Full-cycle data intelligent mapping method and system based on digital model

Abstract

The invention provides a full-cycle data intelligent mapping method and system based on a digital model, and relates to the technical field of data management. The method comprises the steps of obtaining full-period data, uploaded by a user, of to-be-processed equipment, determining a target data uploading intention of the user, determining product modeling data and semantic business data in the full-period data, generating an initial three-dimensional mapping model of the to-be-processed equipment according to the product modeling data, and sending the initial three-dimensional mapping model to the to-be-processed equipment; and constructing a corresponding relationship between the semantic business data and the product modeling data, and according to the corresponding relationship between the semantic business data and the product modeling data, carrying out attribute labeling on the initial three-dimensional mapping model to obtain a target three-dimensional mapping model. The invention aims to improve the problems of fragmentization, non-uniform data formats and disconnection between the equipment state and the three-dimensional model of the existing equipment data management. The defects of an existing method in the aspects of data sharing, real-time monitoring and decision support are overcome, and the accuracy and the intelligent level of equipment management are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data management technology, and in particular to a full-cycle data intelligent mapping method and system based on a digital model. Background Art

[0002] With the increasing complexity of manufacturing equipment and the deepening adoption of intelligent manufacturing concepts, companies are increasingly prioritizing data management and collaborative utilization throughout the equipment lifecycle. In real-world projects, equipment generates a vast amount of structured and unstructured data at every stage, from design, manufacturing, testing, delivery, to operation and maintenance. This data, which contains a wealth of engineering knowledge and status information, is crucial for improving product quality, shortening delivery cycles, and optimizing maintenance strategies.

[0003] However, these full product data are often scattered across different systems, with problems such as inconsistent formats, unclear semantics, and inconsistent granularity, making them difficult to organize and efficiently utilize. Summary of the Invention

[0004] The embodiment of the present application provides a full-cycle data intelligent mapping method based on a digital model. The embodiment of the present application adopts the following technical solutions: In a first aspect, an embodiment of the present application provides a full-cycle data intelligent mapping method based on a digital model, the method comprising: Obtain the full-cycle data of the device to be processed uploaded by the user and determine the user's target data upload intention; Determine the user's target data upload intention, including product modeling data and semantic business data in the full-cycle data; Generate an initial three-dimensional mapping model of the device to be processed based on the product modeling data of the user's target data upload intention; Based on the user's data upload intention, a corresponding relationship is established between the semantic business data that determines the user's target data upload intention and the product modeling data that determines the user's target data upload intention; Based on the product maintenance attribute data in the semantic business data of the user's target data upload intention, the first-level attribute annotation is performed on the initial three-dimensional mapping model of the user's target data upload intention; based on the product operation status attribute data in the semantic business data of the user's target data upload intention, the second-level attribute annotation is performed on the initial three-dimensional mapping model of the user's target data upload intention; based on the product quality inspection and evaluation attribute data in the semantic business data of the user's target data upload intention, the third-level attribute annotation is performed on the initial three-dimensional mapping model of the user's target data upload intention; Integrate the first-level attribute labeling results of the initial three-dimensional mapping model determined by the user's target data upload intention, the second-level attribute labeling results of the initial three-dimensional mapping model determined by the user's target data upload intention, and the third-level attribute labeling results of the initial three-dimensional mapping model determined by the user's target data upload intention to obtain the target three-dimensional mapping model determined by the user's target data upload intention.

[0005] In an optional embodiment, determining the user's target data upload intention, obtaining the full-cycle data of the device to be processed uploaded by the user, and determining the user's target data upload intention includes: Determine the user type data uploading intention of the user's target data uploading intention based on the user type identifier carried in the full-cycle data of the user's target data uploading intention; Determine the user's target data upload intention and the user's data format upload intention according to the data format of the full-cycle data of the user's target data upload intention; The user's target data upload intention is determined according to a matching relationship between the user type data upload intention for determining the user's target data upload intention and the data format upload intention for determining the user's target data upload intention.

[0006] In an optional embodiment, determining the product modeling data and semantic business data in the full-cycle data based on the data type identifier and semantic recognition result carried by the full-cycle data includes: Based on the semantic recognition results, the full-cycle data is screened at the first level to obtain the first data set; Performing a second level of screening on the full-cycle data according to the data type identifier to obtain a second data set; According to the intersection result of the first data set and the second data set, the product modeling data and the semantic business data in the full-cycle data are determined.

[0007] In an optional embodiment, generating an initial three-dimensional mapping model of the device to be processed based on the product modeling data includes: Generate a three-dimensional geometric skeleton model of the equipment to be processed based on the structural topology data and geometric dimension data in the product modeling data; According to the assembly relationship data and component identification information in the product modeling data, the three-dimensional geometric skeleton model is constructed at the component level and the identification is bound to generate an initial three-dimensional mapping model of the device to be processed.

[0008] In an optional embodiment, generating a three-dimensional geometric skeleton model of the device to be processed based on the structural topology data and geometric dimension data in the product modeling data includes: Analyze product modeling data to determine the assemblies that constitute the 3D geometric skeleton model and the assembly identifiers corresponding to the assemblies; Determine the geometric dimension data of each assembly according to the geometric dimension data in the product modeling data, and generate a plurality of three-dimensional geometric skeleton models of each assembly according to the geometric dimension data of each assembly; According to the structural topology data in the product modeling data, the topological connection relationship between each assembly is determined, and according to the topological connection relationship between each assembly, each assembly is assembled to obtain a three-dimensional geometric skeleton model of the device to be processed.

[0009] In an optional implementation, building a correspondence between semantic business data and product modeling data based on the user's data upload intention includes: Parse semantic business data and extract semantic anchor feature information related to the data upload intention. The semantic anchor feature information at least includes equipment location, component name, number, fault type, operation time, etc. Based on the semantic matching strategy corresponding to the data upload intention, the semantic anchor feature information is used to screen and associate the target model objects in the product modeling data, and a binding relationship is established between the semantic business data and the product modeling data.

[0010] In an optional embodiment, integrating the first-level attribute labeling results of the initial three-dimensional mapping model, the second-level attribute labeling results of the initial three-dimensional mapping model, and the third-level attribute labeling results of the initial three-dimensional mapping model to obtain a target three-dimensional mapping model includes: Unified parsing and format standardization of the multi-level attribute annotation results of the initial 3D mapping model; Establish first-level attribute annotation results, second-level attribute annotation results, and third-level attribute annotation results based on component nodes Attribute fusion mapping relationship; Based on the attribute fusion mapping relationship, a target three-dimensional mapping model is generated.

[0011] In a second aspect, an embodiment of the present application provides a full-cycle data intelligent mapping system based on a digital model, the system comprising: The acquisition module is used to obtain the full-cycle data of the device to be processed uploaded by the user, determine the user's target data upload intention, and determine the product modeling data and semantic business data in the full-cycle data; A first determination module is used to determine product modeling data and semantic business data in the full-cycle data; A first model building module is used to generate an initial three-dimensional mapping model of the equipment to be processed based on the product modeling data; The second determination module is used to build a correspondence between semantic business data and product modeling data based on the user's data upload intention; The second model construction module is used to perform first-level attribute annotation on the initial three-dimensional mapping model based on the product maintenance attribute data in the semantic business data, perform second-level attribute annotation on the initial three-dimensional mapping model based on the product operation status attribute data in the semantic business data, and perform third-level attribute annotation on the initial three-dimensional mapping model based on the product quality inspection and evaluation attribute data in the semantic business data, and integrate the first-level attribute annotation results of the initial three-dimensional mapping model, the second-level attribute annotation results of the initial three-dimensional mapping model, and the third-level attribute annotation results of the initial three-dimensional mapping model to obtain the target three-dimensional mapping model.

[0012] In an optional embodiment, the acquisition module includes: A first determination submodule is configured to determine the user's user type data upload intention based on the user type identifier carried in the full-cycle data; The second determination submodule is used to determine the user's data format upload intention based on the data format of the full cycle data; The third determination submodule is used to determine the user's target data upload intention based on the matching relationship between the user's type data upload intention and the data format upload intention.

[0013] In an optional embodiment, the first model building module includes: The skeleton model construction submodule is used to generate a three-dimensional geometric skeleton model of the device to be processed based on the structural topology data and geometric dimension data in the product modeling data; The mapping submodule is used to construct the component hierarchy and bind the identification of the three-dimensional geometric skeleton model according to the assembly relationship data and component identification information in the product modeling data, so as to generate an initial three-dimensional mapping model of the device to be processed.

[0014] In an optional embodiment, the skeleton model construction submodule includes: A parsing unit is used to parse the product modeling data and determine the assemblies constituting the three-dimensional geometric skeleton model and the assembly identifiers corresponding to the assemblies; A determination unit is used to determine the geometric dimension data of each assembly according to the geometric dimension data in the product modeling data, and generate a plurality of three-dimensional geometric skeleton models of each assembly according to the geometric dimension data of each assembly; The splicing unit is used to determine the topological connection relationship between each assembly according to the structural topological data in the product modeling data, and assemble each assembly according to the topological connection relationship between each assembly to obtain a three-dimensional geometric skeleton model of the equipment to be processed.

[0015] In a third aspect, the present application also provides an electronic device, comprising: a memory and one or more processors, the memory being coupled to the processor; wherein computer program code is stored in the memory, the computer program code comprising computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the method in any possible design mode of the above-mentioned first aspect.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium comprising computer instructions; when the computer instructions are executed on an electronic device, the electronic device executes the method in the first aspect and any possible design thereof.

[0017] In a fifth aspect, the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute the method in the first aspect and any possible design thereof.

[0018] This application provides a full-cycle data intelligent mapping method based on a digital model. By deeply integrating various types of data throughout the equipment's life cycle (such as design, manufacturing, operation, maintenance, etc.) with the equipment's three-dimensional digital model, it solves the problems of data silos, information fragmentation, and poor real-time performance in traditional equipment management methods. Through intelligent mapping technology, product modeling data and semantic business data (such as maintenance records, operating status, quality inspection results, etc.) are accurately mapped and applied to the three-dimensional model, which not only improves the intelligence and accuracy of equipment management, but also realizes efficient integration and real-time sharing of cross-system data. Combined with visual display, users can intuitively obtain the comprehensive status of the equipment, quickly perform fault diagnosis, risk prediction, and maintenance decisions, thereby optimizing maintenance strategies, improving product quality, shortening delivery cycles, and significantly reducing operation and maintenance costs.

[0019] Among them, the technical effects of the second to fifth aspects refer to the technical effects of the first aspect and any of its embodiments, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A flowchart of the steps of a full-cycle data intelligent mapping method based on a digital model provided in an embodiment of the present application; Figure 2 A schematic structural diagram of a full-cycle data intelligent mapping system based on a digital model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The terms used in the following embodiments are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and claims of the present application, the singular expressions "a", "a", "above", "the" and "this" are intended to also include expressions such as "one or more", unless there is a clear contrary indication in the context. It should also be understood that in the following embodiments of the present application, "at least one", "one or more" refer to one or more (including two). The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0022] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments.

[0023] In the following, the terms "first," "second," etc., are used for descriptive convenience only and should not be understood as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Thus, a feature defined as "first," "second," etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more. For example, "plurality of processing units" refers to two or more processing units.

[0024] Furthermore, in the embodiments of the present application, "upper," "lower," "left," and "right" are not limited to being defined relative to the orientation of the components schematically shown in the drawings. It should be understood that these directional terms can be relative concepts. They are used for relative description and clarification, and may change accordingly based on changes in the orientation of the components in the drawings. In the drawings, the thickness of layers and regions is exaggerated for clarity, and the dimensional ratios between the components in the drawings do not reflect the actual dimensional ratios.

[0025] In the embodiments of this application, unless otherwise specified or limited, the term "connection" should be understood in a broad sense. For example, "connection" can mean fixed connection, detachable connection, or integration; it can mean direct connection or indirect connection through an intermediate medium. In addition, the term "electrical connection" can mean direct electrical connection or indirect electrical connection through an intermediate medium.

[0026] In the embodiments of the present application, the term "module" generally refers to a functional structure divided according to logic. The "module" can be implemented by pure hardware or a combination of hardware and software. In the embodiments of the present application, "and / or" describes the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, B exists alone, and A and B exist at the same time.

[0027] In the embodiments of this application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] With the increasing complexity of manufacturing equipment and the accelerated implementation of intelligent manufacturing concepts, manufacturing companies are increasingly prioritizing the unified management and collaborative utilization of data generated throughout the equipment lifecycle. In real-world engineering scenarios, equipment generates a vast amount of heterogeneous, multi-source business data at every stage, from project initiation, design and development, manufacturing and processing, quality inspection, delivery and commissioning, to operation and maintenance, troubleshooting, and even decommissioning. This business data contains critical engineering knowledge, process parameters, operating status, and quality information, and is crucial for supporting product quality control, lifecycle management, and intelligent analytical decision-making. However, due to traditional system architectures and data management approaches, current data management throughout the lifecycle faces the following common bottlenecks: First, data is distributed across multiple departments and information systems, resulting in significant data silos. A lack of standardized data interfaces and semantic mapping mechanisms between different systems hinders data flow and sharing. Second, data formats vary and standards are inconsistent, and data from different sources exhibit significant differences in structure, semantics, and granularity, hindering efficient data integration and correlation analysis. Furthermore, much critical data is still stored in manual documents, images, or tables, lacking clear semantics and structure. These documents lack automated parsing and intelligent recognition, making it difficult to support the construction of complex models and real-time data-driven visualizations. Traditional project management and operations systems often rely on static reports or preset views, which struggle to reflect real-time data changes and complex state interactions. This results in delayed project status assessments, untimely risk identification, and insufficient decision-making basis.

[0029] To address the aforementioned issues, this application proposes the following technical concept: By performing structured extraction and semantic recognition on multi-source heterogeneous data generated during the design, manufacturing, testing, delivery, and operation and maintenance stages of equipment, product modeling data and semantic business data are distinguished, and an initial three-dimensional mapping model is constructed based on the product modeling data. Furthermore, through methods such as semantic anchor point recognition and component identification resolution, a correspondence is established between the semantic business data and the three-dimensional model, enabling attribute annotation and intelligent embedding of semantic information in the model. Ultimately, a target three-dimensional mapping model is formed that integrates geometric structure and business semantics and supports dynamic updates and visual analysis.

[0030] In a first aspect, an embodiment of the present application provides a full-cycle data intelligent mapping method based on a digital model, the method comprising: S101: Acquire the full-cycle data of the device to be processed uploaded by the user, and determine the user's target data uploading intention.

[0031] In this embodiment, the equipment to be processed refers to equipment involved in various stages such as design, manufacturing, testing, delivery, use, maintenance and operation and maintenance during the entire life cycle of the equipment. It includes any equipment that requires data processing, status monitoring, fault diagnosis, maintenance or optimization. Specifically, the equipment to be processed can be industrial equipment, mechanical equipment, electronic products, transportation tools, smart devices, etc. in the production line. Users may include designers, manufacturers, testers, operation and maintenance personnel, etc., who generate a large amount of business data at different stages such as equipment design, manufacturing, testing, delivery and operation and maintenance. In order to achieve coverage collection of data throughout the entire life cycle, the system identifies the identities of various users based on preset user permissions and task nodes, and guides them to upload data content that is within the scope of their permissions. At the same time, to ensure the traceability and consistency of data, the system has established a unified data collection interface and version management mechanism, which can record metadata such as the uploading user, time, source system, and associated project nodes of each piece of data.

[0032] A user's target data upload intent refers to the business objectives implicit in uploading full-cycle data at a specific lifecycle point, along with its intended application path within the data management system. Examples include driving structural modeling, supplementing production history, annotating test results, recording fault events, updating maintenance status, or compiling historical archives. This upload intent reflects the business semantics of the data and its corresponding model entity scope, impact dimensions, and attachment strategies. This can be determined through comprehensive analysis of user type identification, data structure characteristics, time tags, data sources, and associated metadata.

[0033] By identifying the user's target data upload intent, semantic analysis of data upload behavior and definition of processing objectives can be achieved, providing the foundation for the system to build a clear data processing path. This process not only helps clarify the "business focus of data content," but also automatically determines data organization and mapping targets, improving the system's classification and processing capabilities for heterogeneous, full-cycle data and improving modeling and integration efficiency.

[0034] The specific steps include: S1011: Determine the user type data uploading intention of the user according to the user type identifier carried in the full cycle data; S1012: Determining the user's data format upload intention based on the data format of the full-cycle data; S1013: Determine the user's target data upload intention based on the matching relationship between the user's type data upload intention and the data format upload intention.

[0035] In the implementation of S1011 to S1013, first, the user type data upload intention is determined based on the user type identifier carried in the full-cycle data. The user type identifier is pre-issued by the upper platform before the user accesses the system. It has unique and structured properties and is usually bound to the user account permissions, business role and project stage. After identifying the identifier, the system determines the regular upload purpose and processing scope of this type of user in the current business context based on the built-in role intention mapping rule set. As an example, design users are usually related to structural modeling or design changes; manufacturing users correspond to process data collection or quality control; detection users focus on reporting detection results or anomaly record annotation; operation and maintenance users pay attention to operation status logs or fault handling records, thereby forming the user type data upload intention. Secondly, the system parses the data format of the uploaded full-cycle data, including but not limited to file encapsulation type, structure specifications, data field composition and its expression granularity, and combines the format type with its conventional usage in the business process to infer the processing target pointed to by the data format and form the data format upload intention. For example, data in a BOM form format might be intended to bind to a material information model, image data might be used to identify surface defects, and structured messages might be used to collect performance metrics. Finally, based on the matching relationship between the user's data upload intent type and the data format, the user's target data upload intent is comprehensively inferred. For example, if the user role is an inspector and the data format is an image file, the matching result might be: defect image annotation.

[0036] S102: Determine product modeling data and semantic business data in the full-cycle data.

[0037] In this implementation, full-lifecycle data related to the equipment being processed is first acquired from multiple data sources. This data includes, but is not limited to, CAD drawings, 3D modeling files, and bills of materials (BOMs) generated during the design phase; process parameters and assembly process records during the manufacturing phase; quality inspection reports and defect images during the testing phase; acceptance records and operating instructions during the delivery phase; and repair work orders, monitoring logs, and maintenance plans generated during the operation and maintenance phase. Because this data is often stored in heterogeneous formats (e.g., PDF, Excel, images, 3D model files) across various systems (e.g., PDM, MES, SCADA, PLM, ERP, etc.), the system first connects to the data sources and performs preliminary data aggregation and standardization to ensure consistency and accuracy in subsequent analysis. After data aggregation, the system uses pre-defined semantic recognition models and data classification rules to perform content analysis and intelligent parsing of the full-lifecycle data. Product modeling data primarily refers to drawings, 3D model files, and their associated attributes, containing information such as equipment structure, dimensions, and connection relationships, which form the fundamental geometric semantics of 3D mapping models. Semantic business data, on the other hand, refers to textual and tagged information related to equipment status, task execution, and maintenance requirements, such as inspection conclusions, fault descriptions, repair recommendations, responsible units, and timelines. This type of data often exists in document records, report descriptions, image annotations, or system logs, requiring in-depth understanding and extraction through technologies such as natural language processing (NLP) and image recognition.

[0038] The specific steps may include: S1021: Determine the product modeling data and semantic business data in the full-cycle data based on the data type identifier and semantic recognition results carried by the full-cycle data.

[0039] In this embodiment, the system then identifies product modeling data and semantic business data within the full-cycle data based on the data type identifier and semantic recognition results. For structured data, the system directly classifies it based on the data type identifier attached during upload. For unstructured data (such as text, images, and tables), the system uses a pre-defined semantic recognition model (such as a natural language processing model or image recognition model) to extract keywords, behavioral characteristics, and semantic context to infer the data's category. Specifically, if the data contains information about the equipment's geometric dimensions, structural topology, or assembly relationships, the system identifies it as product modeling data. If the data reflects semantic information such as equipment status, usage behavior, maintenance records, and operational procedures, it identifies it as semantic business data. Through this process, the system automatically classifies and organizes massive amounts of heterogeneous data, providing a high-quality, structured input data source for subsequent intelligent processing processes such as 3D modeling and attribute annotation, thereby improving the system's data processing efficiency and semantic fusion capabilities.

[0040] In a feasible implementation, determining product modeling data and semantic business data in the full-cycle data based on the data type identifier and semantic recognition result carried by the full-cycle data includes: Based on the semantic recognition results, the full-cycle data is screened at the first level to obtain the first data set; Performing a second level of screening on the full-cycle data according to the data type identifier to obtain a second data set; According to the intersection result of the first data set and the second data set, the product modeling data and the semantic business data in the full-cycle data are determined.

[0041] In this embodiment, the uploaded full-cycle data content is analyzed based on a semantic recognition model (such as natural language processing or image recognition technology) to identify information containing key business semantics, such as equipment structural characteristics, defect descriptions, maintenance behaviors, etc., and data with business significance are screened out to form a first data set; then, the system performs structured classification on the full-cycle data according to the data type identifier bound to each data entry when uploading, to form a second data set; finally, the system takes the intersection of the first data set and the second data set to ensure that the screening results have both valid semantic content and meet the structural type requirements, thereby determining the product modeling data and semantic business data in the full-cycle data.

[0042] For example, suppose the uploaded data includes a 3D structural model file A (data type identified as "CAD model" and semantic recognition results include "assembly relationship, structural dimensions"), an inspection report B (type identified as "inspection report" and content described as "crack depth, weld defects"), an operation and maintenance log C (type identified as "operation and maintenance record" and content described as "parts replacement, troubleshooting"), and a welding image D (type identified as "inspection image" and AI recognition result as "defective area"), then file A is classified as product modeling data, and files B, C, and D are classified as semantic business data.

[0043] S103: Generate an initial three-dimensional mapping model of the device to be processed based on the product modeling data.

[0044] In this implementation, the geometric information contained in the product modeling data is first parsed. This data typically contains information such as the device's geometric dimensions, structural topology, and component relationships, and is typically stored in the form of CAD drawings, 3D models, and assembly instructions. By parsing this data, the 3D geometric shapes of each device component and their connection relationships, such as position, angle, and connection points, are extracted. This geometric data forms the basis for constructing the initial 3D mapping model.

[0045] Next, the extracted geometric data is converted into 3D coordinates, and a preliminary 3D model framework is constructed based on these coordinates. During this process, 3D modeling algorithms (such as B-Rep modeling, mesh modeling, and SolidModeling) are used to generate the initial 3D skeleton of the device. Specifically, each component is positioned appropriately in 3D space based on its size, shape, and positional relationships, generating the basic 3D structure of the entire device.

[0046] After geometric modeling is complete, the model undergoes preliminary verification and optimization. For example, the connections between components are checked to ensure they meet design specifications and to ensure there are no deformation, collision, or other issues. While the initial 3D mapping model of the device captures its shape and basic structure, it lacks business-level information and detailed attribute annotation.

[0047] After completing the initial 3D modeling, metadata such as model number and component identification is created for the 3D model to facilitate subsequent integration with semantic business data. This initial 3D mapping model is merely a physical representation of the device. Subsequent steps will further enrich the model by linking it with semantic business data, ensuring it reflects the device's true attributes, usage status, maintenance requirements, and other information.

[0048] The specific steps may include: S1031: Generate a three-dimensional geometric skeleton model of the device to be processed based on the structural topology data and geometric dimension data in the product modeling data.

[0049] In this implementation, the device's structural topology data is first extracted from the product modeling data. This data describes the relationships and connections between the device's components. This structural topology data typically includes each component's relative position, connection relationships, assembly sequence, connection points, and other structural information. By parsing this data, it is possible to identify how the device's components are assembled and the dependencies between them.

[0050] Next, the device's geometric dimensional data is extracted from the product modeling data. This data typically includes each component's dimensional parameters (such as length, width, height, and diameter), as well as its geometric shape characteristics (such as rectangle, circle, sphere, and complex free-form shapes). This geometric data is used to define the component's shape, ensuring that each component's dimensions in 3D space match the design requirements.

[0051] Combining structural topology data with geometric dimension data, a 3D geometric skeleton model of the device is generated using 3D modeling technology. In this step, the positions and shapes of all components are placed in 3D space based on their geometric dimensions and topological relationships, forming a preliminary 3D framework for the device. This model, called a 3D geometric skeleton model, represents the basic structure and shape of the device but does not include any additional information such as specific materials, colors, or surface treatments.

[0052] In a feasible implementation, generating a three-dimensional geometric skeleton model of the device to be processed includes: Analyze product modeling data to determine the assemblies that constitute the 3D geometric skeleton model and the assembly identifiers corresponding to the assemblies; Determine the geometric dimension data of each assembly according to the geometric dimension data in the product modeling data, and generate a plurality of three-dimensional geometric skeleton models of each assembly according to the geometric dimension data of each assembly; According to the structural topology data in the product modeling data, the topological connection relationship between each assembly is determined, and according to the topological connection relationship between each assembly, each assembly is assembled to obtain a three-dimensional geometric skeleton model of the device to be processed.

[0053] In this implementation, the product modeling data is first parsed to identify and extract the various assemblies that make up the device. Each assembly may be composed of multiple parts, which are integrated according to design requirements to form the functional units of the device. For example, a piece of mechanical equipment may have multiple assemblies, such as "power," "transmission," and "control panel." Each assembly is assigned an assembly identifier, which uniquely identifies the assembly and ensures accurate tracking of its properties, location, and function during subsequent processing.

[0054] Next, the geometric dimensions of each assembly, such as length, width, height, thickness, diameter, and curvature, are determined based on the geometric dimensional data contained in the product modeling data. This geometric dimensional data is typically provided by CAD models, design documents, or other structured data, ensuring that each assembly's dimensional information is consistent with the design requirements. Based on this dimensional data, a 3D geometric skeleton model is generated for each assembly. Each assembly is then treated as an independent 3D structural block with precise geometry and dimensions. The generated 3D geometric skeleton model provides the foundation for subsequent 3D assembly and assembly.

[0055] Based on this, the topological connections between the various assemblies are identified based on the structural topology data within the product modeling data. This involves identifying how the assemblies are connected through elements such as interfaces, connection points, and assembly holes. This topological data typically defines the relative positions, mating methods, and connection types between assemblies. For example, assembly A connects to assembly B via screw holes, or assembly C fits into a slot within assembly D. Based on these topological connections, the assemblies are automatically assembled using appropriate methods to form a complete 3D geometric skeleton model of the device being processed.

[0056] This 3D geometric skeleton model is more than just a simple geometric shape; it accurately represents the connections and relative positions between the various functional modules and components of the device being processed, laying the foundation for subsequent device analysis, simulation, and optimization. After the 3D geometric skeleton model is completed, the device's physical structure is essentially formed and can be further refined and enhanced, such as by adding information such as material properties, color, and kinematic characteristics.

[0057] S1032: Based on the assembly relationship data and component identification information in the product modeling data, perform component hierarchy construction and identification binding on the three-dimensional geometric skeleton model to generate an initial three-dimensional mapping model of the device to be processed.

[0058] In this implementation, the assembly relationship data is first used to identify how the various components of the device are assembled. This data typically describes the connection between different components, such as which components are nested together, which components are combined through connection points or interfaces, and how they are connected together through bolts, welding, clamping, etc. By parsing the assembly relationship data, the overall structure of the device and the logical and physical connection relationships between various components can be understood.

[0059] Next, each component in the 3D geometric skeleton model is assigned a unique identifier based on its component identification information. This information typically includes the component's number, name, material type, and functional category, clearly identifying each component's role, function, and location within the device. By binding this identification information to the corresponding component in the 3D model, each component can be accurately identified, tracked, and managed within the model.

[0060] Next, based on the parsed assembly relationship data and component identification information, a component hierarchy is constructed for the 3D geometric skeleton model. Component hierarchy construction involves breaking down the device into different hierarchical structures based on the interdependencies and assembly relationships between components, starting with the lowest-level components, moving to mid-level subassemblies, and finally to the top-level complete assembly. Components at each level are assigned specific functions and positions so that they are logically presented within the overall device structure. This process helps build a clear device structure tree, thereby improving the efficiency of device management, maintenance, and updates.

[0061] Finally, this hierarchical structure and identification information are bound to the 3D geometric skeleton model to generate an initial 3D mapping model. This initial 3D mapping model not only contains the equipment's geometric information and assembly relationships, but also integrates information such as the function, category, and location of each component through identification binding. This model not only provides visual presentation capabilities but also provides business information related to equipment operation and maintenance.

[0062] S104: Building a correspondence between semantic business data and product modeling data based on the user's data uploading intention.

[0063] In this implementation, semantic business data must first be parsed and identified. This data is typically unstructured or semi-structured and contains information about equipment status, usage, maintenance history, and more. Semantic business data may originate from multiple business domains, such as equipment operating data, sensor data, maintenance records, inspection reports, and operation logs. This data itself often cannot be directly integrated with a 3D model. Therefore, semantic recognition technologies (such as natural language processing, image recognition, and data mining) are needed to convert this unstructured data into structured semantic information. For example, keywords such as "fault code," "part replacement," and "welding defect" can be identified in text and labeled as specific business attributes.

[0064] Next, each data item in the semantic business data is calibrated based on the geometric information contained in the product modeling data (such as each component's identification, dimensions, and location). For example, if a maintenance record mentions a drive shaft failure in a piece of equipment, the "drive shaft failure" in the semantic business data is associated with the "drive shaft component" in the 3D model, ensuring that the maintenance record corresponds to the correct component in the 3D model. Business data can also be accurately mapped to specific components or assemblies in the equipment model using component identification information, location coordinates, or topological relationships.

[0065] At the same time, business data is combined with the 3D model's lifecycle phases based on time information, such as when a fault occurred and when repairs were completed. In actual operation, a component may have different maintenance records or operating status changes at different points in time. Based on this time information, the 3D model display status is dynamically adjusted to reflect the current status of the equipment.

[0066] Finally, the correspondence between the generated semantic business data and the product modeling data is stored in a database in some form for subsequent analysis, query, and visualization. This mapping relationship enables the geometric information of the device to be combined with the business information in subsequent display or analysis. The specific steps include: S1041: Parse semantic business data and extract semantic anchor feature information, where the semantic anchor feature information includes at least equipment location, component name, number, fault type, and operation time; S1042: Based on a preset semantic matching strategy, the semantic anchor feature is matched with the corresponding component identification information to establish a binding relationship between the semantic business data and the product modeling data.

[0067] In the implementation of S1041 to S1042, semantic anchor feature information is first extracted from the semantic business data of the equipment. This feature information typically includes the equipment's location, component name, number, fault type, and operation time. This information often appears in unstructured or semi-structured form and may come from data sources such as maintenance records, fault logs, and operation reports. By performing semantic analysis on this data (using techniques such as natural language processing and image recognition), core information related to the equipment and components can be extracted. Next, based on a pre-set semantic matching strategy, the semantic anchor feature information is precisely matched with the component identification information in the product modeling data. This is first performed through keyword matching, for example, matching "drive shaft" with "drive shaft component identification" in the product modeling data. If the component name or number mentioned in the semantic business data matches the identification information in the product modeling data, they are bound together. Furthermore, matching is performed based on the equipment's location. If the semantic business data mentions that the equipment is located in "Workshop A" and the product modeling data also contains the equipment's specific location, the correspondence between the two data sets is further confirmed. For fault types, the fault type in the semantic business data is compared with the fault description in the equipment modeling data to ensure a matching relationship between the two. For example, "overload fault" is associated with the fault record of the relevant component in the equipment model. In addition, operation time is also an important matching feature. The time information is compared with the maintenance record or inspection log of the equipment to ensure that the equipment's fault and repair history can correspond to the timeline in its three-dimensional model. In addition to simple matching strategies, topological structure matching is also considered, that is, the relationship between semantic data and equipment models is determined by analyzing the assembly relationship of the equipment and the relative position between components. Through these matching strategies, a binding relationship between semantic business data and product modeling data can be established to ensure that the business data of each component (such as fault records, maintenance logs, inspection reports, etc.) can be accurately mapped to its corresponding component in the three-dimensional model.

[0068] S105: Based on the product maintenance attribute data in the semantic business data, the initial three-dimensional mapping model is annotated with first-level attributes. Based on the product operation status attribute data in the semantic business data, the initial three-dimensional mapping model is annotated with second-level attributes. Based on the product quality inspection and evaluation attribute data in the semantic business data, the initial three-dimensional mapping model is annotated with third-level attributes. The first-level attribute annotation results of the initial three-dimensional mapping model, the second-level attribute annotation results of the initial three-dimensional mapping model, and the third-level attribute annotation results of the initial three-dimensional mapping model are integrated to obtain a target three-dimensional mapping model.

[0069] In this embodiment, first, the first-level attribute annotation is performed for each component in the initial three-dimensional mapping model based on the product maintenance attribute data in the semantic business data. Maintenance attribute data usually contains information about the equipment's maintenance history, maintenance records, fault repair status, etc., such as records such as "The drive motor was replaced in December 2019" and "The hydraulic system was inspected and repaired in June 2022." This information will be mapped to the corresponding components in the three-dimensional model. For example, "drive motor replacement" will be annotated to the drive motor component in the three-dimensional model, and the corresponding maintenance time, maintenance items, maintenance frequency, etc. will be displayed. This annotation process allows the historical maintenance status of each component to be clearly presented and helps users quickly understand the maintenance status of the equipment.

[0070] Next, the 3D mapping model is annotated with second-level attributes based on the product operating status attribute data in the semantic business data. Operating status attribute data typically describes the current working status of the equipment, operating efficiency, fault warnings, and other information. For example, data such as "drive motor temperature is too high" or "pump is operating normally." This operating status data will be added to the corresponding components of the 3D model to help users monitor the operating health of the equipment in real time. If the operating status of a component is abnormal (such as excessive temperature, low pressure, etc.), this status can be intuitively displayed in the model through annotation, allowing users to promptly identify potential problems.

[0071] In the third step, the three-dimensional mapping model is annotated with third-level attributes based on the product quality inspection and evaluation attribute data in the semantic business data. Quality inspection and evaluation attribute data typically includes equipment inspection reports, test results, performance evaluations, and other data. For example, data such as "the equipment's compressor has passed quality inspection" or "the material strength of a certain component does not meet the requirements" will be mapped to the corresponding components to display the quality status of each component or whether it has passed inspection. By annotating quality inspection and evaluation data, users can intuitively understand whether the equipment meets quality standards and whether there are any quality risks, further improving the level of refinement of equipment management.

[0072] Finally, the attribute annotation results of the 3D mapping model from the first three steps are integrated, including the first-level (maintenance attributes), second-level (operating status), and third-level (quality inspection and evaluation) annotation results, and this annotation information is fused into the target 3D mapping model. The integrated target 3D mapping model not only contains the equipment's geometric structure but also key information such as its maintenance history, current operating status, and quality inspection results. This model can comprehensively reflect the current status of the equipment and its historical data, providing a dynamic, real-time view of equipment management. In this way, users can intuitively see the status of each component in the 3D model, supporting functions such as equipment fault diagnosis, performance optimization, and preventive maintenance.

[0073] In a feasible implementation, integrating the first-level attribute annotation results of the initial three-dimensional mapping model, the second-level attribute annotation results of the initial three-dimensional mapping model, and the third-level attribute annotation results of the initial three-dimensional mapping model to obtain the target three-dimensional mapping model includes: Unified parsing and format standardization of the multi-level attribute annotation results of the initial 3D mapping model; Establish first-level attribute annotation results, second-level attribute annotation results, and third-level attribute annotation results based on component nodes Attribute fusion mapping relationship; Based on the attribute fusion mapping relationship, a target three-dimensional mapping model is generated.

[0074] In this implementation, all annotated attributes at all levels (first-level maintenance attributes, second-level operating status attributes, and third-level quality inspection and assessment attributes) are first parsed and standardized. Because attribute data from different sources may have inconsistent formats, all annotated results need to be standardized. Specifically, the format and units of each annotated data item (such as time, quantity, and temperature units) are checked and converted to a unified standard format. For example, all dates are standardized to "YYYY-MM-DD," and different temperature units (such as Celsius and Fahrenheit) are standardized to Celsius. This ensures that subsequent data processing and display are not affected by format differences.

[0075] After standardizing the annotation results, an attribute fusion mapping relationship will be established based on the component nodes in the 3D model (that is, the node identifier of each specific component or assembly). As the basic unit in the 3D model, the component node represents each component or component in the model. Different levels of attribute annotations (such as maintenance history, operating status, quality inspection evaluation) will be associated with the node information of each component. For example, the maintenance attribute "drive shaft failure" will be matched with the drive shaft component node, and then the detailed information of the failure (such as failure time, maintenance process, etc.) will be annotated to the node. In this way, by establishing a fusion mapping relationship for each attribute annotation, all business data can be accurately bound to each component of the 3D model, realizing multi-dimensional data integration.

[0076] Finally, based on the established attribute fusion mapping relationships, all annotated attribute information (including maintenance data, operating status, quality assessment, etc.) is applied to each component in the 3D model to generate the final target 3D mapping model. This target model not only contains the equipment's geometric structure information but also integrates all business data to provide a comprehensive, dynamic, and visual digital model of the equipment. Information such as each component's status, history, and fault conditions are presented intuitively, facilitating equipment management, condition monitoring, and decision-making analysis.

[0077] This application provides a full-cycle data intelligent mapping method based on a digital model. By closely integrating various business data throughout the equipment's life cycle with the equipment's three-dimensional digital model, it achieves comprehensive intelligent equipment management. First, the method integrates data from different stages and types (such as design, manufacturing, testing, operation, maintenance, etc.) into the three-dimensional model through intelligent mapping technology. This allows the business data of each component (such as maintenance records, operating status, quality assessment, etc.) to be combined with its geometric structure, assembly relationship and other information to form a comprehensive digital equipment model. This model not only reflects the physical form of the equipment, but also presents the equipment's operating status and maintenance status in real time, thereby effectively improving the level of equipment management throughout its life cycle.

[0078] Secondly, the intelligent mapping method of this application can overcome the data silos and information fragmentation problems existing in existing equipment management methods by deeply associating semantic business data and product modeling data. Existing methods usually store different data of equipment in multiple systems, resulting in problems such as inconsistent data formats, unclear semantics, and inconsistent granularity, making it difficult to achieve effective integration and accurate decision-making from a global perspective. However, this application, through a unified digital model and intelligent mapping mechanism, can achieve efficient circulation and sharing of equipment data across different data sources and systems, greatly improving data utilization efficiency.

[0079] Furthermore, through visualization based on the target 3D mapping model, users can view key information such as the equipment's operating status, historical maintenance records, and fault diagnosis results in real time, enabling more accurate decision-making. For example, maintenance personnel can use the model to quickly identify faulty equipment components and detect potential risks, allowing them to plan maintenance plans in advance, avoid equipment downtime or failure, and reduce operating and maintenance costs. This 3D model-based visualization provides more intuitive and comprehensive data support than traditional text reports and 2D charts.

[0080] To sum up, the beneficial effects of this application are reflected in the following aspects: First, through intelligent mapping technology, seamless integration of equipment life cycle data is achieved, which improves the accuracy and intelligence level of equipment management; second, it breaks the problem of data fragmentation between existing systems and optimizes data sharing and utilization; third, through the target three-dimensional mapping model and visual display, the efficiency of equipment status monitoring and decision support is improved, which helps to improve product quality, shorten delivery cycle, optimize maintenance strategy, and ultimately achieve overall optimization of intelligent manufacturing and equipment operation and maintenance.

[0081] The present application also provides a full-cycle data intelligent mapping system based on a digital model, referring to Figure 2, shows a functional module diagram of a full-cycle data intelligent mapping system 200 based on a digital model of the present application, which may include the following modules: The acquisition module 201 is used to acquire the full-cycle data of the device to be processed uploaded by the user, determine the user's target data upload intention, and determine the product modeling data and semantic business data in the full-cycle data; A first determining module 202 is used to determine product modeling data and semantic business data in the full-cycle data; The first model building module 203 is used to generate an initial three-dimensional mapping model of the equipment to be processed based on the product modeling data; The second determination module 204 is used to build a correspondence between semantic business data and product modeling data according to the user's data upload intention; The second model construction module 205 is used to perform first-level attribute annotation on the initial three-dimensional mapping model based on the product maintenance attribute data in the semantic business data, perform second-level attribute annotation on the initial three-dimensional mapping model based on the product operation status attribute data in the semantic business data, and perform third-level attribute annotation on the initial three-dimensional mapping model based on the product quality inspection and evaluation attribute data in the semantic business data, and integrate the first-level attribute annotation results of the initial three-dimensional mapping model, the second-level attribute annotation results of the initial three-dimensional mapping model, and the third-level attribute annotation results of the initial three-dimensional mapping model to obtain the target three-dimensional mapping model.

[0082] In an optional embodiment, the acquisition module includes: A first determination submodule is configured to determine the user's user type data upload intention based on the user type identifier carried in the full-cycle data; The second determination submodule is used to determine the user's data format upload intention based on the data format of the full cycle data; The third determination submodule is used to determine the user's target data upload intention based on the matching relationship between the user's type data upload intention and the data format upload intention.

[0083] In an optional embodiment, the acquisition module includes: The identification submodule is used to determine the product modeling data and semantic business data in the full-cycle data based on the data type identification and semantic recognition results carried by the full-cycle data.

[0084] In an optional embodiment, the identification submodule includes: A first-level screening unit is used to perform a first-level screening on the full-cycle data according to the semantic recognition result to obtain a first data set; A second-level screening unit is used to perform a second-level screening on the full-cycle data according to the data type identifier to obtain a second data set; The combining unit is used to determine the product modeling data and semantic business data in the full-cycle data according to the intersection result of the first data set and the second data set.

[0085] In an optional embodiment, the first model building module includes: The skeleton model construction submodule is used to generate a three-dimensional geometric skeleton model of the device to be processed based on the structural topology data and geometric dimension data in the product modeling data; The mapping submodule is used to construct the component hierarchy and bind the identification of the three-dimensional geometric skeleton model according to the assembly relationship data and component identification information in the product modeling data, so as to generate an initial three-dimensional mapping model of the device to be processed.

[0086] In an optional embodiment, the skeleton model construction submodule includes: A parsing unit is used to parse the product modeling data and determine the assemblies constituting the three-dimensional geometric skeleton model and the assembly identifiers corresponding to the assemblies; A determination unit is used to determine the geometric dimension data of each assembly according to the geometric dimension data in the product modeling data, and generate a plurality of three-dimensional geometric skeleton models of each assembly according to the geometric dimension data of each assembly; The splicing unit is used to determine the topological connection relationship between each assembly according to the structural topological data in the product modeling data, and assemble each assembly according to the topological connection relationship between each assembly to obtain a three-dimensional geometric skeleton model of the equipment to be processed.

[0087] In this embodiment, an embodiment of the present application further provides an electronic device, which may include a memory and one or more processors. The memory and processors are coupled. The memory is configured to store computer program code, which includes computer instructions. When the processor executes the computer instructions, the electronic device may perform the functions or steps described in the above method embodiments.

[0088] This embodiment further provides a computer-readable storage medium, in which computer instructions are stored. When the computer instructions are executed on an electronic device, the electronic device executes each function or step in the above method embodiment.

[0089] This embodiment further provides a computer program product. When the computer program product is run on a computer, it enables the computer to execute each function or step in the above method embodiment.

[0090] Among them, the electronic device, computer-readable storage medium, and computer program product provided in this embodiment are all used to execute the corresponding methods provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0091] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are fully or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable system. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0092] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0093] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0094] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the system or unit can be electrical, mechanical or other forms.

[0096] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0097] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0098] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program code.

[0099] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A full-cycle data intelligent mapping method based on a digital model, characterized in that: The method comprises: Obtain the full-cycle data of the device to be processed uploaded by the user and determine the user's target data upload intention; Determining product modeling data and semantic business data in the full-cycle data; generating an initial three-dimensional mapping model of the device to be processed according to the product modeling data; Building a correspondence between the semantic business data and the product modeling data based on the user's data uploading intention; Based on the product maintenance attribute data in the semantic business data, the initial three-dimensional mapping model is labeled with first-level attributes. Based on the product operation status attribute data in the semantic business data, the initial three-dimensional mapping model is labeled with second-level attributes. Based on the product quality inspection and evaluation attribute data in the semantic business data, the initial three-dimensional mapping model is labeled with third-level attributes. The first-level attribute labeling results of the initial three-dimensional mapping model, the second-level attribute labeling results of the initial three-dimensional mapping model, and the third-level attribute labeling results of the initial three-dimensional mapping model are integrated to obtain the target three-dimensional mapping model.

2. A full-cycle data intelligent mapping method based on a digital model according to claim 1, characterized in that: The process of obtaining the full-cycle data of the device to be processed uploaded by the user and determining the user's target data uploading intention includes: Determining the user type data uploading intention of the user according to the user type identifier carried by the full cycle data; Determining the user's data format upload intention based on the data format of the full-cycle data; The user's target data upload intention is determined based on the matching relationship between the user type data upload intention and the data format upload intention.

3. The method for intelligent mapping of full-cycle data based on digital models according to claim 1, characterized in that: Determining the product modeling data and semantic business data in the full-cycle data includes: According to the data type identifier and semantic recognition result carried by the full-cycle data, the product modeling data and semantic business data in the full-cycle data are determined.

4. The method for intelligent mapping of full-cycle data based on digital models according to claim 3, characterized in that: The determining, based on the data type identifier and semantic recognition result carried by the full-cycle data, the product modeling data and semantic business data in the full-cycle data includes: Performing a first-level screening on the full-cycle data according to the semantic recognition result to obtain a first data set; Performing a second level of screening on the full-cycle data according to the data type identifier to obtain a second data set; The product modeling data and semantic business data in the full-cycle data are determined according to the intersection result of the first data set and the second data set.

5. The method for intelligent mapping of full-cycle data based on digital models according to claim 1, characterized in that: Generating an initial three-dimensional mapping model of the device to be processed according to the product modeling data includes: Generate a three-dimensional geometric skeleton model of the device to be processed based on the structural topology data and geometric dimension data in the product modeling data; According to the assembly relationship data and component identification information in the product modeling data, the three-dimensional geometric skeleton model is constructed at the component level and identification binding is performed to generate an initial three-dimensional mapping model of the device to be processed.

6. The method for intelligent mapping of full-cycle data based on digital models according to claim 4, characterized in that: Generating a three-dimensional geometric skeleton model of the device to be processed based on the structural topology data and geometric dimension data in the product modeling data includes: Parsing the product modeling data to determine assemblies constituting the three-dimensional geometric skeleton model and assembly identifiers corresponding to the assemblies; Determining geometric dimension data of each assembly according to the geometric dimension data in the product modeling data, and generating a plurality of three-dimensional geometric skeleton models of each assembly according to the geometric dimension data of each assembly; The topological connection relationship between the assemblies is determined according to the structural topological data in the product modeling data, and the assemblies are assembled according to the topological connection relationship between the assemblies to obtain a three-dimensional geometric skeleton model of the device to be processed.

7. The method for intelligent mapping of full-cycle data based on digital models according to claim 1, characterized in that: The step of constructing a correspondence between the semantic business data and the product modeling data according to the user's data upload intention includes: Parsing the semantic business data to extract semantic anchor feature information related to the data upload intention, wherein the semantic anchor feature information includes at least equipment location, component name, number, fault type, operation time, etc.; Based on the semantic matching strategy corresponding to the data uploading intention, the target model objects in the product modeling data are screened and associated using the semantic anchor feature information to establish a binding relationship between the semantic business data and the product modeling data.

8. The method for intelligent mapping of full-cycle data based on digital models according to claim 1, characterized in that: The step of integrating the first-level attribute labeling result of the initial three-dimensional mapping model, the second-level attribute labeling result of the initial three-dimensional mapping model, and the third-level attribute labeling result of the initial three-dimensional mapping model to obtain the target three-dimensional mapping model includes: Performing unified parsing and format standardization on the multi-level attribute annotation results of the initial three-dimensional mapping model; Establishing attribute fusion mapping relationships among first-level attribute annotation results, second-level attribute annotation results, and third-level attribute annotation results based on component nodes; Based on the attribute fusion mapping relationship, the target three-dimensional mapping model is generated.

9. A full-cycle data intelligent mapping system based on digital models, characterized by: For implementing the method according to any one of claims 1 to 8, the system comprises: An acquisition module is used to acquire the full-cycle data of the device to be processed uploaded by the user, determine the user's target data upload intention, and determine the product modeling data and semantic business data in the full-cycle data; A first determination module is used to determine product modeling data and semantic business data in the full-cycle data; A first model building module is used to generate an initial three-dimensional mapping model of the device to be processed according to the product modeling data; A second determination module is configured to construct a correspondence between the semantic business data and the product modeling data according to the user's data upload intention; The second model construction module is used to perform first-level attribute annotation on the initial three-dimensional mapping model based on the product maintenance attribute data in the semantic business data, perform second-level attribute annotation on the initial three-dimensional mapping model based on the product operation status attribute data in the semantic business data, and perform third-level attribute annotation on the initial three-dimensional mapping model based on the product quality inspection and evaluation attribute data in the semantic business data, and integrate the first-level attribute annotation results of the initial three-dimensional mapping model, the second-level attribute annotation results of the initial three-dimensional mapping model, and the third-level attribute annotation results of the initial three-dimensional mapping model to obtain the target three-dimensional mapping model.

10. The full-cycle data intelligent mapping system based on digital model according to claim 9, characterized in that: The acquisition module includes: A first determining submodule, configured to determine the user type data uploading intention of the user according to the user type identifier carried by the full-cycle data; A second determining submodule is configured to determine the user's data format uploading intention according to the data format of the full-cycle data; The third determination submodule is configured to determine the user's target data upload intention based on a matching relationship between the user's type data upload intention and the data format upload intention.

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