critical regions to reduce the amount of model data, effectively reducing the resource consumption for 3D model storage, computation, and transmission. In this way, the target 3D model generated based on differentiated resolutions can prioritize rendering the fine features of critical regions during the display stage, reducing the burden of redundant data from non-critical regions on the visualization engine, and improving the loading speed and smoothness of the 3D model interaction.

[0090] In some embodiments of this application, before performing step 1802 above, such as Figure 6 As shown, the above data processing method may further include steps 1901 and 1902.

[0091] Step 1901: Obtain stress information, functional importance information, and material density information for each structural region.

[0092] Among them, stress information refers to the maximum stress of the structural region under simulated load; functional importance information refers to parameters characterizing the contribution of the structural region to the realization of the core functions of the product; and material density information refers to the density parameters of the structural region.

[0093] For example, to obtain stress information, the stress analysis algorithm in step 150 can be called to perform stress analysis and obtain stress information for each structural region; to obtain functionally important information, the contribution coefficient of each structural region to the product function can be determined by expert scoring based on the design specifications of the structural region and the product functional requirements, and this can be used as functionally important information; to obtain material density information, it can be obtained from the material property data of the reference three-dimensional model, specifically calculated based on the material distribution and the volume data of the actual reference three-dimensional model.

[0094] Step 1902: Determine the key evaluation index value for each structural region based on the stress information, the first weight corresponding to the stress information, the functional importance information, the second weight corresponding to the functional importance information, the material density information, and the third weight corresponding to the material density information.

[0095] Among them, the first weight, the second weight, and the third weight refer to the proportion coefficients of the corresponding evaluation information in the criticality determination. These can be determined by experts based on the product's design requirements, application environment, and safety standards, and are used to adjust the degree of influence of each evaluation information in the calculation of critical evaluation index values.

[0096] For example, in a mechanics-first scenario, the first weight can be set to 0.5, the second weight can be set to 0.3, and the third weight can be set to 0.2; in a function-first scenario, the first weight can be set to 0.3, the second weight can be set to 0.5, and the third weight can be set to 0.2.

[0097] For example, the key evaluation index value of the structural region can be calculated using the following formula (5). : ... (5) in, Indicates stress information in the structural region. This indicates important functional information about the structural region. This indicates the material density information of the structural region. , and These are the weighting coefficients for stress information, functional importance information, and material density information, respectively.

[0098] In a specific example, if a structural region has the following characteristics and weights: stress information MPa, important functional information Points; Material density g / cm³. Weighting coefficient. , , This indicates that in determining the values ​​of key evaluation indicators, functional importance information has the greatest impact, followed by stress information, and finally material density information.

[0099] Based on this, the key performance indicator values ​​are expressed. The calculation process is as follows: Then, Compared with the preset criticality score threshold (85), it indicates that the structural region is of very high importance in the overall product and is a critical region of the first structural region type.

[0100] Therefore, by comprehensively determining the key evaluation data of the structural region through the three dimensions of mechanical properties, functional value and material density, a comprehensive and objective data source can be provided for the calculation of subsequent key evaluation index values, avoiding the one-sidedness of evaluation based on a single dimension.

[0101] Based on the data processing method provided in the above embodiments, this application also provides specific implementations of a data processing apparatus. Please refer to the following embodiments.

[0102] See Figure 7 The data processing device 200 provided in this application embodiment includes a first acquisition module 201, a first determination module 202, an adjustment module 203, and a model construction module 204.

[0103] The first acquisition module 201 is used to acquire a two-dimensional engineering drawing of the product, which includes object feature information of the design object in the product; the first determination module 202 is used to determine a deviation correction strategy that matches the feature deviation information based on the object feature information of the design object and the feature deviation information of the reference object of the design object; the adjustment module 203 is used to adjust the object feature information of the design object according to the deviation correction strategy to obtain the adjusted object feature information; and the model construction module 204 is used to construct a three-dimensional model of the product based on the two-dimensional engineering drawing and the adjusted object feature information.

[0104] Therefore, the first determining module 202 compares the object feature information of the design object in the two-dimensional engineering drawing of the product obtained by the first acquiring module 201 with the standard and correct reference feature information, which can automatically and accurately determine the feature deviation information. Subsequently, based on the feature deviation information, a deviation correction strategy corresponding to the feature deviation information is matched. Then, by adjusting the module 203, the matched deviation correction strategy is executed, which can automatically adjust the erroneous or incomplete object feature information in the two-dimensional engineering drawing to obtain object feature information that meets the standard. There is no need to manually modify the two-dimensional engineering drawing, which directly reduces the manual operation steps that require users to modify the two-dimensional engineering drawing in related technologies, and reduces the manual operation cost. At the same time, this automated correction is not only more efficient than manual modification, but also avoids secondary errors that may occur during manual modification, further ensuring the accuracy of the two-dimensional engineering drawing. Finally, based on the accurate two-dimensional engineering drawing and the adjusted object feature information, the model building module 204 can efficiently and accurately obtain a three-dimensional model that meets the product design requirements. This automated processing flow shortens the conversion cycle from two-dimensional engineering drawing to three-dimensional model, thereby shortening the overall product design cycle and improving product design efficiency.

[0105] In some embodiments of this application, the data processing device 200 in this application may further include a second determining module, a first generating module, and a second generating module.

[0106] The second determining module is used to perform mechanical performance simulation and structural rationality analysis on the three-dimensional model of the product through structural analysis algorithm to obtain structural analysis information, including abnormal areas and abnormal types; the first generating module is used to adjust the structure of the abnormal areas in the three-dimensional model according to the structural adjustment strategy corresponding to the abnormal type to generate a reference three-dimensional model; the second generating module is used to generate a reference two-dimensional engineering drawing of the product based on the reference three-dimensional model.

[0107] In some embodiments of this application, the object feature information includes size information and material type, the reference object feature information includes reference size information and reference material type, and the feature deviation information includes at least one of the following: size deviation information and material deviation information; the data processing device 200 in the embodiments of this application may further include a third determination module.

[0108] The third determining module is used to determine dimensional deviation information based on the reference dimensional information and the dimensional information when the dimensional information and the reference dimensional information do not match; and to determine material deviation information based on the reference material type and the material type when the material type and the reference material type do not match.

[0109] In some embodiments of this application, the first determining module 202 is specifically used to: determine the feature deviation type based on the feature deviation information of the object feature information of the design object and the reference object feature information of the design object; and determine the reference deviation correction strategy associated with the feature deviation type as the deviation correction strategy based on the correlation between the reference feature deviation type and the reference deviation correction strategy.

[0110] In some embodiments of this application, the data processing device 200 in this application may further include a segmentation module, a fourth determination module, a fifth determination module, a model generation module, and a display module.

[0111] The system comprises the following modules: The segmentation module, after executing the structural adjustment strategy corresponding to the anomaly type to adjust the structure of the abnormal regions in the 3D model and generate a reference 3D model, uses a structural region segmentation algorithm to segment the reference 3D model into structural regions, obtaining the segmentation results. The segmentation results include N structural regions, where N is an integer greater than 1. The structural region segmentation algorithm is based on the product's structural and functional design requirements to segment the reference 3D model into structural regions. The fourth determination module determines the structural region type of each of the N structural regions based on the criticality evaluation index value of each region. The structural region type includes either a first structural region type or a second structural region type, with the first type having a higher criticality in product design than the second type. The fifth determination module determines the resolution of each structural region based on its type. The model generation module generates a target 3D model according to the resolution of each structural region and the reference 3D model. The display module displays the target 3D model in the display area.

[0112] In some embodiments of this application, the data processing device 200 in this application may further include a second acquisition module and a sixth determination module.

[0113] The second acquisition module is used to acquire stress information, functional importance information and material density information for each structural region; the sixth determination module is used to determine the key evaluation index value for each structural region based on the stress information, the first weight corresponding to the stress information, the functional importance information, the second weight corresponding to the functional importance information, the material density information and the third weight corresponding to the material density information.

[0114] In some embodiments of this application, the fifth determining module is specifically used to: determine the first preset resolution as the resolution of the structural region when the structural region type is a first structural region type; determine the second preset resolution as the resolution of the structural region when the structural region type is a second structural region type; and the first preset resolution is greater than the second preset resolution.

[0115] In some embodiments of this application, the model building module is specifically used to: obtain the geometric constraints of the design object based on the two-dimensional engineering drawing; generate the model design parameters of the design object based on the adjusted object feature information and geometric constraints; and construct the three-dimensional model of the product based on the model design parameters of the design object.

[0116] The various modules of the data processing apparatus 200 provided in this application embodiment can realize Figures 1 to 6The functions of each step in the provided data processing method, and their corresponding technical effects, will not be elaborated here for the sake of brevity.

[0117] Figure 8 The diagram shows a schematic representation of the hardware structure of an electronic device provided in some embodiments of this application.

[0118] The electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0119] Specifically, the processor 301 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0120] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 302 may include removable or non-removable (or fixed) media. Where appropriate, memory 302 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 302 is non-volatile solid-state memory.

[0121] In certain embodiments, memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory 302 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the data processing methods in the above embodiments according to this application.

[0122] The processor 301 implements any of the data processing methods described in the above embodiments by reading and executing computer program instructions stored in the memory 302.

[0123] In one example, the electronic device may also include a communication interface 303 and a bus 310. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 310 and complete communication with each other.

[0124] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0125] Bus 310 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 310 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0126] The electronic device can execute the data processing method described in the embodiments of this application, thereby achieving the combination Figures 1 to 7 The data processing methods and apparatus described.

[0127] Furthermore, in conjunction with the data processing methods in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the data processing methods in the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0128] Furthermore, in conjunction with the data processing methods in the above embodiments, this application embodiment can provide a computer program product for implementation. This program product is stored in a storage medium and may specifically include a computer program or instructions. When executed by a processor, the computer program or instructions implement any of the data processing methods in the above embodiments. This program product is executed by at least one processor to implement the various processes of the data processing method embodiments described above, and can achieve the same technical effects. To avoid repetition, further details are omitted here.

[0129] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0130] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0131] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0132] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0133] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A data processing method, characterized in that, include: Obtain a two-dimensional engineering drawing of the product, wherein the two-dimensional engineering drawing includes object feature information of the design objects in the product; Based on the feature deviation information of the design object and the reference object feature information of the design object, a deviation correction strategy matching the feature deviation information is determined; The object feature information of the design object is adjusted according to the deviation correction strategy to obtain the adjusted object feature information; Based on the two-dimensional engineering drawing and the adjusted object feature information, a three-dimensional model of the product is constructed.

2. The method according to claim 1, characterized in that, The method further includes: The mechanical performance of the product and the structural rationality analysis are simulated and analyzed using a structural analysis algorithm to obtain structural analysis information, which includes abnormal areas and abnormal types. According to the structural adjustment strategy corresponding to the anomaly type, the structure of the abnormal region in the three-dimensional model is adjusted to generate a reference three-dimensional model; Based on the reference 3D model, a reference 2D engineering drawing of the product is generated.

3. The method according to claim 1, characterized in that, The object feature information includes size information and material type, the reference object feature information includes reference size information and reference material type, and the feature deviation information includes at least one of the following: size deviation information and material deviation information; The method further includes: If the size information and the reference size information do not match, the size deviation information is determined based on the reference size information and the size information. If the material type and the reference material type do not match, the material deviation information is determined based on the reference material type and the material type.

4. The method according to claim 1 or 3, characterized in that, The step of determining a deviation correction strategy matching the feature deviation information based on the object feature information of the design object and the feature deviation information of the reference object of the design object includes: The feature deviation type is determined based on the feature feature information of the design object and the feature deviation information of the reference object of the design object; Based on the correlation between the reference feature deviation type and the reference deviation correction strategy, the reference deviation correction strategy associated with the feature deviation type is determined as the deviation correction strategy.

5. The method according to claim 2, characterized in that, After adjusting the structure of the abnormal region in the 3D model according to the structural adjustment strategy corresponding to the abnormal type to generate a reference 3D model, the method further includes: The reference 3D model is segmented into structural regions using a structural region segmentation algorithm to obtain structural region segmentation results. The structural region segmentation results include N structural regions, where N is an integer greater than 1. The structural region segmentation algorithm is an algorithm that segments the reference 3D model into structural regions based on the structural design requirements and functional design requirements of the product. Based on the criticality evaluation index value of each of the N structural regions, the structural region type of each structural region is determined. The structural region type includes a first structural region type or a second structural region type. The structural region of the first structural region type is more critical in the product design than the structural region of the second structural region type in the product design. The resolution of each structural region is determined based on its structural region type. Generate a target 3D model according to the resolution of each structural region and the reference 3D model; The target 3D model is displayed in the display area.

6. The method according to claim 5, characterized in that, The method further includes: Obtain stress information, functional importance information, and material density information for each structural region; Based on the stress information of each structural region, the first weight corresponding to the stress information, the functional importance information, the second weight corresponding to the functional importance information, the material density information, and the third weight corresponding to the material density information, the key evaluation index value of each structural region is determined.

7. The method according to claim 5 or 6, characterized in that, The step of determining the resolution of each structural region based on its structural region type includes: When the structural region type of the structural region is the first structural region type, the first preset resolution is determined as the resolution of the structural region; When the structural region type of the structural region is the second structural region type, the second preset resolution is determined as the resolution of the structural region; The first preset resolution is greater than the second preset resolution.

8. The method according to claim 1, characterized in that, The step of constructing a three-dimensional model of the product based on the two-dimensional engineering drawing and the adjusted object feature information includes: Based on the two-dimensional engineering drawing, obtain the geometric constraints of the design object; Based on the adjusted object feature information and the geometric constraints, the model design parameters of the design object are generated; Based on the model design parameters of the design object, construct a three-dimensional model of the product.

9. A data processing apparatus, characterized in that, The data processing device includes: The first acquisition module is used to acquire a two-dimensional engineering drawing of the product, wherein the two-dimensional engineering drawing includes object feature information of the design objects in the product; The first determining module is used to determine a deviation correction strategy that matches the feature deviation information based on the object feature information of the design object and the feature deviation information of the reference object feature information of the design object. The adjustment module is used to adjust the object feature information of the design object according to the deviation correction strategy to obtain the adjusted object feature information; The model building module is used to build a three-dimensional model of the product based on the two-dimensional engineering drawing and the adjusted object feature information.

10. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the data processing method as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the data processing method as described in any one of claims 1-8.

12. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device is able to perform the data processing method as described in any one of claims 1-8.

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