Asset digital management method and device and electronic equipment

Abstract

The invention discloses an asset digital management method and device and electronic equipment, and relates to the field of data processing. In the method, attribute information of to-be-managed assets is acquired, and the attribute information comprises an asset type, an asset specification and an asset state; based on the attribute information, generating a multi-dimensional identification code corresponding to the asset, the multi-dimensional identification code including an asset type code, an asset specification code and an asset state code; the multi-dimensional identification code is written into a block chain, an asset identification block is generated, and the asset identification block comprises the multi-dimensional identification code, a timestamp and a hash value of a previous block; establishing an asset full-life-cycle management model, and mapping the asset identification block and the asset full-life-cycle management model to form asset management information; based on the asset management information, an asset management knowledge graph is constructed, and the asset management knowledge graph comprises asset entities, asset attributes and asset relations. By implementing the technical scheme provided by the invention, the asset management efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of data processing, and in particular, to a method, apparatus, and electronic device for digital asset management. Background Art

[0002] With the digital transformation of the global economy, enterprises and organizations are increasingly relying on complex asset management systems to optimize the allocation and utilization of their resources. Asset management, especially in the fields of finance, manufacturing, and public facilities, involves multiple stages such as asset procurement, use, maintenance, and scrapping. Its efficiency and accuracy directly affect the operational efficiency and cost control of enterprises. Therefore, with the development of technology, the demand for a method that can comprehensively and efficiently manage the entire life cycle of assets is increasing.

[0003] Currently, traditional asset management methods mainly rely on centralized database systems for storing and processing asset information. Asset information is scattered in various departments and systems, lacking a unified perspective to analyze and manage the entire life cycle of assets, resulting in insufficient decision-making support and inefficient utilization of resources. Therefore, related technologies have the problem of low asset management efficiency.

[0004] Therefore, there is an urgent need for a method, apparatus, and electronic device for digital asset management. Summary of the Invention

[0005] This application provides a method, apparatus, and electronic device for digital asset management, which improves the efficiency of asset management.

[0006] In a first aspect of this application, a method for digital asset management is provided. The method includes: obtaining attribute information of an asset to be managed, where the attribute information includes asset type, asset specification, and asset status; generating a multi-dimensional identification code corresponding to the asset based on the attribute information, where the multi-dimensional identification code includes an asset type code, an asset specification code, and an asset status code; writing the multi-dimensional identification code into a blockchain to generate an asset identification block, where the asset identification block includes the multi-dimensional identification code, a timestamp, and a hash value of the previous block; establishing an asset full life cycle management model, and mapping the asset identification block to the asset full life cycle management model to form asset management information; constructing an asset management knowledge graph based on the asset management information, where the asset management knowledge graph includes asset entities, asset attributes, and asset relationships.

[0007] By adopting the above technical solution, by obtaining the attribute information of assets, generating multi-dimensional identification codes for assets, writing the multi-dimensional identification codes into the blockchain to generate asset identification blocks, establishing an asset full-life cycle management model, mapping the identification blocks and the management model to form asset management information, and finally constructing an asset management knowledge graph based on the management information. This method can comprehensively and accurately collect the multi-dimensional attribute characteristics of assets, ensure the immutability and traceability of asset identification information through blockchain technology, achieve refined management of the asset full-life cycle through the asset full-life cycle management model, and construct a semantic network for asset management based on knowledge graph technology to support intelligent asset query, analysis and optimization. This method makes full use of blockchain, state machine, knowledge graph, etc., to form a set of digital, intelligent and full-life cycle asset management solutions, which can significantly improve the efficiency, reliability and intelligent level of asset management.

[0008] Optionally, after constructing the asset management knowledge graph based on the asset management information, the method further includes: receiving an asset query request from a user, where the asset query request includes query conditions; performing semantic search in the asset management knowledge graph based on the query conditions to obtain a target asset identification block that matches the query conditions; judging whether the target asset identification block is complete through the blockchain; if it is determined that the target asset identification block is complete, reverse-parsing the target asset identification block into corresponding asset attribute information and returning the asset attribute information to the user.

[0009] By adopting the above technical solution, on the basis of the asset management knowledge graph, an intelligent asset query method is further provided. Users can quickly and accurately retrieve a target asset identification block that matches the conditions by inputting query conditions and using the semantic search ability of the knowledge graph. At the same time, the integrity of the target asset identification block is verified by using the blockchain to ensure the authenticity and credibility of the query result. The queried target asset identification block can be further reverse-parsed into corresponding attribute information and presented to the user. This asset query method based on knowledge graph and blockchain, on the one hand, realizes intelligent and semantic asset retrieval by using the semantic association of the knowledge graph, and on the other hand, ensures the credibility of the query result by using the anti-tampering feature of the blockchain, forming a new model of efficient, reliable and intelligent asset query. Users can quickly locate the required assets and obtain the authoritative attribute information of the assets, greatly improving the convenience of asset management and use.

[0010] Optionally, generating a multi-dimensional identification code corresponding to the asset based on the attribute information specifically includes: performing one-hot encoding on the asset type, the asset specification, and the asset status respectively to obtain an asset type feature vector, an asset specification feature vector, and an asset status feature vector; inputting the asset type feature vector, the asset specification feature vector, and the asset status feature vector into a preset asset identification generation model to generate an asset identification embedding vector; inputting the asset identification embedding vector into a preset encoding function to convert the asset identification embedding vector into a binary encoding sequence of a preset length to form the multi-dimensional identification code; wherein, the preset asset identification generation model is a deep neural network model based on an attention mechanism and is trained through a self-supervised learning method, and the encoding function includes the SHA256 function and the MD5 function.

[0011] By adopting the above technical solution, first, the attributes such as the type, specification, and status of the asset are converted into corresponding feature vectors by using one-hot encoding, and then the feature vectors are input into a preset asset identification generation model to obtain an embedded vector representation of the asset identification. Next, standardized encoding functions such as SHA256 and MD5 are used to convert the embedded vector into a fixed-length binary encoding sequence to form the final multi-dimensional identification code. Among them, the deep neural network model adopts advanced deep learning technologies such as the attention mechanism and self-supervised learning, and can efficiently learn the internal correlations and semantic patterns among asset attributes to generate high-quality asset identification embeddings. The entire process automatically maps the multi-dimensional attributes of the asset into a compact and semantically rich digital identification through an end-to-end deep learning process, overcomes the subjectivity and limitations of the traditional manual coding method, and provides an objective, efficient, and intelligent new method for generating asset identifications.

[0012] Optionally, the step of writing the multi-dimensional identification code into the blockchain to generate an asset identification block specifically includes: concatenating the multi-dimensional identification code, the current timestamp, and the hash value of the previous block to form block data to be written; based on the proof-of-stake consensus mechanism, the nodes in the blockchain network complete the packaging of the block data through a competitive calculation method to obtain packaged data; adding the packaged data to the tail of the blockchain and establishing an association with the previous block to form the asset identification block.

[0013] By adopting the above technical solutions, first, information such as asset identification codes, timestamps, and hash values of the previous block are concatenated in a certain format to form data to be written into the block. Then, using the PoS consensus mechanism, nodes in the blockchain network complete the packaging and confirmation of the original data through competitive calculations, obtaining the block data officially uploaded to the chain. Finally, the packaged block data is added to the tail of the blockchain and a hash connection is established with the previous block to form a new asset identification block. The on-chain method based on PoS consensus makes full use of the decentralized ledger mechanism of the blockchain. Through the computational competition and mutual verification of multiple nodes, it realizes the high efficiency, credibility, and security of asset identification on the chain without relying on any central institution. At the same time, PoS consensus replaces the proof of computing power of PoW with proof of stake, significantly reducing the energy consumption for consensus reaching and improving the consensus efficiency, making the process of asset identification on the chain more environmentally friendly and efficient. The chained associated storage of block data also ensures the persistence, immutability, and traceability of asset identification data.

[0014] Optionally, the establishment of the asset full-life cycle management model specifically includes: for different types of assets, constructing corresponding asset management state machines, where the asset management state machines include each life cycle stage of the asset and the conversion relationships between each of the life cycle stages; converting each life cycle stage of the asset into a state node, and converting the conversion relationships between each of the life cycle stages into edges to construct a state graph model; based on the state graph model, constructing an asset full-life cycle management workflow to form the asset full-life cycle management model.

[0015] By adopting the above technical solutions, for different types of assets, an asset management state machine covering its full life cycle is abstracted, the asset management state machine is formalized into a state graph model, each life cycle stage is abstracted into a state node, and the conversion relationship between stages is abstracted into a directed edge, constructing a graph structure of the asset life cycle. Finally, based on the state graph model, a workflow definition for asset management is further constructed, associating state transitions with actual business processes and management actions to form an executable asset management process network. The modeling method based on the state machine can clearly describe the state changes and transfer processes in asset management, depicting the dynamic characteristics of the asset full life cycle. The modeling method based on the graph model can intuitively display the network structure of the asset management process, revealing the dependency and constraint relationships between states. The combination of the two forms a formal, structured, and semantic asset full-life cycle management modeling paradigm, laying a model foundation for the realization of the automation and intelligence of asset management processes.

[0016] Optionally, mapping the asset identification block to the asset full - life - cycle management model to form asset management information specifically includes: extracting the asset identification block in the asset identification block and matching the asset identification block with the status nodes in the asset full - life - cycle management model; if it is determined that the match is successful, associating the asset identification block to the status node to form the asset management information.

[0017] By adopting the above - mentioned technical solution, the unique identification code information of the asset is extracted from the asset identification block, and then the status node matching the current asset status is searched in the asset management status diagram. If the match is successful, the asset identification block is associated with the corresponding status node, and the current management status of the asset is appended based on the block information. Through this mapping and association, the static identification information of the asset can be integrated with the dynamic management status, realizing the collaborative management of asset identification and management processes. The blockchain provides a trusted basis for asset identification, and the state machine provides a semantic framework for asset management. This method maps and matches the two, enabling the identification information of each asset on the blockchain to form a dynamic association with its status in the management process.

[0018] Optionally, constructing an asset management knowledge graph based on the asset management information specifically includes: extracting asset entities, asset attributes, and asset relationships based on the asset management information; constructing the asset management knowledge graph based on the asset entities, the asset attributes, and the asset relationships, where the asset entities correspond to the entity nodes of the asset management knowledge graph, the asset attributes correspond to the attribute edges of the asset management knowledge graph, and the asset relationships are the relationship edges of the asset management knowledge graph.

[0019] By adopting the above - mentioned technical solution, asset entities, asset attributes, and asset relationships are extracted from the asset management information. Then, the asset entities are mapped to the entity nodes in the knowledge graph, the asset attributes are mapped to the attribute edges associated with the entities, and the asset relationships are mapped to the relationship edges between the entities, finally forming an asset management knowledge graph with rich semantics and complete structure. This kind of knowledge graph not only vividly depicts the structured associations between asset elements, reveals the distribution and role of assets within the organization, but also provides a graph - based semantic representation and calculation model for asset management, strongly supporting the refinement, intelligence, and visualization of asset management.

[0020] In the second aspect of the present application, an asset digital management device is provided, which includes: an acquisition module and a processing module, where: the acquisition module is used to acquire the attribute information of the asset to be managed, and the attribute information includes asset type, asset specification, and asset status; the processing module is used to generate a multi-dimensional identification code corresponding to the asset based on the attribute information, and the multi-dimensional identification code includes an asset type code, an asset specification code, and an asset status code; the processing module is further used to write the multi-dimensional identification code into the blockchain to generate an asset identification block, and the asset identification block includes the multi-dimensional identification code, a timestamp, and the hash value of the previous block; the processing module is further used to establish an asset full life cycle management model, and map the asset identification block to the asset full life cycle management model to form asset management information; the processing module is further used to construct an asset management knowledge graph based on the asset management information, and the asset management knowledge graph includes asset entities, asset attributes, and asset relationships.

[0021] In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, and both the user interface and the network interface are used to communicate with other devices. The processor is used to execute the instructions stored in the memory so that the electronic device executes the method described in any one of the above.

[0022] In the fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method described in any one of the above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By acquiring the attribute information of the asset, generating a multi-dimensional identification code of the asset, writing the multi-dimensional identification code into the blockchain to generate an asset identification block, establishing an asset full life cycle management model, mapping the identification block to the management model to form asset management information, and finally constructing an asset management knowledge graph based on the management information. This method can comprehensively and accurately collect the multi-dimensional attribute characteristics of the asset, ensure the immutability and traceability of the asset identification information through blockchain technology, achieve refined management of the asset full life cycle through the asset full life cycle management model, and construct a semantic network for asset management based on knowledge graph technology to support intelligent asset query, analysis, and optimization. This method makes full use of blockchain, state machines, knowledge graphs, etc. to form a set of digital, intelligent, full life cycle asset management solutions, which can significantly improve the efficiency, reliability, and intelligent level of asset management.

[0024] 2. Based on the asset management knowledge graph, an intelligent asset query method is further provided. Users can input query conditions and utilize the semantic search ability of the knowledge graph to quickly and accurately retrieve the target asset identification block that matches the conditions. At the same time, the integrity of the target asset identification block is verified using blockchain to ensure the authenticity and reliability of the query results. The retrieved target asset identification block can be further reverse-parsed into corresponding attribute information and presented to the user. This asset query method based on the knowledge graph and blockchain, on the one hand, realizes intelligent and semantic asset retrieval using the semantic association of the knowledge graph, and on the other hand, ensures the credibility of the query results using the anti-tampering feature of blockchain, forming a new mode of efficient, reliable, and intelligent asset query. Users can quickly locate the required assets and obtain the authoritative attribute information of the assets, greatly improving the convenience of asset management and use.

[0025] 3. First, use one-hot encoding to convert attributes such as the type, specification, and status of the asset into corresponding feature vectors, and then input the feature vectors into a preset asset identification generation model to obtain the embedded vector representation of the asset identification. Then, use standard encoding functions such as SHA256 and MD5 to convert the embedded vector into a fixed-length binary encoding sequence to form the final multi-dimensional identification code. Among them, the deep neural network model adopts advanced deep learning technologies such as the attention mechanism and self-supervised learning, which can efficiently learn the internal associations and semantic patterns between asset attributes and generate high-quality asset identification embeddings. The entire process automatically maps the multi-dimensional attributes of the asset into a compact and semantically rich digital identification through an end-to-end deep learning process, overcoming the subjectivity and limitations of traditional manual coding methods and providing a new method for objectively, efficiently, and intelligently generating asset identifications. Brief Description of the Drawings

[0026] Figure 1 is a schematic flowchart of an asset digital management method disclosed in an embodiment of the present application; Figure 2 is a schematic block diagram of an asset digital management device disclosed in an embodiment of the present application; Figure 3 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0027] Description of the Reference Numerals: 201, acquisition module; 202, processing module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. Detailed Embodiments

[0028] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments.

[0029] In the description of the embodiments of this application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "for example" or "for illustration" is intended to present the relevant concepts in a specific manner.

[0030] In the description of the embodiments of this application, the term "plurality" means two or more. For example, a plurality of systems means two or more systems, and a plurality of screen terminals means two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] This application provides an asset digital management method, referring to Figure 1 , Figure 1 is a schematic flow chart of an asset digital management method provided in the embodiments of this application. This method is applied to a server. The server is a server that executes an asset digital management program and is used to provide background services for user devices. The server can be a single server, a server cluster composed of multiple servers, or a cloud computing service center. This method includes steps S101 to S105, and the above steps are as follows: Step S101: Obtain the attribute information of the asset to be managed, where the attribute information includes the asset type, asset specification, and asset status.

[0032] In step S101, the server first obtains the attribute information of the assets to be managed, including the asset type, asset specifications, and asset status. This step is the basis of the entire asset digital management method and provides the necessary data sources for subsequent multi-dimensional identification code generation, blockchain writing, full-life cycle management modeling, and knowledge graph construction. Specifically, the server can obtain asset attribute information in various ways. For example, the management personnel can manually enter the attribute information of each asset through a dedicated asset management system interface. This method is suitable for scenarios with a small number of assets and relatively static information. For assets with a large quantity and wide distribution, the server can be connected to various sensors and Internet of Things devices to automatically collect the real-time status data of the assets. For example, in industrial equipment management, the server can collect the operating parameters of the equipment through vibration sensors, temperature sensors, etc.; in vehicle management, the server can collect information such as the location, speed, and fuel consumption of the vehicle through GPS positioning, OBD interfaces, etc. If an enterprise already has other business systems storing some attribute information of the assets, such as the asset ledger in the ERP system and the inventory data in the warehousing system, the server can dock with these systems through data interfaces to regularly synchronize or query in real time the required asset attribute information to avoid duplicate entry.

[0033] For example, assume that an enterprise needs to digitally manage the server equipment in its computer room. In step S101, the asset management server can obtain the attribute information of the server equipment in the following ways: When purchasing new equipment, the asset administrator enters the asset type (such as blade server, rack server, etc.), asset specifications (such as CPU model, memory capacity, hard disk size, etc.), and asset status (such as newly purchased, in stock, deployed, etc.) of the equipment through the management interface. The monitoring agent program deployed on the server equipment regularly collects the operating status of the equipment, such as CPU usage, memory usage, network throughput, etc., and reports it to the asset management server.

[0034] Step S102: Based on the attribute information, generate a multi-dimensional identification code corresponding to the asset. The multi-dimensional identification code includes an asset type code, an asset specification code, and an asset status code.

[0035] In step S102, based on the attribute information, a multi-dimensional identification code corresponding to the asset is generated, specifically including: performing one-hot encoding on the asset type, asset specification, and asset status respectively to obtain an asset type feature vector, an asset specification feature vector, and an asset status feature vector; inputting the asset type feature vector, the asset specification feature vector, and the asset status feature vector into a preset asset identification generation model to generate an asset identification embedding vector; inputting the asset identification embedding vector into a preset encoding function to convert the asset identification embedding vector into a binary encoding sequence of a preset length to form a multi-dimensional identification code; wherein, the preset asset identification generation model is a deep neural network model based on the attention mechanism and is trained through self-supervised learning, and the encoding function includes the SHA256 function and the MD5 function.

[0036] Specifically, the server generates a multi-dimensional identification code corresponding to the asset based on the attribute information of the asset. The multi-dimensional identification code is unique, can accurately represent each asset, and prevent forgery or tampering. The server uses a hybrid method based on deep learning and cryptography to generate the identification code, and the specific process is as follows: First, the server performs one-hot encoding processing on the attribute information such as the asset type, asset specification, and asset status of the asset. In the embodiment of the present application, one-hot encoding can be understood as a method of converting discrete features into continuous vector representations, and it creates a binary feature for each value. For example, assuming that there are three values for the asset type: "equipment", "material", "tool", one-hot encoding will convert it into a vector form of (1, 0, 0), (0, 1, 0), (0, 0, 1). After one-hot encoding processing, the asset type, asset specification, and asset status respectively form a feature vector, and the length of the vector is equal to the number of all possible values of the attribute.

[0037] Next, the server parallelly inputs the one-hot feature vectors of the asset type, asset specification, and asset status into a preset asset identification generation model. This model is a deep neural network based on the attention mechanism, which can automatically learn the internal associations between different attributes and map them into a low-dimensional dense embedding space to form an embedding vector representation of the asset identification. The training of the preset asset identification generation model adopts the self-supervised learning method, and through prediction and comparison tasks on a large amount of asset data, the model autonomously discovers the potential patterns of the asset identification. The identification embedding process can be regarded as a semantic encoding, which compresses multiple attributes of the asset into a fixed-length real-valued vector, retaining both the basic information of the attributes and mining the high-level features between the attributes.

[0038] After generating the asset identification embedding vector, the server does not directly use it as the final identification code. Instead, it further converts the real-valued vector into a fixed-length binary coding sequence through a preset coding function. Here, two hash algorithms can be adopted: SHA256 and MD5. SHA256 can generate a 256-bit (32-byte) hash value, and MD5 can generate a 128-bit (16-byte) hash value. Compared with the original embedding vector, the hash coding has irreversibility and collision resistance, that is, it is impossible to restore the original input from the hash value, and it is very difficult for different inputs to generate the same hash value. This cryptographic coding further ensures the uniqueness and security of the asset identification, preventing the identification from being guessed or cracked. The finally generated multi-dimensional identification code is the result of the hash coding of the asset identification embedding vector, which not only contains rich semantic information of the asset attributes but also has the robust characteristics of cryptography.

[0039] For example, assume that the attribute information of an asset is: asset type = device, asset specification = X01, and asset status = in use. First, the server performs one-hot encoding on these three attributes to obtain three feature vectors: asset type: (1, 0, 0,..., 0); asset specification: (0, 0,..., 1,..., 0); asset status: (0, 1, 0); Then, the server inputs the three feature vectors into the preset asset identification generation model to obtain an asset identification embedding vector of length 256, such as: (0.12, -0.08, 0.02,..., -0.11); Finally, the server performs SHA256 hashing on the asset identification embedding vector to obtain a 256-bit binary coding sequence as the final multi-dimensional identification code of the asset: 10101001...01001110; This identification code can be converted into a hexadecimal string for storage, such as "a4f...38e", and associated with other metadata information of the asset. Subsequent asset management, traceability, verification, etc. operations will all use this multi-dimensional identification code as the unique ID of the asset.

[0040] Step S103: Write the multi-dimensional identification code into the blockchain to generate an asset identification block, and the asset identification block includes the multi-dimensional identification code, timestamp, and the hash value of the previous block.

[0041] In step S103, the steps of writing the multi-dimensional identification code into the blockchain to generate an asset identification block specifically include: concatenating the multi-dimensional identification code, the current timestamp, and the hash value of the previous block to form the block data to be written; based on the proof-of-stake consensus mechanism, the nodes in the blockchain network complete the packaging of the block data through a competitive calculation method to obtain the packaged data; adding the packaged data to the tail of the blockchain and establishing an association with the previous block to form an asset identification block.

[0042] Specifically, the server prepares the data to be written into the blockchain. This data includes: the multi-dimensional identification code of the asset, the current timestamp, and the hash value of the previous block. Among them, the asset identification code is the unique ID generated in the previous step; the timestamp records the creation time of the asset identification block, usually accurate to seconds or milliseconds; the hash value of the previous block is the hash digest of the latest block in the blockchain, which is used to connect new and old blocks to ensure the integrity of the blockchain. The server concatenates these three elements together in a certain format to form the original block data to be written. Then, the server packages the original block data into the blockchain. Here, a proof-of-stake-based consensus mechanism is adopted. Under the proof-of-stake-based consensus mechanism, the server, as a node in the blockchain network, can participate in the block packaging competition. Suppose the server holds a certain number of tokens and it is the server's turn to propose a block. The server can package the original block data to be written, add necessary metadata information (such as block height, difficulty, etc.), and calculate the hash digest of the entire block to form a candidate block. Then, the server broadcasts the candidate block to other nodes for them to verify and confirm. If more than a certain proportion (such as 2 / 3) of the nodes approve the block, it will be officially added to the end of the blockchain and a hash connection will be established with the previous block to form a new asset identification block.

[0043] For example, suppose the server generates an asset identification code "a4f...38e", the current timestamp is "2023-05-20 15:30:00", and the hash value of the previous block is "0x12ab...". The server concatenates these three elements in the format of "identification code, timestamp, previous block hash" to form the original block data: "a4f...38e, 2023-05-20 15:30:00, 0x12ab...". Then, the server competes for the block packaging right according to the PoS consensus. Suppose it successfully obtains the proposal right. Based on the original block data, the server adds metadata such as block height (such as 100) and difficulty (such as 2.5), and calculates the SHA256 hash value of the entire block, such as "0x78cd...", to form a candidate block. Next, the server sends the candidate block to 6 other nodes for verification. Suppose 5 nodes return confirmation messages, reaching the 2 / 3 confirmation ratio. Finally, the server officially adds the candidate block to the blockchain and establishes a connection with the previous block (height 99, hash value "0x12ab..."). The new asset identification block (height 100, hash value "0x78cd...") is thus generated.

[0044] Step S104: Establish an asset full-life cycle management model and map the asset identification block to the asset full-life cycle management model to form asset management information.

[0045] In step S104, an asset full - life - cycle management model is established, which specifically includes: for different types of assets, constructing corresponding asset management state machines. The asset management state machine includes each life - cycle stage of the asset and the conversion relationships between each life - cycle stage; converting each life - cycle stage of the asset into a state node, and converting the conversion relationships between each life - cycle stage into edges to construct a state - diagram model; based on the state - diagram model, constructing an asset full - life - cycle management workflow to form an asset full - life - cycle management model. Mapping the asset identification block to the asset full - life - cycle management model to form asset management information, which specifically includes: extracting the asset identification block in the asset identification block and matching the asset identification block with the state nodes in the asset full - life - cycle management model; if it is determined that the match is successful, associating the asset identification block with the state node to form asset management information.

[0046] Specifically, the server establishes an asset full - life - cycle management model and maps the asset identification block to the management model to form complete asset management information. The purpose of this step is to combine the static identification of assets with the dynamic management process to achieve visual and traceable intelligent management of the asset full - life - cycle. First, the server constructs corresponding asset management state - machine models for different types of assets. A state machine is a mathematical model that describes the transitions of a system between different states and is often used to describe finite - state automata or workflows, etc. In the field of asset management, a state machine can be used to depict the flow process of assets through various life - cycle stages such as procurement, warehousing, requisition, maintenance, and scrapping. For example, the state - machine model of a machine equipment may include states such as newly purchased, under commissioning, normal operation, fault shutdown, under repair, and scrapped, as well as the conversion conditions and conversion actions between each state. In the embodiments of the present application, a series of standardized asset - management state - machine models are abstracted in advance by domain experts summarizing the business processes and management specifications of various assets. Then, the server visualizes the asset - management state - machine model as a state - diagram. A state - diagram is a graphical representation of a state machine and consists of state nodes and transition edges. The server maps each life - cycle stage in the state machine to the nodes of the state - diagram, and maps the conversion relationships between the stages to the directed edges between the nodes to form a directed - graph structure. For example, the above - mentioned equipment state machine can be converted into a state - diagram of newly purchased, under commissioning, normal operation, fault shutdown, under repair, normal operation, and scrapped. The server can use tools such as graph databases or relational databases to persistently store the nodes and edges of the state - diagram for subsequent query and analysis.

[0047] Based on the state diagram, the server further constructs an asset full - life - cycle management workflow. A workflow is a task - orchestration and process - control technology based on a state machine. By associating state transitions with specific business actions, it realizes the automated management of the entire business process. For example, in the transition from the device - failure state to the repaired state, the relevant workflow actions may include: generating a failure report, dispatching a work order to the maintenance personnel, recording the maintenance log, and updating the device status. The server uses tools such as a workflow engine or BPMN to bind the nodes and edges in the state diagram to specific workflow tasks, forming an executable asset - management workflow definition. In this way, an asset full - life - cycle management model is established, including information at multiple levels such as the state machine, state diagram, and workflow.

[0048] Finally, the server needs to map the asset identification block to the asset full - life - cycle management model. Specifically, it associates the asset identification information on the blockchain with the corresponding nodes in the state diagram to form complete asset - management information. The server first extracts the multi - dimensional identification code of the asset from the asset identification block, and then searches for the node in the state diagram that matches the asset type and the current state. If a matching node is found, the block information (such as block height, timestamp, hash value, etc.) is associated with that node, indicating the current management stage of the asset. At the same time, other management information such as the owner, location, and maintenance records can also be attached to the node. In this way, the identification information of each asset on the blockchain is mapped to its life - cycle state in the management model, which can reflect the current management status of the asset in real - time.

[0049] For example, for a newly purchased server device, its blockchain identification is "a4f...38e". After the server extracts this identification code, it searches for the "newly purchased" node in the device - management state diagram and associates the block information with this node, while recording information such as the purchase time and the person in charge of the device. In this way, the block "a4f...38e" is mapped to the state "newly purchased", indicating that the device is currently in the newly purchased stage. As the device state changes, the block information will continuously flow in the state diagram and be associated with the corresponding management nodes, forming a dynamic view of asset - management information.

[0050] Step S105: Based on the asset - management information, construct an asset - management knowledge graph, where the asset - management knowledge graph includes asset entities, asset attributes, and asset relationships.

[0051] In step S105, based on the asset management information, asset entities, asset attributes, and asset relationships are extracted; based on the asset entities, asset attributes, and asset relationships, an asset management knowledge graph is constructed, where the asset entities correspond to the entity nodes of the asset management knowledge graph, the asset attributes correspond to the attribute edges of the asset management knowledge graph, and the asset relationships are the relationship edges of the asset management knowledge graph.

[0052] Specifically, the server further constructs an asset management knowledge graph based on the previously generated asset management information. An asset management knowledge graph is a structured semantic network used to represent knowledge elements such as entities, attributes, relationships, and their interconnections. In the field of asset management, a knowledge graph can integrate scattered asset data into a highly correlated knowledge base and support intelligent asset query, reasoning, decision-making, and other applications. The construction of an asset management knowledge graph marks that asset digital management has entered a higher-level intelligent stage. Specifically in implementation, the server mainly constructs the knowledge graph based on three types of information: asset entities, asset attributes, and asset relationships. Asset entities refer to various objects participating in asset management activities, such as asset identification blocks, management status nodes, physical devices, responsible persons, etc. The server extracts and clusters the asset management information to identify various entity objects and creates a unique identifier (such as URI) for each entity. For example, for an asset identification block "a4f...38e", the server creates an entity identifier " / block / a4f...38e"; for a status node "in use", it creates an identifier " / state / in_use"; for a physical device "Device A", it creates an identifier " / device / A". These entity identifiers will serve as the nodes in the knowledge graph, representing the core objects in asset management. Asset attributes refer to the inherent characteristics of entities, such as the type, specification, value, status, etc. of the assets. The server analyzes the structured data (such as relational tables, JSON documents, etc.) in the asset management information, extracts the attribute information of each entity, and converts it into an attribute edge in the knowledge graph. For example, the server extracts the specification attribute "Model: X01" of Device A from the block information and creates an attribute edge " / device / A Specification X01" in the knowledge graph. Similarly, the server extracts the status attribute "in use" of Device A from the status node information and creates an attribute edge " / device / A Status / state / in_use". The direction of the attribute edge is from the entity to the attribute value, indicating that the entity has a certain attribute.

[0053] Asset relationships refer to the interconnections between entities, such as the association between asset identification blocks and status nodes, and the subordinate relationship between devices and responsible persons. For example, from the mapping information between the asset identification block and the status node, the server extracts the association between the block "a4f...38e" and the status "in use", and creates a relationship edge " / block / a4f...38e associated with / state / in_use" in the knowledge graph. Another example is that the server uses the ontological knowledge of "device usage relationship" to extract the relationship of "Employee B uses Device A" from the device requisition record and creates a relationship edge " / employee / B uses / device / A". The direction of the relationship edge indicates the subject and object of the relationship, and the label indicates the semantic type of the relationship.

[0054] In the above way, the server extracts the elements such as entities, attributes, and relationships in the asset management information one by one, and converts them into nodes and edges of the knowledge graph to form an asset management knowledge graph. Each node in the asset management knowledge graph represents an asset management object, the attributes of the node describe the static characteristics of the object, and the relationships between the nodes reflect the interactions of the objects in the management activities.

[0055] In a possible implementation manner, after step S105, the method further includes: receiving an asset query request from the user, where the asset query request includes query conditions; performing semantic search in the asset management knowledge graph based on the query conditions to obtain a target asset identification block that matches the query conditions; determining whether the target asset identification block is complete through the blockchain; if it is determined that the target asset identification block is complete, reverse-resolving the target asset identification block into corresponding asset attribute information and returning the asset attribute information to the user.

[0056] Specifically, the server first receives and parses the user's asset query request. The user can initiate the query through various means such as a Web page, a mobile App, an API interface, etc. The asset query request contains query conditions for matching assets. For example, the user can specify conditions such as the type, keyword, owner, status, etc. of the asset, like "query assets with type of device, model containing X01, and status of in use". After receiving the request, the server converts these query conditions into corresponding semantic representations in the knowledge graph. For example, the asset type condition is converted into an entity type constraint, the keyword condition is converted into an attribute value match, and the status condition is converted into an entity relationship constraint, etc. Based on the converted query conditions, the server performs semantic search in the asset management knowledge graph. Different from traditional keyword matching search, semantic search makes full use of the semantic association information in the knowledge graph and can achieve more intelligent and comprehensive asset matching. For example, for the query condition of "model containing X01", traditional search can only match assets whose asset attributes contain the string "X01", while semantic search can match assets with model attribute values such as "X01", "X011", "X01 - A", etc. that are semantically similar. The server uses technologies such as knowledge graph query languages (such as SPARQL, Cypher) or graph neural networks to find target assets that match the query conditions in the asset management knowledge graph and obtain the identification block information associated with these assets.

[0057] After finding the target assets, the server verifies the integrity and accuracy of the asset information. Since the asset identification blocks are stored on the blockchain, the server can interact with the blockchain nodes to determine whether the block information of the target assets is complete and consistent with the information in the knowledge graph. Specifically, the server first extracts the block hash values associated with the target assets in the knowledge graph, and then initiates a block query request to the blockchain nodes. If there is a block in the blockchain with a matching hash value and the front and back links of the block are complete, it means that the asset information in the knowledge graph is consistent with that in the blockchain and has not been tampered with; otherwise, if there is no corresponding block in the blockchain or the block links are incomplete, it means that the asset information may be incorrect and needs to be further verified. Through blockchain verification, the server ensures the authenticity and reliability of the asset information returned to the user.

[0058] Finally, the server reverse - parses the verified target asset information into user - readable attribute information and returns the result to the user. Since assets are stored in the knowledge graph in the form of entities, attributes, relationships, etc., these structured information needs to be converted into the form of an attribute list for the user to view. For example, for a device asset, the server extracts its associated type, model, status, value, responsible person, etc. attributes from the asset management knowledge graph and organizes them into a structured JSON or XML document to return to the user.

[0059] Reference Figure 2 In addition, the present application also provides an asset digital management device, which is a server. The server includes an acquisition module 201 and a processing module 202, where: The acquisition module 201 is configured to acquire the attribute information of the asset to be managed, and the attribute information includes the asset type, asset specification, and asset status; The processing module 202 is configured to generate a multi-dimensional identification code corresponding to the asset based on the attribute information, and the multi-dimensional identification code includes an asset type code, an asset specification code, and an asset status code; The processing module 202 is further configured to write the multi-dimensional identification code into the blockchain to generate an asset identification block, and the asset identification block includes the multi-dimensional identification code, a timestamp, and the hash value of the previous block; The processing module 202 is further configured to establish an asset full life cycle management model, and map the asset identification block to the asset full life cycle management model to form asset management information; The processing module 202 is further configured to construct an asset management knowledge graph based on the asset management information, and the asset management knowledge graph includes asset entities, asset attributes, and asset relationships.

[0060] In a possible implementation manner, after the processing module 202 constructs the asset management knowledge graph based on the asset management information, the method further includes: The processing module 202 receives an asset query request from the user, and the asset query request includes a query condition; The processing module 202 performs semantic search in the asset management knowledge graph based on the query condition to obtain a target asset identification block that matches the query condition; The processing module 202 determines whether the target asset identification block is complete through the blockchain; If the processing module 202 determines that the target asset identification block is complete, it reversely parses the target asset identification block into the corresponding asset attribute information and returns the asset attribute information to the user.

[0061] In a possible implementation manner, when the processing module 202 generates a multi-dimensional identification code corresponding to the asset based on the attribute information, it specifically includes: The processing module 202 performs one-hot encoding on the asset type, asset specification, and asset status respectively to obtain an asset type feature vector, an asset specification feature vector, and an asset status feature vector; The processing module 202 inputs the asset type feature vector, the asset specification feature vector, and the asset status feature vector into a preset asset identification generation model to generate an asset identification embedding vector; The processing module 202 inputs the asset identification embedding vector into a preset encoding function to convert the asset identification embedding vector into a binary encoding sequence of a preset length to form a multi-dimensional identification code; Wherein, the preset asset identification generation model is a deep neural network model based on the attention mechanism and is trained through a self-supervised learning method, and the encoding function includes the SHA256 function and the MD5 function.

[0062] In a possible implementation, the step of the processing module 202 writing the multi-dimensional identification code into the blockchain to generate the asset identification block specifically includes: The processing module 202 splices the multi-dimensional identification code, the current timestamp, and the hash value of the previous block to form the block data to be written; The processing module 202, based on the proof-of-stake consensus mechanism, enables the nodes in the blockchain network to complete the packaging of the block data through competitive computing to obtain the packaged data; The processing module 202 adds the packaged data to the tail of the blockchain and establishes an association with the previous block to form the asset identification block.

[0063] In a possible implementation, the processing module 202 establishes an asset full-life cycle management model, which specifically includes: The processing module 202 constructs corresponding asset management state machines for different types of assets. The asset management state machine includes each life cycle stage of the asset and the conversion relationships between each life cycle stage; The processing module 202 converts each life cycle stage of the asset into a state node, and converts the conversion relationships between each life cycle stage into edges to construct a state graph model; The processing module 202 constructs an asset full-life cycle management workflow based on the state graph model to form an asset full-life cycle management model.

[0064] In a possible implementation, the processing module 202 maps the asset identification block to the asset full-life cycle management model to form asset management information, which specifically includes: The processing module 202 extracts the asset identification block in the asset identification block and matches the asset identification block with the state nodes in the asset full-life cycle management model; If the processing module 202 determines that the match is successful, it associates the asset identification block with the state node to form asset management information.

[0065] In a possible implementation, the processing module 202 constructs an asset management knowledge graph based on the asset management information, which specifically includes: The processing module 202 extracts asset entities, asset attributes, and asset relationships based on the asset management information; The processing module 202 constructs an asset management knowledge graph based on the asset entities, asset attributes, and asset relationships. Among them, the asset entities correspond to the entity nodes of the asset management knowledge graph, the asset attributes correspond to the attribute edges of the asset management knowledge graph, and the asset relationships are the relationship edges of the asset management knowledge graph.

[0066] It should be noted that: When the device provided in the above embodiments realizes its functions, only the above-mentioned division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be elaborated here.

[0067] This application also provides an electronic device. Referring to Figure 3 , Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0068] Among them, the communication bus 302 is used to realize the connection and communication between these components.

[0069] Among them, the user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may further include a standard wired interface and a wireless interface.

[0070] Among them, the network interface 304 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0071] Among them, the processor 301 may include one or more processing cores. The processor 301 connects various parts within the entire server through various interfaces and lines, and executes various functions of the server and processes data by running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and by calling the data stored in the memory 305. Optionally, the processor 301 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 301 may integrate one or several combinations of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, the user interface, and application programs, etc.; the GPU is responsible for the rendering and drawing of the content to be displayed on the display screen; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor 301 and may be implemented separately by a single chip.

[0072] Among them, the memory 305 may include a Random Access Memory (RAM), or may also include a Read-Only Memory. Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 305 may include a program storage area and a data storage area. Among them, the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments. Optionally, the memory 305 may also be at least one storage device located far from the aforementioned processor 301. Refer to Figure 3 , in the memory 305 as a computer storage medium, it may include an operating system, a network communication module, a user interface module, and an application program of an asset digital management method.

[0073] In Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user to obtain user input data; and the processor 301 can be used to call the application program of an asset digital management method stored in the memory 305. When executed by one or more processors 301, the electronic device 300 is caused to execute the methods described in one or more of the above embodiments. It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0074] This application also provides a computer-readable storage medium, and the computer-readable storage medium stores instructions. When executed by one or more processors 301, the electronic device 300 is caused to execute the methods described in one or more of the above embodiments.

[0075] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0076] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0077] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0078] In addition, each functional unit in various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present application. And the aforementioned memory includes: various media such as USB flash drives, mobile hard disks, magnetic disks or optical discs that can store program codes.

[0080] The above are only exemplary embodiments of the present disclosure and should not be used to limit the scope of the present disclosure. That is, all equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will easily think of other implementation schemes of the present disclosure after considering the specification and the disclosure of the practical truth.

[0081] The present application aims to cover any variations, uses or adaptive changes of the present disclosure. These variations, uses or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for digital asset management, characterized in that: The method comprises: Acquire attribute information of the asset to be managed, the attribute information including asset type, asset specification and asset status; Based on the attribute information, a multi-dimensional identification code corresponding to the asset is generated, wherein the multi-dimensional identification code includes an asset type code, an asset specification code, and an asset status code; Writing the multi-dimensional identification code into the blockchain to generate an asset identification block, wherein the asset identification block includes the multi-dimensional identification code, a timestamp, and a hash value of a previous block; Establishing an asset life cycle management model, and mapping the asset identification block with the asset life cycle management model to form asset management information; Based on the asset management information, an asset management knowledge graph is constructed, and the asset management knowledge graph includes asset entities, asset attributes, and asset relationships.

2. The method according to claim 1, characterized in that After constructing the asset management knowledge graph based on the asset management information, the method further includes: Receive an asset query request from a user, where the asset query request includes a query condition; Based on the query condition, a semantic search is performed in the asset management knowledge graph to obtain a target asset identification block that matches the query condition; Determine whether the target asset identification block is complete through the blockchain; If it is determined that the target asset identification block is complete, the target asset identification block is reversely parsed into corresponding asset attribute information, and the asset attribute information is returned to the user.

3. The method according to claim 1, characterized in that The generating a multi-dimensional identification code corresponding to the asset based on the attribute information specifically includes: Performing one-hot encoding on the asset type, the asset specification, and the asset status, respectively, to obtain an asset type feature vector, an asset specification feature vector, and an asset status feature vector; Inputting the asset type feature vector, the asset specification feature vector and the asset status feature vector into a preset asset identifier generation model to generate an asset identifier embedding vector; The asset identification embedding vector is input into a preset encoding function, and the asset identification embedding vector is converted into a binary encoding sequence of a preset length to form the multi-dimensional identification code; wherein the preset asset identification generation model is a deep neural network model based on the attention mechanism, which is trained by self-supervised learning, and the encoding function includes a SHA256 function and an MD5 function.

4. The method according to claim 1, characterized in that: The step of writing the multi-dimensional identification code into the blockchain to generate an asset identification block specifically includes: The multi-dimensional identification code, the current timestamp and the hash value of the previous block are concatenated to form the block data to be written; Based on the proof-of-stake consensus mechanism, the nodes in the blockchain network complete the packaging of the block data through competitive calculation to obtain packaged data; The packaged data is added to the end of the blockchain and associated with the previous block to form the asset identification block.

5. The method according to claim 1, characterized in that The establishment of an asset life cycle management model specifically includes: For different types of assets, a corresponding asset management state machine is constructed, wherein the asset management state machine includes various life cycle stages of the asset and the conversion relationship between the various life cycle stages; Convert each life cycle stage of the asset into a state node, convert the conversion relationship between each life cycle stage into an edge, and construct a state graph model; Based on the state diagram model, an asset life cycle management workflow is constructed to form the asset life cycle management model.

6. The method according to claim 1, characterized in that The mapping of the asset identification block with the asset lifecycle management model to form asset management information specifically includes: Extracting an asset identification block from the asset identification block, and matching the asset identification block with a state node in the asset lifecycle management model; If it is determined that the match is successful, the asset identification block is associated with the status node to form the asset management information.

7. The method according to claim 1, characterized in that The constructing of an asset management knowledge graph based on the asset management information specifically includes: Based on the asset management information, extract asset entities, asset attributes and asset relationships; The asset management knowledge graph is constructed based on the asset entity, the asset attributes and the asset relationship, wherein the asset entity corresponds to the entity node of the asset management knowledge graph, the asset attributes correspond to the attribute edge of the asset management knowledge graph, and the asset relationship is the relationship edge of the asset management knowledge graph.

8. An asset digital management device, characterized in that: The device comprises an acquisition module (201) and a processing module (202), wherein: The acquisition module (201) is used to acquire the attribute information of the asset to be managed, wherein the attribute information includes the asset type, asset specification and asset status; The processing module (202) is used to generate a multi-dimensional identification code corresponding to the asset based on the attribute information, wherein the multi-dimensional identification code includes an asset type code, an asset specification code, and an asset status code; The processing module (202) is further used to write the multi-dimensional identification code into the blockchain to generate an asset identification block, wherein the asset identification block includes the multi-dimensional identification code, a timestamp, and a hash value of a previous block; The processing module (202) is also used to establish an asset life cycle management model, and map the asset identification block with the asset life cycle management model to form asset management information; The processing module (202) is also used to construct an asset management knowledge graph based on the asset management information, and the asset management knowledge graph includes asset entities, asset attributes and asset relationships.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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