Component cataloguing method and management method, program product and electronic device

Abstract

The invention belongs to the technical field of data management, and provides a component cataloguing method, a component management method, a program product and electronic equipment. The component cataloguing method comprises the steps of obtaining a project document of an application component; analyzing the project document to obtain application component metadata; in a preset business system data table, obtaining a target data table associated with the application component metadata; in at least one piece of alternative service item data, target service item data is obtained, and the semantic similarity between the target service item data and the application component metadata is larger than a preset threshold value; and filling and reporting the target data table according to the application component metadata and the target service item data. In this way, application component cataloguing filling can be automatically carried out, and the labor cost is reduced.

Description

Technical Field

[0001] This invention relates to the field of data management, and more particularly to component cataloging and management methods, program products, and electronic devices. Background Technology

[0002] Component cataloging represents the process of classifying, ranking, describing, and managing various components in a system.

[0003] Current component cataloging is highly manual, requiring manual extraction of key information such as component function descriptions and interface parameters from scattered, heterogeneous data sources including application documentation, code repositories, and API specifications. Then, manual categorization, naming, and attribute labeling of components are based on subjective experience. This lack of automation in cataloging and reporting results in significant manpower costs. Summary of the Invention

[0004] This application aims to at least solve the technical problems existing in the prior art and provide component cataloging and management methods, program products, and electronic devices.

[0005] Firstly, this application provides a component cataloging method, including:

[0006] Obtain the project documentation for the application components;

[0007] The project documents are parsed to obtain application component metadata;

[0008] In a preset business system data table, a target data table associated with the application component metadata is obtained; and in at least one alternative service item data, target service item data is obtained, wherein the semantic similarity between the target service item data and the application component metadata is greater than a preset threshold.

[0009] The target data table is filled in based on the application component metadata and the target service item data.

[0010] Furthermore, from the preset business system data tables, a target data table associated with the application component metadata is obtained, including:

[0011] The application metadata is semantically analyzed using a pre-defined large language model to obtain the business functions of the application components.

[0012] In the business system data table, obtain the target data table that is associated with the business function.

[0013] Further, from at least one candidate service item data, target service item data is obtained, wherein the semantic similarity between the target service item data and the application component metadata exceeds a preset threshold, including:

[0014] Obtain the first semantic vector of each of the candidate service items data, and obtain the second semantic vector of the application component metadata;

[0015] The first semantic vector is compared with the second semantic vector to obtain the semantic similarity between each of the candidate service item data and the application component metadata.

[0016] Candidate service items with semantic similarity greater than a preset threshold are identified as target service items.

[0017] Further, obtaining the second semantic vector of the application component metadata includes:

[0018] Input the application component metadata into a preset large language model to obtain a summary of the application functions of the application component.

[0019] The application function summary is semantically vectorized to obtain the second semantic vector.

[0020] Furthermore, candidate service item data with semantic similarity greater than a preset threshold are identified as target service item data, including:

[0021] Semantic rearrangement is performed on candidate service item data whose semantic similarity is greater than a preset threshold to obtain initial service item data;

[0022] The initial service item data and the application function summary are input into a preset semantic matching model to obtain the semantic similarity between the initial service item data and the application function summary;

[0023] Initial service item data with semantic similarity greater than a preset threshold are identified as the target service item data.

[0024] Furthermore, before filling in the target data table based on the application component metadata and the target service item data, the process also includes:

[0025] When there are multiple target data tables, the names of fields with the same semantics in each target data table are unified.

[0026] Furthermore, the component cataloging method also includes: adding a unique identifier to each field in the target data table; fields with the same field name and the same data type in different target data tables have the same unique identifier.

[0027] Secondly, this application provides a component cataloging management method, including:

[0028] The target data table obtained from the component cataloging method described in the first aspect is input into the preset cataloging audit model to obtain the audit results.

[0029] Thirdly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the component cataloging method as described in the first aspect or the component cataloging management method as described in the second aspect.

[0030] Fourthly, this application provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor;

[0031] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the component cataloging method as described in the first aspect or the component cataloging management method as described in the second aspect.

[0032] The component cataloging method provided in this application provides an example of an application component cataloging method that parses the project document of the application component to obtain the application component metadata. Based on this metadata, a target data table is obtained from a preset business system data table, and target service item data is obtained from the candidate service item data. This achieves automatic acquisition of the target data table and target service item data. Finally, the target data table is populated based on the application component metadata and the target service item data, thus enabling automatic cataloging of application components. This eliminates the need for manual extraction of component function descriptions and interface parameters, as well as manual classification, naming, and attribute labeling of components, thereby reducing labor costs.

[0033] Furthermore, since manual processes are often more uncertain than automated processes, errors are prone to occur when manually cataloging application components. This application's embodiment, however, automates the cataloging of application components, thus reducing the uncertainties introduced by manual processes and ensuring a higher probability of accuracy.

[0034] Furthermore, when multiple target data tables exist, unifying the names of fields with the same semantics across these tables allows for reuse of the same field name across different tables during data matching. This enables cross-departmental reuse even if the tables belong to different departments. It also reduces the occurrence of incorrect or missed matches due to inconsistent naming. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the component cataloging method provided by the present invention;

[0036] Figure 2 This is a flowchart illustrating a component cataloging management method provided by the present invention;

[0037] Figure 3 This is a schematic diagram of the structure of an electronic device provided by the present invention. Detailed Implementation

[0038] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0039] Current component cataloging relies heavily on manual operations, requiring the extraction of key information such as component function descriptions and interface parameters from scattered, heterogeneous data sources including application documentation, code repositories, and interface specifications. Catalogers must classify, name, and label components based on subjective experience. Due to the lack of unified standards and specifications, different departments exhibit significant differences in naming rules, classification levels, and functional descriptions for similar components, resulting in inconsistent data quality. Furthermore, the review and correction of cataloging results require manual verification of technical details item by item, which is inefficient and has a high error rate. This makes it difficult to ensure the completeness and consistency of component information, severely hindering cross-departmental reuse and platform collaboration efficiency.

[0040] The existing cataloging model suffers from three major pain points: First, it suffers from significant efficiency bottlenecks. The manual processes of information extraction, classification, labeling, and review are time-consuming and labor-intensive, with the average cataloging time for a single component exceeding 30 minutes. Manual costs account for over 60% of the platform's total operating costs. Second, it suffers from insufficient standardization. Component naming and classification rules are vague, functional descriptions rely on free text, and there is a lack of structured metadata, resulting in a cross-system retrieval and matching accuracy rate of less than 70%. Third, it suffers from weak quality control. Manual operations are prone to overlooking key parameters (such as API version numbers and dependencies) or creating description contradictions, leading to frequent compatibility failures when components are called. Furthermore, the lack of an automated risk warning mechanism results in high security risks and governance costs.

[0041] Example 1

[0042] To address the problems in existing component cataloging methods, this application provides a component cataloging method and a component cataloging management method. The execution entities of the component cataloging method and the component cataloging management method include, but are not limited to, at least one of the following electronic devices that can be configured to execute the method provided in the embodiments of this application: a server, a terminal, etc. In other words, the component cataloging method and the component cataloging management method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0043] Combination Figure 1 The flowchart shown illustrates the component cataloging method, which may include the following steps:

[0044] Step S101: Obtain the project documentation for the application components.

[0045] In some embodiments, the application scenario of this application may include a government service system. Correspondingly, the project document refers to the project document for the government service system application.

[0046] In some embodiments, project documentation may include at least one of API (Application Programming Interface) documentation, design documentation, and product documentation.

[0047] API documentation describes the functionality, parameters, return values, and invocation methods of application interfaces. Design documentation explains the application's design approach, such as architecture and database design. It may also include flowcharts illustrating application operation. Product documentation guides users on how to use the product. Product documentation may include a feature introduction and user guide.

[0048] Step S102: Parse the project documents to obtain application component metadata.

[0049] Optionally, UIE (Universal Information Extraction) combined with a large language model can be used to identify and extract application component metadata from documents.

[0050] For example, you can extract the plain text content from the project documentation and then use UIE to extract application component metadata from the documentation.

[0051] Since the plain text content of the project document may contain pronouns referring to application component metadata, the plain text content of the target document can be input into a pre-defined large language model to obtain the specific semantics of the plain text content of the project document. Then, the application component metadata can be extracted from the specific semantics output by the large language model through UIE.

[0052] After obtaining the application component metadata, the application component metadata can be recorded.

[0053] Optionally, multiple application components can exist. Correspondingly, the project documents of each application component can be parsed separately to obtain the application component metadata corresponding to each application component.

[0054] In some embodiments, application component metadata may include one or more of the following: application name, application description, feature introduction, interface registration information, data dependencies, and data assets.

[0055] Step S103: In the preset business system data table, obtain the target data table that is associated with the application component metadata; and in at least one alternative service item data, obtain the target service item data, wherein the semantic similarity between the target service item data and the application component metadata is greater than a preset threshold.

[0056] In one optional implementation provided in this application embodiment, obtaining a target data table associated with the application component metadata from a preset business system data table includes: performing semantic analysis on the application metadata using a preset large language model to obtain the business functions of the application component; and obtaining a target data table associated with the business functions from the business system data table.

[0057] Optionally, you can access the business system through database connection tools or API interfaces to obtain the business system's data tables.

[0058] Optionally, database metadata of the business system can be accessed through database connection tools or API interfaces, and parsed to obtain structured metadata of the business system's data tables. The structured metadata of the business system's data tables includes at least one of the following: table name, field name, field type, constraints, and index information. Field types can include data type, length, and precision. Constraints can include primary key constraints, foreign key constraints, uniqueness constraints, and NOT NULL constraints.

[0059] Database metadata may include at least one of the following: DDL (Data Definition Language) scripts, data dictionary, and schema description file.

[0060] DDL scripts are used to create, modify, or delete database objects in a database. Database objects include one or more of the following: tables, views, indexes, and constraints.

[0061] A data dictionary is used to describe the database structure and contains information about all database objects in the database.

[0062] For example, when the database object is a table, its information may include the table name, creation time, and modification time. When the database object is an index, its information may include the index name, the name of the table associated with the index, and the index type. When the database object is a view, its information may include the view name and a description.

[0063] A schema description file is a metadata description file used to define data structures, interface specifications, or database table structures. Schema description files can describe the format, constraints, fields, and meanings of data.

[0064] Optionally, within the business system data table, a target data table related to the business function can be obtained. This includes inputting the application description and function introduction from the application metadata into a preset large language model to obtain the business functions of the application component. If there is a relationship between the table name or field name of the business system data table and the business function, the business system data table is identified as the target data table.

[0065] For example, if the business function of the application component includes housing provident fund withdrawal, the target data table may include a statistical indicator table and an individual housing provident fund payment information table.

[0066] Furthermore, after obtaining the target data table that is related to the business function, the structured metadata of the target data table can be recorded.

[0067] Optionally, the structured metadata of the target data table is recorded, including: normalizing the structured metadata of the target data table, mapping the field types in the structured metadata to standard types according to a preset business terminology table, and then recording the structured metadata.

[0068] For example, when the field type in the structured metadata is string, a business terminology table can be used to map the string to VARCHAR. Normalizing the structured metadata can include unifying uppercase and lowercase English characters in the structured metadata to uppercase or lowercase. Normalizing the structured metadata can also include removing special characters. Special characters can be any character other than literal characters, or any character other than literal characters and punctuation characters.

[0069] Optionally, you can also retrieve and record the unique identifiers of each field in the target data table. When there are multiple target data tables, fields with the same field name and data type in different target data tables will have the same unique identifier.

[0070] Specifically, the process involves obtaining the unique identifier for each field in the target data table. This includes concatenating the field name, data type, and business meaning of each field to obtain a concatenated string, and then using a hash algorithm to calculate the unique identifier for that field.

[0071] Optionally, if there are multiple target data tables, before concatenating the field name, data type, and business meaning for each field, the method also includes: unifying the names of fields with the same semantics in each target data table.

[0072] For example, in target table a, the field name includes "Order Number". In target table b, the field name includes "Order Number". By using a large language model to obtain the semantics of the field names "Order Number" and "Order Number", it can be determined that "Order Number" and "Order Number" have the same semantics, both used to uniquely identify an order. Therefore, the field names "Order Number" in target table a and "Order Number" in target table b can be unified, for example, by unifying them as "Unique Order Number".

[0073] Alternatively, in target table a, the field name includes "User ID". In target table b, the field name includes "User Identifier". By using a large language model to obtain the semantics of the field names "User ID" and "User Identifier", it can be determined that "User ID" and "User Identifier" have the same semantics, both used to uniquely identify users. Therefore, the field names "User ID" in target table a and "User Identifier" in target table b can be unified, for example, by unifying them into "User Unique Identifier".

[0074] In one optional implementation of this application, obtaining target service item data from at least one candidate service item data includes: obtaining a first semantic vector for each candidate service item data and obtaining a second semantic vector for application component metadata. The first semantic vector is then compared with the second semantic vector to calculate the semantic similarity between each candidate service item data and the application component metadata. Candidate service item data with a semantic similarity greater than a preset threshold are identified as target service item data.

[0075] Optionally, in the case of an application scenario involving a government service system, alternative service item data can be obtained from the system. This data may include basic information such as the item name, processing procedure, required documents, and applicable users. A text embedding model can be used to encode each alternative service item data to obtain a high-dimensional semantic vector, i.e., the first semantic vector.

[0076] Optionally, obtaining the second semantic vector of the application component metadata includes: inputting the application component metadata into a preset large language model to obtain an application function summary of the application component; and semantically vectorizing the application function summary to obtain the second semantic vector.

[0077] For example, a large language model can be used to perform structured extraction and summary generation of application component metadata to obtain an application function summary. A text embedding model can be used to semantically vectorize the application function summary to obtain a second semantic vector.

[0078] Optionally, candidate service item data with semantic similarity greater than a preset threshold are identified as target service item data, including: performing semantic rearrangement on candidate service item data with semantic similarity greater than the preset threshold to obtain initial service item data; inputting the initial service item data and application function summary into a preset semantic matching model to obtain the semantic similarity between the initial service item data and the application function summary; and identifying the initial service item data with semantic similarity greater than the preset threshold as target service item data.

[0079] Step S104: Fill in the target data table based on the application component metadata and target service item data.

[0080] In detail, for each field in the target data table, the field name can be used to search the application component metadata and target service item data to obtain the corresponding field information to be filled. Then, the information to be filled is processed according to the data type of the field, and the processed information is filled into the field. This completes the intelligent filling of the target data table and realizes the cataloging and summarization of application components.

[0081] In some embodiments, the target service item data may include field names and field content. The field content may be content entered by the user through the client's display interface.

[0082] Similarly, application component metadata can also include field names and their corresponding field names. The field names of application component metadata can include one or more of the following: application name, application description, feature introduction, interface registration information, data dependencies, and data assets.

[0083] The component cataloging method provided in this application provides an example of an application component cataloging method that parses the project document of the application component to obtain the application component metadata. Based on this metadata, a target data table is obtained from a preset business system data table, and target service item data is obtained from the candidate service item data. This achieves automatic acquisition of the target data table and target service item data. Finally, the target data table is populated based on the application component metadata and target service item data, enabling automatic cataloging of application components. This eliminates the need for manual extraction of component function descriptions and interface parameters, as well as manual classification, naming, and attribute labeling of components, thus reducing labor costs.

[0084] Furthermore, since manual processes are often more uncertain than automated processes, manual application component cataloging is more prone to errors than automated application component cataloging using program code. This application's embodiment, however, automates application component cataloging, thus reducing the uncertainties introduced by manual processes and increasing the probability of accurate application component cataloging.

[0085] Furthermore, when multiple target data tables exist, unifying the names of fields with the same semantics across these tables allows for reuse of the same field name across different tables during data matching. This enables cross-departmental reuse even if the tables belong to different departments. It also reduces the occurrence of incorrect or missed matches due to inconsistent naming.

[0086] Example 2

[0087] After step S104, since the target service item data contains user-submitted information, and if the user's submitted information does not conform to the submission rules, the obtained component catalog will also not conform to the cataloging rules. Therefore, the completed target data table needs to be reviewed. This embodiment provides a component cataloging management method based on the above embodiment. For details, see [link to related documentation]. Figure 2 As shown, step S201 is executed after step S104.

[0088] Step S201: Input the filled target data table into the preset cataloging and auditing model to obtain the audit results.

[0089] For example, audit rules can be obtained, and then a corresponding cataloging audit model can be built for each audit rule. Then, the target data table that has been filled in can be input into each cataloging audit model to obtain the corresponding audit results.

[0090] The review rules may include duplicate value detection, irrelevant content detection, sensitive word detection, prohibited word detection, and application status detection.

[0091] For example, when the audit rule is duplicate value detection, the corresponding cataloging audit model is used to detect whether the content entered in each field of the target data table is duplicated. Correspondingly, the audit result indicates whether duplicate content exists or not in the target data table.

[0092] When the review rule is sensitive word detection, the corresponding cataloging review model is used to detect whether the content entered in each field of the target data table contains sensitive words. The corresponding review result indicates whether sensitive words exist in the target data table or not.

[0093] When the audit rule is to detect prohibited words, the corresponding cataloging audit model is used to detect whether the content entered in each field of the target data table contains prohibited words. The corresponding audit result indicates whether prohibited words exist in the target data table.

[0094] When the audit rule is to detect irrelevant content, the corresponding cataloging audit model is used to detect whether the content entered in each field of the target data table contains content unrelated to the corresponding field name. The corresponding audit result indicates whether irrelevant content exists or does not exist in the target data table.

[0095] For example, field names might include software name, version number, and developer. The field name "Software Name" could be entered as "Microsoft Office." The field name "Version Number" could be entered as "2021," and the field name "Developer" could be entered as "Microsoft." This is considered excellent. Correspondingly, the audit result would be that irrelevant content exists in the target data table; that is, "Excellent" is unrelated to the required information.

[0096] In this embodiment, the submitted target data table can be input into a preset cataloging audit model according to a preset audit cycle. If multiple cataloging audit models exist, the target data table can also be input into the corresponding cataloging audit model sequentially according to the order of preset audit rules.

[0097] The component cataloging management method provided in this application embodiment can determine whether the content filled in the target data table conforms to the review rules by using a preset cataloging review model, and remind staff to modify the content filled in the target data table, which helps to improve the accuracy of the content filled in the target data table.

[0098] Alternatively, before step S104, the target service item data can be input into a preset cataloging and auditing model to obtain the audit results.

[0099] Similarly, when the audit rule is duplicate value detection, the corresponding cataloging audit model is used to detect whether the content entered in each field of the target service item data is duplicated. The corresponding audit result indicates whether duplicate content exists or not in the target service item data.

[0100] When the audit rule is sensitive word detection, the corresponding cataloging audit model is used to detect whether the content entered in each field of the target service item data contains sensitive words. The corresponding audit result indicates whether sensitive words exist in the target service item data.

[0101] When the audit rule is to detect prohibited words, the corresponding cataloging audit model is used to detect whether the content entered in each field of the target service item data contains prohibited words. The corresponding audit result indicates whether prohibited words exist in the target service item data.

[0102] When the audit rule is "irrelevant content detection," the corresponding cataloging audit model is used to detect whether the content entered in each field of the target service item data contains content unrelated to the corresponding field name. The corresponding audit result indicates whether irrelevant content exists or does not exist in the target service item data. The input prompts for the large language model are as follows:

[0103] Role definition for the large language model: Application cataloging system data review expert and natural language processing engineer.

[0104] Background of the working purpose of the large language model: In application cataloging systems, the information entered by users must strictly conform to the requirements to ensure data accuracy and consistency. However, users may enter irrelevant information during the data entry process, which will affect data quality and subsequent processing efficiency. Therefore, it is necessary to use a large language model to review the entered information, identify and filter out parts that are irrelevant to the required content.

[0105] Correspondingly, the input language model's role definition prompt word "profile" states: You are an expert proficient in application cataloging system data auditing and natural language processing, possessing rich experience in data auditing and a deep understanding of business logic, and able to accurately identify and filter out parts that are irrelevant to the required information.

[0106] Correspondingly, the input capabilities of a large language model require the following skills: You possess strong natural language processing abilities, data auditing capabilities, and a deep understanding of business logic, enabling you to accurately identify and filter out information irrelevant to the required information.

[0107] Input large language model target requirement prompt words: review the information entered by users in the application cataloging system, identify and filter out parts that are not related to the required content, and ensure the accuracy and consistency of the entered information.

[0108] The constraints of the input large language model are as follows: During the review process, the content filled in as required must be strictly judged to ensure the accuracy and objectivity of the review results.

[0109] The output format requirements for inputting a large language model are as follows: The output should include the original information entered by the user, the review results, and the review basis.

[0110] For example, the field names for the target service item data input into the large language model include Software Name, Version Number, and Developer. The field name "Software Name" is entered as "Microsoft Office." The field name "Version Number" is entered as "2021," and the field name "Developer" is entered as "Microsoft," which is excellent.

[0111] The output audit result of the large language model is that there is irrelevant content in the target service item data. The audit basis is: the word "very good" in the content is irrelevant to the required content.

[0112] In this embodiment, the submitted target data table can be input into a preset cataloging audit model according to a preset audit cycle. If multiple cataloging audit models exist, the target data table can also be input into the corresponding cataloging audit model sequentially according to the order of preset audit rules.

[0113] In addition, during the new application project approval stage, for newly added applications, duplicate calculations can be performed on application information such as application name, description, function list, construction department, category, related items, and related data to generate an application duplication report for the application approval department to refer to.

[0114] The present invention also discloses a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the component cataloging method or component cataloging management method provided by the present invention. The computer program product should be understood as a software product that mainly implements its solution through a computer program, such as a program product integrated in the cloud or a software library.

[0115] The present invention also discloses an electronic device, in one embodiment of which the electronic device includes at least one processor; and a memory communicatively connected to the at least one processor; wherein,

[0116] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to execute the component cataloging method or component cataloging management method provided by the present invention.

[0117] like Figure 3 The diagram shown is a structural schematic of an electronic device using a component cataloging method or component cataloging management method according to an embodiment of the present invention. The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program, such as a component cataloging method program or a component cataloging management method program, stored in the memory 11 and executable on the processor 10.

[0118] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing component cataloging methods or component cataloging management methods) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0119] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, FlashCard, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of component cataloging method programs or component cataloging management method programs, but also to temporarily store data that has been output or will be output.

[0120] The communication bus 12 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0121] Communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0122] Figure 3 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 3The structure shown does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0123] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to at least one processor 10 via a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0124] It should be understood that the embodiments are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0125] Furthermore, if the modules / units integrated into the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, a computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0126] In the description of this specification, the references to terms such as "an embodiment," "some embodiments," "example," "specific example," "a implementation," "a preferred implementation," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0127] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A component cataloging method, characterized in that, include: Obtain the project documentation for the application components; The project documents are parsed to obtain application component metadata; From the preset business system data table, obtain the target data table that is associated with the metadata of the application component; And from at least one alternative service item data, obtain the target service item data, wherein the semantic similarity between the target service item data and the application component metadata is greater than a preset threshold; The target data table is filled in based on the application component metadata and the target service item data.

2. The method according to claim 1, characterized in that, From the preset business system data tables, obtain the target data tables that are associated with the metadata of the application components, including: The application metadata is semantically analyzed using a pre-defined large language model to obtain the business functions of the application components. In the business system data table, obtain the target data table that is associated with the business function.

3. The method according to claim 1, characterized in that, From at least one candidate service item data, target service item data is obtained, wherein the semantic similarity between the target service item data and the application component metadata exceeds a preset threshold, including: Obtain the first semantic vector of each of the candidate service items data, and obtain the second semantic vector of the application component metadata; The first semantic vector is compared with the second semantic vector to obtain the semantic similarity between each of the candidate service item data and the application component metadata. Candidate service items with semantic similarity greater than a preset threshold are identified as target service items.

4. The method according to claim 3, characterized in that, The second semantic vector for obtaining the metadata of the application component includes: Input the application component metadata into a preset large language model to obtain a summary of the application functions of the application component. The application function summary is semantically vectorized to obtain the second semantic vector.

5. The method according to claim 3, characterized in that, Candidate service item data with semantic similarity greater than a preset threshold are identified as target service item data, including: Semantic rearrangement is performed on candidate service item data whose semantic similarity is greater than a preset threshold to obtain initial service item data; The initial service item data and the application function summary are input into a preset semantic matching model to obtain the semantic similarity between the initial service item data and the application function summary; Initial service item data with semantic similarity greater than a preset threshold are identified as target service item data.

6. The method according to claim 1, characterized in that, Before filling in the target data table based on the application component metadata and the target service item data, the following steps are also included: When there are multiple target data tables, the names of fields with the same semantics in each target data table are unified.

7. The method according to claim 6, characterized in that, Also includes: Add a unique identifier to each field in the target data table; Fields with the same field name and data type in different target data tables have the same unique identifier.

8. A component cataloging management method, characterized in that, include: The target data table obtained by the component cataloging method according to any one of claims 1 to 7 is input into a preset cataloging audit model to obtain the audit results.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the component cataloging method as described in any one of claims 1-7 or the component cataloging management method as described in claim 8.

10. An electronic device, characterized in that, The electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the component cataloging method as described in any one of claims 1-7 or the component cataloging management method as described in claim 8.

Images