CN112801783A - Entity identification method and device based on digital currency transaction characteristics - Google Patents
Entity identification method and device based on digital currency transaction characteristics Download PDFInfo
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- G06N3/08—Learning methods
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
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- G06Q20/065—Private payment circuits, e.g. involving electronic currency used among participants of a common payment scheme using e-cash
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/382—Payment protocols; Details thereof insuring higher security of transaction
Abstract
The invention discloses an entity identification method and device based on digital currency transaction characteristics, wherein the method comprises the following steps: acquiring transaction characteristic information of each transaction address in a block chain transaction address set belonging to the same entity to be detected; clustering the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets; acquiring transaction information of the transaction addresses in each transaction address subset; analyzing the transaction information of the transaction addresses by using a preset big data analysis method to obtain the judgment result of each transaction address subset; and inputting the judgment result into an entity type identification model, and determining the type of the entity to be detected according to the identification result of the entity type identification model.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to an entity identification method and device based on digital currency transaction characteristics.
Background
The digital currency is a virtual electronic currency based on block chain technology, can be used for real goods and service transaction, is created, issued and circulated by means of verification and password technology, is derived based on a specific algorithm, has limited issuing amount and is encrypted to ensure safety, and is characterized by being issued, managed and circulated by using P2P peer-to-peer network technology.
Digital currency has currency attributes, and in a certain scenario, a transaction entity can use the digital currency to perform transactions, but the digital currency has a characteristic of anonymity compared with legal currency, so an entity identification method based on the digital currency transaction characteristics is urgently needed to distinguish an entity with illegal transaction characteristics from an entity with normal transaction characteristics, so that the purpose of identifying different entity types is achieved, and services are provided for regulatory agencies.
Disclosure of Invention
Therefore, the invention provides an entity identification method and device based on digital currency transaction characteristics, which are used for distinguishing entities with illegal transaction characteristics from entities with normal transaction characteristics, so that the purpose of identifying different entity types is achieved, and services are provided for a supervisory organization.
According to a first aspect, an embodiment of the present invention discloses an entity identification method based on digital currency transaction characteristics, including: acquiring transaction characteristic information of each transaction address in a block chain transaction address set belonging to the same entity to be detected; clustering the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets; acquiring transaction information of the transaction addresses in each transaction address subset; analyzing the transaction information of the transaction addresses by using a preset big data analysis method to obtain the judgment result of each transaction address subset; and inputting the judgment result into an entity type identification model, and determining the type of the entity to be detected according to the identification result of the entity type identification model.
Optionally, the obtaining a block chain transaction address set belonging to the same entity to be detected includes: acquiring a plurality of transaction addresses on a blockchain; and dividing the transaction addresses according to a preset address type determination method to obtain a plurality of block chain transaction address sets belonging to different entities to be detected.
Optionally, the big data analysis method comprises a logistic regression method or a decision tree method.
Optionally, the method for determining a preset address type includes: any of a plurality of methods of capturing internet information, analyzing the correlation of digital currency addresses, and inquiring information from entities corresponding to transaction addresses.
According to a second aspect, an embodiment of the present invention further discloses an entity identification apparatus based on digital currency transaction characteristics, including: the first acquisition module is used for acquiring the transaction characteristic information of each transaction address in the block chain transaction address set belonging to the same entity to be detected; the clustering module is used for clustering the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets; the second acquisition module is used for acquiring the transaction information of the transaction address in each transaction address subset; the judging module is used for analyzing the transaction information of the transaction addresses by using a preset big data analysis method to obtain a judging result of each transaction address subset; and the determining module is used for inputting the judging result into an entity type identification model and determining the type of the entity to be detected according to the identification result of the entity type identification model.
Optionally, the first obtaining module includes an obtaining submodule, configured to obtain a plurality of transaction addresses on a blockchain; and the dividing module is used for dividing the transaction addresses according to a preset address type determining method to obtain a plurality of block chain transaction address sets belonging to different entities to be detected.
Optionally, the big data analysis method comprises a logistic regression method or a decision tree method.
Optionally, the method for determining a preset address type includes: any of a plurality of methods of capturing internet information, analyzing the correlation of digital currency addresses, and inquiring information from entities corresponding to transaction addresses.
According to a third aspect, an embodiment of the present invention further discloses an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the method for entity identification based on digital currency transaction characteristics according to the first aspect or any one of the optional embodiments of the first aspect.
According to a fourth aspect, embodiments of the present invention also disclose a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of the digital money transaction characteristic-based entity identification method according to the first aspect or any one of the optional embodiments of the first aspect.
The technical scheme of the invention has the following advantages:
the entity identification method/device based on the digital currency transaction characteristics obtains transaction characteristic information of each transaction address in a blockchain transaction address set belonging to the same entity to be detected, clusters the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets, obtains the transaction information of the transaction addresses in each transaction address subset, analyzes the transaction information of the transaction addresses by using a preset big data analysis method to obtain a judgment result of each transaction address subset, inputs the judgment result into an entity type identification model, and determines the type of the entity to be detected according to the identification result of the entity type identification model. The method comprises the steps of obtaining transaction information of different types of transaction addresses of the same entity to be detected, analyzing the transaction information to obtain evaluation results of the different types of transaction addresses of the same entity to be detected, determining the type of the entity to be detected according to the multi-dimensional evaluation results, improving the accuracy of identification results of the type of the entity to be detected, and distinguishing the entity with illegal transaction characteristics from the entity with normal transaction characteristics, so that the purpose of identifying different entity types is achieved, and a monitoring mechanism can conveniently monitor the entities with different types.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart showing a specific example of an entity identification method based on digital money transaction characteristics according to an embodiment of the present invention;
FIG. 2 is a schematic block diagram of a specific example of an entity recognition apparatus based on digital money transaction characteristics according to an embodiment of the present invention;
fig. 3 is a diagram of a specific example of an electronic device in an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention discloses an entity identification method based on digital currency transaction characteristics, as shown in figure 1, the method comprises the following steps:
For example, the acquisition mode of the blockchain transaction address set belonging to the same entity to be detected may be a received transaction address set corresponding to the entity to be detected uploaded by the external user node. The method for acquiring the block chain transaction address set belonging to the same entity to be detected is not limited in the embodiment of the application, and can be determined by a person skilled in the art according to actual needs. The full-amount transaction characteristic information of each transaction address in the blockchain transaction address set is acquired, the acquired transaction characteristic information includes but is not limited to transaction hash, transaction time, transaction amount, transaction commission charge and the like, and a person skilled in the art can select the type of the transaction characteristic information to be acquired according to actual needs. The transaction characteristic information of each transaction address can be obtained in a manner that equipment for entity identification communicates with each transaction address, transaction data locally stored by a node corresponding to each transaction address is obtained, and then corresponding transaction characteristic information is obtained.
And step 102, clustering the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets.
For example, the transaction addresses included in the blockchain transaction address set are clustered according to the transaction characteristic information, and the transaction addresses are divided according to a preset classification index corresponding to the transaction characteristic information. If the transaction characteristic information is transaction time, the preset classification index corresponding to the transaction time can be used for classifying the transaction addresses according to the working time and the working time, dividing the transaction addresses transacted in the working time into a transaction address subset, and dividing the transaction addresses transacted in the working time into a transaction address subset. The dividing mode of the transaction address subset according to the transaction time is not limited, and can be determined by a person skilled in the art according to actual needs; when the transaction characteristic information is a transaction amount, the preset classification indexes corresponding to the transaction amount can classify the transaction addresses according to the size relationship between the output amount and the input amount, divide the transaction addresses with the output amount larger than the input amount into a transaction address subset, and divide the transaction addresses with the output amount smaller than the input amount into a transaction address subset. The division mode of the transaction address subset according to the transaction amount is not limited in the embodiment of the application, and can be determined by a person skilled in the art according to actual needs. In the embodiment of the application, the transaction addresses can be clustered by selecting transaction characteristic information of multiple types to obtain transaction address subsets of multiple different dimensions, so that the accuracy of subsequent entity type analysis results is improved.
And 103, acquiring the transaction information of the transaction address in each transaction address subset.
For example, the transaction information of the transaction address in each transaction address subset may include, but is not limited to, transaction frequency, transaction magnitude, transaction amount variation, and the like, and the type and amount of the transaction information of the acquired transaction address are not limited in the embodiments of the present application, and may be determined by those skilled in the art according to actual needs.
And 104, analyzing the transaction information of the transaction address by using a preset big data analysis method to obtain a judgment result of each transaction address subset.
Illustratively, the preset big data analysis method may be a logistic regression method or a decision tree method, or each transaction address subset may be judged by using a pre-trained neural network model according to the acquired transaction information data. The type of the preset big data analysis method is not limited in the embodiment of the application, and can be determined by a person skilled in the art according to actual needs. The characterization form of the judgment result may be a score or a probability magnitude, which is not limited in the embodiments of the present application.
For example, when the transaction frequency corresponding to the transaction address subset divided by the transaction time as the off-duty time is 100 times/hour, and the normal transaction frequency is 20 times/hour, the transaction address subset may be scored by actually obtaining the difference between the transaction frequency and the preset normal transaction frequency. Because the transaction address subset comprises a plurality of transaction address subsets, scoring results of the transaction address subsets with different dimensions can be obtained through a preset big data analysis method.
And 105, inputting the judgment result into an entity type identification model, and determining the type of the entity to be detected according to the identification result of the entity type identification model.
For example, the entity type identification model may be constructed by obtaining real data of each entity in each dimension in advance, inputting the real data into a preset big data analysis method to obtain a real score result, and inputting the real score result into the entity type identification model, that is, the entity type identification model stores real scores of each entity in each dimension. And when a scoring result corresponding to any entity to be detected is received, the entity type identification model compares the actually received scoring result of any entity to be detected with the pre-stored real scoring result of each entity, and the entity type with the highest similarity is used as the entity type of the entity to be detected.
According to the entity identification method based on the digital currency transaction characteristics, provided by the embodiment of the invention, the transaction information of the transaction addresses of different types of the same entity to be detected is obtained and analyzed, the judgment results of the transaction addresses of different types of the same entity to be detected are obtained, the type of the entity to be detected is determined according to the multidimensional judgment results, the accuracy of the identification result of the type of the entity to be detected is improved, the entity with illegal transaction characteristics is distinguished from the entity with normal transaction characteristics, the purpose of identifying different entity types is achieved, and the supervision of different types of entities by a supervision institution is facilitated.
As an optional embodiment of the present invention, the acquiring a blockchain transaction address set belonging to the same entity to be detected includes:
first, a plurality of transaction addresses on a blockchain is obtained.
For example, the acquisition manner of the transaction addresses on the blockchain may be that a device performing entity type identification accesses the blockchain network, and acquires the transaction addresses in the blockchain network through a web crawler technology. The acquisition mode of the transaction address is not limited in the embodiment of the application, and can be determined by a person skilled in the art according to actual needs.
Secondly, dividing the plurality of transaction addresses according to a preset address type determination method to obtain a plurality of block chain transaction address sets belonging to different entities to be detected.
Illustratively, the method for determining the preset address type includes: any of a plurality of methods of capturing internet information, analyzing the correlation of digital currency addresses, and inquiring information from entities corresponding to transaction addresses. The internet information capture method can determine the transaction address by capturing information transmitted to the corresponding transaction address or information sent from the transaction address so as to realize type division of the corresponding transaction address and divide the transaction address with the same function type into the same entity to be detected; the digital currency address correlation analysis method may be a transaction model in which a blockchain to which an acquired transaction address belongs is determined first, and the blockchains of different transaction models correspond to different correlation analysis methods, for example, if a bit currency is an UTXO model, the corresponding digital currency address correlation analysis method is to divide a plurality of input addresses of the same transaction into the same entity; the method for inquiring the information of the entity corresponding to the transaction address can be that an identity information acquisition instruction is sent to the transaction address through the acquired transaction address, and the transaction address is classified according to the identity information fed back by the acquired transaction address. The dividing mode of the transaction address is not limited in the application, and the division mode can be determined by a person skilled in the art according to actual needs.
The embodiment of the invention also discloses an entity identification device based on the digital currency transaction characteristics, as shown in figure 2, the device comprises:
the first obtaining module 201 is configured to obtain transaction characteristic information of each transaction address in a block chain transaction address set belonging to the same entity to be detected;
the clustering module 202 is configured to cluster the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets;
the second obtaining module 203 is configured to obtain transaction information of the transaction addresses in each subset of transaction addresses;
the evaluation module 204 is configured to analyze the transaction information of the transaction address by using a preset big data analysis method to obtain an evaluation result of each transaction address subset;
the determining module 205 is configured to input the evaluation result to an entity type identification model, and determine the type of the entity to be detected according to the identification result of the entity type identification model.
The entity recognition device based on the digital currency transaction characteristics obtains the judgment results of the transaction addresses of the same entity to be detected in different types by obtaining the transaction information of the transaction addresses of the same entity to be detected in different types for analysis, determines the type of the entity to be detected according to the multi-dimensional judgment results, improves the accuracy of the recognition result of the type of the entity to be detected, and distinguishes the entity with illegal transaction characteristics from the entity with normal transaction characteristics, thereby achieving the purpose of recognizing different entity types and facilitating the supervision of different types of entities by a supervision institution.
As an optional implementation manner of the present invention, the first obtaining module 201 includes: the acquisition submodule is used for acquiring a plurality of transaction addresses on the blockchain; and the dividing module is used for dividing the transaction addresses according to a preset address type determining method to obtain a plurality of block chain transaction address sets belonging to different entities to be detected.
As an optional embodiment of the present invention, the big data analysis method includes a logistic regression method or a decision tree method.
As an optional implementation manner of the present invention, the method for determining a preset address type includes: any of a plurality of methods of capturing internet information, analyzing the correlation of digital currency addresses, and inquiring information from entities corresponding to transaction addresses.
An embodiment of the present invention further provides an electronic device, as shown in fig. 3, the electronic device may include a processor 401 and a memory 402, where the processor 401 and the memory 402 may be connected by a bus or in another manner, and fig. 3 takes the connection by the bus as an example.
The memory 402, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the entity identification method based on digital currency transaction characteristics in the embodiments of the present invention. The processor 401 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 402, namely, implements the entity identification method based on digital currency transaction characteristics in the above method embodiments.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 401, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to processor 401 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 402 and when executed by the processor 401 perform a digital currency transaction feature based entity identification method as in the embodiment of fig. 1.
The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD) or a Solid State Drive (SSD), etc.; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (10)
1. An entity identification method based on digital currency transaction characteristics is characterized by comprising the following steps:
acquiring transaction characteristic information of each transaction address in a block chain transaction address set belonging to the same entity to be detected;
clustering the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets;
acquiring transaction information of the transaction addresses in each transaction address subset;
analyzing the transaction information of the transaction addresses by using a preset big data analysis method to obtain the judgment result of each transaction address subset;
and inputting the judgment result into an entity type identification model, and determining the type of the entity to be detected according to the identification result of the entity type identification model.
2. The method of claim 1, wherein obtaining the blockchain transaction address set belonging to the same entity to be detected comprises:
acquiring a plurality of transaction addresses on a blockchain;
and dividing the transaction addresses according to a preset address type determination method to obtain a plurality of block chain transaction address sets belonging to different entities to be detected.
3. The method of claim 1, wherein the big data analysis method comprises a logistic regression method or a decision tree method.
4. The method according to claim 2, wherein the method for determining the default address type comprises: any of a plurality of methods of capturing internet information, analyzing the correlation of digital currency addresses, and inquiring information from entities corresponding to transaction addresses.
5. An entity recognition apparatus based on digital currency transaction characteristics, comprising:
the first acquisition module is used for acquiring the transaction characteristic information of each transaction address in the block chain transaction address set belonging to the same entity to be detected;
the clustering module is used for clustering the transaction addresses in the blockchain transaction set according to the transaction characteristic information of each transaction address to obtain a plurality of transaction address subsets;
the second acquisition module is used for acquiring the transaction information of the transaction address in each transaction address subset;
the judging module is used for analyzing the transaction information of the transaction addresses by using a preset big data analysis method to obtain a judging result of each transaction address subset;
and the determining module is used for inputting the judging result into an entity type identification model and determining the type of the entity to be detected according to the identification result of the entity type identification model.
6. The apparatus of claim 5, wherein the first obtaining module comprises a obtaining sub-module configured to obtain a plurality of transaction addresses on a blockchain; and the dividing module is used for dividing the transaction addresses according to a preset address type determining method to obtain a plurality of block chain transaction address sets belonging to different entities to be detected.
7. The apparatus of claim 5, wherein the big data analysis method comprises a logistic regression method or a decision tree method.
8. The apparatus of claim 6, wherein the preset address type determining method comprises: any of a plurality of methods of capturing internet information, analyzing the correlation of digital currency addresses, and inquiring information from entities corresponding to transaction addresses.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the digital currency transaction characteristic based entity identification method of any one of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the digital money transaction characteristic-based entity identification method according to any one of claims 1 to 4.
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