A transaction address mapping method and device, electronic equipment and readable medium

Abstract

Embodiments of the present disclosure disclose a transaction address mapping method and device, electronic equipment and readable medium. The method comprises: obtaining transaction data of one or more transaction systems, initializing a support range and a confidence threshold; sorting the transaction data according to transaction time, and dividing the transaction data into multiple transaction combinations according to the transaction time; calculating the support of each transaction address, and screening target transaction addresses whose support meets the support range; calculating the confidence of the target transaction addresses and other transaction addresses respectively, screening target transaction addresses and other transaction addresses whose confidence is greater than the confidence threshold in pairs, and generating a mapping relationship. The transaction address mapping method of the embodiments of the present disclosure utilizes the active time of the transaction address in different transaction combinations, and obtains the mapping relationship between the transaction addresses based on the association rule algorithm including the support and the confidence.

Description

Technical Field

[0001] This disclosure relates to transaction address mapping methods, apparatus, electronic devices, and readable media. Background Technology

[0002] In blockchain systems, blockchain addresses are often anonymous and discrete. A complete blockchain transaction chain may span multiple blockchain transaction systems and / or off-chain payment systems. A single transaction may also be broken down into multiple transactions to achieve transaction anonymity, making it difficult to locate the true participants in the transaction chain. Furthermore, in off-chain payment systems, for privacy protection or system anonymization considerations, user identifiers may be used instead of real user information. Therefore, it is necessary to establish a mapping relationship between different transaction addresses of the same user across one or more transaction systems in order to identify risky users and risky transactions. Summary of the Invention

[0003] This disclosure provides a transaction address mapping method, apparatus, electronic device, and readable medium that can obtain the mapping relationship between transaction addresses based on the transaction graph of the transaction system by utilizing the active time of transactions.

[0004] To achieve the above technical objectives, the embodiments of this disclosure adopt the following technical solutions:

[0005] In a first aspect, embodiments of this disclosure provide a transaction address mapping method, the method comprising:

[0006] Acquire transaction data from one or more trading systems, and initialize the support range and confidence threshold, wherein the transaction data includes transaction time and transaction address;

[0007] The transaction data is sorted by transaction time and then divided into multiple transaction groups based on transaction time.

[0008] Calculate the support for each transaction address and filter target transaction addresses whose support meets the support range. The support is used to indicate the frequency of a transaction address in each transaction combination.

[0009] Calculate the confidence scores of the target transaction address and other transaction addresses separately. Pairwise filter the target transaction addresses and other transaction addresses with confidence scores greater than the confidence score threshold to generate a mapping relationship. The confidence score is used to indicate the probability that other transaction addresses and the target transaction address are in the same transaction combination.

[0010] In some possible implementations, the method further includes, before sorting the transaction data by transaction time:

[0011] The transaction data is processed to extract the transaction time and transaction address.

[0012] In some possible implementations, transactions are divided into multiple transaction groups based on transaction time, including:

[0013] Set the time window, trade start time, and trade end time;

[0014] Filter transaction data based on transaction start time and transaction end time;

[0015] The filtered transaction data is sliced ​​into multiple transaction combinations according to time windows.

[0016] In some possible implementations, the support range is defined by the maximum support and / or minimum support.

[0017] In some possible implementations, the support level for each transaction address is calculated, including:

[0018] Extract all transaction addresses within each transaction combination to form a correspondence between transaction combinations and transaction addresses, and calculate the support of each transaction address based on this correspondence.

[0019] Secondly, embodiments of this disclosure provide a transaction address mapping apparatus, the apparatus comprising:

[0020] The initialization module is used to acquire transaction data from one or more trading systems, and initialize the support range and confidence threshold, wherein the transaction data includes transaction time and transaction address;

[0021] The transaction processing module is used to sort transaction data by transaction time and divide it into multiple transaction groups based on transaction time.

[0022] The support calculation module is used to calculate the support of each transaction address and filter target transaction addresses whose support meets the support range. The support is used to indicate the frequency of a transaction address in each transaction combination.

[0023] The mapping generation module is used to calculate the confidence scores of the target transaction address and other transaction addresses respectively, filter the target transaction addresses and other transaction addresses with confidence scores greater than the confidence score threshold in pairs, and generate mapping relationships. The confidence score is used to indicate the probability that other transaction addresses and the target transaction address are in the same transaction combination.

[0024] In some possible implementations, the transaction processing module is further used for:

[0025] The transaction data is processed to extract the transaction time and transaction address.

[0026] In some possible implementations, the transaction processing module is further used for:

[0027] Set the time window, trade start time, and trade end time;

[0028] Filter transaction data based on transaction start time and transaction end time;

[0029] The filtered transaction data is sliced ​​into multiple transaction combinations according to time windows.

[0030] In some possible implementations, the support range is defined by the maximum support and / or minimum support.

[0031] In some possible implementations, the support calculation module is further used for:

[0032] Extract all transaction addresses within each transaction combination to form a correspondence between transaction combinations and transaction addresses, and calculate the support of each transaction address based on this correspondence.

[0033] Thirdly, embodiments of this application provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the transaction address mapping method as described in the first aspect.

[0034] Fourthly, embodiments of this application provide a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the transaction address mapping method as described in the first aspect.

[0035] The first aspect of the technical solution provided by the embodiments of this disclosure brings at least the following beneficial effects: acquiring transaction data from one or more trading systems, initializing support range and confidence threshold; sorting the transaction data by transaction time and dividing it into multiple transaction combinations based on transaction time; calculating the support of each transaction address, and filtering target transaction addresses whose support meets the support range; calculating the confidence of the target transaction address and other transaction addresses respectively, filtering target transaction addresses with confidence greater than the confidence threshold and other transaction addresses in pairs, and generating mapping relationships. The embodiments of this disclosure slice the transaction data according to transaction time, dividing it into multiple transaction combinations, and utilize the active time of transaction addresses in one or more trading systems to obtain mapping relationships between transaction addresses based on an association rule algorithm including support and confidence.

[0036] It should be noted that the technical effects of any of the implementation methods in the second to fourth aspects can be found in the technical effects of the corresponding implementation methods in the first aspect, and will not be repeated here.

[0037] The further effects of the aforementioned unconventional alternative methods will be explained below in conjunction with specific implementation methods. Attached Figure Description

[0038] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Clearly, the drawings described below only relate to some embodiments of this disclosure and are not intended to limit the scope of this disclosure.

[0039] Figure 1 A schematic diagram illustrating the main steps of a transaction address mapping method according to at least one embodiment of the present disclosure is shown.

[0040] Figure 2 A schematic diagram of the main modules of a transaction address mapping apparatus according to at least one embodiment of the present disclosure is shown;

[0041] Figure 3 A schematic diagram of an electronic device according to at least one embodiment of the present disclosure is shown;

[0042] Figure 4 A schematic diagram of a readable storage medium according to at least one embodiment of the present disclosure is shown. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0044] Note that the examples described below are merely specific examples and are not intended to limit the embodiments of this disclosure to the specific shapes, hardware, connections, operations, values, conditions, data, sequences, etc., shown and described. Those skilled in the art can utilize the concepts of this disclosure to construct further embodiments not mentioned herein by reading this specification.

[0045] The terminology used in this disclosure is that which is currently widely used in the art in consideration of the functionality of this disclosure; however, these terms may vary depending on the intent, precedent, or new technology of those skilled in the art. Furthermore, specific terms may be chosen by the applicant, and in such cases, their detailed meanings will be described in the detailed description of this disclosure. Therefore, the terminology used in this specification should not be construed as simple names, but rather based on the meaning of the terms and the overall description of this disclosure.

[0046] To better understand the embodiments of this disclosure, the relevant terms involved in this disclosure will first be defined and explained.

[0047] Blockchain: refers to a chain-like data structure that combines data blocks sequentially in chronological order, and is a distributed ledger that is cryptographically guaranteed to be immutable and unforgeable. Essentially, it is a decentralized database. The blockchains referred to in this disclosure include public blockchains, consortium blockchains, private blockchains, and permissioned blockchains.

[0048] Association rules are a method used to discover meaningful relationships between data items. The goal is to find frequent patterns, associations, or relationships between different items, thereby providing decision support.

[0049] It should be noted that the technical solutions in this disclosure, including the collection, updating, analysis, processing, use, transmission, and storage of user personal information, all comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. Necessary measures are taken to prevent unauthorized access to user personal information data and to safeguard user personal information security, network security, and national security.

[0050] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0051] Figure 1 The diagram illustrates the main steps of a transaction address mapping method 100 according to at least one embodiment of the present disclosure, the method 100 including the following steps:

[0052] Step S101: Obtain transaction data from one or more trading systems, and initialize the support range and confidence threshold, wherein the transaction data includes transaction time and transaction address.

[0053] The disclosed transaction address mapping method 100 is applied to one or more transaction systems. When the transaction system includes a blockchain system, it is difficult to obtain real user information due to the anonymity of blockchain transactions; while when the transaction system includes an off-chain payment system, multiple payment systems may be involved, and for user privacy considerations, or for security or anonymization factors between systems, user identifiers may be used instead of real user information. Therefore, one or more transaction systems may include blockchain systems and / or off-chain transaction systems, without limitation here. When the transaction system is a blockchain system, the transaction address is the on-chain address during the blockchain transaction process; when the transaction system is an off-chain transaction system, the transaction address can be a real user's payment account number or user identifier, etc. It is understood that the transaction address includes the addresses of all parties involved in the transaction; for example, in a transfer transaction, it may include the receiving and sending parties.

[0054] Transaction data, besides the transaction address, may include various information such as transaction time, transaction amount, transaction currency, and transaction type (e.g., purchase, sale, refund). To implement the transaction address mapping method of this disclosure, the transaction data of this disclosure at least includes the transaction time and transaction address. When the obtained transaction data fields include other transaction data besides the transaction time and transaction address, preferably, the transaction data can be processed to extract the transaction time and transaction address. The data processing process helps eliminate sensitive information, reduces the risk of unnecessary information leakage, and, because the transaction data fields are fewer, helps improve the efficiency of subsequent data processing. The method for extracting the transaction time and transaction address can be by filtering to obtain the transaction time and transaction address, or by deleting other transaction data besides the transaction time and transaction address, or other methods that can be implemented in this field; this disclosure does not limit this method.

[0055] Figure 2 A schematic diagram of the main modules of a transaction address mapping device 200 according to at least one embodiment of the present disclosure is shown. The transaction address mapping device 200 can be used to execute the transaction address mapping method 100 of the embodiments of the present disclosure. When one or more transaction systems need to generate mapping relationships for transaction addresses, they can be connected to the transaction address mapping device 200 and the mapping can be implemented through the transaction address mapping device 200. It is understood that one or more transaction systems can process the transaction data before providing it to the transaction address mapping device 200, for example, deleting information other than the transaction time and transaction address, or processing the transaction address to hide the real user information and replace it with a user identifier or user code. If the transaction system does not delete other transaction information in the transaction data, the transaction processing module 220 of the transaction address mapping device 200 can also process the transaction data, for example, deleting unnecessary transaction data.

[0056] Step S102: Sort the transaction data by transaction time and divide it into multiple transaction groups based on the transaction time. The embodiments of this disclosure generate mapping relationships based on the activity time of each transaction address in different transaction groups. Therefore, it is necessary to sort the transaction data by transaction time and divide it into different transaction groups based on the transaction time.

[0057] For example, setting time windows, transaction start times, and transaction end times. First, transaction data can be filtered based on these start and end times, meaning analysis is performed within the transaction analysis timeframes formed by these times. Then, the filtered transaction data is sliced ​​into multiple transaction combinations according to the time window. For example, a rolling time window can be used. Specifically, if the transaction start time is set to 8:00 AM and the transaction end time to 8:00 PM, with a rolling time window of one hour, then 12 different transaction combinations can be formed based on the transaction time. These combinations would be: transaction data between 8:00 AM and 9:00 AM (first combination), transaction data between 9:00 AM and 10:00 AM (second combination), and so on, up to transaction data between 7:00 PM and 8:00 PM (twelfth combination). It's understood that the last transaction combination may not complete a full time window. The size of the time window is determined based on the system parameters of each transaction link, transaction security, or transaction requirements.

[0058] A time window can also be a sliding time window, which can include a time interval and a sliding step. For example, if the trading start time is set to 8:00 AM and the trading end time is set to 9:00 AM, with a time interval of 20 minutes and a sliding step of 5 minutes, then the trading can be divided into 9 different trading combinations based on the trading time. Specifically, the trading data between 8:00 AM and 8:20 AM is the first trading combination, the trading data between 8:05 AM and 8:25 AM is the second trading combination, and so on, until the trading data between 8:40 AM and 9:00 AM is the ninth trading combination.

[0059] Step S103: Calculate the support for each transaction address and filter target transaction addresses whose support meets the support range. Support indicates the frequency of a transaction address appearing in each transaction combination. Specifically, iterate through all transaction addresses involved in the transaction data and calculate the support for each address. The support S(X1) represents the percentage of all transaction combinations containing address X1, which is the frequency of address X1.

[0060] Formula for calculating address X1 support:

[0061] S(X1) = Number of transactions containing address X1 / Total number of transactions %

[0062] For example, for address X1, which has transaction data in 6 of the above 12 transaction combinations (address X1 can be either the payee or the payer, which is not limited here), the support of address X1 is 50%.

[0063] Iterate through the n addresses involved in the 12 transaction combinations, X1, X2, ... X n Support is calculated separately, using the following formula:

[0064] S(X n ) = The transaction combination contains address X n Quantity / Quantity of all trading combinations %

[0065] For the calculated S(X) n ), filter them, and extract m target transaction addresses whose support meets the support range, X1, X2, ... X m .

[0066] Understandably, the support range can be limited by the maximum support and / or minimum support. Support below the minimum support indicates that the address is inactive and not a key focus address; support above the maximum support indicates that the address may be a cross-chain relay address or a public account set up by a payment system, and may also not be a key focus address.

[0067] Support calculation can be performed in two different ways. Taking address X1 as an example, one approach is to directly iterate through all transactions in each transaction combination to determine which transactions(s) contain transaction data for address X1 within each combination. Alternatively, one approach is to first extract all transaction addresses within each transaction combination, forming a correspondence between transaction combinations and transaction addresses. Based on this correspondence, the support for each transaction address can be calculated. Specifically, an example is provided below for forming the correspondence between transaction combinations and transaction addresses:

[0068] Transaction combination 1: Transaction addresses X1, X2, X6, X8, X 12

[0069] Transaction combination 2: Transaction addresses X1, X4, X5, X7

[0070] Transaction combination 3: Transaction addresses X2, X3, X9, X 10 X 12

[0071] Transaction combination 4: Transaction addresses X3, X5, X6, X8, X9, X 12

[0072] Transaction combination 5: Transaction addresses X1, X5, X7, X 11 X 12

[0073] Transaction combination 6: Transaction addresses X1, X8, X9, X 10

[0074] Transaction combination 7: Transaction addresses X4, X5, X6, X8, X 12

[0075] Transaction combination 8: Transaction addresses X5, X7, X8, X9, X 10 X 12

[0076] Transaction combination 9: Transaction addresses X1, X2, X5, X9, X 10

[0077] Transaction combination 10: Transaction addresses X1, X3, X7, X8

[0078] Transaction combination 11: Transaction addresses X6, X7, X8, X9, X 12

[0079] Transaction combination 12: Transaction addresses X2, X4, X6, X9, X 12

[0080] Based on the above correspondence, the transaction address X1-X can be calculated. 12 The support percentages are [50%, 33%, 25%, 25%, 50%, 42%, 42%, 58%, 58%, 33%, 8%, 67%]. This method eliminates the need to repeatedly traverse each transaction in each transaction combination for each transaction address, making support calculation more efficient. If the minimum support is set to 40% and the maximum support to 55%, then transaction addresses X1, X5, X6, and X7 are the target transaction addresses.

[0081] Step S104: Calculate the confidence scores of the target transaction address and other transaction addresses respectively, and filter the target transaction addresses and other transaction addresses with confidence scores greater than the confidence score threshold in pairs to generate a mapping relationship. The confidence score is used to indicate the probability that other transaction addresses and the target transaction address are in the same transaction combination.

[0082] Optionally, other transaction addresses can be transaction addresses of other trading systems, used to establish a mapping between the transaction system where the target transaction address is located and the addresses of other trading systems. They can also be transaction addresses of the same trading system. For example, when a transaction is split into multiple transactions, the same user may correspond to multiple transaction addresses in one trading system. Therefore, it is also necessary to establish a mapping relationship for risk identification.

[0083] Taking transaction address X1 as an example, the process for calculating its confidence level with other transaction addresses is as follows:

[0084] Address X1 and Address X n The support between them is:

[0085] S(X1→X n = The transaction combination contains addresses X1 and X n Quantity / Quantity of all trading combinations %

[0086] In the example above, address X1 is different from other addresses X2-X. 12 The support levels for each of them are: [17%, 8%, 8%, 25%, 8%, 25%, 25%, 17%, 17%, 8%, 17%].

[0087] Further, based on the support calculated above, the confidence level is calculated, and the relationship between address X1 and address X is... n The confidence level between them is:

[0088] C(X1→X n )=(S(X1→X n )) / (S(X1)), indicating address X n The probability of synchronization occurring when address X1 appears.

[0089] In the example above, address X1 is different from other addresses X2-X. 12 The confidence levels between them are: [33%, 17%, 17%, 50%, 17%, 50%, 50%, 33%, 33%, 17%, 33%].

[0090] Based on the set confidence threshold, target transaction addresses are paired with other transaction addresses to obtain the final associated combinations. It can be understood that a confidence threshold greater than this threshold indicates that the paired addresses exhibit a certain degree of transaction synchronization across multiple time windows, meaning there is a high probability of a correlation. Examples include situations where the paired addresses are accounts of the same user on a blockchain or off-chain payment system, or accounts of the same user on different blockchains.

[0091] In the example above, if the confidence threshold is set to 40%, then addresses X5, X7, and X8 may have a mapping relationship with address X1.

[0092] It's important to note that the confidence level between address X and address Y may differ from that between address Y and address X. For example, address Y might be an on-chain address of a cryptocurrency merchant, while X is one of their off-chain receiving accounts. In this case, the confidence level C(X→Y) could be very high, while the confidence level C(Y→X) could be very low (because cryptocurrency merchants might use a large number of different receiving accounts to mitigate risk). Therefore, as long as any confidence level between two addresses is above a certain threshold, the two addresses can be considered to have a high degree of correlation.

[0093] The transaction address mapping method proposed in this disclosure, based on time series and correlation, effectively utilizes the active time of blockchain and off-chain payment transactions. It finds the mapping relationship between blockchain addresses, or between blockchain addresses and off-chain payment accounts, or between off-chain payment accounts based on the correlation rule algorithm. Finally, it locates unidentified blockchain transaction addresses based on the identified blockchain addresses or payment accounts.

[0094] The embodiments disclosed herein have the following advantages: by slicing blockchain transaction time-series data through time windows to transform it into a frequent item mining problem, and by using association rule algorithms to associate and map frequently active blockchain addresses and payment accounts; it relies on fewer data dimensions, requiring only transaction time and tagged account or address information, and there is no risk of information leakage.

[0095] Figure 2 A schematic diagram of the main modules of a transaction address mapping apparatus 200 according to at least one embodiment of the present disclosure is shown. The apparatus 200 includes an initialization module 210, a transaction processing module 220, a support calculation module 230, and a mapping generation module 240, wherein...

[0096] Initialization module 210 is used to acquire transaction data from one or more trading systems and initialize the support range and confidence threshold, wherein the transaction data includes transaction time and transaction address;

[0097] The transaction processing module 220 is used to sort transaction data by transaction time and divide it into multiple transaction combinations based on transaction time.

[0098] The support calculation module 230 is used to calculate the support of each transaction address and filter target transaction addresses whose support meets the support range. The support is used to indicate the frequency of a transaction address in each transaction combination.

[0099] The mapping generation module 240 is used to calculate the confidence scores of the target transaction address and other transaction addresses respectively, filter the target transaction addresses and other transaction addresses with confidence scores greater than the confidence score threshold in pairs, and generate mapping relationships. The confidence score is used to indicate the probability that other transaction addresses and the target transaction address are in the same transaction combination.

[0100] Optionally, the transaction processing module 220 is further used to: process the transaction data and extract the transaction time and transaction address.

[0101] Optionally, the transaction processing module 220 is further configured to: set the time window, the transaction start time, and the transaction end time;

[0102] Filter transaction data based on transaction start time and transaction end time;

[0103] The filtered transaction data is sliced ​​into multiple transaction combinations according to time windows.

[0104] Optionally, the support range is limited by the maximum support and / or minimum support.

[0105] Optionally, the support calculation module 230 is further used to: extract all transaction addresses within each transaction combination, form a correspondence between transaction combinations and transaction addresses, and calculate the support of each transaction address based on the correspondence.

[0106] It should be noted that the above application scenarios are merely exemplary, intended to describe one or more aspects of this disclosure in specific scenarios. However, these aspects are not essential, and various modifications can be made to the application scenario. It is readily understood that the specific application scenarios described in this disclosure are not limited.

[0107] At least some embodiments of this disclosure also provide an electronic device. Figure 3 A schematic diagram of an electronic device 300 according to at least one embodiment of the present disclosure is shown.

[0108] like Figure 3 As shown, the electronic device 300 includes one or more processors 310 and a memory 320. The memory 320 includes one or more computer program modules 321. These computer program modules 321 are stored in the memory 320 and are executed by the processor 310. Each computer program module 321 includes instructions for executing a transaction address mapping method 100 and its additional aspects according to at least one embodiment of the present disclosure. When executed by the processor 310, these instructions can perform one or more steps of the transaction address mapping method 100 and its additional aspects according to at least one embodiment of the present disclosure. The memory 320 and the processor 310 can be interconnected via a bus system and / or other forms of connection mechanisms (not shown). For example, the bus can be a Peripheral Component Interconnect Standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0109] For example, processor 310 may be a central processing unit (CPU), a digital signal processor (DSP), or other processing unit with data processing and / or program execution capabilities, such as a field-programmable gate array (FPGA). Processor 310 may be a general-purpose processor or a special-purpose processor, capable of controlling other components in electronic device 300 to perform desired functions.

[0110] Exemplarily, memory 320 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer program modules 321 may be stored on the computer-readable storage medium, and processor 310 may run one or more computer program modules 321 to implement various functions of electronic device 300. The computer program modules include multiple computer-executable instructions. Various application programs and various data, as well as various data used and / or generated by the application programs, may also be stored in the computer-readable storage medium.

[0111] For example, electronic device 300 may also include input devices such as touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, and gyroscopes; output devices such as liquid crystal displays, speakers, and vibrators; storage devices such as magnetic tapes and hard disks (HDDs or SDDs); and communication devices such as network interface cards like LAN cards and modems. The communication devices allow electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data and perform communication processing via networks such as the Internet. A drive is connected to the I / O interface as needed. Removable storage media, such as disks, optical disks, magneto-optical disks, and semiconductor memories, are installed on the drive as needed so that computer programs read from them can be installed into the storage device as required.

[0112] For example, the electronic device 300 may further include a peripheral interface (not shown in the figure). This peripheral interface can be various types of interfaces, such as a USB interface, a Lightning interface, etc. The communication device can communicate wirelessly with networks and other devices, such as the Internet, intranets and / or wireless networks such as cellular telephone networks, wireless local area networks (LANs) and / or metropolitan area networks (MANs). Wireless communication can use any of a variety of communication standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi (e.g., based on IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, and / or IEEE 802.11n standards), Voice over Internet Protocol (VoIP), Wi-MAX, protocols for email, instant messaging, and / or Short Message Service (SMS), or any other suitable communication protocol.

[0113] The electronic device 300 may be, for example, a system-on-a-chip (SOC) or a device including the SOC. For instance, it can be any device such as a mobile phone, tablet computer, laptop computer, e-reader, game console, television, digital photo frame, navigator, home appliance, communication base station, industrial controller, server, etc., or any combination of data processing devices and hardware. The embodiments of this disclosure do not limit this. The specific functions and technical effects of the electronic device 300 can be found in the above description of the transaction address mapping method 100 and its additional aspects according to at least one embodiment of this disclosure, and will not be repeated here.

[0114] Figure 4 A schematic diagram of a readable storage medium 400 according to at least one embodiment of the present disclosure is shown.

[0115] like Figure 4 As shown, a computer program 410 is stored on a readable storage medium 400, which is a computer-readable storage medium. When the computer program 410 is executed by a processor, it performs one or more steps of the transaction address mapping method 100 and its additional aspects as described above.

[0116] For example, when the program code is read by a computer, the computer can execute the program code stored in the computer storage medium to perform one or more steps to implement, for example, the transaction address mapping method 100 and its additional aspects according to at least one embodiment of the present disclosure.

[0117] For example, the readable storage medium may include a memory card of a smartphone, a storage component of a tablet computer, a hard disk of a personal computer, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), flash memory, and other readable storage media or any combination thereof. The readable storage medium 400 may be a non-transitory readable storage medium.

[0118] At least some of the embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other.

[0119] It should be noted that, in this disclosure, relational terms such as "first," "second," etc., are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved; that is, the preceding or following operations are not necessarily executed precisely in sequence. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0121] The units described in the embodiments of this disclosure can be implemented in software or hardware. The described units can also be located in a processor. The names of these units do not, in some cases, constitute a limitation on the unit itself.

[0122] The following points should be noted regarding this disclosure:

[0123] (1) The accompanying drawings of the embodiments of this disclosure only involve the structures involved in the embodiments of this disclosure. Other structures can be referred to the general design.

[0124] (2) Where there is no conflict, the embodiments of this disclosure and the features in the embodiments can be combined with each other to obtain new embodiments.

[0125] The above description is merely an exemplary embodiment of this disclosure and is not intended to limit the scope of protection of this disclosure, which is determined by the appended claims.

Claims

1. A transaction address mapping method, characterized in that, The method includes: Acquire transaction data from one or more trading systems, and initialize the support range and confidence threshold, wherein the transaction data includes transaction time and transaction address; The transaction data is sorted by transaction time and divided into multiple transaction groups based on the transaction time. Calculate the support for each transaction address, and filter target transaction addresses whose support meets the support range, wherein the support is used to indicate the frequency of the transaction address in each transaction combination; Calculate the confidence scores of the target transaction address and other transaction addresses respectively, and filter the target transaction address and other transaction addresses with confidence scores greater than the confidence score threshold in pairs to generate a mapping relationship. The confidence score is used to indicate the probability that other transaction addresses are in the same transaction combination as the target transaction address. The step of dividing transactions into multiple transaction groups based on the transaction time includes: Set the time window, trade start time, and trade end time; The transaction data is filtered based on the transaction start time and the transaction end time; The filtered transaction data is divided into multiple transaction combinations by time slices according to the time window. The calculation of the support level for each transaction address includes: Extract all transaction addresses within each transaction combination to form a correspondence between transaction combinations and transaction addresses, and calculate the support of each transaction address based on this correspondence.

2. The method according to claim 1, characterized in that, Before sorting the transaction data by transaction time, the method further includes: The transaction data is processed to extract the transaction time and transaction address.

3. The method according to claim 1, characterized in that, The support range is defined by the maximum support and / or minimum support.

4. A transaction address mapping device, characterized in that, The device includes: An initialization module is used to acquire transaction data from one or more trading systems and initialize the support range and confidence threshold, wherein the transaction data includes transaction time and transaction address; The transaction processing module is used to sort the transaction data by transaction time and divide it into multiple transaction combinations according to the transaction time. The support calculation module is used to calculate the support of each transaction address and filter target transaction addresses whose support meets the support range, wherein the support is used to indicate the frequency of the transaction address in each transaction combination; The mapping generation module is used to calculate the confidence scores of the target transaction address and other transaction addresses respectively, filter the target transaction address and other transaction addresses with confidence scores greater than the confidence score threshold in pairs, and generate a mapping relationship. The confidence score is used to indicate the probability that other transaction addresses and the target transaction address are in the same transaction combination. The transaction processing module is further configured as follows: Set the time window, trade start time, and trade end time; The transaction data is filtered based on the transaction start time and the transaction end time; The filtered transaction data is divided into multiple transaction combinations by time slices according to the time window. The support calculation module is further configured as follows: Extract all transaction addresses within each transaction combination to form a correspondence between transaction combinations and transaction addresses, and calculate the support of each transaction address based on this correspondence.

5. The apparatus according to claim 4, characterized in that, The transaction processing module is further used for: The transaction data is processed to extract the transaction time and transaction address.

6. An electronic device, characterized in that, include: One or more processors; Storage device for storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-3.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-3.

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