Asset information management method and device, equipment and storage medium

Abstract

The embodiment of the invention provides an asset information management method and device, equipment and a storage medium, and specifically discloses the method comprising the following steps: obtaining architecture asset data from various heterogeneous data sources through a preset configurable access channel; determining the type of the architecture asset data; the types of the architecture asset data comprise structured data and unstructured data; when the type of the architecture asset data is structured data, analyzing the architecture asset data through a structured data analysis engine to extract field information, and determining an architecture asset label matched with the architecture asset data based on the field information; when the type of the architecture asset data is unstructured data, analyzing the architecture asset data through an unstructured data analysis engine to extract text information, and performing knowledge extraction on the text information to obtain architecture asset description information; and storing at least one of the architecture asset label and the architecture asset description information to a knowledge base, and constructing an architecture asset knowledge graph based on the knowledge base.

Description

Technical Field

[0001] This invention relates to the field of architecture management, and more particularly to an asset information management method, apparatus, device, and storage medium. Background Technology

[0002] In the field of architecture management, comprehensive, accurate, dynamic management and efficient utilization of application asset information are the cornerstones for ensuring architecture visibility, controllability, and sustainable optimization. With the acceleration of enterprise digitalization, the types and attributes of application assets, along with their associated unstructured data such as images and documents, are increasing and changing frequently. This places higher demands on the comprehensive management capabilities of asset information, including intelligent processing, deep correlation, and dynamic presentation.

[0003] Currently, common asset management methods often suffer from broken management chains and insufficient processing depth. Specifically, in the information entry stage, for dynamically changing asset types, attributes, and tags, a crude approach of directly storing the raw text content is often adopted, lacking refined structured processing capabilities. For unstructured data assets such as images and documents, effective parsing and knowledge extraction methods are generally lacking, simply associating them with asset IDs, resulting in a large amount of valuable information not being effectively utilized. Furthermore, when facing diverse data sources, the lack of unified access standards and standardized processing engines leads to system integration relying heavily on customized development, resulting in lengthy management processes and high maintenance costs.

[0004] As can be seen from the above, the existing asset information management methods have the following problems: First, they cannot dynamically adapt to newly added asset types or personalized attributes, making it difficult to quickly respond to business needs for dynamic changes in the architecture assets, resulting in a serious lack of management flexibility and configurability; second, for unstructured data assets such as images and documents, there is a lack of in-depth content analysis and knowledge extraction capabilities, leading to a large amount of valuable information being buried and unable to be effectively utilized; in addition, due to the lack of unified access specifications and standardized processing engines, the efficiency of cross-source asset aggregation is low, and the integration and maintenance costs are high. Summary of the Invention

[0005] The main objective of this invention is to provide an asset information management method, apparatus, device, and storage medium, which aims to solve the problems of insufficient dynamic adaptability, lack of unstructured data parsing, and poor scalability caused by the rigid and extensive management strategies adopted by existing asset management methods.

[0006] In a first aspect, embodiments of this disclosure provide an asset information management method, including: The system acquires architecture asset data from multiple heterogeneous data sources through a pre-configured access channel; wherein the access channel is used to implement at least one communication mode among API interface, message queue and database synchronization. Determine the type of the architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data; When the type of the architecture asset data is structured data, the architecture asset data is parsed using a structured data parsing engine to extract field information, and the architecture asset tags matching the architecture asset data are determined based on the field information. When the type of the architecture asset data is unstructured data, the architecture asset data is parsed using an unstructured data parsing engine to extract text information, and knowledge extraction is performed on the text information to obtain architecture asset description information representing the architecture assets. The architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and association relationships between different architecture asset objects. Store at least one of the architecture asset tags and the architecture asset description information in a knowledge base, and construct an architecture asset knowledge graph based on the knowledge base.

[0007] Secondly, embodiments of this disclosure provide an asset information management device, comprising: The acquisition module is used to acquire architecture asset data from multiple heterogeneous data sources through a pre-configured access channel; wherein, the access channel is used to implement at least one communication mode among API interface, message queue and database synchronization; A determination module is used to determine the type of the architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data; The parsing module is used to parse the architecture asset data using a structured data parsing engine when the data type is structured data, to extract field information, and to determine the architecture asset tags matching the data based on the field information; and to parse the architecture asset data using an unstructured data parsing engine when the data type is unstructured data, to extract text information, and to perform knowledge extraction on the text information to obtain architecture asset description information representing the architecture assets; the architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and association relationships between different architecture asset objects; The construction module is used to store at least one of the architecture asset tags and the architecture asset description information into a knowledge base, and to construct an architecture asset knowledge graph based on the knowledge base.

[0008] Thirdly, embodiments of this disclosure provide an electronic device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the steps of the method described in the first aspect above.

[0009] Fourthly, embodiments of this disclosure provide a computer-readable storage medium for storing computer-executable instructions that, when executed by a processor, implement the steps of the method described in the first aspect.

[0010] Fifthly, embodiments of this disclosure provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements the steps of the method described in the first aspect above.

[0011] The at least one technical solution provided by the embodiments of the present invention can achieve the following technical effects: In this embodiment of the invention, architecture asset data can be obtained from various heterogeneous data sources through a pre-configured access channel to solve the problems of inconsistent integration standards and high customized development costs. The data is then processed by identifying the type of architecture asset data. For structured data, a parsing engine can be used to extract field information and dynamically match asset tags to achieve refined attribute management. For unstructured data, text information can be extracted through parsing technology and further knowledge extraction can be performed to obtain structured description information containing entities, attributes, and relationships. Finally, all extracted architecture asset tags and / or architecture asset description information can be stored in a knowledge base, and an interconnected architecture asset knowledge graph can be built based on this.

[0012] This invention transforms the traditional, crude, static, and isolated asset information management model into a refined, dynamic, and deeply cognitive unified governance process. This effectively solves the problems of low integration efficiency and high operation and maintenance costs caused by the lack of unified access standards and intelligent parsing capabilities. At the same time, through deep analysis and knowledge extraction of unstructured data, it releases the asset information value accumulated in unstructured data, and with the help of standardized processing logic, it achieves rapid adaptation and flexible management of dynamically added asset types, fundamentally improving the automation level and knowledge level of the architecture asset management. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments of this application and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 One of the flowcharts of an asset information management method provided in an embodiment of the present invention; Figure 2 This is a second flowchart illustrating an asset information management method according to an embodiment of the present invention. Figure 3 A schematic diagram of the architecture of an asset information management method provided in one embodiment of the present invention; Figure 4 A schematic diagram of the module composition of an asset information management device 400 provided in one embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0015] The technical solutions provided by the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0016] Please see Figure 1 , Figure 1 This is one of the flowcharts illustrating an asset information management method provided in an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps: Step 102: Obtain architectural asset data from multiple heterogeneous data sources through a pre-configured access channel; wherein, the access channel is used to implement at least one communication mode among API (Application Programming Interface), message queue and database synchronization.

[0017] Step 104: Determine the type of architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data.

[0018] Step 106: When the architecture asset data is structured data, the architecture asset data is parsed using a structured data parsing engine to extract field information, and the architecture asset tags matching the architecture asset data are determined based on the field information; when the architecture asset data is unstructured data, the architecture asset data is parsed using an unstructured data parsing engine to extract text information, and knowledge extraction is performed on the text information to obtain architecture asset description information representing the architecture assets; the architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and the association relationship between different architecture asset objects.

[0019] Step 108: Store at least one of the architecture asset tags and architecture asset description information in the knowledge base, and build an architecture asset knowledge graph based on the knowledge base.

[0020] In one embodiment of the present invention, architectural asset data from various heterogeneous data sources can be acquired through a pre-configured access channel. This access channel can be used to implement at least one communication mode among API interfaces, message queues, and database synchronization. For example, for a cloud resource management platform providing a RESTful API, by configuring its API endpoints and authentication information, it can periodically obtain a list and configuration details of resources such as virtual machines and storage buckets; for systems that publish asset change events to message middleware such as Apache Kafka, by subscribing to corresponding topics, these event streams can be received and processed in real time; and for traditional systems that store asset information in relational databases such as MySQL and PostgreSQL, records in the data table can be periodically synchronized by configuring data source connections and incremental query statements. This embodiment can unify the access specifications of various heterogeneous data sources, transforming what was originally a point-to-point integration requiring custom development into a standardized operation that can be managed through configuration.

[0021] After acquiring the raw architecture asset data, its data type can be determined. Architecture asset data can be structured or unstructured. To automate this process, multi-dimensional feature information can be extracted from the data, including data storage format, data source, and the degree of data content structuring. Data storage format features can be represented by file extensions, MIME (Multipurpose Internet Mail Extensions) types, or Content-Type declared in the communication protocol. Data source features can point to the access channel configured for the data, such as being identified as "CMDB (Configuration Management Database) - API" or "Confluence-Scanner." The degree of data content structuring can be detected using a pre-built parser, for example, by attempting to parse the data as JSON or XML, or by checking whether it contains identifiable, regular delimiters and field names.

[0022] Subsequently, this feature information can be matched against recognition rules in a pre-defined rule base for architectural asset types. This rule base can store logical judgment conditions defined by domain knowledge. For example, one recognition rule might state: "If the data source is 'K8s-API' and the content format is 'application / json', then the type is structured data"; another rule might state: "If the storage format is 'image / png' or the data content is identified as containing image features by the OCR preprocessing module, then the type is unstructured data." Based on all the matched rule results, the final determination of whether the architectural asset data belongs to structured or unstructured data can be made, thereby routing it to the corresponding processing branch.

[0023] In one embodiment of the present invention, when the type of the architecture asset data is determined to be structured data, the data can be parsed using a structured data parsing engine to extract field information. The structured data parsing engine can call the corresponding parser, such as a JSON parser, based on the data format. The parsing engine can extract names and corresponding values ​​from the raw data according to predefined, configurable field mapping rules. For example, from a JSON describing a cloud server, fields such as instance_id, cpu_core, memory_gb, and vpc_id, along with their specific values, can be extracted.

[0024] Based on the extracted field information, the architecture asset tags matching the architecture asset data can be further determined. In one embodiment, the architecture asset tags can be determined by matching the field information with a preset tag rule base. The tag rule base can store the mapping relationship between field names, field value patterns, and architecture asset tags. For example, one rule can be defined as: if the field name is db_engine and the field value is mysql, then the asset is assigned the tag "Database:MySQL"; another rule can be defined as: if the field name matches the regular expression .*port and the field value is 80 or 443, then the tag "Protocol:HTTP / HTTPS" is assigned. One or more tags can be assigned to an asset based on such mapping relationships.

[0025] In another embodiment, architectural asset labels can be determined by inputting field information into a pre-trained label analysis model. This label analysis model can be trained on historical labeled data to learn the complex semantic relationships between field names, field values, and context, enabling intelligent inference for fields not explicitly covered by the rule base or with ambiguous descriptions. For example, for a field named `replica_config` with a value of 3, the model might, considering its context, output the label "High Availability: Multiple Replicas".

[0026] The two methods for determining the asset labels mentioned above can be used independently or in combination to achieve comprehensive, accurate, and adaptive labeling governance of structured assets.

[0027] In one embodiment of the present invention, when the type of architecture asset data is determined to be unstructured data, the architecture asset data can be parsed using an unstructured data parsing engine to extract text information. Specifically, when the unstructured data is an image, text information in the image can be extracted using optical character recognition (OCR) technology. The OCR engine can preprocess, analyze the layout, and recognize characters in input images such as architecture diagrams and deployment topology diagrams, and output all text characters contained in the diagram and their position coordinates. When the unstructured data is a document, text information in the document can be extracted using a document parser. The document parser can support formats such as PDF and Word, and can parse the logical structure of the document, extracting text content and corresponding style, position, and other metadata from titles, paragraphs, lists, and tables.

[0028] After extracting the text information, knowledge extraction can be performed on the text information to obtain architectural asset description information representing the architectural assets. The knowledge extraction process can utilize natural language processing technology to identify entities, attributes, and relationships in the text. Architectural asset description information can include at least one of the following: entity identifiers of architectural asset objects, attribute information of architectural asset objects, and the relationships between different architectural asset objects. For example, from the sentence "The order service calls the query interface of the user service," the entities "order service" and "user service," as well as the "call" relationship between them, can be extracted; from "The database master node runs on host 10.0.0.1," the entity "database master node" and its attribute "host IP: 10.0.0.1" can be extracted.

[0029] Then, at least one of the parsed and extracted architectural asset tags and architectural asset description information can be stored in a knowledge base. The knowledge base, as a centralized storage, can persistently store all standardized asset knowledge.

[0030] In this embodiment of the invention, an architectural asset knowledge graph can be constructed based on a knowledge base. The construction of the knowledge graph relies on a graph database. When the knowledge base includes architectural asset description information, the construction process can include: importing the architectural asset description information into a preset graph database. During this process, entities extracted from the architectural asset description information can be created as graph nodes, and extracted attributes can be set as node attributes. Next, based on the relationships between different architectural asset objects contained in the architectural asset description information, edges can be created between related architectural asset objects in the graph database. These edges are links used to represent asset dependencies or lineage relationships, such as "call," "dependency," and "deployed on," which can intuitively show the connection relationships between components.

[0031] When the knowledge base includes architectural asset tags, another extension approach can be used to construct the knowledge graph: importing the architectural asset tags into the same graph database as attributes of corresponding nodes or tag nodes of specific types. Based on these architectural asset tags, clustering or classification analysis can be performed on architectural asset objects in the graph database to obtain classification results. For example, all nodes with the tags "middleware" and "caching" can be grouped into one category. Based on this classification result, potentially related architectural asset objects can be inferred from the architectural asset objects, such as components belonging to the same business domain or sharing the same technology stack, even if they do not have a direct calling relationship. Subsequently, edges can be created between these related architectural asset objects in the graph database. This edge is a link used to represent the association relationship inferred based on tag semantics, such as "business domain association" or "technology stack association," thereby enriching the semantic hierarchy and business insight capabilities of the knowledge graph.

[0032] Through the above steps, unified access to heterogeneous source data, in-depth targeted analysis of structured and unstructured data, intelligent tagging and knowledge extraction of asset information can be achieved. Ultimately, an architectural asset knowledge graph that integrates explicit technical associations and implicit semantic associations can be constructed, providing a complete data foundation for global asset visualization, impact analysis, compliance governance and architecture optimization.

[0033] In one example, such as Figure 2 As shown, this illustrates the complete end-to-end process for processing architectural asset data.

[0034] Specifically, the architecture asset data can first be type-determined to determine if it is structured data. If the determination result is "yes," meaning the data is structured data, the process can proceed to the branch corresponding to structured data. First, the architecture asset data can be sent to the structured data collection engine. This engine is responsible for connecting to and parsing data with fixed patterns from sources such as databases and API interfaces. One of the engine's core tasks is to adapt the data types and attributes contained in the documents to form a data set. This means it can identify data structures from different sources and map and integrate their fields, values, and other information into a unified, standardized data set within the system. Subsequently, this data set undergoes an ingestion process and is persistently stored in the backend database for later use.

[0035] If the judgment result is "no," meaning the data is not structured data, the process then proceeds to the branch corresponding to unstructured data. The architecture asset data is first parsed by the unstructured data engine. The unstructured data engine specifically handles data lacking a fixed format, and its input sources explicitly include documents, images, and other formats. For images, the unstructured data engine uses technologies such as OCR to recognize text; for documents, it performs content parsing. The purpose of parsing is to organize structured data from unstructured content, that is, to extract implicit information such as entities, attributes, and relationships, and transform it into a structured form. After refinement, part of the structured data is stored in a database, while the remaining original unstructured files, such as original images and documents, may be stored in an object storage service for archiving and backup management.

[0036] Regardless of the data source or processing path, the standardized information ultimately produced will be integrated into the platform's architecture asset display layer. Through the visualization interface, search, and analysis capabilities provided by this layer, a unified and clear view of the architecture assets will be presented to the end user.

[0037] In one example, such as Figure 3 As shown, the support is illustrated. Figure 2The process employs a layered technical architecture. At the top level is the data source for the architecture assets. This layer represents the original source of all data and is a heterogeneous collection of data sources. Specifically, this can include: manual asset entry directly from users; integration with third-party systems from external systems; and various management platforms commonly used within the enterprise, such as CMDB, operations and maintenance platforms, and project management platforms. These data sources collectively constitute the input of the architecture asset data.

[0038] Below the data source of the architecture assets is a unified API access layer. This layer serves as a unified entry point, responsible for receiving data from various upper-layer data sources in a standardized and manageable manner. It abstracts the differences in the underlying data sources, providing a consistent interface for subsequent processing.

[0039] Beneath the unified API access layer, the architecture is branched off by data type, forming two core processing engines. The left branch focuses on processing structured data, with its core being a structured data parsing engine. Surrounding this engine are several supporting modules: a data type adapter for recognizing and matching structured data in different formats; a form builder for dynamically generating data collection or display interfaces; an automated workflow approval module for handling workflows related to data changes; and a sensitive word filter for compliance checks on input content. Furthermore, a permissions module runs throughout this branch, ensuring secure and controlled data processing throughout the entire process.

[0040] The right-hand branch focuses on processing unstructured data, with its core being an unstructured data parsing engine. This engine integrates parsing capabilities for specific formats: an OCR image recognition module can extract text from images; a document parser can process document files of various formats. The parsed text content can then be deeply analyzed by the knowledge extraction module to extract structured knowledge such as entities and relationships. Similarly, the configuration module provides necessary rule and parameter configuration support for this branch.

[0041] The bottom layer is the data storage layer. This layer is the cornerstone of the architecture and is responsible for persistently storing all processed, standardized architectural asset data, metadata, and knowledge information, providing reliable data services for upper-layer applications.

[0042] This architecture diagram, from top to bottom, fully demonstrates the complete technical architecture of data entering from multiple heterogeneous sources, undergoing unified access, intelligent parsing and knowledge extraction, and finally achieving unified storage.

[0043] In this embodiment of the invention, architecture asset data can be obtained from various heterogeneous data sources through a pre-configured access channel to solve the problems of inconsistent integration standards and high customized development costs. The data is then processed by identifying the type of architecture asset data. For structured data, a parsing engine can be used to extract field information and dynamically match asset tags to achieve refined attribute management. For unstructured data, text information can be extracted through parsing technology and further knowledge extraction can be performed to obtain structured description information containing entities, attributes, and relationships. Finally, all extracted architecture asset tags and / or architecture asset description information can be stored in a knowledge base, and an interconnected architecture asset knowledge graph can be built based on this.

[0044] This invention transforms the traditional, crude, static, and isolated asset information management model into a refined, dynamic, and deeply cognitive unified governance process. This effectively solves the problems of low integration efficiency and high operation and maintenance costs caused by the lack of unified access standards and intelligent parsing capabilities. At the same time, through deep analysis and knowledge extraction of unstructured data, it releases the asset information value accumulated in unstructured data, and with the help of standardized processing logic, it achieves rapid adaptation and flexible management of dynamically added asset types, fundamentally improving the automation level and knowledge level of the architecture asset management.

[0045] Figure 4 The asset information management device 400 shown can achieve Figure 1 The method described in the embodiment achieves the same technical effect, and can be specifically referred to in the above description. Figure 1 The asset information management method of the illustrated embodiment will not be described in detail here. The asset information management device 400 includes: The acquisition module 401 is used to acquire architecture asset data from multiple heterogeneous data sources through a pre-set configurable access channel; wherein, the access channel is used to implement at least one communication mode among application programming interface (API) interface, message queue and database synchronization. The determining module 402 is used to determine the type of the architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data; The parsing module 403 is used to parse the architecture asset data using a structured data parsing engine when the type of the architecture asset data is structured data, to extract field information, and to determine the architecture asset tag matching the architecture asset data based on the field information; when the type of the architecture asset data is unstructured data, it is used to parse the architecture asset data using an unstructured data parsing engine to extract text information, and to perform knowledge extraction on the text information to obtain architecture asset description information representing the architecture asset; the architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and association relationships between different architecture asset objects; The construction module 404 is used to store at least one of the architecture asset tags and the architecture asset description information into a knowledge base, and to construct an architecture asset knowledge graph based on the knowledge base.

[0046] Optionally, the determining module 402 is used to: Feature information is extracted from the architecture asset data; wherein the feature information includes at least one of the following: data storage format, data source, and degree of data content structuring; The feature information is matched with the identification rules in the preset architecture asset type rule base; wherein, the identification rules are used to determine the type of architecture asset data based on the feature information; Based on the matching results, it is determined whether the architecture asset data belongs to structured data or unstructured data.

[0047] Optionally, the parsing module 403 is used for: In the case where the unstructured data is an image, text information in the image is extracted using optical character recognition technology; and in the case where the unstructured data is a document, text information in the document is extracted using a document parser.

[0048] Optionally, the parsing module 403 is used for: The field information is matched with a preset tag rule base, and at least one architecture asset tag is assigned to the architecture asset data based on the matching result; wherein, the tag rule base includes the mapping relationship between field values, field names and architecture asset tags; or, The field information is input into a pre-trained label analysis model, which then outputs at least one architecture asset label.

[0049] Optionally, the building module 404 is used for: Import the architecture asset description information into a preset graph database; Based on the relationships between different architectural asset objects contained in the architectural asset description information, edges are created between the associated architectural asset objects in the graph database; the edges are links used to represent asset dependencies or lineage relationships.

[0050] Optionally, the building module 404 is used for: Import the architecture asset tags into a preset graph database; Based on the architecture asset tags, the architecture asset objects are classified in the graph database to obtain classification results; Based on the classification results, identify related architectural asset objects with associated relationships from the architectural asset objects; Edges are created between the associated architecture asset objects in the graph database; the edges are links used to represent the association relationship.

[0051] In this embodiment of the invention, architecture asset data can be obtained from various heterogeneous data sources through a pre-configured access channel to solve the problems of inconsistent integration standards and high customized development costs. The data is then processed by identifying the type of architecture asset data. For structured data, a parsing engine can be used to extract field information and dynamically match asset tags to achieve refined attribute management. For unstructured data, text information can be extracted through parsing technology and further knowledge extraction can be performed to obtain structured description information containing entities, attributes, and relationships. Finally, all extracted architecture asset tags and / or architecture asset description information can be stored in a knowledge base, and an interconnected architecture asset knowledge graph can be built based on this.

[0052] This invention transforms the traditional, crude, static, and isolated asset information management model into a refined, dynamic, and deeply cognitive unified governance process. This effectively solves the problems of low integration efficiency and high operation and maintenance costs caused by the lack of unified access standards and intelligent parsing capabilities. At the same time, through deep analysis and knowledge extraction of unstructured data, it releases the asset information value accumulated in unstructured data, and with the help of standardized processing logic, it achieves rapid adaptation and flexible management of dynamically added asset types, fundamentally improving the automation level and knowledge level of the architecture asset management.

[0053] Figure 5 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Please refer to it. Figure 5At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0054] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0055] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0056] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a non-contiguous transfer configuration at the logical level. The processor executes the program stored in memory and specifically performs the following operations: The system acquires architecture asset data from multiple heterogeneous data sources through a pre-configured access channel; wherein the access channel is used to implement at least one communication mode among application programming interface (API) interface, message queue and database synchronization. Determine the type of the architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data; When the type of the architecture asset data is structured data, the architecture asset data is parsed using a structured data parsing engine to extract field information, and the architecture asset tags matching the architecture asset data are determined based on the field information. When the type of the architecture asset data is unstructured data, the architecture asset data is parsed using an unstructured data parsing engine to extract text information, and knowledge extraction is performed on the text information to obtain architecture asset description information representing the architecture assets. The architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and association relationships between different architecture asset objects. Store at least one of the architecture asset tags and the architecture asset description information in a knowledge base, and construct an architecture asset knowledge graph based on the knowledge base.

[0057] The above is as stated in this application. Figure 1 The asset information management method disclosed in the embodiments described above can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in one or more embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in one or more embodiments of this application can be directly implemented by a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0058] The electronic device can also perform Figure 1The asset information management method described herein will not be elaborated further in this application.

[0059] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by a portable electronic device including multiple applications, enable the portable electronic device to perform... Figure 1 The methods of the embodiments shown are not described in detail here.

[0060] This application also proposes a computer program product, which is stored in a storage medium and executed by at least one processor to implement... Figure 1 The methods of the embodiments shown are not described in detail here.

[0061] Of course, in addition to software implementation, the electronic device of this application does not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. In other words, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0062] In summary, the above description is merely a preferred embodiment of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this application should be included within the scope of protection of one or more embodiments of this application.

[0063] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0064] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined in the embodiments of this application, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0065] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0066] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

Claims

1. An asset information management method, characterized in that, include: The system acquires architecture asset data from multiple heterogeneous data sources through a pre-configured access channel; wherein the access channel is used to implement at least one communication mode among application programming interface (API) interface, message queue and database synchronization. Determine the type of the architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data; When the type of the architecture asset data is structured data, the architecture asset data is parsed using a structured data parsing engine to extract field information, and the architecture asset tags matching the architecture asset data are determined based on the field information. When the type of the architecture asset data is unstructured data, the architecture asset data is parsed using an unstructured data parsing engine to extract text information, and knowledge extraction is performed on the text information to obtain architecture asset description information representing the architecture assets. The architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and association relationships between different architecture asset objects. Store at least one of the architecture asset tags and the architecture asset description information in a knowledge base, and construct an architecture asset knowledge graph based on the knowledge base.

2. The method according to claim 1, characterized in that, Determining the type of the architecture asset data includes: Feature information is extracted from the architecture asset data; wherein the feature information includes at least one of the following: data storage format, data source, and degree of data content structuring; The feature information is matched with the identification rules in the preset architecture asset type rule base; wherein, the identification rules are used to determine the type of architecture asset data based on the feature information; Based on the matching results, it is determined whether the architecture asset data belongs to structured data or unstructured data.

3. The method according to claim 1, characterized in that, The architecture asset data is parsed using an unstructured data parsing engine to extract text information, including: In the case where the unstructured data is an image, text information in the image is extracted using optical character recognition technology; and in the case where the unstructured data is a document, text information in the document is extracted using a document parser.

4. The method according to claim 1, characterized in that, The step of determining the architecture asset tag matching the architecture asset data based on the field information includes: The field information is matched with a preset tag rule base, and at least one architecture asset tag is assigned to the architecture asset data based on the matching result; wherein, the tag rule base includes the mapping relationship between field values, field names and architecture asset tags; or, The field information is input into a pre-trained label analysis model, which then outputs at least one architecture asset label.

5. The method according to claim 1, characterized in that, The knowledge base includes the architectural asset description information, and the construction of the architectural asset knowledge graph based on the knowledge base includes: Import the architecture asset description information into a preset graph database; Based on the relationships between different architectural asset objects contained in the architectural asset description information, edges are created between the associated architectural asset objects in the graph database; the edges are links used to represent asset dependencies or lineage relationships.

6. The method according to claim 1, characterized in that, The knowledge base includes the architecture asset tags, and the construction of the architecture asset knowledge graph based on the knowledge base includes: Import the architecture asset tags into a preset graph database; Based on the architecture asset tags, the architecture asset objects are classified in the graph database to obtain classification results; Based on the classification results, identify related architectural asset objects with associated relationships from the architectural asset objects; Edges are created between the associated architecture asset objects in the graph database; the edges are links used to represent the association relationship.

7. An asset information management device, characterized in that, include: The acquisition module is used to acquire architecture asset data from multiple heterogeneous data sources through a pre-configured access channel; wherein, the access channel is used to implement at least one communication mode among API interface, message queue and database synchronization; A determination module is used to determine the type of the architecture asset data; wherein, the type of architecture asset data includes structured data and unstructured data; The parsing module is used to parse the architecture asset data using a structured data parsing engine when the data type is structured data, to extract field information, and to determine the architecture asset tags matching the data based on the field information; and to parse the architecture asset data using an unstructured data parsing engine when the data type is unstructured data, to extract text information, and to perform knowledge extraction on the text information to obtain architecture asset description information representing the architecture assets; the architecture asset description information includes at least one of the following: entity identifier of the architecture asset object, attribute information of the architecture asset object, and association relationships between different architecture asset objects; The construction module is used to store at least one of the architecture asset tags and the architecture asset description information into a knowledge base, and to construct an architecture asset knowledge graph based on the knowledge base.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions that, when executed by a processor, implement the steps of the method described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1 to 6.

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