Data processing method and device, equipment and storage medium

Abstract

The application relates to a data processing method, device and equipment and a storage medium, and relates to the technical field of computers, and is used for efficiently and reasonably counting transaction information related to a blockchain address, thereby facilitating detection of the blockchain address. The data processing method comprises the following steps: acquiring to-be-processed blockchain data; dividing the to-be-processed blockchain data according to a plurality of transaction occurrence times to obtain a plurality of to-be-processed subsets; performing a first operation on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets; the first operation comprises constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure; the feature information is used for representing the transaction behavior feature of each blockchain address in the to-be-processed subset; and the plurality of feature information is fused to obtain a processing result of the to-be-processed blockchain data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and particularly relates to a data processing method and device, equipment and storage medium. BACKGROUND

[0002] In the blockchain technology, a transaction is usually completed based on a blockchain address. Considering that the blockchain address is usually anonymous, the blockchain address is detected through transaction information related to the blockchain address, so as to improve the legality and security of the transaction.

[0003] However, the data volume of the transaction information related to the blockchain address in the blockchain is usually large, and is inconvenient for statistical processing. Therefore, an effective method needs to be explored to process the transaction information related to the blockchain address, so as to support the detection of the blockchain address. SUMMARY

[0004] The present application provides a data processing method and device, equipment and storage medium, so as to at least efficiently and reasonably statistically process the transaction information related to the blockchain address, thereby facilitating the detection of the blockchain address. The technical scheme of the present application is as follows:

[0005] According to a first aspect of an embodiment of the present application, a data processing method is provided, comprising: obtaining to-be-processed blockchain data; the to-be-processed blockchain data comprises a plurality of blockchain addresses and a plurality of blockchain transactions, and a plurality of transaction occurrence times corresponding to the plurality of blockchain transactions one by one; there is an association relationship between the blockchain address and at least one blockchain transaction; dividing the to-be-processed blockchain data according to the plurality of transaction occurrence times, to obtain a plurality of to-be-processed subsets; respectively performing a first operation on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets one by one; the first operation comprises constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure; the feature information is used to represent the transaction behavior feature of each blockchain address in the to-be-processed subset; and performing fusion processing on the plurality of feature information to obtain a processing result of the to-be-processed blockchain data.

[0006] In an optional implementation manner, the method of constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure, specifically comprises:

[0007] determining a directed graph corresponding to the to-be-processed subset by taking each blockchain address and each blockchain transaction in the to-be-processed subset as a node, and taking the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset as a directed edge;

[0008] The node features of the nodes corresponding to the blockchain addresses in the statistical directed graph are counted to obtain feature information.

[0009] In an optional implementation, the method for dividing the to-be-processed blockchain data according to a plurality of transaction occurrence times to obtain a plurality of to-be-processed subsets includes the following steps.

[0010] The to-be-processed blockchain data is sorted according to the order of the plurality of transaction occurrence times to obtain sorted blockchain data.

[0011] The time period corresponding to the sorted blockchain data is divided to obtain a plurality of continuous sub-time periods, so as to divide the sorted blockchain data into a plurality of to-be-processed subsets corresponding to the plurality of sub-time periods.

[0012] In an optional implementation, the method for performing the first operation on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets includes the following steps.

[0013] A plurality of feature statistical tasks corresponding to the plurality of sub-time periods are determined, and the feature statistical tasks perform the first operation.

[0014] The plurality of task execution services are called to perform parallel processing on the plurality of feature statistical tasks to obtain the plurality of feature information.

[0015] In an optional implementation, the method for taking the association relationship between the blockchain addresses and the blockchain transactions in the to-be-processed subset as a directed edge includes the following steps.

[0016] When the association relationship between the blockchain address and the blockchain transaction is a transaction initiator, the directed edge is configured to be from the node corresponding to the blockchain address and to the node corresponding to the blockchain transaction.

[0017] When the association relationship between the blockchain address and the blockchain transaction is a transaction receiver, the directed edge is configured to be from the node corresponding to the blockchain transaction and to the node corresponding to the blockchain address.

[0018] In an optional implementation, the fusion processing includes at least one of the following: splicing the information of the blockchain addresses in different dimensions in different feature information and weighted summation of the information of the blockchain addresses in the same dimension in different feature information.

[0019] In an optional implementation, the node features include at least one of the following: out-degree information, in-degree information, degree centrality information, and hop count information of the node.

[0020] According to a second aspect of the embodiments of the present application, a data processing apparatus is provided, including an acquisition unit and a processing unit.

[0021] The acquisition unit is configured to acquire to-be-processed blockchain data; the to-be-processed blockchain data includes a plurality of blockchain addresses and a plurality of blockchain transactions, and a plurality of transaction occurrence times corresponding to the plurality of blockchain transactions; there is an association relationship between the blockchain address and at least one blockchain transaction;

[0022] The processing unit is configured to divide the to-be-processed blockchain data according to the plurality of transaction occurrence times to obtain a plurality of to-be-processed subsets;

[0023] The processing unit is further configured to respectively perform a first operation on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets; the first operation includes constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure; the feature information is used to represent the transaction behavior feature of each blockchain address in the to-be-processed subset;

[0024] The processing unit is further configured to perform fusion processing on the plurality of feature information to obtain a processing result of the to-be-processed blockchain data.

[0025] In an optional implementation manner, the processing unit is specifically configured to perform:

[0026] Determine a directed graph corresponding to the to-be-processed subset by taking each blockchain address and each blockchain transaction in the to-be-processed subset as a node, and taking the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset as a directed edge;

[0027] Obtain the feature information by counting the node features of the nodes corresponding to each blockchain address in the directed graph.

[0028] In an optional implementation manner, the processing unit is specifically configured to perform:

[0029] Perform sorting processing on the to-be-processed blockchain data according to the chronological order of the plurality of transaction occurrence times to obtain sorted blockchain data;

[0030] Divide the time period corresponding to the sorted blockchain data into a plurality of continuous sub-time periods to divide the sorted blockchain data into a plurality of to-be-processed subsets corresponding to the plurality of sub-time periods.

[0031] In an optional implementation manner, the processing unit is specifically configured to perform:

[0032] Determine a plurality of feature statistical tasks corresponding to the plurality of sub-time periods; the feature statistical task performs the first operation;

[0033] The plurality of feature statistical tasks are processed in parallel by calling a plurality of task execution services, and a plurality of feature information is obtained.

[0034] In an optional implementation, the processing unit is specifically configured to perform:

[0035] When the association between the blockchain address and the blockchain transaction is a transaction initiator, the directed edge is configured to be from a node corresponding to the blockchain address and to a node corresponding to the blockchain transaction;

[0036] When the association between the blockchain address and the blockchain transaction is a transaction receiver, the directed edge is configured to be from a node corresponding to the blockchain transaction and to a node corresponding to the blockchain address.

[0037] In an optional implementation, the fusion processing includes at least one of the following: splicing information of the blockchain address in different dimensions in different feature information and weighted summation of information of the blockchain address in the same dimension in different feature information.

[0038] In an optional implementation, the node feature includes at least one of the following: out-degree information, in-degree information, degree centrality information, and hop information of the node.

[0039] According to a third aspect of the embodiments of the present application, an electronic device is provided, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement any optional data processing method in the first aspect.

[0040] According to a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, and the computer-readable storage medium stores instructions, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device can execute any optional data processing method in the first aspect.

[0041] According to a fifth aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and the computer program is executed by an electronic device to implement any optional data processing method in the first aspect.

[0042] The embodiments of the present application provide the technical solutions of the first aspect, which bring at least the following beneficial effects: after obtaining the to-be-processed blockchain data including a plurality of blockchain addresses, a plurality of blockchain transactions and a plurality of transaction occurrence times, the to-be-processed blockchain data can be divided according to the plurality of transaction occurrence times to obtain a plurality of to-be-processed subsets, and a first operation is respectively performed on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets one by one, so as to further perform fusion processing on the plurality of feature information to obtain a processing result of the to-be-processed blockchain data, so as to determine the transaction behavior features of each blockchain address. The first operation includes constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure.

[0043] Based on this, when facing a large amount of blockchain data, the present application can divide the to-be-processed blockchain data into a plurality of to-be-processed subsets according to the transaction time for processing respectively, thereby improving the data processing efficiency. Moreover, the present application can construct the graph structure of each to-be-processed subset, and perform efficient and convenient statistical processing based on the graph structure, so as to determine the transaction behavior features of each blockchain address in the to-be-processed blockchain data. Therefore, the present application can be used to efficiently and reasonably perform statistical processing on the transaction information related to the blockchain address, thereby facilitating the detection of the blockchain address.

[0044] It should be noted that the technical effects brought by any one of the implementation manners of the second aspect to the fifth aspect can refer to the technical effects brought by the corresponding implementation manners in the first aspect, which will not be repeated here.

[0045] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present application. BRIEF DESCRIPTION OF DRAWINGS

[0046] The accompanying drawings incorporated into the specification and forming a part thereof illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the present application, and do not constitute an undue limitation on the present application.

[0047] Figure 1 is a schematic diagram of an implementation architecture shown by an embodiment of the present application;

[0048] Figure 2 is a flowchart of a data processing method shown by an embodiment of the present application;

[0049] Figure 3 is a structural schematic diagram of a directed graph shown by an embodiment of the present application;

[0050] Figure 4 is a schematic diagram of a data processing flow shown by an embodiment of the present application;

[0051] Figure 5 is a block diagram of a data processing apparatus according to an embodiment of the present application;

[0052] Figure 6 is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the ordinary person skilled in the art better understand the technical solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The implementation described in the following exemplary embodiments does not represent all implementations consistent with the present application. Rather, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0055] Before the data processing method provided by the present application is described in detail, the application scenarios and implementation environments involved in the present application are briefly introduced.

[0056] In the blockchain technology, transactions are usually completed based on blockchain addresses. Considering that blockchain addresses are often anonymous, blockchain addresses are usually detected through transaction information related to the blockchain addresses to improve the legality and security of transactions.

[0057] However, the data volume of transaction information related to the blockchain address in the blockchain is often large, which is not convenient for statistical processing. Therefore, it is necessary to explore effective methods to process transaction information related to the blockchain address, so as to support the detection (such as type identification, etc.) of the blockchain address.

[0058] To solve the above problems, the present application provides a data processing method. After obtaining the to-be-processed blockchain data including a plurality of blockchain addresses, a plurality of blockchain transactions and a plurality of transaction times, the to-be-processed blockchain data can be divided according to the plurality of transaction times to obtain a plurality of to-be-processed subsets, and a first operation is performed on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets, so as to further fuse the plurality of feature information to obtain a processing result of the to-be-processed blockchain data, so as to determine the transaction behavior characteristics of each blockchain address. The first operation includes constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure.

[0059] Based on this, when facing a large amount of blockchain data, the present application can divide the to-be-processed blockchain data into a plurality of to-be-processed subsets according to the transaction time for processing respectively, thereby improving the data processing efficiency. Moreover, the present application can construct the graph structure of each to-be-processed subset, and efficiently and conveniently perform statistical processing based on the graph structure, so as to determine the transaction behavior characteristics of each blockchain address in the to-be-processed blockchain data. Therefore, the present application can be used to efficiently and reasonably perform statistical processing on the transaction information related to the blockchain address, thereby facilitating the detection of the blockchain address.

[0060] Figure 1 The present application provides an implementation architecture diagram. As shown in Figure 1 The data processing system 100 provided by the present application embodiment can include an electronic device 101 and a plurality of blockchain nodes 102. The electronic device 101 can be in communication connection with any one of the blockchain nodes 102. The plurality of blockchain nodes 102 can constitute a blockchain system, and different blockchain nodes 102 can be in communication connection.

[0061] For ease of understanding, the present application takes the communication connection between the electronic device 101 and one of the blockchain nodes 102 as an example for description.

[0062] In one possible manner, Figure 1 The electronic device 101 in the present application can have functions of data statistical processing, data fusion processing and data parallel processing, and is used to provide data processing services.

[0063] Optionally, Figure 1 The electronic device 101 and the blockchain node 102 in the present application can be functional modules integrated in the same device, or can be two devices independently arranged. The present application embodiment does not limit this.

[0064] It is easy to understand that when electronic device 101 and blockchain node 102 are functional modules integrated into the same device, the communication between electronic device 101 and blockchain node 102 is based on communication between internal modules of the device. In this case, the communication process between the two is the same as the communication process between electronic device 101 and blockchain node 102 when they are independently installed.

[0065] For ease of understanding, this application is mainly explained by taking the example of the electronic device 101 and the blockchain node 102 being independently set up.

[0066] Alternatively, the electronic device 101 may be an independent device outside of the blockchain system composed of multiple blockchain nodes 102. Alternatively, the electronic device 101 may be a blockchain node and may form a blockchain system together with multiple blockchain nodes 102.

[0067] Optionally, Figure 1 The electronic device 101 may be a terminal, a server, or other device that supports functions such as model processing and data processing. Figure 1 The figure is merely an example of the device form of the electronic device 101 and does not constitute a limitation thereto.

[0068] In one possible way, Figure 1 The multiple blockchain nodes 102 in the blockchain can form a blockchain network, which can be used to provide data storage and data query services for medical institutions and banking institutions. The blockchain network can be regarded as a peer-to-peer (P2P) network.

[0069] In one possible approach, multiple blockchain nodes 102 may store the same blockchain. A blockchain generally includes multiple blocks and is a chain structure that maintains blocks, which can maintain a continuously growing, shareable, and tamper-proof data record. The blockchain can store transaction information of multiple blockchain addresses. For example, the blockchain may include: Figure 1 Multiple blocks are shown, including initial block 0, block 1, block 2, and block 3.

[0070] The blockchain can be private, consortium, or public. A private chain is a blockchain with strict access control. A consortium chain, also known as a community blockchain, is a blockchain where the consensus process is controlled by preselected nodes. In a consortium chain, all or some functions are only accessible to the nodes that store the consortium chain. Each node in the consortium chain can customize read, write, and query permissions based on their needs. A public chain is a blockchain where any individual or group in the world can send transactions and participate in the consensus process.

[0071] Optionally,Figure 1 The blockchain node 102 in the blockchain system 100 can be a terminal, a server, or other device supporting functions such as blockchain maintenance. Figure 1 The device form of the blockchain node 102 in the blockchain system 100 is only an example and does not constitute a limitation.

[0072] Optionally, when the electronic device 101 and the blockchain node 102 are terminals, the terminals can be devices such as mobile phones, tablet computers, desktop computers, laptop computers, handheld computers, notebook computers, ultra-mobile personal computers (UMPC), netbooks, and personal digital assistants (PDA). The terminals can also interact with users through one or more of a keyboard, a touchpad, a touch screen, a remote control, voice interaction, or a handwriting device. The specific form of the terminal is not limited in the present application.

[0073] When the electronic device 101 and the blockchain node 102 are servers, the servers can be a single server or a server cluster composed of multiple servers. In some embodiments, the server cluster can also be a distributed cluster. The specific implementation of the server is not limited in the present application.

[0074] For ease of understanding, the data processing method provided in the present application is specifically introduced below in combination with the accompanying drawings.

[0075] Figure 2 is a flowchart of a data processing method according to an exemplary embodiment, which is applied to the electronic device 101 in the blockchain system 100. As shown in Figure 1 , the data processing method includes S201-S204. Figure 2

[0076] S201, the electronic device acquires to-be-processed blockchain data.

[0077] The to-be-processed blockchain data can include multiple blockchain addresses, multiple blockchain transactions, and multiple transaction occurrence times corresponding to the multiple blockchain transactions. There can be an association relationship between one blockchain address and at least one blockchain transaction. There can be an association relationship between one blockchain transaction and two or more blockchain addresses.

[0078] In one possible manner, the blockchain address can be a string composed of multiple letters and / or numbers, used to support transactions on the blockchain. That is, the blockchain address can be a transaction address on the blockchain, also known as a wallet address. One blockchain transaction can be a transaction between two blockchain addresses, or a transaction between multiple blockchain addresses.​

[0079] In a possible implementation, the plurality of blockchain transactions included in the to-be-processed blockchain data can be sequentially sorted according to time sequence. Subsequently, when constructing the directed graph, the electronic device can sequentially construct nodes corresponding to the plurality of blockchain transactions, and nodes corresponding to the blockchain addresses connected between the plurality of blockchain transactions, so that the nodes in the directed graph are connected according to time sequence, to facilitate determination of the sequence relationship between the plurality of blockchain transactions, thereby effectively supporting detection of the blockchain address.

[0080] In a possible implementation, in combination with Figure 1 The electronic device can obtain the blockchain from the blockchain node in real time or periodically, and parse the obtained blockchain to obtain transaction information related to the blockchain address. The electronic device can be configured with a storage module, and the transaction information related to the blockchain address can be stored in the configured storage module as to-be-processed blockchain data.

[0081] Based on this, the electronic device can read the to-be-processed blockchain data from the configured storage module when triggered by a timing trigger instruction or a condition trigger instruction, to perform statistical processing.

[0082] Alternatively, the electronic device can also be configured with an input module such as a keyboard and a mouse. Based on this, the staff can also perform a feature statistical operation through the input module of the electronic device, for example, click a statistical button. In response to the feature statistical operation, the electronic device can read the to-be-processed blockchain data from the configured storage module to perform statistical processing.

[0083] S202, the electronic device divides the to-be-processed blockchain data according to the plurality of transaction occurrence times, to obtain a plurality of to-be-processed subsets.

[0084] Optionally, the electronic device can sort the to-be-processed blockchain data according to the sequence of the plurality of transaction occurrence times, to obtain sorted blockchain data, and further divide the time period corresponding to the sorted blockchain data to obtain a plurality of continuous sub-time periods, to divide the sorted blockchain data into a plurality of to-be-processed subsets corresponding to the plurality of sub-time periods.

[0085] Alternatively, the electronic device can also use a division clustering algorithm, configure a plurality of time points as a plurality of clustering centers, and cluster the plurality of transaction occurrence times to obtain a plurality of clusters corresponding to the plurality of clustering centers, and further determine the blockchain transactions corresponding to each cluster to obtain a plurality of to-be-processed subsets corresponding to the plurality of clusters.

[0086] In a possible implementation, the to-be-processed subset can include two or more blockchain addresses and at least one blockchain transaction.

[0087] S203, the electronic device performs a first operation on each to-be-processed sub-set respectively to obtain a plurality of feature information corresponding to the plurality of to-be-processed sub-sets.

[0088] The first operation includes constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed sub-set, and determining the feature information of the to-be-processed sub-set according to the graph feature of the graph structure. The feature information of the to-be-processed sub-set can be used to represent the transaction behavior characteristics of each blockchain address in the to-be-processed sub-set.

[0089] Optionally, the graph structure constructed by the electronic device according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed sub-set can be an undirected graph or a directed graph.

[0090] In one possible implementation, when the electronic device constructs an undirected graph according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed sub-set, the electronic device can construct nodes corresponding to each blockchain address and nodes corresponding to each blockchain transaction in the undirected graph, and connect each blockchain address and each blockchain transaction having an association relationship to obtain the undirected graph.

[0091] In one possible implementation, when a directed graph is constructed according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed sub-set, the electronic device can construct nodes corresponding to each blockchain address and nodes corresponding to each blockchain transaction in the directed graph, and construct directed edges connecting each node according to the association relationship between each blockchain address and each blockchain transaction to determine the directed graph corresponding to the to-be-processed blockchain data.

[0092] In one possible implementation, the graph feature of the graph structure can be a node feature of each node in the graph structure. For example, the number of undirected edges or directed edges connected to each node.

[0093] In one possible implementation, when the graph structure is an undirected graph, the electronic device can determine the feature information of the to-be-processed blockchain data according to the graph feature of the graph structure by counting the degree information and hop count information of the nodes corresponding to each blockchain address in the undirected graph.

[0094] In one possible implementation, when the graph structure is a directed graph, the electronic device can determine the feature information of the to-be-processed blockchain data according to the graph feature of the graph structure by counting the out-degree information of the nodes corresponding to each blockchain address in the directed graph, counting the in-degree information of the nodes corresponding to each blockchain address in the directed graph, counting the degree centrality information of the nodes corresponding to each blockchain address in the directed graph, and the like, thereby obtaining the feature information representing the transaction behavior characteristics of each blockchain address.

[0095] Exemplarily, the out-degree information of the node corresponding to the blockchain address can be used to represent the number of blockchain transactions initiated by the blockchain address, and can also be understood as the transaction demand degree of the user corresponding to the blockchain address. The in-degree information of the node corresponding to the blockchain address can be used to represent the number of blockchain transactions responded by the blockchain address, and can also be understood as the transaction response capability of the user corresponding to the blockchain address.

[0096] In S204, the electronic device performs fusion processing on the plurality of feature information to obtain a processing result of the to-be-processed blockchain data.

[0097] In the fusion processing, the information of the blockchain address in different dimensions in different feature information can be spliced, and / or the information of the blockchain address in the same dimension in different feature information can be weighted and summed.

[0098] In a possible manner, the processing result can be used to represent the transaction behavior characteristics of each blockchain address in the to-be-processed blockchain data.

[0099] In a possible manner, in the electronic device, the feature information can be represented by a vector. Based on this, when the electronic device performs fusion processing on the plurality of feature information, the vectors of the same blockchain address in different feature information can be merged into one vector. When the electronic device splices the information of the same blockchain address in different dimensions in different feature information, the vector values of the information in different dimensions can be configured in the vector of the same blockchain address. When the electronic device performs weighted summation on the information of the same blockchain address in the same dimension in different feature information, the vector values of the information in the same dimension can be weighted and summed.

[0100] Optionally, when the electronic device performs weighted summation on the information in the same dimension in different feature information, different weights can be configured according to the time corresponding to different feature information, or the same weight can be configured for different feature information.

[0101] Based on the method described in S201-S204, after the electronic device obtains the to-be-processed blockchain data including a plurality of blockchain addresses, a plurality of blockchain transactions, and a plurality of transaction occurrence times, the electronic device can divide the to-be-processed blockchain data according to the plurality of transaction occurrence times to obtain a plurality of to-be-processed subsets, and perform a first operation on each to-be-processed subset to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets one by one, so as to further perform fusion processing on the plurality of feature information to obtain a processing result of the to-be-processed blockchain data, and determine the transaction behavior characteristics of each blockchain address. The first operation includes constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure.

[0102] Based on this, when facing a large amount of blockchain data, the application can divide the to-be-processed blockchain data into multiple to-be-processed subsets according to transaction time for separate processing, thereby improving data processing efficiency. Moreover, the application can construct a graph structure of each to-be-processed subset, and efficiently and conveniently perform statistical processing based on the graph structure, so as to determine the transaction behavior characteristics of each blockchain address in the to-be-processed blockchain data. Therefore, the application can be used to efficiently and reasonably perform statistical processing on the transaction information related to the blockchain address, thereby facilitating detection of the blockchain address.

[0103] In one embodiment, when the electronic device performs the first operation on one to-be-processed subset in S203, that is, the electronic device constructs a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determines the characteristic information of the to-be-processed blockchain data according to the graph feature of the graph structure, the application embodiment provides an optional implementation manner, including S301-S302.

[0104] S301, the electronic device determines a directed graph corresponding to the to-be-processed subset by taking each blockchain address and each blockchain transaction in the to-be-processed subset as a node, and taking the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset as a directed edge.

[0105] In one possible manner, when the association relationship between the blockchain address and the blockchain transaction is the transaction initiator, the directed edge between the blockchain address and the blockchain transaction can be from the node corresponding to the blockchain address and point to the node corresponding to the blockchain transaction. When the association relationship between the blockchain address and the blockchain transaction is the transaction receiver, the directed edge between the blockchain address and the blockchain transaction can be from the node corresponding to the blockchain transaction and point to the node corresponding to the blockchain address.

[0106] In one possible manner, the directed graph corresponding to the to-be-processed subset can include multiple subgraphs. The subgraphs can not be connected. One subgraph can include at least three nodes, that is, two nodes corresponding to blockchain addresses and one node corresponding to a blockchain transaction.

[0107] In one possible example, as shown in FIG. 3, the to-be-processed subset can include three nodes, that is, two nodes corresponding to blockchain addresses and one node corresponding to a blockchain transaction. Figure 3As shown, it is a structure schematic diagram of a directed graph provided by an embodiment of the present application. It is assumed that the to-be-processed sub-set includes blockchain addresses a1, a2, a3 and a4, and blockchain transactions b1 and b2. The blockchain addresses a1 and a2 can have an association relationship with the blockchain transaction b1, and the blockchain addresses a1 and a2 are transaction initiators. The blockchain address a3 can have an association relationship with the blockchain transactions b1 and b2 respectively, and the association relationship between the blockchain address a3 and the blockchain transaction b1 is a transaction receiver, and the association relationship between the blockchain address a3 and the blockchain transaction b2 is a transaction initiator. The blockchain address a4 can have an association relationship with the blockchain transaction b2, and the blockchain address a4 is a transaction receiver.

[0108] Therefore, in the directed graph corresponding to the to-be-processed sub-set, the node corresponding to a1 and the node corresponding to a2 can be connected with the node corresponding to b1 through the directed edges pointing to the node corresponding to b1 respectively. The node corresponding to b1 and the node corresponding to a3 can be connected through the directed edge pointing to the node corresponding to a3. The node corresponding to a3 and the node corresponding to b2 can be connected through the directed edge pointing to the node corresponding to b1. The node corresponding to b2 and the node corresponding to a4 can be connected through the directed edge pointing to the node corresponding to a4.

[0109] In S302, the electronic device counts the node features of the nodes corresponding to the blockchain addresses in the directed graph, to obtain the feature information.

[0110] In a possible manner, the node features can include at least one of the following: out-degree information, in-degree information, degree centrality information and hop count information of the node.

[0111] The out-degree information of the node corresponding to the blockchain address can be used to represent the number of directed edges starting from the node corresponding to the blockchain address.

[0112] The in-degree information of the node corresponding to the blockchain address can be used to represent the number of directed edges pointing to the node corresponding to the blockchain address.

[0113] The degree centrality information of the node corresponding to the blockchain address can be used to represent the association degree of the node corresponding to the blockchain address with other nodes in the directed graph, which can be the sum of the out-degree information and the in-degree information of the node corresponding to the blockchain address.

[0114] The hop count information of the node corresponding to the blockchain address can be used to represent the number of nodes connected by the directed edges starting from the node corresponding to the blockchain address.

[0115] In an embodiment, in S202 described above, that is, when the electronic device divides the to-be-processed blockchain data according to the plurality of transaction occurrence times to obtain a plurality of to-be-processed sub-sets, an optional implementation manner is provided, which includes S401-S402.

[0116] S401, the electronic device sorts the to-be-processed blockchain data in the order of the transaction occurrence times of the blockchain transactions in the to-be-processed blockchain data to obtain sorted blockchain data.

[0117] In a possible manner, the electronic device can sort the blockchain transactions in the to-be-processed blockchain data in the order of the transaction occurrence times of the blockchain transactions to obtain sorted blockchain data.

[0118] Based on this, when the directed graph is subsequently constructed, the electronic device can sequentially construct the nodes corresponding to the blockchain transactions and the nodes corresponding to the blockchain addresses connected between the blockchain transactions in the directed graph in the order of time, so that the nodes in the directed graph are connected in the order of time, to facilitate determination of the occurrence order between the blockchain transactions, thereby effectively supporting detection of the blockchain addresses.

[0119] In a possible manner, the electronic device can further perform preprocessing such as variable coding on the to-be-processed blockchain data to facilitate construction of the directed graph.

[0120] S402, the electronic device divides a time period corresponding to the sorted blockchain data into a plurality of continuous sub-time periods to divide the sorted blockchain data into a plurality of to-be-processed sub-sets corresponding to the plurality of sub-time periods.

[0121] In a possible manner, the start time of the time period corresponding to the sorted blockchain data can be the transaction occurrence time of the first blockchain transaction in the sorted blockchain data, and the end time can be the transaction occurrence time of the last blockchain transaction in the sorted blockchain data.

[0122] It should be noted that in the case where the data amount of the sorted blockchain data is large, the sorted blockchain data is divided into a plurality of data blocks to process the plurality of data blocks in parallel, thereby improving the efficiency of the feature statistical processing process.

[0123] Based on this, the electronic device can divide the time period corresponding to the sorted blockchain data into a plurality of continuous sub-time periods. The electronic device can divide the sorted blockchain data into a plurality of to-be-processed blockchain data corresponding to the plurality of sub-time periods. That is, the data of the sorted blockchain data in each sub-time period can be regarded as a to-be-processed blockchain data.

[0124] Optionally, the lengths of the plurality of sub-time periods can be the same or different. The embodiments of the present application do not limit this.

[0125] In one embodiment, in the S203, the electronic device performs the first operation on each to-be-processed sub-set to obtain a plurality of feature information corresponding to the plurality of to-be-processed sub-sets, the present embodiment provides an optional implementation manner, including: S501-S502.

[0126] S501, the electronic device determines a plurality of feature statistical tasks corresponding to a plurality of sub-time periods.

[0127] The feature statistical task can be used to construct a graph structure corresponding to the to-be-processed sub-set and determine the feature information of the to-be-processed sub-set. That is, the feature statistical task can be used to perform the first operation.

[0128] In one implementation manner, in order to improve the efficiency of the feature statistical processing process, the electronic device can configure a plurality of feature statistical tasks corresponding to a plurality of sub-time periods, so as to support parallel processing of the blockchain data in the to-be-processed sub-set corresponding to each sub-time period, thereby avoiding the problem of low processing efficiency caused by large amount of to-be-processed blockchain data.

[0129] In one possible manner, one feature statistical task can include the start time and end time of one sub-time period, and the storage location information of the sorted blockchain data. Alternatively, one feature statistical task can also include the storage location information of the to-be-processed sub-set corresponding to one sub-time period. Based on this, the electronic device can obtain the blockchain data corresponding to each feature statistical task when performing each feature statistical task.

[0130] In one possible manner, the electronic device can create a task queue and configure a plurality of feature statistical tasks in the task queue according to the time sequence.

[0131] In one possible example, the length of each sub-time period is the same, and the length of the sub-time period (also referred to as the time window) is S. If the length T of the time period corresponding to the sorted blockchain data cannot be divided by S, the number of the plurality of feature statistical tasks can be obtained by dividing T by S and directly counting the value after the decimal point as 1.

[0132] If the length T of the time period corresponding to the sorted blockchain data can be divided by S, the number of the plurality of feature statistical tasks can be obtained by dividing T by S. In this case, the start time of each feature statistical task can be x, and the end time can be obtained by adding S to x. x can start from the start time of the time period corresponding to the sorted blockchain data and change by S in turn.

[0133] S502, the electronic device calls a plurality of task execution services to perform parallel processing on the plurality of feature statistical tasks to obtain a plurality of feature information.

[0134] The task execution service can be a control flow for executing the feature statistics task. For example, the task execution service can be a thread.

[0135] Optionally, the number of the plurality of task execution services and the number of the plurality of feature statistics tasks can be the same or different. Embodiments of the present application do not limit this.

[0136] In one possible manner, the electronic device can be pre-configured with a plurality of task execution services. When one task execution service is executed, a graph structure corresponding to one to-be-processed sub-set can be constructed, and the feature information of the one to-be-processed sub-set can be determined. Based on this, the electronic device can execute the plurality of task execution services in parallel, and perform parallel processing on the plurality of feature statistics tasks, to obtain the graph structure corresponding to each to-be-processed sub-set, and determine the feature information of each to-be-processed sub-set.

[0137] It should be understood that the implementation manner of constructing a graph structure corresponding to one to-be-processed sub-set by one task execution service, and determining the feature information of the one to-be-processed sub-set can refer to the specific description in S301-S302, which will not be repeated here.

[0138] In one implementable manner, when the task execution service is a thread, the electronic device can create a plurality of threads to execute the plurality of feature statistics tasks in parallel through the plurality of threads. Specifically, each thread can independently read the feature statistics task in the task queue, and execute the read feature statistics task. After the execution of one feature statistics task is completed, each thread can continue to read new tasks from the task queue. The number of the plurality of threads can be reasonably configured by the worker as needed. For example, the number of the plurality of threads can be 10 or 100.

[0139] In one possible manner, when the electronic device executes one feature statistics task, the electronic device can read the blockchain data covered by the sub-time period corresponding to the one feature statistics task (i.e., the to-be-processed sub-set) from the sorted blockchain data.

[0140] In one embodiment, when the electronic device takes the association relationship between each blockchain address and each blockchain transaction in the to-be-processed sub-set as a directed edge, an optional implementation manner provided by embodiments of the present application includes S601-S602.

[0141] S601, when the association relationship between the blockchain address and the blockchain transaction is a transaction initiator, the electronic device configures the directed edge as starting from the node corresponding to the blockchain address and pointing to the node corresponding to the blockchain transaction.

[0142] S602, when the association relationship between the blockchain address and the blockchain transaction is a transaction receiver, the electronic device configures the directed edge to be from the node corresponding to the blockchain transaction and to the node corresponding to the blockchain address.

[0143] In an embodiment, as shown in FIG. 6, a schematic diagram of a data processing flow provided by an embodiment of the present application is provided. The electronic device can initialize the start time corresponding to the data (i.e., the sorted blockchain data), the end time corresponding to the data, the number of threads, and the sub-time period corresponding to each feature statistical task, and start the threads. Figure 4

[0144] The electronic device can read the feature statistical tasks in the task queue through the threads, and execute the feature statistical tasks through the threads, to construct the directed graph based on the blockchain address and the blockchain transaction, and extract the feature information corresponding to each blockchain address in the directed graph.

[0145] After one task is executed, the electronic device can determine whether the task queue is empty. If the task queue is not empty (i.e., no), the electronic device reads a new feature statistical task from the task queue through the threads and executes it. If the task queue is empty (i.e., yes), the electronic device can end the threads. After all threads exit (i.e., all feature statistical tasks in the task queue are executed), the electronic device can obtain the feature information corresponding to each feature statistical task and perform feature fusion.

[0146] The present application constructs a directed graph based on the transaction information of the blockchain address, and constructs a discrete graph structure of the transaction information by slicing the time based on the continuous features of the transaction information in time. In addition, in order to accelerate the feature extraction speed, multi-thread task division can be performed. Therefore, the present application can effectively and efficiently extract features from the transaction information of the blockchain address.

[0147] Based on this, when facing a large amount of blockchain data, the present application can divide the to-be-processed blockchain data into multiple to-be-processed sub-sets according to the transaction time for separate processing, thereby improving the data processing efficiency. In addition, the present application can construct the graph structure of each to-be-processed sub-set, and perform efficient and convenient statistical processing based on the graph structure, to determine the transaction behavior features of each blockchain address in the to-be-processed blockchain data. Therefore, the present application can be used to efficiently and reasonably perform statistics on the transaction information related to the blockchain address, thereby facilitating the detection of the blockchain address.

[0148] ​The above describes the solutions provided by the embodiments of the present application from the method perspective. To implement the above functions, the electronic device includes hardware structures and / or software modules corresponding to each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of the examples described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0149] The embodiments of the present application can divide the functional modules of the electronic device according to the above method. For example, the electronic device can include functional modules corresponding to each function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in the form of hardware or software functional module. It should be noted that the division of modules in the embodiments of the present application is illustrative, and is only a logical functional division. Actual implementation can have another division manner.

[0150] For example, the embodiments of the present application also provide a data processing apparatus.

[0151] Figure 5 is a block diagram of a data processing apparatus according to an exemplary embodiment. Referring to Figure 5 , the data processing apparatus can include: comprising: an acquisition unit 701 and a processing unit 702;

[0152] The acquisition unit 701 is configured to perform acquisition of to-be-processed blockchain data; the to-be-processed blockchain data includes a plurality of blockchain addresses and a plurality of blockchain transactions, and a plurality of transaction occurrence times corresponding to the plurality of blockchain transactions; there is an association relationship between the blockchain address and at least one blockchain transaction;

[0153] The processing unit 702 is configured to perform division of the to-be-processed blockchain data according to the plurality of transaction occurrence times, to obtain a plurality of to-be-processed subsets;

[0154] The processing unit 702 is further configured to perform the first operation on each to-be-processed subset respectively, to obtain a plurality of feature information corresponding to the plurality of to-be-processed subsets; the first operation includes constructing a graph structure according to the association relationship between each blockchain address and each blockchain transaction in the to-be-processed subset, and determining the feature information of the to-be-processed subset according to the graph feature of the graph structure; the feature information is used to represent the transaction behavior feature of each blockchain address in the to-be-processed subset;

[0155] The processing unit 702 is further configured to perform fusion processing on the plurality of feature information to obtain a processing result of the to-be-processed blockchain data.

[0156] In an optional implementation, the processing unit 702 is specifically configured to perform the following.

[0157] Determine a directed graph corresponding to the to-be-processed sub-set by taking each blockchain address and each blockchain transaction in the to-be-processed sub-set as a node and taking the association relationship between each blockchain address and each blockchain transaction in the to-be-processed sub-set as a directed edge.

[0158] Obtain the feature information by counting the node features of the nodes corresponding to each blockchain address in the directed graph.

[0159] In an optional implementation, the processing unit 702 is specifically configured to perform the following.

[0160] Perform sorting processing on the to-be-processed blockchain data according to the chronological order of the plurality of transaction times to obtain sorted blockchain data.

[0161] Divide the time period corresponding to the sorted blockchain data into a plurality of continuous sub-time periods to divide the sorted blockchain data into a plurality of to-be-processed sub-sets corresponding to the plurality of sub-time periods.

[0162] In an optional implementation, the processing unit 702 is specifically configured to perform the following.

[0163] Determine a plurality of feature statistic tasks corresponding to the plurality of sub-time periods; and the feature statistic task performs a first operation.

[0164] Call the plurality of task execution services to perform parallel processing on the plurality of feature statistic tasks to obtain the plurality of feature information.

[0165] In an optional implementation, the processing unit 702 is specifically configured to perform the following.

[0166] When the association relationship between the blockchain address and the blockchain transaction is a transaction initiator, the directed edge is configured to start from the node corresponding to the blockchain address and point to the node corresponding to the blockchain transaction.

[0167] When the association relationship between the blockchain address and the blockchain transaction is a transaction receiver, the directed edge is configured to start from the node corresponding to the blockchain transaction and point to the node corresponding to the blockchain address.

[0168] In an optional implementation, the fusion processing includes at least one of the following: splicing information of the blockchain address in different dimensions in different feature information and weighted summation of information of the blockchain address in the same dimension in different feature information.

[0169] In an optional implementation, the node features include at least one of the following: out-degree information, in-degree information, degree centrality information, and hop information of the node.

[0170] As to the apparatus in the above-mentioned embodiments, the specific manners in which various modules perform operations have been described in detail in the embodiments of the method, and thus will not be described in detail here.

[0171] Figure 6 is a block diagram of an electronic device according to an exemplary embodiment.

[0172] As Figure 6 shown, the electronic device 200 includes but is not limited to a processor 210 and a memory 220.

[0173] The memory 220 described above is configured to store executable instructions of the processor 210. It can be understood that the processor 210 is configured to execute the instructions to implement the data processing method in the above-mentioned embodiments.

[0174] It should be noted that those skilled in the art can understand Figure 6 that the electronic device structure shown in the above-mentioned embodiments does not constitute a limitation on the electronic device, and the electronic device can include more or fewer components than Figure 6 shown, or combine certain components, or different component arrangements.

[0175] The processor 210 is the control center of the electronic device, and connects various parts of the electronic device through various interfaces and lines. The processor 210 performs various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 220 and calling data stored in the memory 220, thereby overall monitoring the electronic device. The processor 210 can include one or more processing units. Optionally, the processor 210 can integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, etc., and the modem processor mainly processes wireless communication. It can be understood that the above-mentioned modem processor can also not be integrated into the processor 210.

[0176] The memory 220 can be used to store software programs and various data. The memory 220 can mainly include a program storage area and a data storage area, wherein the program storage area can store operating systems, application programs required by at least one functional module, etc. In addition, the memory 220 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other volatile solid-state memory device.

[0177] In the example embodiment, a computer readable storage medium including instructions, for example, the memory 220 including instructions, is also provided, which can be executed by the processor 210 of the electronic device 200 to implement the data processing method in the above embodiment.

[0178] Optionally, the computer readable storage medium can be a non-transitory computer readable storage medium, for example, the non-transitory computer readable storage medium can be a Read-Only Memory (ROM), a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0179] In the example embodiment, the embodiment of the application also provides a computer program product including one or more instructions, which can be executed by the processor 210 of the electronic device 200 to complete the data processing method in the above embodiment.

[0180] It should be noted that the instructions in the above computer readable storage medium or the one or more instructions in the computer program product are executed by the processor 210 of the electronic device 200 to realize each process of the above data processing method embodiment, and can achieve the same technical effect as the above data processing method. To avoid repetition, it will not be repeated here.

[0181] Through the description of the above embodiments, those skilled in the art can clearly understand that, for the convenience and brevity of description, only the above division of each functional module is taken as an example for illustration, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete the above described full classification part or part of the function.

[0182] In several embodiments provided in the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the above described device embodiments are only schematic, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed each other can be through some interface, indirect coupling or communication connection between devices or units, which can be electrical, mechanical or other forms.

[0183] The units described as separate components may or may not be physically separate, and the components displayed as units may be a physical unit or multiple physical units, that is, may be located in one place, or also can be distributed to multiple different places. Part or all of the classified units can be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0184] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present alone, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0185] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application essentially or the part that contributes to the general technology or the whole classification or part of the technical solutions can be embodied in the form of a software product. The software product is stored in a storage medium, including a plurality of instructions for making a device (which can be a single-chip microcomputer, a chip, etc.) or a processor execute all or part of the steps of the method of each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk and various program code storage media.

[0186] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A data processing method, characterized in that: include: Obtaining blockchain data to be processed; the blockchain data to be processed includes multiple blockchain addresses and multiple blockchain transactions, and multiple transaction occurrence times corresponding to the multiple blockchain transactions; there is an association between the blockchain address and at least one of the blockchain transactions; Dividing the to-be-processed blockchain data according to the occurrence times of the multiple transactions to obtain multiple to-be-processed subsets; Performing a first operation on each of the subsets to be processed to obtain a plurality of feature information corresponding one-to-one to the plurality of subsets to be processed; the first operation includes constructing a graph structure based on the association relationship between each blockchain address and each blockchain transaction in the subset to be processed, and determining the feature information of the subset to be processed based on the graph features of the graph structure; The characteristic information is used to characterize the transaction behavior characteristics of each of the blockchain addresses in the subset to be processed; The multiple feature information are fused to obtain a processing result of the blockchain data to be processed.

2. The data processing method according to claim 1, wherein: The step of constructing a graph structure based on the association between each blockchain address and each blockchain transaction in the subset to be processed, and determining feature information of the subset to be processed based on graph features of the graph structure, includes: Determine a directed graph corresponding to the subset to be processed by taking each blockchain address and each blockchain transaction in the subset to be processed as a node, and taking the association between each blockchain address and each blockchain transaction in the subset to be processed as a directed edge; Counting node features of the nodes corresponding to each of the blockchain addresses in the directed graph to obtain the feature information.

3. The data processing method according to claim 1, wherein: The block chain data to be processed is divided according to the occurrence time of the multiple transactions to obtain multiple subsets to be processed, including: Sorting the to-be-processed blockchain data according to the chronological order of the multiple transactions to obtain sorted blockchain data; The time period corresponding to the sorted blockchain data is divided into a plurality of consecutive sub-time periods, so as to divide the sorted blockchain data into the plurality of to-be-processed subsets corresponding one-to-one to the plurality of sub-time periods.

4. The data processing method according to claim 3, wherein: The performing the first operation on each of the to-be-processed subsets to obtain a plurality of feature information corresponding one-to-one to the plurality of to-be-processed subsets includes: Determining a plurality of feature statistical tasks corresponding one-to-one to the plurality of sub-time periods; the feature statistical tasks performing the first operation; Multiple task execution services are called to process the multiple feature statistics tasks in parallel to obtain the multiple feature information.

5. The data processing method according to claim 2, wherein: The process of using the association between each blockchain address and each blockchain transaction in the to-be-processed subset as a directed edge includes: When the association relationship between the blockchain address and the blockchain transaction is the transaction initiator, the directed edge is configured to start from the node corresponding to the blockchain address and point to the node corresponding to the blockchain transaction; When the association relationship between the blockchain address and the blockchain transaction is a transaction recipient, the directed edge is configured to start from the node corresponding to the blockchain transaction and point to the node corresponding to the blockchain address.

6. The data processing method according to any one of claims 1 to 5, characterized in that: The fusion processing includes at least one of the following: splicing information of different dimensions in different feature information of the blockchain address and performing weighted summation on information of the same dimension in different feature information of the blockchain address.

7. The data processing method according to claim 2, characterized in that: The node characteristics include at least one of the following: out-degree information, in-degree information, degree centrality information and hop count information of the node.

8. A data processing device, characterized in that: include: Acquisition unit and processing unit; The acquisition unit is configured to execute acquisition of blockchain data to be processed; the blockchain data to be processed includes multiple blockchain addresses and multiple blockchain transactions, and multiple transaction occurrence times corresponding to the multiple blockchain transactions; there is an association relationship between the blockchain address and at least one of the blockchain transactions; The processing unit is configured to divide the to-be-processed blockchain data according to the occurrence times of the multiple transactions to obtain multiple to-be-processed subsets; The processing unit is further configured to perform a first operation on each of the to-be-processed subsets to obtain a plurality of feature information corresponding one-to-one to the plurality of to-be-processed subsets; the first operation comprises constructing a graph structure based on the association between each of the blockchain addresses and each of the blockchain transactions in the to-be-processed subsets, and determining the feature information of the to-be-processed subsets based on the graph features of the graph structure; The characteristic information is used to characterize the transaction behavior characteristics of each of the blockchain addresses in the subset to be processed; The processing unit is further configured to perform fusion processing on the multiple feature information to obtain a processing result of the blockchain data to be processed.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the data processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the data processing method according to any one of claims 1 to 7.

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