Ethereum exchange name identification method and device based on counterparty matching

Abstract

The present invention discloses an Ethereum exchange name recognition method and device based on counterparty matching, the method comprising: obtaining Ethereum transaction details data; obtaining exchange label categories, and using centralized transaction deposit and withdrawal business logic to identify exchange hot wallet addresses; training label category recognition models to obtain supervised multi-classification label recognition models; using the supervised multi-classification label recognition model and a preset label threshold to predict suspected exchange category addresses; aggregating a user deposit address set of a known exchange platform; extracting a suspected exchange user deposit address set; and identifying the name of the suspected exchange address. The present invention can quickly expand the exchange's common wallet address based on the existing exchange label library, can improve the timeliness of manually expanding the exchange label address, and provide more accurate and reliable technical and data support for relevant institutions to supervise blockchain.

Description

Technical Field

[0001] The present invention belongs to the technical field of blockchain application security, and in particular relates to an Ethereum exchange name recognition method and device based on counterparty matching. Background Art

[0002] Faced with the ever-expanding virtual currency market in the blockchain industry, its supervision is still very difficult. The corresponding technical characteristics of blockchain are mainly reflected in the following aspects:

[0003] (1) Globality. Virtual currencies are widely circulated around the world, and different countries have very different attitudes towards virtual currencies, which makes the virtual currency market extremely "guerrilla". Even if a project is banned by law in a certain country, the project organizer can still get market support by changing its "mountain".

[0004] (2) Anonymity. Virtual currencies generally have the characteristic of anonymity. After on-chain transactions have been obfuscated and transferred multiple times, it will be very difficult to trace and verify. This makes asset tracking have certain technical thresholds and time windows, such as whether there are a large number of on-chain address tags and whether timely warnings can be issued after the target asset changes.

[0005] (3) Complexity. The on-chain transaction environment of virtual currencies is complex. Various transaction forms such as currency-to-currency, mixed currency, cross-chain, DEX, and DeFi lending can be mixed on the chain, which greatly increases the complexity of asset tracking.

[0006] In summary, although many countries have expressed their regulatory attitudes, the complex status of the virtual currency market makes the regulation of virtual currencies by relevant institutions uncertain.

[0007] Compared with traditional finance, virtual currency, as a virtual currency circulated in cyberspace, has some similarities with the traditional real financial system. For example, the "exchange" platform plays a role similar to that of a bank, and holders can save and exchange various virtual currencies in the exchange; the "wallet address" of virtual currency is equivalent to a personal bank account, and the wallet address can be used to trace the exchange to which it belongs (except for cold wallets).

[0008] In order to solve the problem of tracing the exchange to which the wallet address belongs, most solutions are based on collecting exchange wallet addresses through different open source channels. However, the information of many unknown exchanges on the blockchain cannot be obtained. This method means that we can only conduct illegal fund investigation from known exchange wallets. Since the open source channels are limited in collecting exchange label addresses, it is too limited. Summary of the invention

[0009] In order to solve the problem of limitations in collecting exchange tag addresses through different open source channels, the present invention provides an Ethereum exchange name identification method based on counterparty matching. The present invention can identify the Ethereum exchange wallet address in the Ethereum scenario.

[0010] The present invention is achieved through the following technical solutions:

[0011] The Ethereum exchange name identification method based on counterparty matching includes:

[0012] Obtain and process detailed data of Ethereum historical transactions through the Ethereum full node to obtain detailed data of Ethereum transactions;

[0013] Get the exchange tag category and use the centralized trading deposit and withdrawal business logic to identify the exchange hot wallet address;

[0014] Extracting transaction behavior feature data according to the Ethereum transaction details data, and training a label category recognition model based on the extracted feature data to obtain a supervised multi-classification label recognition model;

[0015] The supervised multi-classification label recognition model and the preset label threshold are used to predict the suspected exchange category address;

[0016] According to the hot wallet address of the exchange, aggregate the user deposit address set of the known exchange platform;

[0017] Extract the set of inbound counterparties with addresses suspected to be exchanges, that is, obtain the set of deposit addresses of users suspected to be exchanges;

[0018] Based on the suspected exchange user deposit address set and the known exchange user deposit address set, the suspected exchange address name is identified.

[0019] Preferably, the steps of obtaining the exchange tag category and identifying the exchange hot wallet address using the centralized transaction deposit and withdrawal business logic of the present invention specifically include:

[0020] Get all the tags of websites related to Ethereum transactions and get the small tags of each tag address;

[0021] Classify and label the acquired small tags to obtain the tag categories of each exchange;

[0022] For each exchange label category, identify the hot wallet addresses of each exchange based on the deposit and withdrawal business logic of the centralized exchange.

[0023] Preferably, the present invention extracts transaction behavior feature data according to the Ethereum transaction details data, and trains a label category recognition model based on the extracted feature data to obtain a supervised multi-classification label recognition model, and the steps specifically include:

[0024] According to the Ethereum transaction details data, obtain the transaction behavior feature data of the label address;

[0025] Input the acquired transaction behavior feature data into a preset supervised multi-classification algorithm to train a label category recognition model, thereby obtaining a supervised multi-classification label recognition model;

[0026] During the training process, the Ethereum wallet address whose model output category score probability exceeds the category label threshold is determined as the current category label, otherwise it is determined as other unknown category labels.

[0027] Preferably, the category label threshold of the present invention can be set to 90% or 95%.

[0028] Preferably, the step of using the supervised multi-classification label recognition model and a preset label threshold to predict a suspected exchange address in the present invention specifically includes:

[0029] The trained supervised multi-classification label recognition model is used to predict the exchange category of Ethereum wallet addresses with unknown labels and transaction times exceeding the threshold;

[0030] The addresses with a score probability exceeding the label threshold corresponding to the exchange category are regarded as suspected exchange category addresses.

[0031] Preferably, the step of aggregating a set of user deposit addresses of a known exchange platform according to the hot wallet address of the exchange in the present invention specifically includes:

[0032] Get the incoming counterparties of the hot wallet addresses of each exchange category, that is, the user deposit address set;

[0033] Aggregate user deposit addresses according to exchange names to obtain a set of user deposit addresses on known exchange platforms.

[0034] Preferably, the present invention performs the step of identifying the name of the suspected exchange address based on the suspected exchange user deposit address set and the user deposit address set of the known exchange platform, specifically including:

[0035] Find the intersection of the obtained suspected exchange user deposit address set and the known exchange user deposit address set;

[0036] Calculate the intersection proportion score;

[0037] Compare whether the intersection ratio score is greater than the ratio score threshold. If so, return the small label of the currently known exchange platform, which is the suspected exchange address label; otherwise, identify the next suspected exchange address label.

[0038] In a second aspect, the present invention proposes an Ethereum exchange name recognition device based on counterparty matching, including a data processing unit, a labeling unit, a model training unit, a category prediction unit, an aggregation unit, an extraction unit, and a recognition unit;

[0039] The data processing unit obtains and processes the detailed data of Ethereum historical transactions through the Ethereum full node to obtain the detailed data of Ethereum transactions;

[0040] The labeling unit is used to obtain the exchange label category and identify the exchange hot wallet address using the centralized transaction deposit and withdrawal business logic;

[0041] The model training unit extracts transaction behavior feature data according to the Ethereum transaction details data, and performs label category recognition model training based on the extracted feature data to obtain a supervised multi-classification label recognition model;

[0042] The category prediction unit predicts the suspected exchange category address using the supervised multi-classification label recognition model and a preset label threshold;

[0043] The aggregation unit is used to obtain a set of user deposit addresses on a known exchange platform;

[0044] The extraction unit extracts a set of incoming transaction counterparties of suspected exchange category addresses, that is, obtains a set of suspected exchange user deposit addresses;

[0045] The identification unit identifies the name of the suspected exchange address according to the suspected exchange user deposit address set and the user deposit address set of the known exchange platform.

[0046] In a third aspect, the present invention proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of the method of the present invention when executing the computer program.

[0047] In a fourth aspect, the present invention proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method of the present invention when executed by a processor.

[0048] The present invention has the following advantages and beneficial effects:

[0049] 1. The present invention can quickly expand the common wallet addresses of exchanges based on the existing exchange tag library, improve the time efficiency of manually expanding the exchange tag addresses, and provide more accurate and reliable technical and data support for relevant institutions to supervise blockchain.

[0050] 2. The present invention is based on common address category labels collected and sorted through open source channels, trained through a supervised multi-classification model, and adjusts the output of the model to achieve the goal of preferring missed judgments to misjudgment.

[0051] 3. The present invention identifies specific transaction behaviors of ordinary addresses of each category based on a supervised multi-classification model, identifies more suspected exchange label addresses, and then expands the exchange category label addresses in combination with business rules. The combination of business rules and machine learning greatly improves the accuracy of exchange label addresses.

[0052] 4. The present invention is based on a combination of business rules and machine learning, and can be iterated regularly, so as to continuously expand the exchange tag addresses and form a closed loop of data use. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0054] Figure 1 It is a schematic diagram of the identification method flow of the present invention.

[0055] Figure 2 This is a schematic diagram of the deposit and withdrawal process of a centralized exchange.

[0056] Figure 3 It is a schematic diagram of the computer device structure of the present invention.

[0057] Figure 4 This is a principle block diagram of the identification device of the present invention. DETAILED DESCRIPTION

[0058] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0059] Example 1

[0060] This embodiment proposes a method for identifying the name of an Ethereum exchange based on counterparty matching, which solves the problem that the information collected through open source channels about the wallet addresses of Ethereum exchanges is too limited.

[0061] like Figure 1As shown, the identification method of this embodiment includes the following steps:

[0062] Step 101, obtain and process the detailed data of Ethereum historical transactions through the Ethereum full node to obtain the Ethereum transaction details data.

[0063] The Ethereum transaction details data obtained in step 101 of this embodiment include transaction Hash, transaction input address, transaction output address, transaction amount and other data.

[0064] Step 102, obtain the exchange tag category, and use the centralized transaction deposit and withdrawal business logic to identify the exchange hot wallet address.

[0065] Step 102 of this embodiment specifically includes the following sub-steps:

[0066] Step 201, obtain all tags in the tag cloud of existing Ethereum transaction-related websites (for example, Etherscan official website), and obtain small tags of each tag address, such as Huobi Exchange, Binance Exchange, Imtoken Wallet, etc.;

[0067] Step 202, classify and label the obtained small tags, and finally obtain major categories such as exchanges, service providers, wallets, blacklists, etc.;

[0068] Step 203, for the label addresses of each major exchange category, identify the hot wallet addresses of each exchange based on the deposit and withdrawal business logic of the centralized exchange.

[0069] Specific as Figure 2 As shown in the figure, the business logic of the centralized exchange's deposit and withdrawal is as follows:

[0070] The centralized transaction offline host generates multiple wallet addresses in batches, with public and private keys stored separately, and different wallets are generated for different currencies;

[0071] When a user registers, the exchange statically assigns the user a wallet address whose private key is controlled by the exchange. This address is also used as the user's recharge address.

[0072] When the user completes the recharge, the background program automatically scans the wallet transaction record, and the currency is recharged when it is scanned. The currency at this address is regularly transferred from the exchange to the exchange hot wallet within a certain period of time;

[0073] The money in the hot wallet is regularly transferred to the cold wallet for storage, and enough virtual currency for withdrawal is retained;

[0074] When a user triggers a withdrawal, the user needs to fill in the withdrawal address, withdrawal amount, and generate an order. The exchange will transfer the specified amount of virtual currency to the withdrawal address through the hot wallet.

[0075] Step 103, extracting transaction behavior feature data based on the Ethereum transaction details data, and training a label category recognition model based on the extracted feature data to obtain a supervised multi-classification label recognition model.

[0076] Step 103 of this embodiment specifically includes the following sub-steps:

[0077] Step 301, according to the Ethereum transaction details data obtained in step 103, obtain the transaction behavior characteristic data of the tag address. In this embodiment, the characteristic data is obtained from the Ethereum transaction details data from two aspects: the Ethereum transaction node and the Ethereum transaction network structure;

[0078] Step 302, inputting the acquired feature data into a preset supervised multi-classification algorithm to train a label category recognition model, thereby obtaining a supervised multi-classification label recognition model;

[0079] This embodiment sets the threshold of each exchange category label by the output of the supervised multi-classification label recognition model in step 302, so that the accuracy reaches the preset threshold, so that the label recognition effect can achieve the purpose of rather missing than misjudging. If the score probability (the accuracy of the model corresponding to the category) exceeds the preset category label threshold, the Ethereum wallet address is determined to be the current category label, otherwise it is other unknown category labels.

[0080] The thresholds of various types of labels (major labels) set in this embodiment can be set to 90%, 95%, etc.

[0081] This embodiment first preliminarily determines the major category to which the Ethereum wallet address belongs, and then determines that the Ethereum wallet address belongs to the category only when the possibility (accuracy) of the Ethereum wallet address belonging to the category reaches a preset threshold, so as to further improve the classification accuracy and reliability.

[0082] This embodiment first performs a preliminary label determination on the category to which the exchange address belongs, and then finally determines the known label category of the exchange address by setting a threshold to improve the recognition effect, that is, to ensure the recognition accuracy by reducing the number of known label data to be recognized.

[0083] Step 104: Use a supervised multi-classification label recognition model and a preset label threshold to predict the suspected exchange category address.

[0084] Step 104 of this embodiment specifically includes the following sub-steps:

[0085] Step 401, using the supervised multi-classification label recognition model trained in step 302 to predict the exchange category of Ethereum wallet addresses with unknown labels and transaction times exceeding a preset threshold;

[0086] Step 402: The score probability exceeds the label threshold corresponding to the exchange category in step 303, and is regarded as a suspected exchange category address.

[0087] Step 105, aggregate and obtain a set of user deposit addresses of known exchange platforms of each blockchain.

[0088] Step 105 of this embodiment specifically includes the following sub-steps:

[0089] Obtain the incoming counterparty of the hot wallet address of the exchange category (major category) in step 202, that is, the user deposit address set;

[0090] The user deposit addresses are aggregated according to the exchange name (small label), so as to obtain the user deposit address set A of each known exchange platform of the blockchain.

[0091] Step 106, extract the set of incoming transaction counterparties of the suspected exchange addresses, that is, obtain the set B of suspected exchange user deposit addresses.

[0092] Step 107, identifying the name of the suspected exchange address based on the suspected exchange user deposit address set and the known exchange platform user deposit address set.

[0093] Step 107 of this embodiment specifically includes the following sub-steps:

[0094] Step 501, finding the intersection C of the suspected exchange user deposit address set B obtained in step 106 and the user deposit address set A of the known exchange platform obtained in step 105;

[0095] Step 502, calculate the intersection proportion score, the specific calculation formula is as follows:

[0096] Score=Length(C) / Min(Length(A),Length(B))

[0097] Among them, Length(C) represents the length of the intersection C, Length(A) represents the length of the user deposit address set A of the known exchange platform, Length(B) represents the length of the user deposit address set B of the suspected exchange platform, and Min() represents taking the minimum value.

[0098] Step 503, compare whether the calculated intersection proportion score is greater than the proportion score threshold. If so, return the small label of the currently known exchange platform, that is, the name of Huobi Exchange, Binance Exchange, etc.; if not, identify the next suspected exchange address name.

[0099] The percentage score threshold in this embodiment can be set to 0.2, 0.5, 0.7, etc.

[0100] This embodiment also provides a computer device for executing the above method of this embodiment.

[0101] Specific as Figure 3 As shown, the computer device includes a processor, an internal memory and a system bus; various device components including the internal memory and the processor are connected to the system bus. The processor is a piece of hardware used to execute computer program instructions through basic arithmetic and logical operations in the computer system. The internal memory is a physical device used to temporarily or permanently store computer programs or data (for example, program status information). The system bus can be any of the following types of bus structures, including a memory bus or a storage controller, a peripheral bus, and a local bus. The processor and the internal memory can communicate data through the system bus. The internal memory includes a read-only memory (ROM) or a flash memory (not shown in the figure), and a random access memory (RAM), and RAM usually refers to the main memory loaded with the operating system and computer programs.

[0102] Computer devices generally include an external storage device. The external storage device can be selected from a variety of computer-readable media, which refers to any available media that can be accessed by the computer device, including both removable and fixed media. For example, computer-readable media include, but are not limited to, flash memory (micro SD card), CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic tape cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by the computer device.

[0103] Computer devices can be logically connected to one or more network terminals in a network environment. Network terminals can be personal computers, servers, routers, smart phones, tablets, or other public network nodes. Computer devices are connected to network terminals through network interfaces (local area network LAN interfaces). Local area network (LAN) refers to a network of computers interconnected within a limited area, such as a home, school, computer laboratory, or office building using network media. WiFi and twisted pair cabling Ethernet are the two most commonly used technologies for building LANs.

[0104] It should be noted that other computer systems including more or fewer subsystems than a computer device may also be suitable for the invention.

[0105] As described in detail above, the computer device applicable to this embodiment can perform the specified operations of the Ethereum exchange name recognition method. The computer device performs these operations in the form of software instructions running in a computer-readable medium by a processor. These software instructions can be read into the memory from a storage device or from another device through a local area network interface. The software instructions stored in the memory enable the processor to execute the above-mentioned group member information processing method. In addition, the present invention can also be implemented by hardware circuits or hardware circuits combined with software instructions. Therefore, the implementation of this embodiment is not limited to any specific combination of hardware circuits and software.

[0106] Example 2

[0107] This embodiment proposes an Ethereum exchange name recognition device based on counterparty matching, such as Figure 4 As shown, the device of this embodiment includes a data processing unit, a labeling unit, a model training unit, a category prediction unit, an aggregation unit, an extraction unit and a recognition unit.

[0108] Among them, the data processing unit obtains and processes the detailed data of Ethereum historical transactions through the Ethereum full node to obtain the Ethereum transaction details data.

[0109] The labeling unit is used to obtain the exchange label category and use the centralized trading deposit and withdrawal business logic to identify the exchange hot wallet address.

[0110] The model training unit extracts transaction behavior feature data based on the Ethereum transaction details data, and trains the label category recognition model based on the extracted feature data to obtain a supervised multi-classification label recognition model.

[0111] The category prediction unit uses a supervised multi-classification label recognition model and a preset label threshold to predict the suspected exchange category address.

[0112] The aggregation unit is used to obtain the user deposit address set of each known exchange platform of each blockchain.

[0113] The extraction unit extracts the set of incoming transaction counterparties of the suspected exchange category addresses, that is, obtains the set of deposit addresses of the suspected exchange users.

[0114] The identification unit identifies the name of the suspected exchange address based on the suspected exchange user deposit address set and the user deposit address set of the known exchange platform.

[0115] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An Ethereum exchange name identification method based on counterparty matching, characterized in that: include: Obtain and process detailed data of Ethereum historical transactions through the Ethereum full node to obtain detailed data of Ethereum transactions; Get the exchange tag category and use the centralized trading deposit and withdrawal business logic to identify the exchange hot wallet address; Extracting transaction behavior feature data according to the Ethereum transaction details data, and training a label category recognition model based on the extracted feature data to obtain a supervised multi-classification label recognition model; The supervised multi-classification label recognition model and the preset label threshold are used to predict the suspected exchange category address; According to the hot wallet address of the exchange, aggregate the user deposit address set of the known exchange platform; Extract the set of inbound counterparties with addresses suspected to be exchanges, that is, obtain the set of deposit addresses of users suspected to be exchanges; Based on the suspected exchange user deposit address set and the known exchange user deposit address set, identify the suspected exchange address name; According to the suspected exchange user deposit address set and the known exchange user deposit address set, the steps of identifying the suspected exchange address name specifically include: Find the intersection of the obtained suspected exchange user deposit address set and the known exchange user deposit address set; Calculate the intersection proportion score; Compare whether the intersection ratio score is greater than the ratio score threshold. If so, return the small label of the currently known exchange platform, which is the suspected exchange address label; otherwise, identify the next suspected exchange address label.

2. The Ethereum exchange name identification method based on counterparty matching according to claim 1, characterized in that: The steps to obtain the exchange tag category and use the centralized trading deposit and withdrawal business logic to identify the exchange hot wallet address include: Get all the tags of websites related to Ethereum transactions and get the small tags of each tag address; Classify and label the acquired small tags to obtain the tag categories of each exchange; For each exchange label category, identify the hot wallet addresses of each exchange based on the deposit and withdrawal business logic of the centralized exchange.

3. The Ethereum exchange name identification method based on counterparty matching according to claim 1, characterized in that: The steps of extracting transaction behavior feature data according to the Ethereum transaction details data, and training a label category recognition model based on the extracted feature data to obtain a supervised multi-classification label recognition model specifically include: According to the Ethereum transaction details data, obtain the transaction behavior feature data of the label address; Input the acquired transaction behavior feature data into a preset supervised multi-classification algorithm to train a label category recognition model, thereby obtaining a supervised multi-classification label recognition model; During the training process, the Ethereum wallet address whose model output category score probability exceeds the category label threshold is determined as the current category label, otherwise it is determined as other unknown category labels.

4. The Ethereum exchange name identification method based on counterparty matching according to claim 3 is characterized in that: The category label threshold can be set to 90% or 95%.

5. The Ethereum exchange name identification method based on counterparty matching according to claim 1, characterized in that: The steps of using the supervised multi-classification label recognition model and the preset label threshold to predict the suspected exchange address specifically include: The trained supervised multi-classification label recognition model is used to predict the exchange category of Ethereum wallet addresses with unknown labels and transaction times exceeding the threshold; The addresses with a score probability exceeding the label threshold corresponding to the exchange category are regarded as suspected exchange category addresses.

6. The Ethereum exchange name identification method based on counterparty matching according to claim 1, characterized in that: According to the hot wallet address of the exchange, the steps of aggregating the set of user deposit addresses of the known exchange platform include: Get the incoming counterparties of the hot wallet addresses of each exchange category, that is, the user deposit address set; Aggregate user deposit addresses according to exchange names to obtain a set of user deposit addresses on known exchange platforms.

7. An Ethereum exchange name recognition device based on counterparty matching, characterized in that: It includes a data processing unit, a labeling unit, a model training unit, a category prediction unit, an aggregation unit, an extraction unit, and a recognition unit; The data processing unit obtains and processes the detailed data of Ethereum historical transactions through the Ethereum full node to obtain the detailed data of Ethereum transactions; The labeling unit is used to obtain the exchange label category and identify the exchange hot wallet address using the centralized transaction deposit and withdrawal business logic; The model training unit extracts transaction behavior feature data according to the Ethereum transaction details data, and performs label category recognition model training based on the extracted feature data to obtain a supervised multi-classification label recognition model; The category prediction unit predicts the suspected exchange category address using the supervised multi-classification label recognition model and a preset label threshold; The aggregation unit is used to obtain a set of user deposit addresses on a known exchange platform; The extraction unit extracts a set of incoming transaction counterparties of suspected exchange category addresses, that is, obtains a set of suspected exchange user deposit addresses; The identification unit identifies the name of the suspected exchange address according to the suspected exchange user deposit address set and the user deposit address set of the known exchange platform; The identification of the suspected exchange address name based on the suspected exchange user deposit address set and the known exchange platform user deposit address set specifically includes: Find the intersection of the obtained suspected exchange user deposit address set and the known exchange user deposit address set; Calculate the intersection proportion score; Compare whether the intersection ratio score is greater than the ratio score threshold. If so, return the small label of the currently known exchange platform, which is the suspected exchange address label; otherwise, identify the next suspected exchange address label.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images