Business component data processing method and device, electronic equipment and readable storage medium

Abstract

The present disclosure relates to the technical field of data processing, and provides a business component data processing method and device, electronic equipment and readable storage medium. The method comprises: performing feature analysis processing on the to-be-processed business component data to obtain business component feature data; performing encoding mapping processing on the business component feature data based on a preset mapping rule library to obtain business component standard encoding data; performing encoding solidification processing on the to-be-processed business component data based on the business component standard encoding data to obtain standardized business component file data; and performing asset record generation processing on the standardized business component file data to obtain target component asset record data. In this way, the stability of the standard encoding information and the physical component file in the data level is improved, and the digital asset creation process is shortened. The automation degree of the business component data standardization processing and the heterogeneous file processing capability are improved, and the efficiency and consistency of the digital asset management are improved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and readable storage medium for processing business component data. Background Technology

[0002] In the processing of business component data such as Building Information Modeling (BIM), the conventional approach is to manually identify, look up, and encode component files according to a standard coding library. This results in low processing efficiency, a high error rate, and difficulty in ensuring the consistency between component data and the standard coding system. Existing technologies can assist manual coding by developing semi-automatic tools, but this relies on manual judgment, and the improvement in accuracy and efficiency is limited. Alternatively, a batch import method based on fixed tags can be used, but this requires the data source to strictly adhere to a specific format beforehand, resulting in poor flexibility, inability to handle heterogeneous files, and insufficient ability to match complex and fuzzy features.

[0003] It is evident that existing technologies suffer from several problems: the reliance on manual identification and table lookup for business component data processing leads to low efficiency and a high risk of errors; the fixed-label batch import method lacks flexibility; the simple keyword matching rule base is insufficient for matching complex and fuzzy features; resulting in low automation of standardized processing of business component data; weak heterogeneous file processing capabilities; a disconnect between standard information and physical files; and a long and inconsistent digital asset creation process. Summary of the Invention

[0004] In view of this, the present disclosure provides a business component data processing method, apparatus, electronic device, and readable storage medium to solve the problems in the prior art where business component data processing relies on manual identification and table lookup, which is inefficient and prone to errors; the fixed label batch import method is inflexible; the simple keyword matching rule base is insufficient for matching complex and fuzzy features; resulting in low automation of business component data standardization processing; weak heterogeneous file processing capability; disconnect between standard information and physical files; and long and inconsistent digital asset creation process.

[0005] A first aspect of this disclosure provides a business component data processing method, comprising: performing feature parsing processing on business component data to be processed to obtain business component feature data; performing encoding mapping processing on the business component feature data based on a preset mapping rule base to obtain business component standard encoding data; performing encoding and solidification processing on the business component data to be processed based on the business component standard encoding data to obtain standardized business component file data; and performing asset record generation processing on the standardized business component file data to obtain target component asset record data.

[0006] In some embodiments, the business component data to be processed is encoded and solidified based on the standard encoding data of the business component to obtain standardized business component file data, including: performing field positioning processing on preset parameter field data based on the standard encoding data of the business component to obtain target parameter field data; performing encoding and writing processing on the target parameter field data based on the standard encoding data of the business component to obtain parameter field writing data; and performing file encapsulation processing on the business component data to be processed based on the parameter field writing data to obtain standardized business component file data.

[0007] In some embodiments, the business component feature data is encoded and mapped based on a preset mapping rule library to obtain standard encoded data of the business component, including: performing condition retrieval processing on the business component feature data based on a preset mapping condition library to obtain matching condition data; performing condition judgment processing on the business component feature data based on the matching condition data to obtain condition matching result data; and performing standard encoding generation processing on the condition matching result data to obtain standard encoded data of the business component.

[0008] In some embodiments, feature parsing processing is performed on the business component data to be processed to obtain business component feature data, including: performing structured parsing processing on the business component data to be processed to obtain parsed element data; performing family identifier extraction processing on the parsed element data to obtain family identifier data; performing attribute category extraction processing on the parsed element data to obtain attribute category data; and performing feature aggregation processing on the family identifier data and attribute category data to obtain business component feature data.

[0009] In some embodiments, asset record generation processing is performed on standardized business component file data to obtain target component asset record data, including: encoding and reading standardized business component file data to obtain record encoding data; performing asset record creation processing on the record encoding data to obtain initial asset record data; and associating and binding the initial asset record data and standardized business component file data to obtain target component asset record data.

[0010] In some embodiments, the target parameter field data is encoded and written based on the standard encoding data of the business component to obtain parameter field written data, including: encoding value writing processing of the target parameter field data based on the standard encoding data of the business component to obtain encoded value written data; classification path writing processing of the target parameter field data based on the standard encoding data of the business component to obtain classification path written data; and field content aggregation processing of the encoded value written data and the classification path written data to obtain parameter field written data.

[0011] In some embodiments, conditional judgment processing is performed on the feature data of business components based on matching condition data to obtain conditional matching result data, including: performing feature comparison processing on the feature data of business components based on matching condition data to obtain feature comparison result data; and performing matching judgment processing on the feature comparison result data to obtain conditional matching result data.

[0012] A second aspect of this disclosure provides a business component data processing apparatus, comprising: a first processing module for performing feature parsing processing on business component data to be processed to obtain business component feature data; a second processing module for performing encoding mapping processing on the business component feature data based on a preset mapping rule base to obtain business component standard encoding data; a third processing module for performing encoding solidification processing on the business component data to be processed based on the business component standard encoding data to obtain standardized business component file data; and a fourth processing module for performing asset record generation processing on the standardized business component file data to obtain target component asset record data.

[0013] A third aspect of this disclosure provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0014] A fourth aspect of this disclosure provides a readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0015] The beneficial effects of this disclosed embodiment compared with the prior art are as follows: By performing feature parsing processing on the business component data to be processed, business component feature data is obtained; based on a preset mapping rule base, the business component feature data is encoded and mapped to obtain standard business component encoding data; based on the standard business component encoding data, the business component data to be processed is encoded and solidified to obtain standardized business component file data; and asset record generation processing is performed on the standardized business component file data to obtain target component asset record data. This improves the efficiency of automatic parsing and feature extraction of heterogeneous business component files, enhances the accuracy and mapping coverage of automatic conversion of heterogeneous data to the standard encoding system, improves the stability of the data-level unity between standard encoding information and physical component files, enhances the guarantee of information consistency among the digital asset library, standard encoding, and component files, and shortens the digital asset creation process; it also improves the automation level of business component data standardization processing and heterogeneous file processing capabilities, thereby improving the efficiency and consistency of digital asset management. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure; Figure 2 This is a flowchart illustrating a business component data processing method provided in an embodiment of this disclosure; Figure 3 This is a flowchart illustrating another business component data processing method provided in this embodiment of the disclosure; Figure 4 This is a schematic diagram of the structure of a business component data processing device provided in an embodiment of this disclosure; Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, so as to provide a thorough understanding of the embodiments of this disclosure. However, those skilled in the art will understand that this disclosure may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this disclosure with unnecessary detail.

[0019] It should be noted that the user information (including but not limited to terminal device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0020] A business component data processing method and apparatus according to an embodiment of the present disclosure will now be described in detail with reference to the accompanying drawings.

[0021] Figure 1 This is a schematic diagram illustrating an application scenario of an embodiment of this disclosure. The application scenario may include terminal devices 1, 2, and 3, server 4, and network 5.

[0022] Terminal devices 1, 2, and 3 can be hardware or software. When terminal devices 1, 2, and 3 are hardware, they can be various electronic devices with displays and supporting communication with server 4, including but not limited to smartphones, tablets, laptops, and desktop computers. When terminal devices 1, 2, and 3 are software, they can be installed in the aforementioned electronic devices. Terminal devices 1, 2, and 3 can be implemented as multiple software programs or software modules, or as a single software program or software module; this disclosure does not limit this. Furthermore, various applications can be installed on terminal devices 1, 2, and 3, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

[0023] Server 4 can be a server that provides various services, such as a backend server that receives requests sent by terminal devices with which it has established communication connections. This backend server can receive and analyze the requests sent by the terminal devices and generate processing results. Server 4 can be a single server, a server cluster consisting of several servers, or a cloud computing service center. This disclosure embodiment does not limit this.

[0024] It should be noted that server 4 can be either hardware or software. When server 4 is hardware, it can be various electronic devices that provide various services to terminal devices 1, 2, and 3. When server 4 is software, it can be multiple software programs or software modules that provide various services to terminal devices 1, 2, and 3, or it can be a single software program or software module that provides various services to terminal devices 1, 2, and 3. This disclosure does not limit the scope of the embodiments.

[0025] Network 5 can be a wired network using coaxial cable, twisted pair, and fiber optic connection, or it can be a wireless network that enables interconnection of various communication devices without wiring, such as Bluetooth, Near Field Communication (NFC), and Infrared. This disclosure does not limit the scope of the network.

[0026] Users can establish a communication connection with server 4 via network 5 through terminal devices 1, 2, and 3 to receive or send information. Specifically, server 4 can obtain business component data to be processed through terminal devices 1, 2, and 3, and obtain business component feature data by performing feature parsing processing on the business component data to be processed; perform encoding mapping processing on the business component feature data based on a preset mapping rule base to obtain standard encoded data of business components; perform encoding and solidification processing on the business component data to be processed based on the standard encoded data of business components to obtain standardized business component file data; and perform asset record generation processing on the standardized business component file data to obtain target component asset record data.

[0027] It should be noted that the specific types, quantities, and combinations of terminal devices 1, 2, and 3, server 4, and network 5 can be adjusted according to the actual needs of the application scenario, and this disclosure embodiment does not impose any restrictions on this.

[0028] Figure 2 This is a flowchart illustrating a business component data processing method provided in an embodiment of this disclosure. Figure 2 The business component data processing method can be provided by Figure 1 The server executes the command. For example... Figure 2 As shown, the data processing method for this business component includes: S201, Perform feature parsing processing on the business component data to be processed to obtain business component feature data.

[0029] Specifically, the business component data to be processed can be initial file data containing component design information from an external submitter. This business component data to be processed can be used to trigger the entire business component data processing flow. This business component data to be processed can contain the initial design features and attribute parameters of the component. This business component data to be processed can be an initial component file submitted to the system by an external submitter via an upload interface or interactive terminal.

[0030] In addition, feature parsing can be a data processing procedure that transforms the content of unstructured initial component files into structured feature data. This feature parsing can be used to read and analyze the initial component files to extract key feature elements that can be used for subsequent automated matching. Based on preset parsing rules, this feature parsing can extract metadata and identify attribute parameters of the data inside the component files to obtain business component feature data.

[0031] Specifically, the business component feature data can be a structured data set extracted from the business component data to be processed, which can be used to characterize the key attributes and geometric features of the component. This business component feature data can be used to compare and match with the mapping conditions in the preset mapping rule base. The business component feature data can contain structured elements such as family identifier data, attribute parameter data, and geometric category data. This business component feature data can be obtained by reading files and extracting features from the business component data to be processed through feature parsing processing.

[0032] Furthermore, the feature parsing process can specifically include performing structured parsing on the business component data to be processed to obtain parsed element data; extracting family identifiers from the parsed element data to obtain family identifier data; extracting attribute categories from the parsed element data to obtain attribute category data; and performing feature aggregation on the family identifier data and attribute category data to obtain business component feature data.

[0033] This application embodiment obtains parsed element data by structuring and parsing the business component data to be processed, extracts family identifier data and attribute category data from the parsed elements, and performs feature aggregation on the family identifier data and attribute category data to obtain business component feature data. This enhances the ability to transform unstructured initial component files into structured feature data and improves the extraction accuracy of key attributes and geometric features of components.

[0034] For example, in Building Information Modeling (BIM) management, an external designer submits initial file data of a door component as business component data to be processed via an upload terminal. This triggers feature parsing processing to read and analyze the business component data. Structured parsing processing transforms the unstructured content within the file into parsed feature data. Then, family identifier extraction processing can be performed on the parsed feature data to obtain family identifier data containing family name and family type name. Attribute category extraction processing can be performed on the parsed feature data to obtain attribute category data containing width, height, material, and geometric categories. Finally, feature aggregation processing can be performed on the family identifier data and attribute category data to obtain business component feature data containing all key features.

[0035] S202, based on the preset mapping rule base, the feature data of the business components are encoded and mapped to obtain the standard encoded data of the business components.

[0036] Specifically, the preset mapping rule base can be a pre-configured dataset containing multiple sets of mapping conditions and corresponding standard encoding output rules. This preset mapping rule base can be used to provide matching basis and judgment criteria for encoding mapping processing, realizing the automatic conversion of heterogeneous feature data to the standard encoding system. The preset mapping rule base can contain condition combinations and corresponding encoding mapping relationships based on dimensions such as component family identifier, attribute parameters, and geometric categories. The preset mapping rule base can be a database or rule file dynamically maintained and configured by the administrator according to business standards updates.

[0037] Furthermore, the encoding mapping process can be a data processing procedure that automatically matches and maps heterogeneous business component feature data with a preset standard encoding system. This encoding mapping process can be used to convert component features into standard codes. Based on the conditional rules in the preset mapping rule base, this encoding mapping process can perform item-by-item retrieval and conditional judgment on the business component feature data to obtain the standard encoding data of the business components.

[0038] Among them, the standard coding data of business components can be the unique identification data of components that conform to the preset standard system and are generated through coding mapping processing. The standard coding data of business components can be used to write back to the component file and create asset records. The standard coding data of business components can include standardized identity elements such as unique coding data, classification path data and standard attribute data. The standard coding data of business components can be the standardized output data generated after the rule matching and condition judgment of the feature data of business components through coding mapping processing.

[0039] Furthermore, the encoding mapping process may specifically include performing rule retrieval processing on the feature data of business components based on a preset mapping rule base to obtain matching rule data; performing condition judgment processing on the feature data of business components based on the matching rule data to obtain rule matching result data; and performing standard encoding generation processing on the rule matching result data to obtain standard encoding data of business components.

[0040] This application embodiment performs rule retrieval processing on business component feature data based on a preset mapping rule base to obtain matching rule data; performs condition judgment processing on business component feature data based on the matching rule data to obtain rule matching result data; and performs standard encoding generation processing on the rule matching result data to obtain standard encoding data for business components. This improves the automation level and mapping efficiency of standardized mapping of business component data, enhances the accuracy and consistency of standard encoding generation, and improves the response speed and adaptability of standard system updates in automated processing.

[0041] For example, in building information modeling (BIM) management, business component feature data may include a family name of "fire door" and a width parameter within a preset threshold range. Based on a preset mapping rule base, rule retrieval processing can be performed on this business component feature data to retrieve matching rule data related to "fire door" and the width parameter. Then, based on the matching rule data, conditional judgment processing can be performed on the business component feature data to verify whether the feature parameters meet the rule conditions, obtaining rule matching result data. Finally, standard encoding generation processing can be performed on the rule matching result data to obtain standard code data for the business component containing a unique standard code and classification path.

[0042] S203: Based on the standard coding data of business components, the data of the business components to be processed is encoded and solidified to obtain standardized business component file data.

[0043] Specifically, the encoding solidification process can be a data processing procedure that reverses the generated standard encoding data and writes it into specific fields inside the initial component file to achieve physical solidification of standard identity information. This encoding solidification process can be used to ensure that the standard identity of the component flows with the file itself. This encoding solidification process can locate and write the content of the preset parameter fields inside the component file based on the standard encoding data of the business component to obtain standardized business component file data.

[0044] Specifically, the standardized business component file data can be component file data that has been encoded and solidified, embedding standard coded data and related information. This standardized business component file data can be used to create consistent asset records in the digital asset library. This standardized business component file data can contain the entire content of the initial component file and solidified identity elements such as the standard coded data written back. This standardized business component file data can be obtained by writing back standard information and encapsulating the business component data to be processed through encoding and solidification.

[0045] Furthermore, the encoding solidification process may specifically include: performing field location processing on preset parameter field data based on the standard encoding data of business components to obtain target parameter field data; performing encoding and writing processing on the target parameter field data based on the standard encoding data of business components to obtain parameter field writing data; and performing file encapsulation processing on the business component data to be processed based on the parameter field writing data to obtain standardized business component file data.

[0046] This application embodiment obtains target parameter field data by performing field location processing on preset parameter field data based on business component standard coding data; performs encoding and writing processing on the target parameter field data based on business component standard coding data to obtain parameter field written data; and performs file encapsulation processing on the business component data to be processed based on the parameter field written data to obtain standardized business component file data. This improves the stability of the integration of business component standard identity information and physical file, enhances the identifiability and traceability of standard coding data in the cross-system flow process, and improves the standardization and physical consistency of digital asset files.

[0047] For example, in Building Information Modeling (BIM) management, business component data to be processed can be encoded and solidified based on standard code data containing unique standard codes and classification paths. Preset parameter field data can be located based on the standard code data to determine the target parameter field data for writing standard information inside the file. Then, the target parameter field data can be encoded and written based on the standard code data to write the standard code and classification path to the corresponding position to obtain parameter field writing data. The parameter field writing data can be used to encapsulate the business component data to be processed to obtain standardized business component file data with embedded standard identity information.

[0048] S204, perform asset record generation processing on standardized business component file data to obtain target component asset record data.

[0049] Specifically, the asset record generation process can be a data processing process that automatically creates digital asset records and establishes file associations based on the standard information that has been fixed in the standardized business component file data. This asset record generation process can be used to realize the automatic conversion of component files to digital assets and ensure the consistency between asset records and physical file information. This asset record generation process can encode and read and create records based on the standard encoded data in the standardized business component file data to obtain the target component asset record data.

[0050] Specifically, the target component asset record data can be structured record data created in the digital asset library through asset record generation processing, used to uniquely identify and manage standardized business components. This target component asset record data can serve as the formal identity certificate of the component in the digital asset library, enabling the component to be queried, managed, and reused. The target component asset record data can include core asset information such as coded field data, classification path data, and attribute field data. The target component asset record data can also be asset registration data generated by encoding, reading, creating, and associating standardized business component file data.

[0051] Furthermore, the asset record generation process may specifically include encoding and reading standardized business component file data to obtain record encoding data; performing asset record creation processing on the record encoding data to obtain initial asset record data; and associating and binding the initial asset record data and standardized business component file data to obtain target component asset record data.

[0052] This application embodiment obtains coded record data by encoding and reading standardized business component file data; it then performs asset record creation processing on the coded record data to obtain initial asset record data; finally, it associates and binds the initial asset record data with the standardized business component file data to obtain target component asset record data. This improves the accuracy and efficiency of the automated conversion of business components from files to assets, enhances the consistency between the recorded information in the digital asset library and the physical file information, and improves the automation level of the digital asset creation process and the quality of asset data.

[0053] For example, in building information modeling management, code reading can be performed to read the fixed unique standard codes and classification paths from standardized business component file data to obtain record code data. Then, asset record creation processing can be performed on the record code data to create initial asset record data containing code fields and classification path fields in the digital asset library. The initial asset record data and standardized business component file data can be associated and bound to establish the correspondence between asset records and physical files to obtain target component asset record data.

[0054] According to the technical solution provided in this disclosure, feature parsing processing is performed on the business component data to be processed. Through structured parsing, family identifier extraction, attribute category extraction, and feature aggregation, business component feature data is obtained. Based on a preset mapping rule base, the business component feature data is encoded and mapped. Through rule retrieval, condition judgment, and standard code generation, standard code data for the business component is obtained. Based on the standard code data, the business component data to be processed is encoded and solidified. Through field positioning, encoding writing, and file encapsulation, standardized business component file data is obtained. Asset record generation processing is performed on the standardized business component file data. Through encoding reading, asset record creation, and association binding, target component asset record data is obtained. This enhances the ability to convert unstructured component files into structured feature data; improves the accuracy and efficiency of automated mapping of component features to standard codes; enhances the stability and cross-system traceability of the integration of standard identity information and physical files; realizes automated conversion and consistent association of component files to digital assets; improves the efficiency of automatic parsing and feature extraction of heterogeneous business component files; shortens the digital asset creation process; and improves the automation level of business component data standardization processing and heterogeneous file processing capabilities.

[0055] In some embodiments, the business component data to be processed is encoded and solidified based on the standard encoding data of the business component to obtain standardized business component file data, including: performing field positioning processing on preset parameter field data based on the standard encoding data of the business component to obtain target parameter field data; performing encoding and writing processing on the target parameter field data based on the standard encoding data of the business component to obtain parameter field writing data; and performing file encapsulation processing on the business component data to be processed based on the parameter field writing data to obtain standardized business component file data.

[0056] Specifically, the preset parameter field data can be a pre-configured set of parameters within the component file containing standard encoding information. This preset parameter field data can be used to provide a candidate set of target locations for which standard information can be written. This preset parameter field data can contain structured location information elements such as shared parameter definitions and parameter name identifiers. This preset parameter field data can be a data container pre-configured according to the target file format specification and standardization requirements.

[0057] In addition, the field location processing can be a data processing process that searches for and determines the corresponding target write location in a preset parameter field data set based on the encoding type and attribute information indicated by the standard encoding data of the business component. This field location processing can be used to establish a mapping relationship between standard encoding data and the internal storage location of the file. This field location processing can perform parameter matching and location identification on the standard encoding data of the business component based on preset field mapping rules to obtain the target parameter field data.

[0058] The target parameter field data can be the write location data corresponding to the standard coding data of the business component, which is identified from the preset parameter field data set through field positioning processing. The target parameter field data can be used to indicate the specific parameter field to which the standard coding information should be written. The target parameter field data can include location identification information such as parameter name data and parameter type data. The target parameter field data can be obtained by scanning, matching and identifying the correspondence of the preset parameter field data through field positioning processing.

[0059] Furthermore, the field location processing may specifically include parsing the standard coded data of the business component to obtain coded type data; performing parameter scanning processing on the preset parameter field data based on the coded type data to obtain candidate parameter field data; and performing matching and confirmation processing on the candidate parameter field data to obtain target parameter field data.

[0060] In addition, the encoding and writing process can be a data processing procedure that writes the standard information contained in the standard encoding data of the business component to the corresponding parameter position of the component file according to the position indicated by the target parameter field data. This encoding and writing process can be used to inject and solidify the standard information at the physical file level, so that the component file itself carries standardized identity information. This encoding and writing process can extract the content and assign parameters to the standard encoding data of the business component based on the target parameter field data to obtain the parameter field writing data.

[0061] Among them, the parameter field write data can be the data status data of the target parameter field after the encoding write process is completed, which is assigned specific standard encoding information. This parameter field write data can be used as an intermediate data product connecting standard encoding and standardization documents. This parameter field write data can contain field content carrying standardization information, such as updated parameter names and parameter values. This parameter field write data can be obtained after assigning standard information and updating the content of the target parameter field data through encoding write process.

[0062] Furthermore, the encoding and writing process can specifically include extracting content from the standard encoded data of the business component to obtain standard encoded content data; confirming the writing position of the target parameter field data to obtain writing position data; and assigning parameters to the standard encoded content data based on the writing position data to obtain parameter field writing data.

[0063] In addition, file encapsulation processing can be a data processing process that integrates and confirms the format of business component data to be processed with updated standard parameters based on the data written to the parameter fields. This file encapsulation processing can be used to produce standardized digital asset files that are deliverable and transferable. This file encapsulation processing can save parameters and generate file versions of business component data to be processed based on the data written to the parameter fields to obtain standardized business component file data.

[0064] Furthermore, the file encapsulation process may specifically include performing write status verification on the data written to the parameter fields to obtain status verification data; performing parameter saving processing on the data of the business component to be processed based on the status verification data to obtain parameter saved data; and performing file version generation processing on the parameter saved data to obtain standardized business component file data.

[0065] For example, in Building Information Modeling (BIM) management, standard coding data for business components can include unique standard codes and classification paths. Based on the standard coding data, preset parameter fields can be located. Through code type parsing and parameter scanning, target parameter fields corresponding to the standard coding type can be identified from a preset shared parameter set. Then, based on the standard coding data, the target parameter fields can be encoded and written. Standard information such as standard codes and classification paths can be written to the corresponding target parameter field positions to obtain parameter field writing data. Based on the parameter field writing data, the business component data to be processed can be encapsulated. Through parameter saving and file version generation, standardized business component file data with embedded standard identity information can be obtained.

[0066] According to the technical solution provided in this disclosure, the preset parameter field data is located based on the standard coding data of the business component. The target parameter field data is obtained through coding type parsing, parameter scanning, and matching confirmation. The target parameter field data is then encoded and written based on the standard coding data of the business component. The written parameter data is obtained through content extraction, location confirmation, and parameter assignment. The business component data to be processed is then encapsulated based on the written parameter data. Standardized business component file data is obtained through status verification, parameter saving, and file version generation. This improves the accurate mapping capability between standard coding data and the internal storage location of the file; realizes the injection and solidification of standard identity information at the physical file level; and enhances the cross-system identifiability and traceability of standardized digital asset files.

[0067] In some embodiments, the business component feature data is encoded and mapped based on a preset mapping rule library to obtain standard encoded data of the business component, including: performing condition retrieval processing on the business component feature data based on a preset mapping condition library to obtain matching condition data; performing condition judgment processing on the business component feature data based on the matching condition data to obtain condition matching result data; and performing standard encoding generation processing on the condition matching result data to obtain standard encoded data of the business component.

[0068] Specifically, the conditional retrieval process can be a data processing procedure that queries and compares feature item information in the business component feature data in a preset mapping condition library to filter potential association rules. This conditional retrieval process can be used to filter candidate rules related to the current component features from preset rules, thereby improving subsequent matching efficiency. This conditional retrieval process can also extract feature items and scan the condition library based on preset retrieval rules to obtain matching condition data.

[0069] The matching condition data can be a set of candidate rules that can potentially be related to the feature data of business components, which are filtered from a preset mapping condition library through condition retrieval processing. The matching condition data can be used to determine whether the mapping conditions are met. The matching condition data can include candidate rule information such as condition rule identifier and rule condition description. The matching condition data can be obtained by performing feature association scanning and rule filtering on the preset mapping condition library through condition retrieval processing.

[0070] Furthermore, the conditional retrieval process may specifically include extracting feature items from the feature data of business components to obtain feature item data; performing rule scanning on a preset mapping condition library based on the feature item data to obtain candidate rule data; and performing association filtering on the candidate rule data to obtain matching condition data.

[0071] In addition, the condition determination process can be a data processing process that verifies whether the business component feature data meets its specified conditions for each rule in the matching condition data. This condition determination process can be used to realize the mapping decision from non-standard features to standard codes and ensure the accuracy of the mapping results. This condition determination process can perform condition comparison and logical verification on the matching condition data and business component feature data based on preset determination rules to obtain the condition matching result data.

[0072] Among them, the conditional matching result data can be the judgment result data generated by the conditional judgment process, indicating whether a complete matching rule has been found and the specific matching rule identifier. The conditional matching result data can be used to determine whether to generate standard codes or transfer to other processing flows. The conditional matching result data can contain judgment information such as matching status identifier and matching rule pointer. The conditional matching result data can be obtained by performing rule-by-rule verification and logical judgment on the matching condition data through the conditional judgment process.

[0073] Furthermore, the condition determination process may specifically include performing rule parsing on the matching condition data to obtain rule condition data; performing condition comparison on the business component feature data based on the rule condition data to obtain comparison result data; and performing logical verification on the comparison result data to obtain condition matching result data.

[0074] In addition, the standard coding generation process can be a data processing process that generates a code that conforms to a preset standard system by executing corresponding mapping actions based on the rule information that is successfully matched in the conditional matching result data. This standard coding generation process can be used to automatically generate the identity identifier and complete attribute list of the component in the standard system. This standard coding generation process can extract codes and expand attributes based on the matching rule pointers in the conditional matching result data to obtain the standard coding data of the business component.

[0075] Furthermore, the standard encoding generation process may specifically include performing matching status recognition processing on the condition matching result data to obtain status recognition data; performing encoding extraction processing on the preset encoding rules based on the status recognition data to obtain basic encoding data; and performing attribute extension processing on the basic encoding data to obtain standard encoding data for business components.

[0076] For example, in Building Information Modeling (BIM) management, conditional retrieval processing of business component feature data can be performed based on a preset mapping condition library. By extracting feature items and scanning rules, candidate rules related to the current component features can be selected from the preset mapping condition library to obtain matching condition data. Then, conditional judgment processing of business component feature data can be performed based on the matching condition data. By parsing rules and comparing conditions, it can be verified whether the component features meet the rule conditions to obtain conditional matching result data. Standard encoding generation processing can be performed on the conditional matching result data. By identifying the matching status and extracting the encoding, standard encoding data of business components containing unique codes and classification paths can be generated.

[0077] According to the technical solution provided in this disclosure, the feature data of business components is processed by condition retrieval based on a preset mapping condition library. Matching condition data is obtained through feature extraction, rule scanning, and association filtering. Based on the matching condition data, the feature data of business components is processed by condition judgment. Condition matching result data is obtained through rule parsing, condition comparison, and logical verification. Standard encoding generation is performed on the condition matching result data. Standard encoding data of business components is obtained through matching status recognition, encoding extraction, and attribute expansion. This improves the screening accuracy and matching efficiency of candidate rules; enhances the automated mapping decision-making capability from features to standard encoding; and ensures the accuracy and attribute integrity of standard encoding generation.

[0078] In some embodiments, feature parsing processing is performed on the business component data to be processed to obtain business component feature data, including: performing structured parsing processing on the business component data to be processed to obtain parsed element data; performing family identifier extraction processing on the parsed element data to obtain family identifier data; performing attribute category extraction processing on the parsed element data to obtain attribute category data; and performing feature aggregation processing on the family identifier data and attribute category data to obtain business component feature data.

[0079] Specifically, structured parsing can be a data processing procedure that reads and extracts content from unstructured initial component files to generate a structured data set with clear field and value correspondences. This structured parsing can be used to transform non-standardized files containing complex internal metadata, attributes, and geometric information into intermediate data with a unified format. This structured parsing can also identify metadata and extract parameters from the internal data of component files based on preset parsing rules to obtain parsed feature data.

[0080] Among them, the parsing element data can be a set of structured data representing the key content of the component extracted from the business component file to be processed through structured parsing processing. The parsing element data can be used to extract core features such as family affiliation and attribute dimensions. The parsing element data can contain structured elements such as file internal metadata, key attribute parameters and geometric category information. The parsing element data can be obtained by reading the file and organizing the content of the business component data to be processed through structured parsing processing.

[0081] Furthermore, the structured parsing process can specifically include: reading the business component data to be processed to obtain file content data; performing metadata identification processing on the file content data to obtain metadata element data; extracting parameters from the file content data to obtain parameter element data; performing category identification processing on the file content data to obtain geometric category data; and aggregating the metadata element data, parameter element data, and geometric category data to obtain parsed element data.

[0082] In addition, the family identifier extraction process can be a data processing procedure that identifies and extracts key identifier information that can characterize the family to which a component belongs from structured parsed element data. This family identifier extraction process can be used to filter out the core features that best represent the family affiliation of a component from the parsed information. This family identifier extraction process can perform field positioning and value extraction on the parsed element data based on preset identifier rules to obtain family identifier data.

[0083] Among them, the family identifier data can be key identifier information data representing the family to which the component belongs, which is extracted from the parsed feature data through family identifier extraction processing. This family identifier data can be used as the core feature field for feature aggregation processing, and can be used to represent the family affiliation and type identifier of the component. This family identifier data can include family affiliation information such as family name identifier and family type identifier. This family identifier data can be obtained by reading fields and extracting values ​​from the metadata feature data in the parsed feature data through family identifier extraction processing.

[0084] Furthermore, the family identifier extraction process may specifically include performing metadata field location processing on the parsed element data to obtain metadata field data; performing identifier field filtering processing on the metadata field data to obtain identifier candidate data; and performing priority determination processing on the identifier candidate data to obtain family identifier data.

[0085] In addition, attribute category extraction processing can be a data processing process that identifies and extracts category information representing the key physical or functional attributes of a component from structured parsed feature data. This attribute category extraction processing can be used to obtain the key attribute dimension information of the component. Based on preset category mapping rules, this attribute category extraction processing can perform name analysis and category classification on the parameter feature data in the parsed feature data to obtain attribute category data.

[0086] Among them, the attribute category data can be classification information data that can characterize the key attribute dimensions of the component, which can be extracted from the parsed feature data through attribute category extraction processing. This attribute category data can be used to provide a classification description of the component's attribute dimensions. This attribute category data can include attribute dimension information such as size category, material category, and function category. This attribute category data can be obtained by name mapping and category classification of the parameter feature data in the parsed feature data through attribute category extraction processing.

[0087] Furthermore, the attribute category extraction process may specifically include reading parameter names from the parsed feature data to obtain parameter name data; performing category mapping on the parameter name data based on preset category mapping rules to obtain parameter category data; and performing category aggregation on the parameter category data to obtain attribute category data.

[0088] In addition, feature aggregation processing can be a data processing process that combines the extracted family identifier data and attribute category data according to a predetermined logic to form a complete component comprehensive feature data record. This feature aggregation processing can be used to integrate scattered key feature information into a unified data object. This feature aggregation processing can perform field splicing and structural assembly of family identifier data and attribute category data based on preset aggregation rules to obtain business component feature data.

[0089] Furthermore, the feature aggregation process may specifically include assigning values ​​to the family identifier data to obtain identifier field data; assigning values ​​to the attribute category data to obtain category field data; and performing structural assembly processing on the identifier field data and category field data to obtain business component feature data.

[0090] For example, in Building Information Modeling (BIM) management, the business component data to be processed can be an initial component file containing component design information. The business component data to be processed can be subjected to structured parsing processing. By reading the file, identifying the metadata, extracting parameters, and identifying categories, parsing element data containing metadata elements, parameter elements, and geometric category information can be obtained. Then, family identifier extraction processing can be performed on the parsing element data. By locating metadata fields and filtering identifier fields, family identifier data representing the component family affiliation can be obtained. At the same time, attribute category extraction processing can be performed on the parsing element data. By reading parameter names and mapping categories, attribute category data representing the component attribute dimensions can be obtained. The family identifier data and attribute category data can be subjected to feature aggregation processing. By assigning field values ​​and assembling the structure, business component feature data containing family identifier fields and attribute category fields can be obtained.

[0091] According to the technical solution provided in this disclosure, structured parsing processing is performed on the business component data to be processed. This involves file reading, metadata identification, parameter extraction, and category identification followed by aggregation to obtain parsed element data. Family identifier extraction is then performed on the parsed element data, obtaining family identifier data through metadata field location, identifier field filtering, and priority determination. Attribute category extraction is then performed on the parsed element data, obtaining attribute category data through parameter name reading, category mapping, and category aggregation. Finally, feature aggregation processing is performed on the family identifier data and attribute category data. Through field assignment and structure assembly, business component feature data is obtained. This enhances the ability to transform unstructured initial component files into structured parsed elements and improves the extraction accuracy and classification accuracy of family identifier and attribute category information.

[0092] In some embodiments, asset record generation processing is performed on standardized business component file data to obtain target component asset record data, including: encoding and reading standardized business component file data to obtain record encoding data; performing asset record creation processing on the record encoding data to obtain initial asset record data; and associating and binding the initial asset record data and standardized business component file data to obtain target component asset record data.

[0093] Specifically, the encoding reading process can be a data processing procedure that extracts standard encoding information from the parameter fields already written inside the standardized business component file data. This encoding reading process can be used to obtain the unique identifier of the component. Based on preset reading rules, this encoding reading process can scan the target parameter fields in the standardized business component file data and extract the encoding to obtain the recorded encoded data.

[0094] Specifically, the record encoding data can be standard encoding information data extracted from standardized business component file data through encoding reading processing. This record encoding data can serve as a key index and core field value for asset record creation processing, used to create new records containing standard codes in the digital asset library. This record encoding data can contain standardized identity elements such as unique encoding identifiers and classification path information. This record encoding data can be obtained by extracting the content of parameter fields in standardized business component file data through encoding reading processing.

[0095] Furthermore, the encoding reading process can specifically include performing parameter field location processing on standardized business component file data to obtain encoded field data; performing content scanning processing on the encoded field data to obtain encoded content data; and performing encoding extraction processing on the encoded content data to obtain record encoded data.

[0096] In addition, the asset record creation process can be a data processing process that generates new asset records containing standard coded fields in a digital asset library based primarily on record coded data. This asset record creation process can be used to realize the digital registration of component assets at the database level. This asset record creation process can fill in the fields of the record coded data and generate records based on a preset record template to obtain the initial asset record data.

[0097] The initial asset record data can be component asset record data generated in the digital asset library through asset record creation processing, containing standard coded fields. This initial asset record data can be used to establish a correspondence with standardized business component file data. This initial asset record data can contain core asset information such as coded fields, classification path fields, and attribute fields. This initial asset record data can be obtained by filling the record template and writing to the database through asset record creation processing of the record coded data.

[0098] Furthermore, the asset record creation process may specifically include extracting coded fields from the coded record data to obtain coded field value data; filling the coded field value data with fields based on a preset record template to obtain filled record data; and writing the filled record data to the database to obtain initial asset record data.

[0099] In addition, the association binding process can be a data processing procedure that establishes a link between initial asset record data and standardized business component file data in the digital asset library. This association binding process can be used to achieve a one-to-one correspondence and unified management between digital asset records and their physical files, ensuring the integrity and traceability of asset information. This association binding process can identify and match initial asset record data and standardized business component file data and establish relationships based on preset association rules to obtain target component asset record data.

[0100] Furthermore, the association binding process can specifically include extracting record identifiers from the initial asset record data to obtain record identifier data; extracting file identifiers from the standardized business component file data to obtain file identifier data; and establishing a relationship between the record identifier data and the file identifier data to obtain the target component asset record data.

[0101] For example, in Building Information Modeling (BIM) management, asset record generation can be performed on standardized business component file data that embeds standard coded data. The standardized business component file data can be encoded and read, and the standard codes and classification paths can be extracted from the file through parameter field positioning and content scanning to obtain record coded data. Then, asset record creation can be performed on the record coded data. Initial asset record data containing standard coded fields can be generated in the digital asset library by extracting coded fields and filling record templates. The initial asset record data and standardized business component file data can be associated and bound. The target component asset record data containing complete asset information and file association can be obtained by establishing the relationship between record identifier and file identifier.

[0102] According to the technical solution provided in this disclosure, standardized business component file data is encoded and read. Record encoding data is obtained through parameter field positioning, content scanning, and encoding extraction. Asset record creation is performed on the recorded encoding data. Initial asset record data is obtained through encoding field extraction, template filling, and database writing. The initial asset record data and standardized business component file data are then associated and bound. Target component asset record data is obtained through record identifier extraction, file identifier extraction, and relationship establishment. This improves the efficient transmission capability of standard encoded information from physical files to digital asset records; achieves accurate association and unified management between component asset records and physical files; and enhances the integrity and traceability of component records in the digital asset library.

[0103] In some embodiments, the target parameter field data is encoded and written based on the standard encoding data of the business component to obtain parameter field written data, including: encoding value writing processing of the target parameter field data based on the standard encoding data of the business component to obtain encoded value written data; classification path writing processing of the target parameter field data based on the standard encoding data of the business component to obtain classification path written data; and field content aggregation processing of the encoded value written data and the classification path written data to obtain parameter field written data.

[0104] Specifically, the encoding value writing process can be a data processing procedure that writes the unique encoding identifier in the standard encoding data of the business component into the corresponding encoding field of the target parameter field data. This encoding value writing process can be used to inject and solidify the unique identity of the component into the physical file parameter field. This encoding value writing process can fill the content and update the value of the encoding field in the target parameter field data based on the unique encoding data in the standard encoding data of the business component to obtain the encoding value writing data.

[0105] Specifically, the encoded value written data can be parameter status data obtained by filling the unique encoded identifier into the corresponding encoded field of the target parameter field data through the encoded value writing process. This encoded value written data can be used to provide standardized unique identity content. This encoded value written data can contain standardized encoded information such as the updated encoded field name and encoded field value. This encoded value written data can be obtained by locating the encoded field and assigning unique encoded data to the target parameter field data through the encoded value writing process.

[0106] Furthermore, the coded value writing process may specifically include performing unique code extraction processing on the standard coded data of the business component to obtain unique coded data; performing coded field positioning processing on the target parameter field data to obtain coded field position data; and performing field filling processing on the unique coded data based on the coded field position data to obtain coded value writing data.

[0107] In addition, the classification path writing process can be a data processing procedure that writes the classification hierarchy path information in the standard coding data of business components into the corresponding classification field of the target parameter field data. This classification path writing process can be used to inject and solidify the hierarchical belonging information of components in the standard classification system into the physical file parameter field. This classification path writing process can fill the content and write the path to the classification field in the target parameter field data based on the classification path data in the standard coding data of business components to obtain the classification path writing data.

[0108] Among them, the classification path writing data can be parameter status data obtained by filling the classification hierarchy path information into the corresponding classification field of the target parameter field data through classification path writing processing. This classification path writing data can be used to provide standardized hierarchical belonging information content; this classification path writing data can contain standardized classification information such as updated classification field names and classification field values; this classification path writing data can be obtained by locating the classification field and assigning classification path data to the target parameter field data through classification path writing processing.

[0109] Furthermore, the classification path writing process can specifically include performing classification path extraction processing on the standard coding data of business components to obtain classification path data; performing classification field positioning processing on the target parameter field data to obtain classification field position data; and performing field filling processing on the classification path data based on the classification field position data to obtain classification path writing data.

[0110] In addition, the field content aggregation process can be a data processing procedure that merges the standardized coded field content in the coded value written data and the standardized classification field content in the classification path written data into the same data structure while retaining other necessary parameter information. This field content aggregation process can be used to generate parameter field written data containing complete standardized identity information. This field content aggregation process can merge fields and integrate structures of coded value written data and classification path written data based on preset aggregation rules to obtain parameter field written data.

[0111] Furthermore, the field content aggregation processing can specifically include extracting the encoded field from the encoded value written data to obtain the encoded field content data; extracting the category field from the category path written data to obtain the category field content data; and merging the encoded field content data and the category field content data to obtain the parameter field written data.

[0112] For example, in Building Information Modeling (BIM) management, target parameter field data obtained through field location processing can be encoded and written based on business component standard coding data containing unique standard codes and classification paths. Encoded values ​​can be written to target parameter field data based on business component standard coding data. Standard code values ​​are then filled into corresponding coding fields through unique code extraction and coding field location to obtain encoded value written data. Furthermore, classification paths can be written to target parameter field data based on business component standard coding data. Standard classification paths are then filled into corresponding classification fields through classification path extraction and classification field location to obtain classification path written data. Finally, the encoded value written data and classification path written data can be aggregated into field content. Encoded field extraction and classification field extraction merge the standardized fields from the two dimensions into unified parameter field written data.

[0113] According to the technical solution provided in this disclosure, the target parameter field data is processed by writing encoded values ​​based on the standard encoded data of business components. This is achieved through unique code extraction, encoded field positioning, and field filling to obtain the written encoded values. The target parameter field data is then processed by writing classification paths based on the standard encoded data of business components. This is achieved through classification path extraction, classification field positioning, and field filling to obtain the written classification paths. The written encoded values ​​and written classification paths are then aggregated for field content. Finally, through code field extraction, classification field extraction, and field merging, the written parameter fields are obtained. This improves the synchronous injection and solidification capability of unique encoded identifiers and classification hierarchical paths in the physical file parameter fields, and enhances the integrity and accuracy of writing standard identity information.

[0114] In some embodiments, conditional judgment processing is performed on the feature data of business components based on matching condition data to obtain conditional matching result data, including: performing feature comparison processing on the feature data of business components based on matching condition data to obtain feature comparison result data; and performing matching judgment processing on the feature comparison result data to obtain conditional matching result data.

[0115] Specifically, feature comparison processing can be a data processing process that compares and verifies each feature element in the business component feature data with the rule conditions in the matching condition data one by one or in combination. This feature comparison processing can be used to determine whether the component features meet the preset mapping rule conditions. Based on the preset comparison rules, this feature comparison processing can extract condition items and compare feature values ​​of the matching condition data and the business component feature data to obtain feature comparison result data.

[0116] The feature comparison result data can be intermediate result data generated through feature comparison processing, which can be used to indicate the comparison status between component features and matching conditions. This feature comparison result data can be used to characterize the preliminary matching status and the degree of condition satisfaction. This feature comparison result data can include intermediate status information such as condition satisfaction identifier and comparison details data. This feature comparison result data can be obtained by performing rule condition comparison and status recording on the matching condition data and business component feature data through feature comparison processing.

[0117] Furthermore, the feature comparison processing can specifically include performing rule condition extraction processing on the matching condition data to obtain rule condition item data; performing feature item extraction processing on the business component feature data to obtain feature item data; and performing condition comparison processing on the rule condition item data and feature item data to obtain feature comparison result data.

[0118] Furthermore, the matching determination process can be a data processing procedure that makes a matching decision based on a preset logic according to the comparison status indicated in the feature comparison result data. This matching determination process can be used to determine whether the business component has successfully matched the standard code based on the preliminary comparison results and generate the corresponding conclusion output. This matching determination process can perform status recognition and conclusion generation on the feature comparison result data based on the preset determination logic to obtain conditional matching result data.

[0119] Furthermore, the matching determination process may specifically include performing state recognition processing on the feature comparison result data to obtain comparison state data; performing logical verification processing on the state data based on a preset determination logic comparison to obtain verification result data; and performing conclusion generation processing on the verification result data to obtain condition matching result data.

[0120] For example, in Building Information Modeling (BIM) management, conditional judgment processing can be performed on business component feature data containing family identifier data and attribute parameter data based on matching condition data containing candidate rule information. Feature comparison processing can be performed on business component feature data based on matching condition data. By extracting rule conditions and feature items, the component features are compared with the rule conditions item by item to obtain feature comparison result data indicating the condition satisfaction state. Then, matching judgment processing can be performed on the feature comparison result data. By identifying the state and verifying the logic, a matching decision is made based on the comparison state to obtain conditional matching result data containing the matching state identifier.

[0121] According to the technical solution provided in this disclosure, feature comparison processing is performed on the feature data of business components based on matching condition data. Feature comparison result data is obtained through rule condition extraction, feature item extraction and condition comparison. Matching judgment processing is performed on the feature comparison result data. Condition matching result data is obtained through state recognition, logical verification and conclusion generation. This enhances the ability to accurately compare component features with mapping rule conditions and record state; and improves the logical rigor and reliability of matching judgment.

[0122] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this disclosure, and will not be described in detail here.

[0123] Figure 3 This is a schematic diagram of another business component data processing method provided in this disclosure embodiment. For example... Figure 3 As shown, the data processing method for this business component includes: 1. Component file parsing and feature extraction: When a BIM component file (business component data to be processed) (such as a door .rfa file) is submitted through the upload plugin or web interface, the background can trigger the file parsing service.

[0124] This service reads the file and extracts a set of features that can be used for matching, including but not limited to: Internal metadata of the file: the original "Family Name" and "Type Name".

[0125] Key attribute values: such as the names and values ​​of parameters like "width", "height", and "material".

[0126] Geometric features: The type of component (such as "window" or "column") and simple geometric features.

[0127] Transform unstructured BIM file content into structured feature data.

[0128] 2. Rule-based feature matching and standard encoding generation: The extracted feature information can be compared with the mapping rules pre-set in the standard coding library. The mapping rules are in the form of "IF-THEN", for example: The IF family name contains "fire door" AND width ≈ 1000mm THEN and maps to the standard code "S6-C1-FIRE-DOOR-1000".

[0129] The encoding mapping engine performs rule matching. If the match is successful, it generates a unique code, a complete classification path, and a list of standard attribute values ​​for the component that conform to the Lifecycle Classification (LFC) standard.

[0130] If a match fails (e.g., due to ambiguous features or incomplete rule coverage), a pending task is generated, prompting the administrator to manually review and map the task.

[0131] 3. Standard information reverse writing and asset record creation: For successfully matched components, the file reverse write service can be invoked. This service writes information such as standard codes and classifications into reserved or specific shared parameter fields in the original BIM component file. For example, the standard code S6-C1-FIRE-DOOR-1000 is written into a parameter named "LFC_Code" in the "door" family file.

[0132] After the reverse writing is complete, a new asset record can be created in the digital asset library. The core information of this record, such as the "encoding" field, is directly taken from the standard information in the reverse-written file, ensuring consistency between the library, the encoding, and the file.

[0133] The reverse-written standard BIM file is used as the master file, stored on a file server (as shown in the BIM library), and associated with the asset records.

[0134] According to the technical solution provided in this disclosure, a complete method for intelligent matching between a rule engine and the client's standard coding library is presented. This method automates the traditional manual coding mapping process by writing client-standard coding and other information back into the original BIM file. This physically unifies standard identity information with the model file, connecting the multiple independent steps of parsing, mapping, reverse writing, and database entry into a pipeline through a service-oriented interface. This pipeline achieves seamless and efficient conversion of external heterogeneous BIM data into internal standardized digital assets, building the core production capability of the BIM asset library.

[0135] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0136] Figure 4 This is a schematic diagram of a business component data processing device provided in an embodiment of this disclosure. Figure 4 As shown, the data processing device for this business component includes: The first processing module 401 is used to perform feature parsing processing on the business component data to be processed, and obtain business component feature data. The second processing module 402 is used to perform encoding mapping processing on the feature data of business components based on a preset mapping rule library to obtain standard encoded data of business components. The third processing module 403 is used to encode and solidify the business component data to be processed based on the standard coding data of the business component, so as to obtain standardized business component file data. The fourth processing module 404 is used to process the standardized business component file data to generate asset records, thereby obtaining the target component asset record data.

[0137] According to the technical solution provided in this disclosure, feature parsing processing is performed on the business component data to be processed. Through structured parsing, family identifier extraction, attribute category extraction, and feature aggregation, business component feature data is obtained. Based on a preset mapping rule base, the business component feature data is encoded and mapped. Through rule retrieval, condition judgment, and standard code generation, standard code data for the business component is obtained. Based on the standard code data, the business component data to be processed is encoded and solidified. Through field positioning, encoding writing, and file encapsulation, standardized business component file data is obtained. Asset record generation processing is performed on the standardized business component file data. Through encoding reading, asset record creation, and association binding, target component asset record data is obtained. This enhances the ability to convert unstructured component files into structured feature data; improves the accuracy and efficiency of automated mapping of component features to standard codes; enhances the stability and cross-system traceability of the integration of standard identity information and physical files; realizes automated conversion and consistent association of component files to digital assets; improves the efficiency of automatic parsing and feature extraction of heterogeneous business component files; shortens the digital asset creation process; and improves the automation level of business component data standardization processing and heterogeneous file processing capabilities.

[0138] In some embodiments, the third processing module 403 is specifically used to: perform field positioning processing on preset parameter field data based on business component standard encoding data to obtain target parameter field data; perform encoding and writing processing on target parameter field data based on business component standard encoding data to obtain parameter field writing data; and perform file encapsulation processing on the business component data to be processed based on parameter field writing data to obtain standardized business component file data.

[0139] In some embodiments, the second processing module 402 is specifically used to: perform condition retrieval processing on the feature data of the business component based on a preset mapping condition library to obtain matching condition data; perform condition judgment processing on the feature data of the business component based on the matching condition data to obtain condition matching result data; and perform standard encoding generation processing on the condition matching result data to obtain standard encoding data of the business component.

[0140] In some embodiments, the first processing module 401 is specifically used to perform structured parsing processing on the business component data to be processed to obtain parsed element data; perform family identifier extraction processing on the parsed element data to obtain family identifier data; perform attribute category extraction processing on the parsed element data to obtain attribute category data; and perform feature aggregation processing on the family identifier data and attribute category data to obtain business component feature data.

[0141] In some embodiments, the fourth processing module 404 is specifically used to: perform encoding reading processing on the standardized business component file data to obtain record encoding data; perform asset record creation processing on the record encoding data to obtain initial asset record data; and perform association binding processing on the initial asset record data and the standardized business component file data to obtain target component asset record data.

[0142] In some embodiments, the process of encoding and writing target parameter field data based on business component standard encoding data to obtain parameter field written data is specifically used as follows: encoding value writing processing is performed on target parameter field data based on business component standard encoding data to obtain encoded value written data; classification path writing processing is performed on target parameter field data based on business component standard encoding data to obtain classification path written data; and field content aggregation processing is performed on encoded value written data and classification path written data to obtain parameter field written data.

[0143] In some embodiments, performing condition determination processing on the feature data of business components based on matching condition data to obtain condition matching result data is specifically used for: performing feature comparison processing on the feature data of business components based on matching condition data to obtain feature comparison result data; and performing matching determination processing on the feature comparison result data to obtain condition matching result data.

[0144] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this disclosure.

[0145] Figure 5 This is a schematic diagram of the electronic device 5 provided in an embodiment of this disclosure. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 501, a memory 502, and a computer program 503 stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program 503, it implements the steps in the various method embodiments described above. Alternatively, when the processor 501 executes the computer program 503, it implements the functions of each module / unit in the various device embodiments described above.

[0146] Electronic device 5 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 5 may include, but is not limited to, processor 501 and memory 502. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or different components.

[0147] The processor 501 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0148] The memory 502 can be an internal storage unit of the electronic device 5, such as a hard disk or RAM of the electronic device 5. The memory 502 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 5. The memory 502 can also include both internal and external storage units of the electronic device 5. The memory 502 is used to store computer programs and other programs and data required by the electronic device.

[0149] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0150] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a readable storage medium (e.g., a computer-readable storage medium). Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable storage medium may include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0151] The above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit it. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this disclosure, and should all be included within the protection scope of this disclosure.

Claims

1. A method for processing business component data, characterized in that, include: The data of the business components to be processed is subjected to feature parsing to obtain the feature data of the business components; The feature data of the business components are encoded and mapped based on a preset mapping rule base to obtain standard encoded data of the business components. Based on the standard coding data of the business components, the data of the business components to be processed is encoded and solidified to obtain standardized business component file data. The standardized business component file data is processed to generate asset records, resulting in target component asset record data.

2. The business component data processing method according to claim 1, characterized in that, The process of encoding and solidifying the business component data to be processed based on the standard encoding data of the business component to obtain standardized business component file data includes: Based on the standard encoding data of the business components, the preset parameter field data is processed to obtain the target parameter field data; Based on the standard encoding data of the business component, the target parameter field data is encoded and written to obtain the parameter field written data; Based on the data written in the parameter fields, the data of the business component to be processed is encapsulated into a file to obtain the standardized business component file data.

3. The business component data processing method according to claim 1, characterized in that, The process of encoding and mapping the feature data of the business components based on a preset mapping rule base yields standard encoded data for the business components, including: Based on the preset mapping condition library, the feature data of the business components are subjected to condition retrieval processing to obtain matching condition data; Based on the matching condition data, the feature data of the business component is subjected to condition determination processing to obtain condition matching result data; The condition matching result data is processed by standard encoding to obtain the standard encoded data of the business component.

4. The business component data processing method according to claim 1, characterized in that, The feature parsing process is performed on the data of the business components to be processed to obtain the feature data of the business components, including: The data of the business components to be processed is subjected to structured parsing to obtain parsed element data; The parsed element data is processed by extracting family identifiers to obtain family identifier data; The attribute category data is extracted from the parsed element data to obtain attribute category data; The family identifier data and the attribute category data are subjected to feature aggregation processing to obtain the business component feature data.

5. The business component data processing method according to claim 1, characterized in that, The step of performing asset record generation processing on the standardized business component file data to obtain target component asset record data includes: The standardized business component file data is encoded and read to obtain the recorded encoded data; The recorded coded data is processed to create asset records, resulting in initial asset record data; The initial asset record data and the standardized business component file data are associated and bound together to obtain the target component asset record data.

6. The business component data processing method according to claim 2, characterized in that, The process of encoding and writing the target parameter field data based on the standard encoding data of the business component to obtain the parameter field written data includes: Based on the standard encoding data of the business component, the target parameter field data is processed to write the encoded value to obtain the encoded value written data. Based on the standard encoding data of the business component, the target parameter field data is processed by classification path writing to obtain classification path writing data; The encoded value and the classification path data are aggregated to obtain the parameter field data.

7. The business component data processing method according to claim 3, characterized in that, The conditional judgment processing of the business component feature data based on the matching condition data to obtain conditional matching result data includes: Based on the matching condition data, feature comparison processing is performed on the feature data of the business component to obtain feature comparison result data; The feature comparison result data is subjected to matching judgment processing to obtain the condition matching result data.

8. A business component data processing device, characterized in that, include: The first processing module is used to perform feature parsing on the business component data to be processed, and obtain the business component feature data. The second processing module is used to perform encoding mapping processing on the feature data of the business components based on a preset mapping rule library to obtain standard encoded data of the business components. The third processing module is used to encode and solidify the business component data to be processed based on the standard coding data of the business component to obtain standardized business component file data. The fourth processing module is used to perform asset record generation processing on the standardized business component file data to obtain the target component asset record data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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