CN111462327B - Unstructured data analysis method for three-dimensional inspection model of three-dimensional modeling software - Google Patents
Unstructured data analysis method for three-dimensional inspection model of three-dimensional modeling software Download PDFInfo
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Abstract
The invention discloses an unstructured data analysis method for a three-dimensional inspection model of three-dimensional modeling software, and aims to provide an unstructured data analysis method which can improve the compatibility and the applicability of a system and can be smoothly identified or used. The invention is realized by the following technical scheme: according to the internal connection of three-dimensional modeling software, a three-dimensional vector finite element calculation flow based on a three-dimensional modeling unstructured grid is established, when data are stored, assembly nodes in an assembly digital-to-analog structure are stored in a design end product data management system in a structure presentation mode to serve as a manufacturing end PDM system of an application layer, an assembly part document is regenerated according to a position coordinate matrix recorded in a database, an unstructured three-dimensional model data analyzer is introduced into the manufacturing end PDM system to construct an unstructured three-dimensional model data analyzer, geometric data and non-geometric data in three-dimensional model data and required non-geometric data labels are analyzed, and all xml information is converted into identifiable unstructured three-dimensional model data.
Description
Technical Field
The invention relates to a visual unstructured three-dimensional model data analysis method for analyzing unstructured data (unstructured data) of a three-dimensional inspection model of three-dimensional modeling software into a computer understandable format.
Background
Data in the world is growing in an explosive situation, wherein the content of unstructured data occupies 80% of the current data ocean, and more than 80% of the data can be unstructured data with greater processing difficulty. The exponentially increasing amount of data makes it almost impossible to accomplish the data management process using human labor, and computers have become an important tool for data management. However, most computer software can only understand some data with a specific structure according to a preset program, so that the computer is not distracted when managing unstructured data. The unstructured data generally refers to data with an indefinite length or an unfixed format, such as files on a disk of a mail, a word document, and the like. With the continuous improvement of electronic information technology, a great deal of unstructured data containing a great deal of information useful for human development and technology development is emerging, and mainstream unstructured data types include text, graphics, images, voice (audio), video, and the like. Unstructured data is so called because, unlike conventional data structures used by traditional databases, these data lack or even lack the formal semantics needed by computer software to parse the data. Traditional relational databases can only manage structured data, but cannot deal with complex types of unstructured data, particularly data of relational data structures widely adopted by databases, such as texts, images, sounds, videos and the like, how to effectively manage and analyze massive unstructured data and analyze and mine useful information from the massive unstructured data. However, conventional relational databases have been difficult to deal with non-relational data generated by new applications. The reason is that the traditional database is mainly based on the relational data model invented in the seventies of the last century, and the content of the traditional database comprises a complex and lengthy relational algebra theory, relational operation, SQL query language and the like. Many unstructured data occupy an increasing proportion of the total amount of data. Unstructured data does not have a predefined data model and is not convenient to be represented by a database two-dimensional logic table.
With the development of science and technology and the promotion of enterprise informatization, the storage formats of enterprise data are more and more diversified. A large number of digitizing devices generate a huge amount of data, so that data resources are increasing day by day. Among these data, unstructured data, which mainly includes electronic documents, mails, reports, audios, videos, and graphic images, grows very rapidly, while structured data in relational databases are relatively slow. The most immediate problem caused by this phenomenon is the storage and management of large amounts of unstructured data. Generally, storage formats for data generated by a digitizing device are classified into three categories, one is data that can be represented by data or a uniform structure, such as numbers, symbols, etc., and is called structured data; the other is data which cannot be represented by numbers or uniform structures, such as texts, images, sounds and the like, and is called unstructured data; yet another class is data that is intermediate between unstructured and structured data, such as HTML pages and XML documents, and the like, referred to as semi-structured data. Structured data refers to data having a fixed format or a finite length, such as a database, metadata, and the like. Data in computer informatization systems is typically divided into structured data, unstructured data, and semi-structured data. The structured data is data logically expressed and realized by a two-dimensional table structure, strictly follows the data format and length specification, is mainly stored and managed by a relational database and is expressed as two-dimensional data. The general characteristics are as follows: data is in row units, one row of data represents information of one entity, and the attribute of each row of data is the same. Structured data, also referred to as row data, is data that can be logically represented and implemented by a two-dimensional table structure, with a clear definition for each column of the table structure. Structured data is data that is highly organized and well-formatted. It is the type of data that can be put into tables and spreadsheets. It may not be the data type that one finds most easily, but is certainly the more easily used of the two, as compared to unstructured data. On the other hand, the computer can easily search for it. Structured data, also referred to as quantitative data, is information that can be represented by data or a uniform structure, such as numbers, symbols. The data stored and managed are typically relational databases, and when structured query language or SQL is used, the clear relationships of structured data make the data very convenient to use and computer programs can easily search for the terms. Unstructured data is essentially anything but structured data, much more so than structured data, which is difficult to understand without human or computer translation. It does not conform to any predefined model, so it is stored in a non-relational database and queried using NoSQL. It may be textual or non-textual, and may be human or machine generated. Simply, unstructured data is data whose fields are variable. Unstructured data is not as easy to organize or format. The greatest difference is the convenience of analyzing structured data versus unstructured data. There are mature analytical tools for structured data, but the analytical tools used to mine unstructured data are in the infancy and developmental stages. Structured data and unstructured data are two types of big data, and there is no real conflict between the two. How to choose is not based on the data structure but on the application that uses them: relational databases are used for structured data and most other types of applications are used for unstructured data. Semi-structured data, which is different from both of the above categories, between structured data and unstructured data, is data with a structure that varies greatly because the data cannot be simply organized into a file to be processed according to unstructured data, and a table cannot be simply created to correspond to the data due to the large structural variation. It is generally self-describing, and the structure and content of the data is mixed together without obvious distinction. The most important difference between structured data, semi-structured data and unstructured data is whether a predefined data model exists, or not, and more specifically, a conceptual data model. Structured data can be represented by a uniform certain structure. The unstructured data has no limitation of a conceptual data model form and can be freely expressed; while semi-structured data has some structure, but the data itself has the meaning of a structure. Unstructured data is very diverse in format, diverse in standard, technically more difficult to standardize and understand than structured information, and very difficult for any system to handle in the face of unstructured data. With respect to structured data, unstructured data has the following characteristics: the data storage occupation ratio is high, the data formats are various, the structure is not standard and complex, the information quantity is rich, and the processing threshold is high. Unstructured data generally cannot be used directly and needs to be processed by means of algorithms and the like. But the processing difficulty is high due to the characteristics of unstructured data. The unstructured data usually cannot directly know the content of the unstructured data, the unstructured data can be opened and browsed only through corresponding software, and the unstructured data can be stored in a BLOB field only by a database, so that great trouble is caused to later data retrieval. Moreover, the data is not easy to understand, and the meaning of the expression cannot be directly obtained from the data. Unstructured data does not have a specified structure, cannot be standardized, and is not easy to manage, so that a more intelligent system is required for querying, storing, updating, and using the unstructured data. For the processing of unstructured data, algorithms cannot be separated, high threshold of the algorithms and high requirements of services restrict the release of the capability of the unstructured data. Tools and algorithms are mainly provided for unstructured processing at present, and no solution is provided for data per se.
One difficulty with unstructured data storage is that the content of metadata to be extracted cannot be determined in advance, and the content and format of the metadata may change as the system requirements change. In fact, pure unstructured data is not present. Firstly, the unstructured data may contain partially structured information and many different types of features, wherein the unstructured data may contain data with format information, such as tables; secondly, the unstructured data can be converted into structured data under the translation of a proper interpreter, and structured meta information such as the storage mode of the data, the producer of the data, the transmission mode of the data and even the data can be extracted from the unstructured data. Relational databases the files used to store data, which are unstructured before database management loads and parses, are typically binary files stored on disk. The method has the defects that the structure is complex, the implementation difficulty is increased, in addition, the machine can automatically mine and understand the structured part in the unstructured data through means of machine learning, natural language processing and the like, and the structure of the data is learned according to the method. The data generally has two storage modes, the first mode is to store the data in a file system and store a file link in a database, and the mode has certain safety problem and needs extra work to ensure the ACID characteristic of a transaction; the other mode is that large data types provided by a database system are used for storage, such as Blob of Oracle, the mode increases IO work in the database query process, and therefore efficiency is low, some database systems provide a columnar storage model to solve the problem, metadata is used as the feature of unstructured data, and compared with original data, the metadata has certain structurality, and the feature can be described by using a feature type and a feature value binary group, so that a corresponding relation exists between the metadata and an entity of a relational data model. Second, the complexity of unstructured data determines the versatility of its metadata type, and even unstructured data of similar type may have disparate metadata types. Due to the diversity of unstructured data, the whole entity relationship model is abnormally bloated, and therefore the complexity of a data management program is increased. One difficulty with unstructured data storage is that the content of metadata to be extracted cannot be determined in advance, and as the system requirements change, the content and format of the metadata may change. Over time and with the increasing number of users, the original data scheme gradually faces some problems: the application system is frequently changed, so that new data and old data are difficult to be compatible; the system needs to process a large amount of unstructured data, and the management requirement is variable; the data volume is larger and larger, the original copying technology not only wastes the storage space, but also can not improve the execution efficiency of single complex query operation. If the structure is complicated, the difficulty of implementation is increased.
In a modern manufacturing enterprise, process data is the most important and basic technical data for the transformation of the process data of the enterprise. For a long time, due to the lack of professional auxiliary software support, in the information society, the data types of information are mainly three types: the traditional process is mostly manually compiled by office software, and the process data mostly exist in an independent electronic document form. With the lapse of time, process data accumulate day by day, the data volume is huge, and is dispersedly stored in an unstructured data form, the process data sharing and the reusability are poor, the knowledge contained in the data resources cannot be fully mined and utilized, and the problems of abundant data and poor knowledge are very serious. The traditional process rule usually relies on WORD software to create a one-dimensional table and describe process information in the form of the one-dimensional table, and belongs to a typical unstructured data type.
With the rapid development of information technology, the method and means for designing products in the mechanical industry undergo the leap-type development from manual drawing to two-dimensional CAD technology and then to three-dimensional modeling technology, the transformation from planar projection technology to full three-dimensional model digital prototype and complete digital product definition is realized, and the analysis and verification of product performance and manufacturing capability are finished by relying on a computer application system more and more. The three-dimensional labeling technology really opens the full three-dimensional digital design and manufacturing era, really realizes the three-dimensional digital and drawing-free design and manufacturing technology, enables the two-dimensional engineering drawing or the engineering drawing to become history, is no longer the manufacturing authority basis even if the two-dimensional engineering drawing or the engineering drawing exists, and is only an auxiliary expression mode of data under specific conditions. However, although the product design process has all the engineering and manufacturing information contained in the three-dimensional model, when the data of the three-dimensional model is transmitted to the product manufacturing process, the data is stored in an unstructured mode, and only special tool software, such as typical unstructured three-dimensional modeling software CATIA, UG and the like, can be used for analyzing and extracting the data for being identified or applied by a relevant technician or an information system. These software relational databases are unstructured before the files used to store the data, which are typically binary files stored on disk, are loaded and parsed by the database management. Metadata is used as the characteristics of unstructured data, and the characteristics are divided into basic attributes, semantic characteristics, underlying characteristics and original data to represent data into dimensions, variables, attributes and original data. However, the metadata of unstructured data is not necessarily able to simply correspond. First, the data type of the metadata is not necessarily an atomic type, and may be a list or even unstructured data, so the actual corresponding result is a tree structure. The complexity of unstructured data determines the versatility of its metadata types, even though unstructured data of similar types may exist in disparate metadata types. The complexity of unstructured data makes it often inadequate to utilize views alone, and database management systems typically do not support update operations across table views. With the increase of time and user quantity, the original data scheme gradually faces some problems that an application system is frequently changed, so that new and old data are difficult to be compatible; the system needs to process a large amount of unstructured data, such as logs and images, and the management requirements are variable; the data volume is larger and larger, the original copying technology not only wastes the storage space, but also can not improve the execution efficiency of single complex query operation. The unstructured data structure is complex, the realization difficulty is high, and the problem of management and data version compatibility is difficult to solve.
Most of CAD/CAM software commonly used in the mechanical industry at present has a function of parametric modeling, but some CAD/CAM software needs to be realized through interface programming with software, and developers are required to have higher programming level; some of them cannot be visualized, and need the designer to be skilled in the related commands and operations of the software. The three-dimensional modeling software CATIA adopts a characteristic modeling and parameterization modeling technology, and has a unified user interface, data management and compatible database and application program interfaces. The space view of the three-dimensional model can be used for watching the model from any direction, and the structure of the part and the interference between the parts can be easier to observe visually than the plane projection views of the two-dimensional model. The quality of three-dimensional parametric modeling depends largely on the degree of conformance of the size constraints in the two-dimensional graph to the physical parameters. An ideal model can be established only by grasping the CATIA modeling characteristics and adopting reasonable two-dimensional and three-dimensional modeling methods. The CATIA product structure tree reflects the tree expression form of product composition and reflects the assembly hierarchical relation of product components. Before the CATIA model file is issued, the file is sorted to meet the requirement of the CATIA model issuing state. And (3) coordinate system: the current coordinate system of the assembly part model is a default coordinate system of CATIA software, namely an airplane body coordinate system; 0. adding zero and component attribute information into a CATIA model file of the component; a "contaminated" model cannot be sent out, nor can a "contaminated" model be written into the library; in the CATIA software system, the "contaminated" model is deleted and replaced with the correct one; the "dirty" model (corruptmodel) does not work properly and can repeatedly cause a model of system error. The key of driving the graph by using the system parameters is how to convert the parameters extracted from the real objects into CATIA (computer-graphics aided three-dimensional interactive application) for controlling the characteristic parameters of the three-dimensional model. In the case of a three-dimensional entity, the drawing is performed while looking with reference to a three-dimensional entity model, so that the drawing is slow and is easy to make mistakes. In the operation process of the simulation platform, a large amount of data is generated in a simulation mode and used for generating simulation output files, and some files contain massive data information. The system needs to determine the size of a table of the database to store data in at least one simulation output file, and excessive data can cause huge pressure on the system and even cause system crash, and is inconvenient for the system to retrieve and manage the data. And the simulation output file generated by analyzing the big data XML document has huge data quantity. After the class file is converted into the XML document, the size of the generated XML document is generally more than 3 times of the size of the file. The general parsing method can parse XML documents with small data volume well, but cannot meet the requirement of XML documents with large data volume. In the analysis process, a large amount of memory resources of a computer are occupied, and the system is possibly crashed due to the error of memory overflow. The system needs a set of scheme to deal with the big data XML document, correctly analyzes the big data XML document, effectively obtains the data in the big data XML document, improves the compatibility of the system, and realizes that when the mass data is quickly inserted into the database table, the running speed of a computer is slow due to huge data volume. Meanwhile, the time for inserting each piece of data is not changed, and the amount of data is huge, so that a computer needs a lot of time to complete the operation. Thus, it is possible that a large data file generated by simulation may not complete its transformation in a significant amount of time and lose its meaning of transformation. The existing three-dimensional modeling software unstructured three-dimensional model data analysis method has the problems that the method cannot be conveniently identified or applied in the whole design, manufacture and inspection process, and the application environment is greatly limited. It has been shown that unstructured three-dimensional model data greatly limits the application environment, making it impossible to identify or use it successfully throughout the design, manufacturing and inspection processes. Is not beneficial to the smooth development of the informatization of the manufacturing industry and the improvement of the data management and application level of the machinery industry. Clearly, changing relational database data schemas is extremely inefficient and in some cases even infeasible.
Disclosure of Invention
The invention provides a three-dimensional modeling software unstructured three-dimensional model data analysis method which can improve the compatibility and the applicability of a system and can be smoothly identified or used, aiming at solving the problems that the three-dimensional modeling software unstructured three-dimensional model data analysis method in the prior art cannot be conveniently identified or applied in the whole design, manufacture and inspection process and the application environment is greatly limited.
The above object of the present invention can be achieved by a method for analyzing unstructured data of a three-dimensional inspection model of three-dimensional modeling software, comprising the steps of: firstly, establishing a three-dimensional MT vector finite element calculation flow based on a three-dimensional modeling unstructured grid according to the internal relation among basic attributes, semantic features, bottom layer features and original data composition elements of three-dimensional modeling software unstructured data, storing assembly nodes in an assembly digital-analog structure in a design end product data management PPDM system in a structure presentation form when three-dimensional model data is stored, using the assembly nodes as a manufacturing end product data management PDM system of an application layer, regenerating assembly part documents according to a position coordinate matrix recorded in a database, and importing the assembly part documents into the manufacturing end product data management PDM system; secondly, a file format definition module is constructed, the data abstraction merging module, the object retrieval and distribution module, the data query analysis module, the module data storage module and the exception handling module form an unstructured three-dimensional model data analyzer in sequence, wherein the file format definition module imports assembly part text data into a database, extracts fields in each record in the assembly part text data through a developed program, inserts the fields into a target database, and performs necessary data type conversion, data extraction and data generation according to application requirements; after sharing the configuration information of the data, the data abstraction merging module organizes the metadata by using a three-dimensional data relationship model, introduces an inheritance relationship, performs unified management on unstructured data based on the metadata, and manages an association relationship, a subclass and data table mapping relationship and integrity constraint among different data tables; the object retrieval and distribution module processes data query requests from different application systems according to a computing process, automatically carries out sub-classification on classes with overlarge data quantity, automatically completes the decomposition of query and the mapping of sub-query, and stores different sub-tables in different physical databases; the data query analysis module is used for segmenting data based on types according to query fields, loading structural tree information of the three-dimensional model data into an unstructured three-dimensional model data model, converting an unstructured storage mode into a structured storage mode from the three-dimensional model data, respectively generating structured document data and unstructured model data into corresponding xml and file extension GEOM files, executing data analysis operation, analyzing geometric data and non-geometric data and required non-geometric data labels in the three-dimensional model data, and forwarding sub-queries to other computing nodes Map; the data storage module takes semi-structured XML as a bridge, adopts the gradual conversion of unstructured data → semi-structured data → structured data, stores the geometric data of the three-dimensional model as a file extension GEOM file, stores the non-geometric data as an XML file, defines a data table according to the hierarchical relationship between the structured data and the three-dimensional model structure tree after storage and analysis, stores the data table between two product data management PDMs of a flow in the system in a structural presentation form, and stores the data table in a relational database in a data table form; the exception handling module reads an xml file of a feature tree of the three-dimensional modeling software part, extracts structure tree information, generates an xml file from information such as parameter names, parameter values, geometric figure sets and the like, sequentially writes geometric tolerance marking information associated with the model into the same xml file, respectively marks all information with data according to xml tags, analyzes abnormal errors generated in the process of processing data and the execution process of the storage module, converts all xml information into unstructured three-dimensional model data which can be identified and used, and stores the unstructured three-dimensional model data in a predefined database table.
Compared with the prior art, the invention has the following beneficial effects.
According to the invention, a three-dimensional MT vector finite element calculation process based on a three-dimensional modeling unstructured grid is established according to the basic attributes, semantic features, bottom layer features and internal relations among original data composition elements of three-dimensional modeling software unstructured data, and after configuration information of a data abstraction merging module is shared, data query requests from different application systems can be processed in a non-interfering manner by each calculation process, so that concurrent query operation is supported. The computing process can also adopt a mode similar to MapReduce, and the parallel processing of merging the query results is realized. The three-dimensional model data is converted from an unstructured storage mode to a structured storage mode, so that the application environment of the unstructured three-dimensional model data is greatly expanded, the unstructured three-dimensional model data can be conveniently identified or applied in the whole design, manufacture and inspection process, the informatization development of the manufacturing industry can be smoothly carried out, and the data management and application level of enterprises can be improved.
The data query analysis module is used for segmenting data based on types according to query fields, loading structural tree information of three-dimensional model data into an unstructured three-dimensional model data model, converting an unstructured storage mode into a structured storage mode from the three-dimensional model data, respectively generating structured document data and unstructured model data into corresponding xml and file extension GEOM files, executing data analysis operation, and forwarding sub-queries to other computing nodes (Map); different query conditions may be added to a particular field in a sub-query to segment the query according to that field. This is especially important for the consolidation of large numbers of query results. The object retrieval and distribution module automatically completes the decomposition of the query and the mapping of the sub-query, different sub-tables are stored in different physical databases, the working process of the system is not influenced actually, the subclassing can be automatically carried out on the class with overlarge data volume, and the transparent data segmentation (Partition) based on the type is realized. Image attributes, height, width and compression format of images with resolutions up to 46000x46000 are extracted. The method is characterized in that a format graph is created and is easy to self-define by using a powerful programming method driven by a model, information can be managed, protected, inquired and managed by the highest level of performance, and the guidance on services can be realized by easy scheduling and use; second, it may not rely on dedicated application or device logic.
The invention adopts a data storage module which takes semi-structured XML as a bridge, adopts the gradual conversion of unstructured data → semi-structured data → structured data to realize the conversion from the unstructured data to the structured data, stores geometric data as a file extension GEOM file, stores the non-geometric data as an XML file, defines a data table according to the hierarchical relationship of the structured data and a three-dimensional model structure tree after storage and analysis, stores the data table between two product data management PDMs of a flow in a system in a structural presentation form, and stores the data table in a relational database in a distributed manner, thereby breaking through the storage capacity limit of a single host, eliminating the isomerism and the inconsistency of the data, helping to realize the conversion of the isomerism data, and ensuring that the data access process of single query is completed in parallel, and improving the input and output efficiency.
The method comprises the steps of reading an xml file of a feature tree of a three-dimensional modeling software part by adopting an exception handling module, extracting structure tree information, generating the information such as a parameter name, a parameter value and a geometric figure set into the xml file, sequentially writing geometric tolerance marking information associated with a model into the same xml file, respectively marking data on all the information according to xml tags, collecting scattered unstructured data together to form a complete data set of the xml file generated by the information such as the parameter name, the parameter value and the geometric figure set, and forming a complete geometric view. After data are collected, the data can go deep into the industry, professional knowledge of the industry is formed, the business is ploughed in deeply, the use threshold of an algorithm is fully reduced, the value of the data is fully exerted, and the visual angle of each BU for seeing the data is not isolated any more and is not limited any more. Meanwhile, a format definition module is added to support data conversion of unstructured files of the same type and different structures, and functions of the system are further improved. On the basis, the performance of the initial system is optimized, so that the large data file is analyzed, mass data can be quickly imported, and the compatibility and the applicability of the system are improved.
The invention is suitable for storing and processing large-scale unstructured data. The unstructured data is converted to structured data.
Drawings
The present invention is illustrated and described with reference to the accompanying drawings, where like method steps/system components are optionally identified, and in which:
FIG. 1 is a flow chart of the unstructured data parser of the three-dimensional modeling software three-dimensional inspection model of the present invention.
Detailed Description
See fig. 1. According to the invention, firstly, according to the internal relation among the basic attribute, semantic feature, bottom layer feature and original data composition elements of the three-dimensional modeling software unstructured data, a three-dimensional MT vector finite element calculation flow based on a three-dimensional modeling unstructured grid is established, when three-dimensional model data is stored, assembly nodes in an assembly digital-analog structure are stored in a design end product data management PPDM system in a structure presentation mode and serve as a manufacturing end product data management PDM system of an application layer, an assembly file is regenerated according to a position coordinate matrix recorded in a database, and the assembly file is imported into the manufacturing end product data management PDM system; secondly, a file format definition module is constructed, the data abstraction merging module, the object retrieval and distribution module, the data query analysis module, the module data storage module and the exception handling module form an unstructured three-dimensional model data analyzer in sequence, wherein the file format definition module imports assembly part text data into a database, extracts fields in each record in the assembly part text data through a developed program, inserts the fields into a target database, and performs necessary data type conversion, data extraction and data generation according to application requirements; after sharing the configuration information of the data, the data abstraction merging module organizes the metadata by using a three-dimensional data relationship model, introduces an inheritance relationship, performs unified management on unstructured data based on the metadata, and manages an association relationship, a subclass and data table mapping relationship and integrity constraint among different data tables; the object retrieval and distribution module processes data query requests from different application systems according to a computing process, automatically carries out sub-classification on classes with overlarge data quantity, automatically completes the decomposition of query and the mapping of sub-query, and stores different sub-tables in different physical databases; the data query analysis module is used for segmenting data based on types according to query fields, loading structural tree information of the three-dimensional model data into an unstructured three-dimensional model data model, converting an unstructured storage mode into a structured storage mode from the three-dimensional model data, respectively generating structured document data and unstructured model data into corresponding xml and file extension GEOM files, executing data analysis operation, analyzing geometric data and non-geometric data and required non-geometric data labels in the three-dimensional model data, and forwarding sub-queries to other computing nodes Map; the data storage module takes semi-structured XML as a bridge, adopts the gradual conversion of unstructured data → semi-structured data → structured data, stores the geometric data of the three-dimensional model as a file extension GEOM file, stores the non-geometric data as an XML file, defines a data table according to the hierarchical relationship between the structured data after storage and analysis and the structural tree of the three-dimensional model, stores the data table between two product data management PDMs in the system in a structural presentation form, and stores the data table in a relational database in a data table form; the exception handling module reads an xml file of a feature tree of the three-dimensional modeling software part, extracts structure tree information, generates an xml file from information such as parameter names, parameter values, geometric figure sets and the like, sequentially writes geometric tolerance marking information associated with the model into the same xml file, respectively marks all information with data according to xml tags, analyzes abnormal errors generated in the process of processing data and the execution process of the storage module, converts all xml information into unstructured three-dimensional model data which can be identified and used, and stores the unstructured three-dimensional model data in a predefined database table.
The required non-geometric data tags include: part number, part name, version, weight, and comment tag information.
The information of the relational database table includes: table names, field names, data types, field lengths, value constraints, primary foreign key constraints, and the like. The tables are very convenient to query and count, simple to operate and easy to maintain.
The user can specify the code of a specific interface realized by a high-level language, the data abstraction merging module carries out format conversion according to a processing mode configured by the user, the user code is called through a reflection mechanism of the high-level language, a configuration result is converted into a format required by the user, then each sub-query data is connected, all elements in the Join array are placed into one character for sequencing and grouping, the query results of each sub-query are merged, and the data conversion process is completed, so that the compatibility of an original application system based on a relational database is realized. In implementation, the data abstraction merging module may also be implemented using a process independent of the actual database system, and may generally merge the computing process with the process of the data query parsing distribution module.
The object retrieval and distribution module receives and analyzes the query statement of the data user, and distributes the query to the actual database management system according to the mapping relation provided by the data abstract merging module.
The object retrieval and distribution module adopts a heuristic optimization scheme and a characteristic cache to perform query optimization, reads the configuration information of the data abstraction merging module, automatically maps the subclass of the configuration information to a physical data table, processes data query and data writing operation separately, and automatically expands a new class to modify the format of the data table. In actual implementation, this module is done by a process that is independent of the actual database management system.
In a practical system, the data storage module is composed of independent database systems, and the databases may be ordinary database systems or some database systems specially designed for meeting polymorphic functions. The data do not show the relationship among the data in the module, and the relationship information is maintained by the data abstract merging module.
In an optional embodiment, the exception handling module adopts different conversion rules to convert XML files generated by parameter names, parameter values and geometric figure sets into standard XML documents respectively, analyzes the mapping relation between the XML documents and a relational database, and creates a simulation result table structure in the database according to a generated file template through structural mapping and semantic mapping; the XML document is converted into a relational database table according to the conversion rule, a management module is established to acquire and manage the file structure required in the conversion process of the unstructured file, and corresponding information is stored in the corresponding database table, so that the conversion process is integrated.
The abnormal processing module reads corresponding XML documents according to information in the template based on the relational database, extracts metadata of document template files, analyzes the XML documents by using an analysis tool, extracts data content in the XML documents, distinguishes the template files by using a uniform conversion interface, converts the structures of the template files by using different programs respectively to generate one-to-many relationship standard structure files, then selects a document template, guides data in a plurality of simulation output files into the same database table, inserts the standard structure files into the created result table, and completes conversion from semi-structured data to structured data. By realizing the conversion of the standard structure file, the system can greatly simplify the code writing from the unstructured data conversion to the structured data conversion. Thus, a set of program codes for data conversion does not need to be specially written aiming at files with different structures.
In the modeling specification, all the data expressed in the form of a structure tree and a parameter are structured data and can be viewed in a text mode; all the information contained in the geometric model is unstructured data and is expressed in a binary mode.
In another optional embodiment, the exception handling module stores the geometric data of the unstructured three-dimensional model as an extension file, an extension geo file, and stores the non-geometric data as an extension xml file, wherein the extension geo file of the file is still reserved in an unstructured storage manner, and the xml file meeting the international uniform requirement is defined in a tag manner, for example, "PartNumber" is a tag, and its value is a specific part drawing number. And the xml file is converted into a structured storage mode according to the label definition. During conversion, if structured data expressed in a structure tree form and a parameter form is encountered, an xml file is written, and unstructured data such as the rest geometric model information and the like are reserved.
And the exception handling module is used for handling exception errors occurring in the data analysis and storage module execution process. When the three-dimensional model data are analyzed, the analyzer automatically opens the model in a program background and extracts the structure tree information; generating an xml file by using information such as 'parameter name', 'parameter value', 'geometric figure set' and the like; writing the 'geometric tolerance marking' information associated with the model into the same xml file in sequence, and marking all the information according to xml tags respectively; the three-dimensional model is stored in a file extension GEOM format and can be opened and viewed by special graphic software; after the suffix is changed into a file extension GEOM and an xml file, the former can only be opened by special graphic software, and the latter can be opened by any text editor. All xml information is eventually stored in a database table defined in advance.
And traversing the whole structural tree information by the exception handling module according to the modeling specification, reading a file with a CATPArt suffix and an xml file of a product and a part with a CATProduct suffix in a CATIA three-dimensional model database in the unstructured three-dimensional model, analyzing the tag information of the xml file and a required non-geometric data tag, traversing the whole xml tag during analysis, reading tag values in a one-to-one correspondence manner, generating a file extension GEOM file for storing the xml file of the structured data and the unstructured data, and storing the tag information of the xml file.
The anomaly processing module defines a data table according to the hierarchical relationship of a structure tree of a CATIA (computer-graphics aided three-dimensional interactive application) at a manufacturing end, separates out non-geometric data from non-structural three-dimensional model data, analyzes the geometric data and the non-geometric data of the structural relationship in the CATIA three-dimensional model, analyzes the structural tree information of the three-dimensional model data, distinguishes the geometric data and the non-geometric data, performs information characteristic identification according to modeling specifications, stores the analyzed xml file label information in a relational database in a data table mode according to the hierarchical relationship of the CATIA structure tree, and stores the analyzed structural data in a database table.
When the three-dimensional model data are analyzed, the analyzer automatically opens the model in a program background and extracts the structure tree information; generating an xml file from information such as parameter names, parameter values, geometric figure sets and the like; writing the 'geometric tolerance labeling' information associated with the model into the same xml file in sequence, and labeling all the information according to xml tags respectively; the three-dimensional model is stored in a file extension GEOM format and can be opened and viewed by special graphic software; all xml information is eventually stored in a database table defined in advance. After the suffix is changed into a file extension GEOM and an xml file, the suffix can only be opened by special graphic software, and the suffix can be opened by any text editor.
Although the invention has been illustrated and described above with reference to preferred embodiments and specific examples thereof, those skilled in the art will readily appreciate that other embodiments and examples may perform similar functions and/or achieve similar results. It is therefore to be understood that all such equivalent embodiments and examples are within the spirit and scope of the present invention and are intended to be covered by the appended claims.
Claims (10)
1. A three-dimensional modeling software three-dimensional inspection model unstructured data analysis method is characterized by comprising the following steps: firstly, establishing a three-dimensional MT vector finite element calculation flow based on a three-dimensional modeling unstructured grid according to the internal relation among the basic attribute, the semantic feature, the bottom layer feature and the original data composition elements of three-dimensional modeling software unstructured data, storing assembly nodes in an assembly digital-analog structure in a design end product data management PPDM system in a structure presentation mode when three-dimensional model data is stored, using the assembly nodes as a manufacturing end product data management PDM system of an application layer, regenerating an assembly document according to a position coordinate matrix recorded in a database, and importing the assembly document into the manufacturing end product data management PDM system; secondly, a file format definition module is constructed, the data abstraction merging module, the object retrieval and distribution module, the data query analysis module, the module data storage module and the exception handling module form an unstructured three-dimensional model data analyzer in sequence, wherein the file format definition module imports assembly part text data into a database, extracts fields in each record in the assembly part text data through a developed program, inserts the fields into a target database, and performs necessary data type conversion, data extraction and data generation according to application requirements; after sharing the configuration information of the data, the data abstraction merging module organizes the metadata by using a three-dimensional data relationship model, introduces an inheritance relationship, performs unified management on unstructured data based on the metadata, and manages an association relationship, a subclass and data table mapping relationship and integrity constraint among different data tables; the object retrieval and distribution module processes data query requests from different application systems according to a computing process, automatically carries out sub-classification on classes with overlarge data quantity, automatically completes the decomposition of query and the mapping of sub-query, and stores different sub-tables in different physical databases; the data query analysis module is used for segmenting data based on types according to query fields, loading structural tree information of the three-dimensional model data into an unstructured three-dimensional model data model, converting an unstructured storage mode into a structured storage mode from the three-dimensional model data, respectively generating structured document data and unstructured model data into corresponding xml and file extension GEOM files, executing data analysis operation, analyzing geometric data and non-geometric data and required non-geometric data labels in the three-dimensional model data, and forwarding sub-queries to other computing nodes Map; the data storage module takes semi-structured XML as a bridge, adopts the gradual conversion of unstructured data → semi-structured data → structured data, stores the geometric data of the three-dimensional model as a file extension GEOM file, stores the non-geometric data as an XML file, defines a data table according to the hierarchical relationship between the structured data and the three-dimensional model structure tree after storage and analysis, stores the data table between two product data management PDMs of a flow in the system in a structural presentation form, and stores the data table in a relational database in a data table form; the exception handling module reads an xml file of a feature tree of the three-dimensional modeling software part, extracts structure tree information, generates an xml file by using parameter names, parameter values and geometric figure set information, and associates geometric tolerances with the model
And the marked information is written into the same xml file in sequence, all the information is respectively marked with data according to xml tags, abnormal errors occurring in the data processing and storage module executing process are analyzed, all the xml information is converted into unstructured three-dimensional model data which can be identified and used, and the unstructured three-dimensional model data are stored in a database table which is defined in advance.
2. The method for analyzing unstructured data of the three-dimensional inspection model of the three-dimensional modeling software according to claim 1, characterized in that: the non-geometric data tag includes: part number, part name, version, weight, and comment specification tag information.
3. The method for analyzing unstructured data of the three-dimensional inspection model of the three-dimensional modeling software according to claim 1, characterized in that: the information of the relational database table includes: table name, field name, data type, field length, value constraints, and primary foreign key constraints.
4. The method for analyzing unstructured data of the three-dimensional test model of the three-dimensional modeling software according to claim 1, wherein: the data abstraction merging module carries out format conversion according to a processing mode configured by a user, calls a user code through a reflection mechanism of a high-level language, converts a configuration result into a format required by the user, then connects all sub-query data, puts all elements in a Join array into one character for sequencing and grouping, merges all sub-query results, and completes a data conversion process so as to realize compatibility of an original application system based on a relational database.
5. The method for analyzing unstructured data of the three-dimensional inspection model of the three-dimensional modeling software according to claim 1, characterized in that: the object retrieval and distribution module receives and analyzes the query statement of the data user, and distributes the query to the actual database management system according to the mapping relation provided by the data abstract merging module.
6. The method for analyzing unstructured data of the three-dimensional inspection model of the three-dimensional modeling software according to claim 1, characterized in that: the object retrieval and distribution module adopts a heuristic optimization scheme and a characteristic cache to carry out query optimization, reads the configuration information of the data abstraction merging module, automatically maps the configuration information subclass to the physical data table, and carries out separate processing on data query and data write-in operation, and automatically expands a new class to modify the format of the data table.
7. The method for analyzing unstructured data of the three-dimensional inspection model of the three-dimensional modeling software according to claim 1, characterized in that: the exception handling module adopts different conversion rules to respectively convert XML files generated by parameter names, parameter values and geometric figure sets into standard XML files, analyzes the mapping relation between the XML files and a relational database, and creates a simulation result table structure in the database according to a generated file template through structural mapping and semantic mapping; and converting the XML document into a relational database table according to the conversion rule, creating a management module to acquire and manage a file structure required in the unstructured file conversion process, and storing corresponding information into a corresponding database table to integrate the conversion process.
8. The method for analyzing unstructured data of the three-dimensional test model of the three-dimensional modeling software according to claim 1, wherein: the abnormal processing module reads corresponding XML documents according to the information in the template based on the relational database, extracts the metadata of the document template file, analyzes the XML documents by using an analysis tool, extracts the data content in the XML documents, distinguishes the template files by using a uniform conversion interface, converts the structure of the template files by using different programs respectively to generate a one-to-many relation standard structure file, then selects the document template, imports the data in a plurality of simulation output files into the same database table, inserts the standard structure file into the created result table, and completes the conversion from semi-structured data to structured data.
9. The method for analyzing unstructured data of the three-dimensional inspection model of the three-dimensional modeling software according to claim 1, characterized in that: and traversing the whole structural tree information by the exception handling module according to the modeling specification, reading a file with a CATPArt suffix and an xml file of a product and a part with a CATProduct suffix in a CATIA three-dimensional model database in the unstructured three-dimensional model, analyzing the tag information of the xml file and a required non-geometric data tag, traversing the whole xml tag during analysis, reading tag values in a one-to-one correspondence manner, generating a file extension GEOM file for storing the xml file of the structured data and the unstructured data, and storing the tag information of the xml file.
10. The method for analyzing unstructured data of the three-dimensional test model of the three-dimensional modeling software according to claim 1, wherein: the anomaly processing module defines a data table according to the hierarchical relationship of a structure tree of a CATIA (computer-graphics aided three-dimensional interactive application) at a manufacturing end, separates out non-geometric data from non-structural three-dimensional model data, analyzes the geometric data and the non-geometric data of the structural relationship in the CATIA three-dimensional model, analyzes the structural tree information of the three-dimensional model data, distinguishes the geometric data and the non-geometric data, performs information characteristic identification according to modeling specifications, stores the analyzed xml file label information in a relational database in a data table mode according to the hierarchical relationship of the CATIA structure tree, and stores the analyzed structural data in a database table.
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