Blockchain data supervision method, system and computer equipment based on knowledge graph

Abstract

The present invention discloses a blockchain data supervision method, system and computer equipment based on knowledge graph. The method includes: knowledge modeling, defining business entities, relationships and attributes according to different blockchain business meanings and data types; knowledge extraction, extracting business entities, relationships and attribute data in each chain; knowledge fusion, merging entities, entity attributes and relationships of data in different blockchains; knowledge storage, storing blockchain data in a graph database to form a blockchain data knowledge graph; knowledge calculation, defining data supervision rules and corresponding reasoning logic based on the blockchain data knowledge graph, and finally obtaining reasoning conclusions based on supervision rules by using knowledge reasoning and graph mining methods to achieve the purpose of data supervision. The present invention adopts knowledge graph technology to provide a universal blockchain data supervision method and system, which can flexibly configure supervision rules, achieve the purpose of blockchain data supervision, and improve the security of blockchain business.

Description

Technical Field

[0001] The present invention belongs to the fields of computer systems and blockchain technology, and in particular to a blockchain data supervision method, system and computer equipment based on knowledge graphs. Background Art

[0002] Blockchain is a distributed data ledger technology that connects data blocks in chronological order, forming a chained data structure. This structure is decentralized and stored by all participants, and cryptographic techniques ensure that the data is tamper-proof, unforgeable, and traceable. The application of blockchain technology ensures the integrity and reliability of information, effectively addressing trust and security issues inherent in information exchange. The financial industry, characterized by high trust and collaboration costs among participants and demanding the security and integrity of transaction records, is well-suited to the application of blockchain technology. Its applications in the financial sector encompass a wide range of businesses, including supply chain finance, trade finance, fund management, payment and clearing, and digital assets.

[0003] As a trusted infrastructure, blockchain's immutability, transparency, and other characteristics can solve many practical problems, but they also raise information security concerns. In a blockchain system, data is written and read by all peer nodes. Therefore, it's impossible to prevent illegal or infringing information from being written to the blockchain. For blockchain systems used in financial scenarios, where user assets are involved, on-chain asset data must be audited. Furthermore, because blockchain data cannot be easily queried, aggregated, or analyzed like relational databases, it makes information auditing and regulation difficult. Therefore, methods for blockchain-specific data regulation are urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to address the deficiencies of the existing technology and provide a blockchain data supervision method, system and computer device based on knowledge graph.

[0005] According to a first aspect of the present invention, a blockchain data supervision method based on a knowledge graph is provided, the method comprising:

[0006] Knowledge modeling: defining business entities, relationships, and attributes based on the business meaning and data types of different blockchains, and establishing business models;

[0007] Knowledge extraction: obtaining transaction data from each chain and extracting corresponding entity, relationship, and attribute data based on the established business model;

[0008] Knowledge fusion: The extracted data is cleaned through data preprocessing, and the data from different blockchains are merged into entities, entity attributes and relationships through knowledge fusion technology;

[0009] Knowledge storage uses a graph-based storage method to store entities, relationships, and attribute data in a graph database, indexes relevant business identification information, and obtains a blockchain data knowledge graph;

[0010] Knowledge computing, based on the constructed blockchain data knowledge graph, defines data supervision rules and corresponding reasoning logic, and adopts graph-based knowledge reasoning and graph mining methods to quickly obtain results based on supervision rules.

[0011] Furthermore, knowledge modeling of blockchain data involves manually establishing entities, relationships, and attributes based on the business type on the blockchain and the entity definitions (structures, classes, etc.) and transaction definitions of the blockchain contract. For example, in a proof chain, the proof party and the proof can be defined as entities, the proof party information and proof information can be defined as attributes, and the proof transaction can be defined as a relationship.

[0012] Furthermore, knowledge extraction of blockchain data includes data acquisition and information extraction;

[0013] Data acquisition refers to obtaining data from the blockchain. There are three ways to do this: first, directly read data from the blockchain's native database, which is mostly a key-value storage database; second, read data from the blockchain's data archive database. Currently, mainstream blockchain platforms all have data archiving capabilities. To facilitate data use, archived data is mostly stored in relational databases; third, obtain data through the query API provided by the blockchain platform.

[0014] Information extraction is to extract entities, attributes and relationships from the acquired blockchain data according to the established business model to obtain the basic data needed to build the knowledge graph.

[0015] Furthermore, knowledge fusion is performed on the extracted data. In reality, the same entity may store different information in different blockchains, so data preprocessing is required to clean the data and entity fusion technology is used to merge the entities. For example, in a supply chain finance scenario blockchain network, the information stored in different blockchains for the same company may be the company's full name, company abbreviation, or company organization code, so the same entity from different blockchains needs to be merged; in a medical scenario blockchain network, the diagnostic information of the same patient in different medical institutions also needs to be merged.

[0016] The data preprocessing includes data format standardization, sensitive information processing, redundant data filtering, etc.

[0017] The knowledge fusion technology includes entity alignment, entity linking, entity disambiguation, and the merging of entity attributes and relationships.

[0018] Furthermore, the knowledge storage is to store the fused data based on a graph structure, and use the attribute graph model to convert entities, relationships, and attribute data into vertex, edge, attribute, and label data. The graph database selects mainstream graph database products, such as neo4j. Compared with traditional relational databases, graph databases have the advantages of fast query speed, simple operation, rich relationship display methods, and intuitive visualization. After the data is stored in the graph database, the relevant business identification fields are indexed.

[0019] Furthermore, the knowledge calculation is based on the constructed blockchain data knowledge graph, defines specific data supervision rules and corresponding reasoning logic according to the actual supervision and audit regulations, and adopts knowledge reasoning and graph mining methods to obtain reasoning conclusions to achieve the purpose of supervision and audit.

[0020] Furthermore, the data supervision rules include rules such as sensitive word detection, transaction risk detection, and personal sensitive information detection; the knowledge reasoning and graph mining methods include keyword retrieval, graph query retrieval, association analysis, path search, node classification and other methods.

[0021] According to a second aspect of the present invention, a blockchain data supervision system based on a knowledge graph is provided, comprising the following modules:

[0022] Knowledge modeling module: defines business entities, relationships, and attributes based on the business meaning and data types of different blockchains, and establishes business models;

[0023] Knowledge extraction module: obtains transaction data from each chain and extracts corresponding entity, relationship, and attribute data based on the established business model;

[0024] Knowledge fusion module: This module cleanses the extracted data through data preprocessing and merges entities, entity attributes, and relationships from different blockchains through knowledge fusion technology.

[0025] Knowledge storage module: Uses a graph-based storage method to store entities, relationships, and attribute data in a graph database, indexes relevant business identification information, and obtains a blockchain data knowledge graph;

[0026] Knowledge computing module: Based on the constructed blockchain data knowledge graph, define data supervision rules and corresponding reasoning logic, and use graph-based knowledge reasoning and graph mining methods to quickly obtain results based on supervision rules.

[0027] According to a third aspect of the present invention, a computer device is provided, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps in the above-mentioned knowledge graph-based blockchain data supervision method.

[0028] According to a fourth aspect of the present invention, a storage medium storing computer-readable instructions is provided. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps in the above-mentioned knowledge graph-based blockchain data supervision method.

[0029] The beneficial effects of the present invention are: the present invention adopts knowledge graph technology to provide a universal blockchain data supervision method and system, which can flexibly configure supervision rules, achieve the purpose of blockchain data supervision, and improve the security of blockchain business. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0031] Figure 1 This is a flow chart of a knowledge graph-based blockchain data supervision method according to one embodiment of the present invention;

[0032] Figure 2 This is a diagram of a blockchain data business model according to one embodiment of the present invention;

[0033] Figure 3 This is a schematic diagram of a blockchain data knowledge graph according to one embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0035] The embodiment of the present invention provides a blockchain data supervision method based on knowledge graph, the method flow chart is as follows: Figure 1 As shown, including:

[0036] Knowledge modeling: defining business entities, relationships, and attributes based on the business meaning and data types of different blockchains, and establishing business models;

[0037] Knowledge extraction: obtaining transaction data from each chain and extracting corresponding entity, relationship, and attribute data based on the established business model;

[0038] Knowledge fusion: The extracted data is cleaned through data preprocessing, and the data from different blockchains are merged into entities, entity attributes and relationships through knowledge fusion technology;

[0039] Knowledge storage uses a graph-based storage method to store entities, relationships, and attribute data in a graph database, indexes relevant business identification information, and obtains a blockchain data knowledge graph;

[0040] Knowledge computing, based on the constructed blockchain data knowledge graph, defines data supervision rules and corresponding reasoning logic, and adopts graph-based knowledge reasoning and graph mining methods to quickly obtain results based on supervision rules.

[0041] In the knowledge modeling phase, entities, relationships, and attributes are manually established based on the business type on the chain and the entity definition (structure, class, etc.) and transaction definition of the contract in the blockchain. Figure 2 In the blockchain data business model diagram shown, business models are established for evidence chain A, evidence chain B and supply chain finance chain C respectively. For the evidence chain, the evidence party and evidence can be defined as entities, and the evidence party information and evidence information can be defined as attributes. As shown in the figure, Company A and Company C are evidence parties, including attributes such as company name and company code. Evidence 001 and Evidence 01 are on-chain evidence, including attributes such as evidence number, evidence time, and evidence details. For the supply chain finance chain, supply chain participants and accounts receivable are defined as entities, participant information and accounts receivable details are defined as attributes, and the relationship between participants, the relationship between participants and accounts receivable, and the flow relationship of accounts receivable are defined as relationships, among which participant information, accounts receivable details, etc. are used as attributes. As shown in the figure, the supply chain participant entities include Company A, Bank A, Company B, and Company C. The attributes include information such as company name and company code. The accounts receivable entities include Accounts Receivable 001, Accounts Receivable 002, etc. The attributes include information such as accounts receivable number, amount, and status. The relationship entities include the relationship between supply chain participants, the relationship between supply chain participants and the issuance and holding of accounts receivable, and the flow relationship of accounts receivable. The relationship attributes include serial number, type, time, etc.

[0042] The knowledge extraction stage includes data acquisition and information extraction. Data acquisition refers to obtaining data from the blockchain. There are three methods for this: first, directly reading data from the blockchain's native database, which is often a key-value store; second, reading data from the blockchain's archived database. Currently, mainstream blockchain platforms all have data archiving capabilities. To facilitate data use, archived data is often stored in relational databases; and third, obtaining data through query APIs provided by the blockchain platform. Information extraction involves extracting entities, attributes, and relationships from the acquired blockchain data according to the established business model to obtain the basic data needed to construct the knowledge graph.

[0043] In the knowledge fusion stage, in reality, the same entity may have different information stored in different blockchains, so it is necessary to perform data cleaning through data preprocessing and merge entities using entity fusion technology. For example, in the supply chain finance scenario blockchain network, the information stored in different blockchains for the same enterprise may be the company's full name, company abbreviation, or company organization code, so the same entity from different blockchains needs to be merged. Figure 3 Shown Figure 2 The result of the fusion of Evidence Chain A, Evidence Chain B, and Supply Chain Finance Chain C. In this example, the information of Enterprise A and Enterprise C is merged. Data preprocessing includes standardizing data formats, processing sensitive information, and filtering redundant data. Knowledge fusion techniques include entity alignment, entity linking, entity disambiguation, and the merging of entity attributes and relationships.

[0044] In the knowledge storage stage, the fused data is stored based on a graph structure, and the attribute graph model is used to convert entities, relationships, and attribute information into vertex, edge, attribute, and label data. The graph database uses mainstream graph database products, such as neo4j. Figure 3 As shown in the figure, the attribute graph model is used to transform the data after knowledge fusion. The vertices include enterprises, accounts receivable, and evidence. The edges include the relationship between enterprises, the relationship between enterprises and evidence, the relationship between enterprises and accounts receivable, and the relationship between accounts receivable and accounts receivable. The attributes include enterprise information, evidence information, accounts receivable information and other attributes.

[0045] In the knowledge calculation stage, based on the blockchain data knowledge graph established in the above steps, specific data supervision rules and corresponding reasoning logic are defined according to the actual supervision and audit regulations. Knowledge reasoning and graph mining methods are used to obtain reasoning conclusions to achieve supervision and audit purposes. Data supervision rules include sensitive word detection, transaction risk detection, personal sensitive information detection and other rules. Knowledge reasoning and graph mining methods include keyword retrieval, graph query retrieval, association analysis, path search, node classification and other methods. Figure 3 For example, we need to supervise the on-chain evidence storage information and the capital flow chain in the supply chain:

[0046] Sensitive word detection and supervision rules are defined for the on-chain evidence information. If it is necessary to globally search for all stored sensitive information, the corresponding rule is a full-graph search of vertices whose vertex type is evidence and whose attributes contain corresponding sensitive words. The supervisory user only needs to enter the sensitive word information to obtain the required evidence results through the rules; if it is necessary to search for all on-chain evidence of enterprise A, the corresponding rule is a vertex search of vertices whose vertex type is evidence and whose attributes contain corresponding sensitive words that are once associated with enterprise A. The supervisory user only needs to enter the corresponding enterprise name and sensitive word information to obtain the required evidence results through the rules.

[0047] Regulatory rules are defined for the capital flow chain in the supply chain. Assume that company B now faces major operating risks and needs to test its capital chain. In the supply chain financial chain C, transactions between supply chain participants are conducted through accounts receivable. Therefore, the corresponding regulatory rules are path search algorithms that include accounts receivable held by company B in the figure and the relationship type is accounts receivable transaction type. As shown in the figure, company B holds accounts receivable 003. According to the rules, the entire path from accounts receivable 001 to accounts receivable 004 can be found. The path contains the holder information of each accounts receivable, including company A, bank A, company C, and the transfer information of accounts receivable, including the split number of accounts receivable, transfer amount, etc. The resulting data is directly used by regulators.

[0048] In one embodiment, a knowledge graph-based blockchain data supervision system is provided, comprising the following modules:

[0049] Knowledge modeling module: defines business entities, relationships, and attributes based on the business meaning and data types of different blockchains, and establishes business models;

[0050] Knowledge extraction module: obtains transaction data from each chain and extracts corresponding entity, relationship, and attribute data based on the established business model;

[0051] Knowledge fusion module: This module cleanses the extracted data through data preprocessing and merges entities, entity attributes, and relationships from different blockchains through knowledge fusion technology.

[0052] Knowledge storage module: Uses a graph-based storage method to store entities, relationships, and attribute data in a graph database, indexes relevant business identification information, and obtains a blockchain data knowledge graph;

[0053] Knowledge computing module: Based on the constructed blockchain data knowledge graph, define data supervision rules and corresponding reasoning logic, and use graph-based knowledge reasoning and graph mining methods to quickly obtain results based on supervision rules.

[0054] In one embodiment, a computer device is proposed, including a memory and a processor, wherein computer-readable instructions are stored in the memory. When the computer-readable instructions are executed by the processor, the processor executes the steps of the blockchain data supervision method based on the knowledge graph in the above-mentioned embodiments.

[0055] In one embodiment, a storage medium storing computer-readable instructions is provided. When executed by one or more processors, the computer-readable instructions cause the one or more processors to perform the steps of the knowledge graph-based blockchain data supervision method described in each of the above embodiments. The storage medium may be a non-volatile storage medium.

[0056] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0057] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A blockchain data supervision method based on knowledge graph, characterized in that: The method includes: Knowledge modeling: Define business entities, relationships, and attributes based on the business meanings and data types of different blockchains to establish a business model; for knowledge modeling of blockchain data, manually establish entities, relationships, and attributes based on the business types on the chain and the entity definitions and transaction definitions of the contracts in the blockchain; the entities include the depositor, the deposit, the supply chain participants, and accounts receivable; the attributes include depositor information, deposit information, participant information, and accounts receivable details; the depositor information includes the company name and company code; the deposit information includes the deposit number, deposit time, and deposit details; the participant information includes the company name and company code; the accounts receivable details include the accounts receivable number, amount, and status; the relationships include the relationship between companies, the relationship between companies and deposits, the relationship between companies and accounts receivable, and the relationship between accounts receivable and accounts receivable; Knowledge extraction: obtaining transaction data from each chain and extracting corresponding entity, relationship, and attribute data based on the established business model; knowledge extraction from blockchain data includes data acquisition and information extraction; There are three ways to obtain data: one is to directly read the data from the blockchain native database; the second is to read the data from the blockchain data archive database; the third is to obtain data through the query API provided by the blockchain platform; The information extraction is to extract entities, attributes and relationships from the acquired blockchain data according to the established business model to obtain the basic data for the knowledge graph to be constructed; Knowledge fusion: The extracted data is cleaned through data preprocessing, and the data from different blockchains are merged into entities, entity attributes, and relationships through knowledge fusion technology. The data preprocessing includes data format standardization, sensitive information processing, and redundant data filtering. The knowledge fusion technology includes entity alignment, entity linking, entity disambiguation, and the merging of entity attributes and relationships. Knowledge storage uses a graph-based storage method to store entities, relationships, and attribute data in a graph database, indexes relevant business identification information, and obtains a blockchain data knowledge graph; Knowledge computing, based on the constructed blockchain data knowledge graph, defines data supervision rules and corresponding reasoning logic, and adopts graph-based knowledge reasoning and graph mining methods to quickly obtain results based on supervision rules.

2. A blockchain data supervision method based on knowledge graph according to claim 1, characterized in that: The knowledge storage is to store the fused data based on a graph structure, and use an attribute graph model to convert entities, relationships, and attribute data into vertex, edge, attribute, and label data. After the data is stored in the graph database, an index is established for the relevant business identification field.

3. A blockchain data supervision method based on knowledge graph according to claim 1, characterized in that: The knowledge calculation is based on the constructed blockchain data knowledge graph, defines specific data supervision rules and corresponding reasoning logic according to the actual supervision and audit regulations, and uses knowledge reasoning and graph mining methods to obtain reasoning conclusions to achieve supervision and audit purposes.

4. A blockchain data supervision method based on knowledge graph according to claim 3, characterized in that: In the knowledge calculation, the data supervision rules include sensitive word detection, transaction risk detection, and personal sensitive information detection rules; the knowledge reasoning and graph mining methods include keyword retrieval, graph query retrieval, association analysis, path search, and node classification methods.

5. A blockchain data supervision system based on a knowledge graph implemented by the method according to any one of claims 1 to 4, characterized in that: The system includes the following modules: Knowledge modeling module: defines business entities, relationships, and attributes based on the business meaning and data types of different blockchains, and establishes business models; Knowledge extraction module: obtains transaction data from each chain and extracts corresponding entity, relationship, and attribute data based on the established business model; Knowledge fusion module: This module cleanses the extracted data through data preprocessing and merges entities, entity attributes, and relationships from different blockchains through knowledge fusion technology. Knowledge storage module: Uses a graph-based storage method to store entities, relationships, and attribute data in a graph database, indexes relevant business identification information, and obtains a blockchain data knowledge graph; Knowledge computing module: Based on the constructed blockchain data knowledge graph, define data supervision rules and corresponding reasoning logic, and use graph-based knowledge reasoning and graph mining methods to quickly obtain results based on supervision rules.

6. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps in the knowledge graph-based blockchain data supervision method as described in any one of claims 1 to 4.

7. A storage medium storing computer-readable instructions, which, when executed by one or more processors, causes the one or more processors to perform the steps of the knowledge graph-based blockchain data supervision method as described in any one of claims 1 to 4.

Images