Target Information Generation Method, Apparatus, Computer Device, and Storage Medium

Abstract

The present application relates to a method for generating target information, and the method includes: obtaining transaction data of the dark web, where the transaction data includes user numbers and transaction information corresponding to the user numbers; performing standardization processing on the transaction data to generate user standardized data and transaction standardized data; determining whether there is an association between user numbers based on the transaction data; in the case of an existing association, generating group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers; in the case of no existing association, generating individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers. By determining the type of target information through the association between user numbers, the integrity and effectiveness of transaction data analysis are improved.

Description

Technical Field

[0001] The present application relates to the technical field of information processing, and in particular, to a method, apparatus, computer device, and storage medium for generating target information. Background Art

[0002] The dark web is a place where anonymous users conduct transactions. While ensuring privacy, a large number of transactions and sales with transaction natures and items not meeting legal requirements have emerged on the dark web. At the same time, using virtual currency as the transaction currency makes transactions even more difficult to trace. Therefore, information detection based on dark web transactions becomes particularly important. If effective transaction information can be obtained, it plays a key role in determining the category of transactions, transaction items, and the identities of both parties to the transaction. Regarding the problem of how to find effective target information from dark web transaction data, the existing technology usually collects data for specific dark webs and then analyzes the text through natural language analysis means such as semantic analysis and keyword discovery in the dark web data. However, the analysis method of this way is relatively single, and the effect of finding effective information is not good when the text language has strong concealment.

[0003] Regarding the problem that the effect of finding effective information by natural language analysis means in the related technology is not good, no effective solution has been proposed yet. Summary of the Invention

[0004] In this embodiment, a method, apparatus, computer device, and storage medium for generating target information are provided to solve the problem that the effect of finding effective information by natural language analysis means in the related technology is not good.

[0005] In a first aspect, in this embodiment, a method for generating target information is provided. The method includes:

[0006] Obtain transaction data of the dark web, where the transaction data includes user numbers and transaction information corresponding to the user numbers;

[0007] Perform standardization processing on the transaction data to generate user standardized data and transaction standardized data;

[0008] Based on the transaction data, determine whether there is an association between the user numbers;

[0009] In the case of an association, generate group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers;

[0010] In the case of no association, generate individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers.

[0011] In some of these embodiments, the user standardized data includes the user number and the corresponding virtual wallet address. Determining whether there is an association between the user numbers based on the transaction data includes:

[0012] Obtaining the matching relationship between the virtual wallet address and the user number in the user standardized data;

[0013] In the case where the same virtual wallet address matches at least two user numbers, determining that there is an association between the at least two user numbers.

[0014] In some of these embodiments, the transaction standardized data includes a transaction number, the transaction content corresponding to the transaction number, and the user number that publishes the transaction content. Determining whether there is an association between the user numbers based on the transaction data includes:

[0015] Obtaining the matching relationship between the transaction content and the user number in the transaction standardized data;

[0016] In the case where the same transaction content matches at least two user numbers, determining that there is an association between the at least two user numbers.

[0017] In some of these embodiments, the transaction standardized data includes transaction freshness and transaction popularity. After generating the group target information or the individual target information, the method further includes:

[0018] Based on the transaction freshness and transaction popularity in the group target information or the individual target information, determining the priority of the group target information or the individual target information.

[0019] In some of these embodiments, after generating the group target information or the individual target information, the method further includes:

[0020] Performing identity matching on the group target information or the individual target information based on a matching rule;

[0021] In the case of successful matching, obtaining the identity information corresponding to the user number;

[0022] Associating the identity information with the group target information or the individual target information, and determining that the priority of the group target information or the individual target information is high.

[0023] In some of these embodiments, the transaction standardized data includes a transaction category. Generating group target information based on the user standardized data and the transaction standardized data corresponding to the associated user numbers includes:

[0024] Perform keyword matching on the transaction content based on a keyword library;

[0025] Determine the transaction category corresponding to the transaction content based on the result of the keyword matching;

[0026] Determine the category of the group target information based on the transaction category.

[0027] In some embodiments, the standardizing the transaction data to generate user standardized data and transaction standardized data includes:

[0028] Obtain the transaction freshness in the transaction standardized data based on the release time, merchant online time, and user latest comment time in the transaction data;

[0029] Obtain the transaction popularity in the transaction standardized data based on the transaction status, transaction volume, and user comment quantity in the transaction data.

[0030] In a second aspect, a target information generation device is provided in this embodiment. The target information generation device includes:

[0031] A first acquisition module, configured to acquire dark web transaction data, where the transaction data includes a user number and transaction information corresponding to the user number;

[0032] A first generation module, configured to perform standardization processing on the transaction data to generate user standardized data and transaction standardized data;

[0033] A first determination module, configured to determine whether there is an association between the user numbers based on the transaction data;

[0034] A second generation module, configured to generate group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers when there is an association;

[0035] A third generation module, configured to generate individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers when there is no association.

[0036] In a third aspect, a computer device is provided in this embodiment, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the target information generation method described in the first aspect above is implemented.

[0037] Fourthly, in this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the target information generation method described in the first aspect above are implemented.

[0038] Compared with the related art, in the target information generation method provided in this embodiment, by obtaining dark web transaction data and performing normalization processing, extracting valid information from the transaction data and performing associated storage, a database of user normalization data and transaction normalization data is generated, improving the utilization efficiency of transaction data; determining whether there is an association between user numbers based on the transaction data, that is, determining whether there is a situation where the normalized data items corresponding to different user numbers in the database are the same. If so, it indicates that there may be a joint transaction or an organized transaction composed of multiple users. Based on the user numbers and transaction data corresponding to this group, group target information is generated; if not, based on the user number and transaction data of this user, individual target information is generated, and the type of target information is determined through the association between user numbers, improving the integrity and effectiveness of transaction data analysis.

[0039] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more concise and understandable. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments and descriptions thereof of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the drawings:

[0041] Figure 1 is a hardware structure block diagram of a terminal of the target information generation method according to an embodiment of the present application;

[0042] Figure 2 is a flowchart of the target information generation method according to an embodiment of the present application;

[0043] Figure 3 is a flowchart of the high-priority target information generation method according to an embodiment of the present application;

[0044] Figure 4 is a flowchart of the method for determining the category of group target information according to an embodiment of the present application;

[0045] Figure 5 is a flowchart of the target information generation method according to a preferred embodiment of the present application;

[0046] Figure 6 is a structure block diagram of the target information generation device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To better understand the purpose, technical solution, and advantages of this application, the following describes and explains this application in conjunction with the accompanying drawings and embodiments.

[0048] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by those with ordinary skills in the technical field to which this application belongs. In this application, words such as "a", "an", "one kind", "the", "these", etc. do not indicate a limitation in quantity, and they can be singular or plural. The terms "including", "comprising", "having" and any variants thereof involved in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device including a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent in these processes, methods, products, or devices. The terms "connected", "coupled", etc. involved in this application do not limit to physical or mechanical connections, but may include electrical connections, whether directly or indirectly connected. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, and B exists alone. Usually, the character " / " indicates that the objects associated before and after are in an "or" relationship. The terms "first", "second", "third", etc. involved in this application are only used to distinguish similar objects and do not represent a specific sorting for the objects.

[0049] The method embodiment provided in this embodiment can be executed on a terminal, a computer, a server, or a similar computing device. For example, running on a terminal, Figure 1 is the hardware structure block diagram of the terminal of the target information generation method in this embodiment. As Figure 1 shown, the terminal may include one or more ( Figure 1 only one is shown in the figure) processors 102 and a memory 104 for storing data. Among them, the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The above terminal may also include a transmission device 106 for communication functions and an input / output device 108. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above terminal. For example, the terminal may also include more or fewer components than Figure 1 shown in the figure, or have a different configuration from Figure 1 shown in the figure.

[0050] The memory 104 can be used to store computer programs, such as software programs and modules of application software, such as the computer program corresponding to the target information generation method in this embodiment. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implements the above-mentioned method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 may further include a memory remotely disposed relative to the processor 102, and these remote memories may be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0051] The transmission device 106 is used to receive or send data via a network. The above network includes a wireless network provided by a communication provider of the terminal. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, abbreviated as NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0052] In this embodiment, a target information generation method is provided. Figure 2 It is a flowchart of the target information generation method in this embodiment, as Figure 2 shown, and the process includes the following steps:

[0053] Step S201, obtain the transaction data of the dark web, and the transaction data includes a user number and transaction information corresponding to the user number.

[0054] Through the known dark web trading platform website, the dark web transaction data can be crawled and stored in text format. As a trading platform, the dark web transaction data may include a user number for identifying different users; and transaction information corresponding to the user number, which is used to reflect whether the user participates in the transaction, the role played in the transaction, and the data reflecting the content and transaction status of the transaction. The transaction information may include the status of the user, the virtual wallet address of the user, the transaction number published or participated in by the user, the merchant user number and comment user number corresponding to the transaction number, the transaction release time, the transaction content, the comment content, the merchant online time, the number of user comments, the latest comment time, the trading volume, the transaction status, etc.

[0055] Among them, the user status refers to whether the user is a seller or a buyer in this transaction. The same user can be a buyer in one transaction and a seller in another transaction. The virtual wallet address is the virtual currency wallet address corresponding to the user, which can be a hash value. The transaction content is the published transaction copywriting. The merchant online time is the time when the seller was last online. The transaction status is open or closed.

[0056] Step S202: Standardize the transaction data to generate user standardized data and transaction standardized data.

[0057] Generate user standardized and transaction standardized data based on the transaction data obtained in step S201. Among them, the fields of the transaction standardized data can include:

[0058] Transaction number, which is the number assigned by the transaction website to identify this transaction internally. This number is the unique identifier of this transaction.

[0059] Merchant number, which is the user number of the transaction publisher and interacts with the user standardized data, presented in the format of the user standardized data.

[0060] Publication time, which is the time when the transaction was published.

[0061] Transaction content, which is the published transaction content.

[0062] Transaction category, which is classified based on the text of the transaction content.

[0063] Transaction freshness, which is a comprehensive evaluation of this transaction from the time dimension.

[0064] Transaction popularity, which is a comprehensive evaluation of this transaction from dimensions such as the number of transactions.

[0065] Among them, the transaction number, merchant information, publication time, and transaction content can be directly obtained from the transaction data. The transaction category, transaction freshness, and transaction popularity can be obtained by extracting information from the transaction data and making judgments based on a pre-set algorithm.

[0066] When converting the transaction data into the corresponding transaction standardized data, you can first generate the corresponding fields according to the above-mentioned transaction standardized data fields, and then search for this transaction number in the dark web transaction database to check whether this transaction exists. If it does, update the information of this transaction standardized module; if not, construct a transaction standardized module. A transaction standardized module can correspond to a transaction number.

[0067] The fields of the user standardized data can include:

[0068] User number;

[0069] User status, determine whether the user is a seller or a buyer based on the dimension of whether the user publishes transaction information or comments on transactions;

[0070] Publishing transaction information means the transaction number and category published by the user;

[0071] Commenting on transaction information means the transaction number and category commented by the user;

[0072] Virtual wallet address, which is the virtual currency wallet address used for transactions.

[0073] When converting transaction data into corresponding user standardized data, first generate the user standardized data of the seller according to the transaction number, merchant number, transaction category, and merchant virtual wallet address in the transaction data, in the format of the above user standardized data fields, and the user status is the seller; then search for the user number in the dark web user database to check whether the user exists. If so, update the record of the user in the database, add a new record under the original record, and add the user status, the transaction numbers and categories published by the user. In the existing data of the database, the user status can be the seller or the buyer, or both. If the user does not exist in the database, create a new user record, including the user number, transaction number, transaction category, virtual wallet address, and the user status is the seller. One user standardization module can correspond to one user number.

[0074] The generation method of the user standardized data of the buyer is similar to that of the seller. The transaction numbers and categories published by the above seller can be replaced with the transaction numbers and categories commented by the buyer. Interaction can be carried out between the user standardized data and the transaction standardized data through the user number.

[0075] Step S203, based on the transaction data, determine whether there is an association between user numbers.

[0076] Based on the user standardized data and transaction standardized data generated from the transaction data, determine whether different user numbers have the same record items, and determine whether there is an association between user numbers according to the same record items. For example, the same record items can be the same virtual wallet address, the same transaction content, etc.

[0077] Step S204, in the case of an association, generate group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers.

[0078] If there are identical record items, that is, there is an association between user IDs, it indicates that there is an association or joint transaction between these user IDs, which may involve organized or group behavior. Since the harm of group behavior or organized transactions is usually greater than that of individual transactions, such information can be separately generated into group target information and submitted to the relevant department for investigation. The group target information includes the user standardized data and transaction standardized data corresponding to the mutually associated user IDs.

[0079] Step S205, in the case of no association, generate individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user IDs.

[0080] Through steps S201 to S205, the target information generation method provided in this embodiment, by obtaining dark web transaction data and performing standardized processing, extracts the valid information in the transaction data and stores it in an associated manner, generates a database of user standardized data and transaction standardized data, and improves the utilization efficiency of transaction data; determines whether there is an association between user IDs based on the transaction data, that is, determines whether there is a situation where the standardized data items corresponding to different user IDs in the database are the same. If so, it indicates that there may be a joint transaction or organized transaction composed of multiple users. Based on the user IDs and transaction data corresponding to this group, generate group target information; if not, generate individual target information based on the user ID and transaction data of this user, and determine the type of target information through the association between user IDs, improving the integrity and effectiveness of transaction data analysis.

[0081] In some of these embodiments, a process for determining whether there is an association between user IDs based on transaction data is involved. The user standardized data may include user IDs and corresponding virtual wallet addresses. The process includes the following steps:

[0082] Step S11, obtain the matching relationship between the virtual wallet addresses and user IDs in the user standardized data.

[0083] Statistically analyze all the virtual wallet addresses in the user standardized data to determine the user ID corresponding to each virtual wallet address.

[0084] Step S12, in the case where the same virtual wallet address matches at least two user IDs, determine that there is an association between the at least two user IDs.

[0085] The virtual wallet address corresponds to the user's virtual currency account. If two user IDs use the same virtual wallet address, it can be determined that there is fund sharing between the user IDs.

[0086] Through steps S11 to S12, the target information generation method provided in this embodiment determines the correlation between user IDs based on whether the virtual wallet addresses corresponding to the user IDs are the same, so as to determine whether there is group common trading or organized trading among different users, and provides group target information for this situation, improving the integrity and effectiveness of the target information.

[0087] In some of these embodiments, a process for determining whether there is a correlation between user IDs based on transaction data is involved. The transaction standardized data includes a transaction number, the transaction content corresponding to the transaction number, and the user ID that publishes the transaction content. The process includes the following steps:

[0088] Step S21, obtain the matching relationship between the transaction content and the user ID in the transaction standardized data.

[0089] Statistically analyze all the transaction contents in the transaction standardized data to determine the user ID corresponding to each transaction content.

[0090] Step S22, when the same transaction content matches at least two user IDs, determine that there is a correlation between the at least two user IDs.

[0091] The transaction content is the copywriting content used by the user when publishing a transaction. If the copywriting contents used by different users when publishing transactions are exactly the same, it can be considered that there is common trading or organized trading between the user IDs.

[0092] Through steps S21 to S22, the target information generation method provided in this embodiment determines the correlation between user IDs based on whether the transaction contents corresponding to the user IDs are the same, so as to determine whether there is group common trading or organized trading among different users, and provides group target information for this situation, improving the integrity and effectiveness of the target information.

[0093] In some of these embodiments, a method for determining the priority of group target information or individual target information is involved. The transaction standardized data includes transaction freshness and transaction popularity. After generating the group target information or generating the individual target information, the target information generation method further includes:

[0094] Based on the transaction freshness and transaction popularity in the group target information or individual target information, determine the priority of the group target information or individual target information.

[0095] In transaction data, there are some historical transactions with low trading volumes or long time intervals from the current time. Due to poor transaction execution and timeliness, the corresponding target information has low value and a low processing priority. Therefore, the priority of the corresponding target information can be determined by the trading freshness and trading heat in the transaction standardized data. Trading freshness reflects the timeliness of transactions; trading heat reflects the transaction execution and attention situation.

[0096] For example, the trading freshness and trading heat can be divided into three levels respectively: high, medium, and low. When both the trading freshness and trading heat are high, the priority of the corresponding target information is determined to be high; when either the trading freshness or trading heat is low, the priority of the corresponding target information is determined to be low; in other cases, the priority of the target information is medium. Of course, there can be other corresponding methods for levels, or the priority of the target information can be obtained by means of weighted scoring.

[0097] The method for generating target information provided in this embodiment determines the priority of the corresponding group target information or individual target information through the trading freshness and trading heat in the transaction standardized data, provides a method for judging the value of target information, and improves the effectiveness and processing efficiency of target information.

[0098] In some of these embodiments, it also involves determining the priority of target information through identification information. Figure 3 It is a flowchart of the method for generating high-priority target information in this embodiment. As Figure 3 shown, after generating group target information or individual target information, this process further includes the following steps:

[0099] Step S301, perform identification matching on the group target information or individual target information based on matching rules.

[0100] Perform content cleaning on the user standardized data and transaction standardized data in the group target information or individual target information, and perform matching on the cleaned content through matching rules. The keywords for identification matching can be social accounts, and the matching rules can be keyword matching, keyword length, digital features, etc. For example, a mobile phone number has 11 digits, and WeChat IDs, QQ accounts, email addresses, etc. also have similar digital features, as well as text features such as V numbers and penguin numbers as keywords.

[0101] Step S302, in the case of successful matching, obtain the identification information corresponding to the user number.

[0102] In the case of successful matching, it can be considered that the group target information or individual target information contains identification information, and this identification information can directly locate an individual.

[0103] Step S303: Associate the identification information with the group target information or individual target information, and determine that the priority of the group target information or individual target information is high.

[0104] The target information containing the identification information is of high value for obtaining the detailed information of the transaction. Therefore, the corresponding target information has a high priority.

[0105] Through steps S301 - S303, the target information generation method provided in this embodiment obtains the corresponding identification information by performing identification matching on the target information, and determines that the corresponding priority is high, which improves the effectiveness and value of the target information and provides assistance for the subsequent analysis of the transaction.

[0106] In some of these embodiments, it involves the acquisition process of the group target information category. Figure 4 It is the flowchart of the method for determining the group target information category in the embodiments of this application, as Figure 4 shown, this process includes the following steps:

[0107] Step S401: Perform keyword matching on the transaction content based on the keyword library.

[0108] According to the transaction content in the transaction standardization data, perform keyword matching through the keyword library. For example, "customer data", "email password", "intrusion service", etc. can be used as the matching keywords.

[0109] Step S402: Determine the transaction category corresponding to the transaction content based on the result of the keyword matching.

[0110] According to the type of the keyword, determine the successfully matched transaction content as the type corresponding to the keyword. For example, "customer data" and "email password" can be corresponding to the "data transaction" category, and "intrusion service" can be corresponding to the "service business" category. There can also be other categories such as "virtual item transaction" and "physical item transaction". The transaction content that fails to match can be corresponding to the "other" category.

[0111] Step S403: Determine the category of the group target information based on the transaction category.

[0112] Through steps S401 - S403, the target information generation method provided in this embodiment classifies the target information according to the transaction category through keyword matching, increases the effective information volume of the target information, enables the target information processing personnel to complete the subsequent processing according to the category corresponding to the transaction, and improves the effectiveness and processing efficiency of the target information.

[0113] In some of these embodiments, it involves the process of standardizing the transaction data and obtaining the transaction freshness and transaction heat. This process includes:

[0114] Step S31: Obtain the transaction freshness in the transaction standardization data based on the release time, merchant online time, and user's latest comment time in the transaction data.

[0115] The transaction freshness reflects the timeliness of the transaction and can be measured by the release time, merchant online time, and user's latest comment time in the transaction data. For example, the transaction freshness can be measured by the following algorithm:

[0116] If the release time is within 24 hours, the transaction freshness is high;

[0117] If the release time exceeds 24 hours and the merchant online time is within 24 hours, the transaction freshness is high;

[0118] If the release time exceeds 24 hours, the merchant online time exceeds 24 hours and is less than 3 days, the transaction freshness is medium;

[0119] If the release time exceeds 24 hours, the merchant online time exceeds 3 days, and the user comment time is within 48 hours, the transaction freshness is medium;

[0120] If the release time exceeds 24 hours, the merchant online time exceeds 3 days, and the user comment time exceeds 48 hours (or there is no comment), the transaction freshness is low.

[0121] Step S32: Obtain the transaction popularity in the transaction standardization data based on the transaction status, trading volume, and user comment count in the transaction data.

[0122] The transaction popularity reflects the transaction's closing situation and attention. It can be measured by the transaction status, trading volume, and user comment count in the transaction data. For example, the transaction popularity can be measured by the following algorithm:

[0123] If the transaction status is open and the trading volume is greater than 50, the transaction popularity is high;

[0124] If the transaction status is open, the trading volume is less than 50, and the user comment count is greater than 10, the transaction popularity is medium;

[0125] If the transaction status is open, the trading volume is less than 50, and the user comment count is less than 10, the transaction popularity is low;

[0126] If the transaction status is closed, the transaction popularity is zero.

[0127] Among them, the order of steps S31 and S32 can be swapped.

[0128] The target information generation method provided in this embodiment extracts relevant information from transaction data and obtains the transaction freshness and transaction heat in the transaction standardized data based on a preset algorithm, so as to obtain the priority data of the target information, which is used to improve the effectiveness and processing efficiency of the target information.

[0129] The following describes and explains this embodiment through preferred embodiments.

[0130] Figure 5 It is a flowchart of the target information generation method of this preferred embodiment. As Figure 5 shown, this process includes the following steps:

[0131] S501, crawl the dark web transaction data through the dark web trading platform website and store it in text format;

[0132] S502, obtain the information of 13 fields of the transaction data, including the transaction number, the user number of the publishing merchant, the publishing time, the transaction content, the comment content, the merchant online time, the user number of the comment, the number of user comments, the latest user comment time, the trading volume, the transaction status, the merchant virtual wallet address, and the buyer virtual wallet address;

[0133] S503, perform keyword matching on the transaction content in the transaction data based on the keyword library, and determine the corresponding transaction category according to the keyword matching result;

[0134] S504, obtain the transaction freshness in the transaction standardized data based on the publishing time, the merchant online time, and the latest user comment time in the transaction data;

[0135] S505, obtain the transaction heat in the transaction standardized data based on the transaction status, the trading volume, and the number of user comments in the transaction data;

[0136] Steps S503, S504, and S505 can be arranged in a different order.

[0137] S506, generate transaction standardized data according to steps S502 - S505;

[0138] S507, search for this transaction number in the dark web transaction database to check whether this transaction exists. If it exists, update the information of this transaction standardized module; if not, construct a transaction standardized module;

[0139] S508, generate the user standardized data of the seller according to the merchant user number, the transaction number, the transaction category, and the merchant virtual wallet address;

[0140] S509. Search for the user number in the dark web user database to check if the user exists. If so, update the user standardization module information of the seller; if not, create a user standardization module for the seller.

[0141] S510. Generate the user standardization data for the buyer based on the comment user number, transaction number, transaction category, and buyer's virtual wallet address.

[0142] S511. Search for the user number in the dark web user database to check if the user exists. If so, update the user standardization module information of the buyer; if not, create a user standardization module for the buyer.

[0143] Steps S508 - S509 and S510 - S511 can be swapped.

[0144] S512. Complete the construction of the transaction standardization module and the user standardization module.

[0145] S513. Obtain the matching relationship between the virtual wallet address and the user number in the user standardization data.

[0146] S514. When the same virtual wallet address is matched with at least two user numbers, determine that there is an association between the at least two user numbers.

[0147] S515. Obtain the matching relationship between the transaction content and the user number in the transaction standardization data.

[0148] S516. When the same transaction content is matched with at least two user numbers, determine that there is an association between the at least two user numbers.

[0149] Steps S513 - S514 and S515 - S516 can be swapped.

[0150] S517. When there is an association, generate group target information based on the user standardization data and transaction standardization data corresponding to the associated user numbers.

[0151] S518. When there is no association, generate individual target information based on the user standardization data and transaction standardization data corresponding to the unassociated user numbers.

[0152] S519. Determine the priority of the group target information or individual target information based on the transaction freshness and transaction heat in the group target information or individual target information.

[0153] S520. Perform identity matching on the group target information or individual target information based on the matching rules.

[0154] S521. When the matching is successful, obtain the identification information corresponding to the user number;

[0155] S522. Associate the identification information with the group target information or the individual target information, and determine that the priority of the group target information or the individual target information is high.

[0156] The order of steps S519 and S520 - S522 can be swapped.

[0157] Through the above steps S501 to S522, by obtaining the dark web transaction data, extracting the valid information in the corresponding fields of the transaction data for associated storage, and obtaining the category, freshness, and heat of the transaction through keyword matching and corresponding algorithms to obtain the key information of the transaction; generating a database of user standardized data and transaction standardized data based on the transaction data to improve the data utilization efficiency; determining whether there is an association between user numbers based on the virtual wallet address and the transaction content. If there is, it indicates that there may be a joint transaction or an organized transaction composed of multiple users, and generate group target information; if not, it indicates that the transaction of this user is an individual transaction, and generate individual target information, thereby distinguishing the importance of the target information; determining the priority of the target information according to the transaction freshness and heat, and determining the identity of the transaction participants through identity matching, improving the utilization efficiency and effectiveness of the target information.

[0158] It should be noted that the steps shown in the above process or the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions. And although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from here.

[0159] In some embodiments, the present application also provides a target information generation device. This target information generation device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. The following terms such as "module", "unit", "sub - unit", etc. can be a combination of software and / or hardware that can achieve a predetermined function.

[0160] In some embodiments, Figure 6 is the structural block diagram of the target information generation device of this embodiment. As Figure 6 shown, this device includes:

[0161] The first acquisition module 61 is used to acquire the transaction data of the dark web, and this transaction data includes the user number and the transaction information corresponding to the user number;

[0162] The first generation module 62 is used to perform standardization processing on the transaction data to generate user standardized data and transaction standardized data;

[0163] The first determination module 63 is configured to determine whether there is an association between user numbers based on transaction data;

[0164] The second generation module 64 is configured to, when there is an association, generate group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers;

[0165] The third generation module 65 is configured to, when there is no association, generate individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers.

[0166] In the target information generation device of this embodiment, the dark web transaction data is obtained through the first acquisition module 61, and standardized processing is performed through the first generation module 62 to extract the valid information in the transaction data and perform associated storage to generate a database of user standardized data and transaction standardized data; the first determination module 63 determines whether there is an association between user numbers based on the transaction data, that is, determines whether there is a situation where the transaction information data items corresponding to different user numbers in the database are the same. If so, it indicates that there may be an organized transaction composed of multiple users. The second generation module 64 generates group target information based on the associated user numbers and transaction data; if not, it indicates that the transaction of this user is an individual transaction, and the third generation module 65 generates individual target information based on the user number and transaction data of this user, and obtains group and individual target information respectively according to the number of associated user numbers and the transaction information corresponding to the user numbers, improving the effectiveness of dark web data analysis.

[0167] In some of these embodiments, the user standardized data includes a user number and the corresponding virtual wallet address. The first determination module includes a second acquisition module and a second determination module. The second acquisition module is configured to obtain the matching relationship between the virtual wallet address and the user number in the user standardized data; the second determination module is configured to determine that there is an association between the at least two user numbers when the same virtual wallet address matches at least two user numbers.

[0168] In the target information generation device of this embodiment, the matching relationship between the virtual wallet address and the user number is obtained through the second acquisition module, and the association between the user numbers caused by the virtual wallet address is determined through the second determination module to determine whether there is a group common transaction or an organized transaction between different users, and group target information is provided for this situation, improving the integrity and effectiveness of the target information.

[0169] In some of these embodiments, the transaction standardization data includes a transaction number, the transaction content corresponding to the transaction number, and the user number that publishes the transaction content. The first determination module includes a third acquisition module and a third determination module. The third acquisition module is used to acquire the matching relationship between the transaction content and the user number in the transaction standardization data; the third determination module is used to determine that there is an association between at least two user numbers when the same transaction content matches at least two user numbers.

[0170] In the target information generation device in this embodiment, the third acquisition module acquires the matching relationship between the transaction content and the user number, and the third determination module determines the relevance between the user numbers caused by the transaction content, so as to determine whether there is group common trading or organized trading between different users, and provides group target information for this situation, improving the integrity and effectiveness of the target information.

[0171] In some of these embodiments, the transaction standardization data includes transaction freshness and transaction popularity. The target information generation device further includes a fourth determination module, which is used to determine the priority of the group target information or the individual target information based on the transaction freshness and transaction popularity in the group target information or the individual target information.

[0172] In the target information generation device in this embodiment, the fourth determination module determines the priority of the corresponding group target information or individual target information based on the transaction freshness and transaction popularity in the transaction standardization data, so as to judge the value of the target information according to the priority, improving the effectiveness and processing efficiency of the target information.

[0173] In this embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0174] Optionally, the above computer device may further include a transmission device and input / output devices. Among them, the transmission device is connected to the above processor, and the input / output devices are connected to the above processor.

[0175] Optionally, in this embodiment, the above processor may be configured to execute the following steps through a computer program:

[0176] S1, acquire the transaction data of the dark web, where the transaction data includes user numbers and transaction information corresponding to the user numbers;

[0177] S2, perform standardization processing on the transaction data to generate user standardization data and transaction standardization data;

[0178] S3, based on the transaction data, determine whether there is an association between user numbers;

[0179] S4. In the case of an association, generate group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers.

[0180] S5. In the case of no association, generate individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers.

[0181] It should be noted that for the specific examples in this embodiment, reference can be made to the examples described in the above embodiments and optional implementation manners, which will not be elaborated in this embodiment.

[0182] In addition, in combination with the target information generation method provided in the above embodiments, a storage medium can also be provided in this embodiment to implement it. A computer program is stored on the storage medium; when the computer program is executed by a processor, any one of the target information generation methods in the above embodiments is implemented.

[0183] It should be noted that for the specific examples in this embodiment, reference can be made to the examples described in the above embodiments and optional implementation manners, which will not be elaborated in this embodiment.

[0184] It should be understood that the specific embodiments described here are only used to explain this application, rather than to limit it. According to the embodiments provided in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of this application.

[0185] Obviously, the drawings are only some examples or embodiments of this application. For those of ordinary skill in the art, this application can also be applied to other similar situations based on these drawings without creative efforts. Additionally, it can be understood that although the work done during this development process may be complex and time-consuming, for those of ordinary skill in the art, certain design, manufacturing, or production changes based on the technical content disclosed in this application are only conventional technical means and should not be regarded as insufficient disclosure of this application.

[0186] The term "embodiment" in this application means that the specific features, structures, or characteristics described in combination with the embodiment can be included in at least one embodiment of this application. The phrase appears in various positions in the specification and does not necessarily mean the same embodiment, nor does it mean being independent or alternative to other embodiments and mutually exclusive. Those of ordinary skill in the art can clearly or implicitly understand that the embodiments described in this application can be combined with other embodiments without conflict.

[0187] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of patent protection. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method for generating target information, characterized in that The method includes: Obtaining transaction data of the dark web, where the transaction data includes user numbers and transaction information corresponding to the user numbers; Performing standardization processing on the transaction data to generate user standardized data and transaction standardized data; Based on the transaction data, determining whether there is an association between the user numbers; In the case of an existing association, generating group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers; In the case of no association, generating individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers; The user standardized data includes the user number and the corresponding virtual wallet address. Based on the transaction data, determining whether there is an association between the user numbers includes: Obtaining the matching relationship between the virtual wallet address and the user number in the user standardized data; In the case where the same virtual wallet address matches at least two user numbers, determining that there is an association between the at least two user numbers.

2. The target information generation method according to claim 1, wherein The transaction standardized data includes a transaction number, the transaction content corresponding to the transaction number, and the user number that publishes the transaction content. Based on the transaction data, determining whether there is an association between the user numbers includes: Obtaining the matching relationship between the transaction content and the user number in the transaction standardized data; In the case where the same transaction content matches at least two user numbers, determining that there is an association between the at least two user numbers.

3. The method for generating target information according to claim 1, wherein The transaction standardized data includes transaction freshness and transaction popularity. After generating the group target information or the individual target information, the method further includes: Based on the transaction freshness and transaction popularity in the group target information or the individual target information, determining the priority of the group target information or the individual target information.

4. The method for generating target information according to claim 3, wherein After generating the group target information or the individual target information, the method further includes: Performing identity matching on the group target information or the individual target information based on a matching rule; In the case of successful matching, obtaining the identity information corresponding to the user number; Associating the identity information with the group target information or the individual target information and determining that the priority of the group target information or the individual target information is high.

5. The method for generating target information according to claim 2, wherein The transaction standardized data includes a transaction category. Generating group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers includes: Performing keyword matching on the transaction content based on a keyword library; Determining the transaction category corresponding to the transaction content based on the result of the keyword matching; Determining the category of the group target information based on the transaction category.

6. The target information generation method according to claim 1, wherein Performing standardization processing on the transaction data to generate user standardized data and transaction standardized data includes: Based on the release time, merchant online time, and user's latest comment time in the transaction data, obtaining the transaction freshness in the transaction standardized data; Based on the transaction status, trading volume, and user comment quantity in the transaction data, obtaining the transaction popularity in the transaction standardized data.

7. A target information generation device, characterized in that, The target information generation device includes: A first acquisition module, configured to acquire transaction data of the dark web, where the transaction data includes user numbers and transaction information corresponding to the user numbers; A first generation module, configured to perform standardization processing on the transaction data to generate user standardized data and transaction standardized data; A first determination module, configured to determine whether there is an association between the user numbers based on the transaction data; A second generation module, configured to generate group target information based on the user standardized data and transaction standardized data corresponding to the associated user numbers when there is an association; A third generation module, configured to generate individual target information based on the user standardized data and transaction standardized data corresponding to the unassociated user numbers when there is no association; The user standardized data includes a user number and a corresponding virtual wallet address. The first determination module includes a second acquisition module and a second determination module; the second acquisition module is configured to acquire the matching relationship between the virtual wallet address and the user number in the user standardized data; the second determination module is configured to determine that there is an association between at least two user numbers when the same virtual wallet address matches at least two user numbers.

8. A computer device, comprising a memory and a processor, characterized in that, A computer program is stored in the memory, and the processor is configured to run the computer program to execute the target information generation method according to any one of claims 1 to 6.

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

Images