Abnormal transaction processing method and device for network game

Abstract

The application provides an abnormal transaction processing method and device for network games, which can be used in the financial field or other fields. The method comprises the following steps: obtaining user basic information authorized by a user; obtaining multiple user account information of the same operator authorized by the user according to the user basic information; obtaining user transaction data corresponding to the user account information according to the user account information; determining an abnormal transaction user according to a preset game transaction rule and the user transaction data; and determining an abnormal transaction gang by using a preset user association index according to the user basic information and the user transaction data of the abnormal transaction user. The application accurately identifies abnormal transaction users and abnormal transaction gangs by associating and processing the transaction data of multiple user accounts of the same operator, accurately identifies abnormal transaction gangs in network games, reduces the risk of abnormal transactions in network games, promotes the healthy and orderly development of games, and improves the user experience.

Description

Technical Field

[0001] This invention relates to the field of abnormal transactions, and more particularly to a method and apparatus for handling abnormal transactions in online games. Background Technology

[0002] With the rapid development of the Internet and the widespread application of financial technology, abnormal transaction methods have become more covert and complex. Abnormal transaction behaviors are characterized by a high degree of organization, a large number of participants, and dense fund transfer networks, which increases the demand for effective monitoring of abnormal transactions.

[0003] Currently, online game studios recruit professional gamers to provide services such as game leveling, quest completion assistance, and farming in-game currency, equipment, mounts, and honor points. Meanwhile, illicit trading groups establish studios to use illicit funds to recharge and upgrade in-game equipment, which they then sell to other players for in-game currency. This currency is then traded through in-game trading platforms or third-party payment platforms, converting it into legitimate in-game revenue. This process breeds illicit trading activities. Currently, non-bank financial institutions lack effective mechanisms to combat illicit transactions, but these scenarios are rapidly expanding from traditional banks and non-bank financial institutions to other industries, with cases involving illicit transactions through online games becoming increasingly common. Summary of the Invention

[0004] To address the problems existing in the prior art, the main objective of this invention is to provide a method and apparatus for handling abnormal transactions in online games, thereby accurately identifying abnormal transaction groups in online games and reducing the risks arising from abnormal transactions.

[0005] To achieve the above objectives, embodiments of the present invention provide a method for handling abnormal transactions in online games, the method comprising:

[0006] Obtain user basic information authorized by the user, and based on the user basic information, obtain information on multiple user accounts belonging to the same operator authorized by the user.

[0007] Based on user account information, obtain user transaction data that is authorized by the user and corresponds one-to-one with the user account information;

[0008] Identify users with abnormal transactions based on preset game trading rules and user transaction data;

[0009] By using preset user association indicators, abnormal trading groups can be identified based on the basic user information and transaction data of users with abnormal transactions.

[0010] Optionally, in one embodiment of the present invention, user transaction data includes: user daily transaction data, account recharge data, game property transaction data, game equipment transaction data, and account login data.

[0011] Optionally, in one embodiment of the present invention, the abnormal transaction users are identified based on preset game transaction rules and user transaction data, including:

[0012] Based on user transaction data, including daily user transaction data, account recharge data, game property transaction data, and game equipment transaction data, determine the transaction frequency, transaction amount, and the proportion of game property outflow.

[0013] Users with abnormal transactions are identified based on preset game transaction rules, transaction frequency, transaction amount, and the proportion of game assets flowing out.

[0014] Optionally, in one embodiment of the present invention, determining abnormal transaction users based on preset game transaction rules and the user transaction data further includes:

[0015] Determine the account login frequency based on the account login data in the user transaction data;

[0016] Based on preset game trading rules and account login frequency, identify users with abnormal trading activity.

[0017] Optionally, in one embodiment of the present invention, the user association indicators include identity association indicators and transaction association indicators.

[0018] Optionally, in one embodiment of the present invention, by utilizing preset user association indicators and based on the basic user information and transaction data of users with abnormal transactions, the abnormal transaction groups are identified as including:

[0019] By using the identity association indicator among the preset user association indicators, the basic information of users corresponding to abnormal transactions is matched to identify the abnormal transaction groups.

[0020] Optionally, in one embodiment of the present invention, determining the abnormal transaction group by utilizing preset user association indicators and based on the user's basic information and transaction data corresponding to the abnormal transaction user further includes:

[0021] By using the pre-set user association indicators, transaction association indicators are used to match the transaction data of users with abnormal transactions, thereby identifying abnormal transaction groups.

[0022] This invention also provides an abnormal transaction processing device for online games, the device comprising:

[0023] The user information module is used to obtain user basic information authorized by the user, and based on the user basic information, to obtain information on multiple user accounts belonging to the same operator authorized by the user.

[0024] The transaction data module is used to obtain user transaction data that is authorized by the user and corresponds one-to-one with the user's account information, based on the user's account information;

[0025] The abnormal transaction module is used to identify users with abnormal transactions based on preset game transaction rules and the user transaction data.

[0026] The user association module is used to identify abnormal trading groups based on the basic user information and transaction data of users with abnormal transactions, using preset user association indicators.

[0027] Optionally, in one embodiment of the present invention, user transaction data includes: user daily transaction data, account recharge data, game property transaction data, game equipment transaction data, and account login data.

[0028] Optionally, in one embodiment of the present invention, the abnormal transaction module includes:

[0029] The transaction data unit is used to determine the transaction frequency, transaction amount, and outflow ratio of game assets based on the user's daily transaction data, account recharge data, game property transaction data, and game equipment transaction data in the user transaction data.

[0030] The first abnormal transaction unit is used to identify users with abnormal transactions based on preset game transaction rules, transaction frequency, transaction amount, and the proportion of game property outflow.

[0031] Optionally, in one embodiment of the present invention, the abnormal transaction module further includes:

[0032] The account login unit is used to determine the account login frequency based on the account login data in the user transaction data;

[0033] The second abnormal transaction unit is used to identify users with abnormal transactions based on preset game transaction rules and account login frequency.

[0034] Optionally, in one embodiment of the present invention, the user association indicators include identity association indicators and transaction association indicators.

[0035] Optionally, in one embodiment of the present invention, the user association module is further configured to use the identity association index among the preset user association indicators to perform association matching on the basic user information corresponding to the abnormal transaction user, and to identify the abnormal transaction group.

[0036] Optionally, in one embodiment of the present invention, the user association module is further configured to use the transaction association indicators in the preset user association indicators to perform association matching on the user transaction data corresponding to the abnormal transaction user, and to identify the abnormal transaction group.

[0037] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0038] The present invention also provides a computer-readable storage medium storing a computer program that performs the above-described methods by a computer.

[0039] The present invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described method.

[0040] This invention accurately identifies users and groups engaging in abnormal transactions by correlating transaction data from multiple user accounts within the same operator. This enables the accurate identification of such groups in online games, reduces the risks associated with abnormal transactions, promotes the healthy and orderly development of games, and enhances the user experience. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating an abnormal transaction handling method for an online game according to an embodiment of the present invention;

[0043] Figure 2 This is a flowchart for determining users with abnormal transactions in an embodiment of the present invention;

[0044] Figure 3 This is a flowchart for determining users with abnormal transactions in another embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the structure of an abnormal transaction processing device for an online game according to an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the abnormal transaction module in an embodiment of the present invention;

[0047] Figure 6 This is a schematic diagram of the abnormal transaction module in another embodiment of the present invention;

[0048] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0049] This invention provides a method and apparatus for handling abnormal transactions in online games, which can be used in the financial field and other fields. It should be noted that the method and apparatus for handling abnormal transactions in online games of this invention can be used in the financial field, or in any field other than the financial field. The application field of the method and apparatus for handling abnormal transactions in online games of this invention is not limited.

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] like Figure 1 The diagram shows a flowchart of an abnormal transaction handling method for online games according to an embodiment of the present invention. The execution subject of this method includes, but is not limited to, a computer. This invention accurately identifies users and groups engaging in abnormal transactions by correlating transaction data from multiple user accounts within the same operator. This enables accurate identification of such groups, reduces the risks associated with abnormal transactions in online games, promotes the healthy and orderly development of games, and enhances user experience. The method shown in the diagram includes:

[0052] Step S1: Obtain the user's basic information authorized by the user, and based on the user's basic information, obtain the information of multiple user accounts belonging to the same operator authorized by the user.

[0053] The basic user information includes the user's real name, ID number, age, and gender. First, the user's account information related to a specific online game is retrieved. Specifically, the user account information includes the user ID, etc.

[0054] Furthermore, by utilizing the user's basic information, the system can obtain the account information of other users of the same user under the same operator, that is, obtain the user's account information in other online games under the same operator.

[0055] Step S2: Based on the user account information, obtain user transaction data that is authorized by the user and corresponds one-to-one with the user account information.

[0056] This involves acquiring user account information, such as user transaction data under a user ID, as well as user transaction data under other user accounts belonging to the same user. Specifically, user transaction data includes daily user transaction data, account recharge data, game property transaction data, game equipment transaction data, and account login data.

[0057] Step S3: Identify users with abnormal transactions based on preset game transaction rules and user transaction data.

[0058] The basic transaction data processing, which involves analyzing daily user transaction data, account recharge data, game asset transaction data, and game equipment transaction data, yields transaction frequency, transaction amount, and the outflow ratio of game assets. The conventional calculation methods used to process the transaction data are not detailed here.

[0059] Furthermore, statistical calculations are performed on the account login data within the user transaction data to obtain data such as account login frequency. The conventional statistical calculation methods used are not detailed here.

[0060] Furthermore, the preset game transaction rules include transaction frequency thresholds, transaction amount thresholds, game asset outflow ratio thresholds, and account login frequency thresholds. The user's transaction frequency, transaction amount, game asset outflow ratio, and account login frequency thresholds are compared with the thresholds in the user's transaction data. If they exceed the corresponding thresholds, the user is identified as an abnormal transaction user. In addition, this abnormal transaction user is designated as the primary user.

[0061] Step S4: Using preset user association indicators, identify the abnormal trading group based on the user's basic information and transaction data corresponding to the abnormal trading user.

[0062] Among them, user-related metrics include identity-related metrics and transaction-related metrics. Specifically, identity-related metrics also include attribute-related metrics and business-related metrics, while transaction-related metrics include counterparty-related metrics and behavioral-related metrics.

[0063] Furthermore, the identity association indicator within the user association metrics is used to match and associate abnormal transaction users who are the main users. Specifically, with the user's authorization, the user's basic information is matched and associated according to the identity association indicator to obtain other account information belonging to the same user. For example, the user's basic information, such as real-name information, contact information, IP address, and MAC address, is matched and associated with the main user to find identity-related users, and the main user and identity-related users are identified as an abnormal transaction group.

[0064] Furthermore, transaction association metrics within the user association metrics are used to match and associate users who are engaging in abnormal transactions with the main user. Specifically, with the user's authorization, user transaction data is matched and associated according to transaction association metrics to identify users who have engaged in abnormal transactions with the main user. For example, matching and associating relevant information such as the main user's trading counterparties, the flow of traded items, and the transaction map identifies users who have transactional relationships with the main user, and the main user and these users are then identified as an abnormal transaction group.

[0065] Furthermore, the main user, the user associated with the identity, and the user associated with the transaction will be considered as an abnormal transaction group.

[0066] As an embodiment of the present invention, user transaction data includes: user daily transaction data, account recharge data, game property transaction data, game equipment transaction data, and account login data.

[0067] In this embodiment, as Figure 2 As shown, based on the preset game transaction rules and the user transaction data, users with abnormal transactions are identified as including:

[0068] Step S21: Based on the user's daily transaction data, account recharge data, game property transaction data, and game equipment transaction data in the user transaction data, determine the transaction frequency, transaction amount, and game property outflow ratio;

[0069] Step S22: Identify users with abnormal transactions based on preset game transaction rules, transaction frequency, transaction amount, and the proportion of game assets flowing out.

[0070] Among them, by performing basic transaction data processing on user daily transaction data, account recharge data, game property transaction data, and game equipment transaction data, we can obtain transaction frequency, transaction amount, and game property outflow ratio.

[0071] Furthermore, the preset game transaction rules include transaction frequency thresholds, transaction amount thresholds, and game asset outflow ratio thresholds. The user's transaction frequency, transaction amount, and game asset outflow ratio are compared with the thresholds in the user's transaction data. If the values ​​exceed the corresponding thresholds, the user is identified as an abnormal transaction user.

[0072] In this embodiment, as Figure 3 As shown, based on the preset game trading rules and user trading data, users identified as having engaged in abnormal trading also include:

[0073] Step S31: Determine the account login frequency based on the account login data in the user transaction data;

[0074] Step S32: Identify users with abnormal transactions based on preset game transaction rules and account login frequency.

[0075] Among them, statistical calculations are performed on the account login data in the user transaction data to obtain data such as account login frequency.

[0076] Furthermore, the preset game transaction rules include an account login frequency threshold. The user's account login frequency threshold is compared with the threshold in the user's transaction data; if it exceeds the corresponding threshold, the user is identified as an abnormal transaction user.

[0077] As an embodiment of the present invention, user association indicators include identity association indicators and transaction association indicators.

[0078] Among them, user-related metrics include identity-related metrics and transaction-related metrics. Specifically, identity-related metrics also include attribute-related metrics and business-related metrics, while transaction-related metrics include counterparty-related metrics and behavioral-related metrics.

[0079] In this embodiment, the determination of abnormal transaction groups by using preset user association indicators and based on the user basic information and user transaction data corresponding to abnormal transaction users includes: using the identity association indicators in the preset user association indicators to perform association matching on the user basic information corresponding to abnormal transaction users to determine the abnormal transaction groups.

[0080] Specifically, the identity association indicator within the user association metrics is used to match and associate users who are engaging in abnormal transactions, with the main user as the primary user. With user authorization, the basic user information is matched and associated according to the identity association indicator to obtain information about other accounts belonging to the same user. For example, the basic user information of the primary user is matched and associated with information such as real-name information, contact information, IP address, and MAC address to find users with an identity association with the primary user, and the primary user and the users with the identity association are identified as an abnormal transaction group.

[0081] In this embodiment, determining the abnormal transaction group by using preset user association indicators and based on the user's basic information and transaction data corresponding to the abnormal transaction user also includes: using the transaction association indicators in the preset user association indicators to perform association matching on the user transaction data corresponding to the abnormal transaction user to determine the abnormal transaction group.

[0082] Specifically, the transaction association indicator within the user association metrics is used to match and associate users who are engaging in abnormal transactions with the main user. With user authorization, user transaction data is matched and associated according to the transaction association indicator to identify users who have engaged in abnormal transactions with the main user. For example, matching and associating information such as the main user's trading partners, the flow of traded items, and the transaction map identifies users who have a transactional relationship with the main user, and these main user and their associated users are considered an abnormal transaction group. Thus, the main user, users associated with the main user's identity, and users associated with the main user's transactions are considered an abnormal transaction group.

[0083] In one specific embodiment of the present invention, as the black market for games continues to evolve, online game operators, for profit, do not restrict players from using physical and virtual assets offline without affecting the operation of the game. This results in frequent occurrences of abnormal transactions through game platforms. The present invention monitors abnormal transactions through the circulation of game currency and equipment, thereby reducing the risk of games being used for abnormal transactions.

[0084] In this embodiment, a network relationship chain model is constructed based on information such as game user information, in-game transaction information, and team information. This model is used to monitor and analyze abnormal transaction entities for game operators. When assisting in the analysis of abnormal transactions, the model displays and pushes game players that are related to the entity being alerted. Through information such as user ID, relationship, and transaction intimacy, the model supports game operators in conducting a comprehensive analysis of such teams, further determining the degree of suspicion of the abnormal transaction entity, and determining whether the user is involved in game black market activities or suspected of abnormal transactions.

[0085] Step 1: Identify suspected abnormal transaction scenarios. Game operators retrieve user data through the backend, and comprehensively judge whether there are any abnormal situations based on users' daily transaction characteristics, recharge status, virtual property consumption, equipment purchase status, and account login status, while comparing user participation in other games of the same operator. For example, game points can be freely circulated among game players through game currency or game item trading, and the transaction price is agreed upon between players. Some online games also have gifting and payment on behalf functions. Abnormal transaction groups use real money to recharge game points, use the points to buy game currency and game items from players, and then trade and exchange game currency and game equipment with other players, ultimately realizing the conversion of virtual property into real property.

[0086] Among these, the abnormal trading groups use methods such as decentralized point card top-ups, game item gifting, and payment on behalf of others to separate assets of unknown origin. Multiple game accounts trade with each other, merging legitimate income with assets of unknown origin to achieve a superficial legitimacy and complete the abnormal trading activities. Anomaly detection focuses on game players frequently trading game equipment, materials, and in-game currency. Trigger criteria are set for each situation, such as transaction amount, transaction frequency, review period, and inflow / outflow ratio of game assets, to determine whether the main user is exhibiting abnormal behavior. For example, normal users purchase equipment according to their game level, but abnormal users may trade high-level equipment at low levels or frequently trade multiple pieces of equipment.

[0087] Step 2: Determine the association between abnormal groups. The association keys between the main body and the group are shown in Table 1.

[0088] Table 1

[0089]

[0090]

[0091] Among them, users with abnormal behavior are identified based on the daily transactions of all game players on the server. Users who engage in player-to-player transactions are screened every day (including in-game equipment trading platforms). Reasonable ranges are designed based on the value of traded game currency and traded equipment (items). Users who frequently trade with abnormal users or engage in transactions exceeding a certain value are classified as a group.

[0092] Furthermore, the criteria for judging association can be based on transaction association as an example: abnormal trading groups need to buy / sell a large number of equipment (items) and game currency to carry out transactions. The account transaction mode has characteristics such as concentrated transfer of game assets into the account, dispersed transfer out of the account, or quick in and out of the account, with obvious transition. At the same time, abnormal trading groups will select commodities with high prices, wide liquidity, and wide range of uses for trading. Therefore, the abnormal characteristics can be judged by comprehensively judging whether there are abnormal characteristics through the type of equipment (items) traded, transaction frequency, transaction amount, and transaction mode.

[0093] This invention accurately identifies users and groups engaging in abnormal transactions by correlating transaction data from multiple user accounts within the same operator. This enables the accurate identification of such groups in online games, reduces the risks associated with abnormal transactions, promotes the healthy and orderly development of games, and enhances the user experience.

[0094] like Figure 4 The diagram shown is a structural schematic of an abnormal transaction processing device for an online game according to an embodiment of the present invention. The device shown in the diagram includes:

[0095] User information module 10 is used to obtain user basic information authorized by the user, and based on the user basic information, obtain information on multiple user accounts belonging to the same operator authorized by the user.

[0096] The transaction data module 20 is used to obtain user transaction data that is authorized by the user and corresponds one-to-one with the user account information, based on the user account information;

[0097] The abnormal transaction module 30 is used to identify users with abnormal transactions based on preset game transaction rules and the user transaction data.

[0098] The user association module 40 is used to identify abnormal trading groups based on the basic user information and transaction data of users with abnormal transactions, using preset user association indicators.

[0099] As an embodiment of the present invention, user transaction data includes: user daily transaction data, account recharge data, game property transaction data, game equipment transaction data, and account login data.

[0100] In this embodiment, as Figure 5 As shown, the abnormal transaction module 30 includes:

[0101] The transaction data unit 31 is used to determine the transaction frequency, transaction amount, and outflow ratio of game assets based on the user's daily transaction data, account recharge data, game property transaction data, and game equipment transaction data in the user transaction data.

[0102] The first abnormal transaction unit 32 is used to identify abnormal transaction users based on preset game transaction rules, transaction frequency, transaction amount and game property outflow ratio.

[0103] In this embodiment, as Figure 6 As shown, the abnormal transaction module 30 also includes:

[0104] Account login unit 33 is used to determine the account login frequency based on the account login data in the user transaction data;

[0105] The second abnormal transaction unit 34 is used to identify abnormal transaction users based on preset game transaction rules and account login frequency.

[0106] As an embodiment of the present invention, user association indicators include identity association indicators and transaction association indicators.

[0107] In this embodiment, the user association module 40 is also used to use the identity association index in the preset user association index to match the basic information of users corresponding to abnormal transactions and identify the abnormal transaction group.

[0108] In this embodiment, the user association module 40 is also used to use the transaction association indicators in the preset user association indicators to perform association matching on the user transaction data corresponding to the abnormal transaction users, and to identify the abnormal transaction group.

[0109] Based on the same concept as the aforementioned method for handling abnormal transactions in online games, this invention also provides an apparatus for handling abnormal transactions in online games. Since the principle by which this apparatus solves the problem is similar to that of the aforementioned method, the implementation of this apparatus can refer to the implementation of the aforementioned method, and will not be repeated here.

[0110] This invention accurately identifies users and groups engaging in abnormal transactions by correlating transaction data from multiple user accounts within the same operator. This enables the accurate identification of such groups in online games, reduces the risks associated with abnormal transactions, promotes the healthy and orderly development of games, and enhances the user experience.

[0111] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0112] The present invention also provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the above-described method.

[0113] The present invention also provides a computer-readable storage medium storing a computer program that performs the above-described methods by a computer.

[0114] like Figure 7 As shown, the electronic device 600 may also include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily need to include these components. Figure 7 All components shown; in addition, the electronic device 600 may also include Figure 7 For components not shown, please refer to existing technologies.

[0115] like Figure 7 As shown, the central processing unit 100, sometimes also referred to as a controller or operating control, may include a microprocessor or other processor device and / or logic device. The central processing unit 100 receives inputs and controls the operation of various components of the electronic device 600.

[0116] The memory 140 may be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It may store the aforementioned failure-related information, and also store a program for executing that information. The central processing unit 100 may execute the program stored in the memory 140 to perform information storage or processing, etc.

[0117] Input unit 120 provides input to central processing unit 100. Input unit 120 may be, for example, a keypad or touch input device. Power supply 170 provides power to electronic device 600. Display 160 displays images and text. Display may be, for example, an LCD display, but is not limited thereto.

[0118] The memory 140 can be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), a SIM card, etc. It can also be a memory that retains information even when power is off, can be selectively erased, and contains more data; examples of this type of memory are sometimes referred to as EPROMs. The memory 140 can also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142 for storing application programs and function programs or processes for executing the operation of the electronic device 600 via the central processing unit 100.

[0119] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for the electronic device's communication functions and / or for performing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0120] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processing unit 100 to provide input signals and receive output signals, which can be the same as in a conventional mobile communication terminal.

[0121] Based on different communication technologies, multiple communication modules 110 can be configured in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless LAN modules. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132, thereby enabling typical telecommunications functions. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. Additionally, the audio processor 130 is coupled to a central processing unit 100, enabling on-device recording via the microphone 132 and on-device playback of stored audio via the speaker 131.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0124] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0125] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0126] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for handling abnormal transactions in online games, characterized in that, The method includes: Obtain user basic information authorized by the user, and based on the user basic information, obtain information on multiple user accounts belonging to the same operator authorized by the user. Based on the user account information, obtain user transaction data that is authorized by the user and corresponds one-to-one with the user account information; Based on the preset game transaction rules and the user transaction data, abnormal transaction users are identified. The preset game transaction rules include: transaction frequency threshold, transaction amount threshold, game property outflow ratio threshold, and account login frequency threshold. The process of identifying abnormal transaction users based on the preset game transaction rules and the user transaction data includes: determining the transaction frequency, transaction amount, and game asset outflow ratio based on the user's daily transaction data, account recharge data, game property transaction data, and game equipment transaction data in the user transaction data; and identifying abnormal transaction users based on the preset game transaction rules, the transaction frequency, the transaction amount, and the game asset outflow ratio. Determining the abnormal transaction user based on the preset game transaction rules and the user transaction data further includes: determining the account login frequency based on the account login data in the user transaction data; and determining the abnormal transaction user based on the preset game transaction rules and the account login frequency. By using preset user association indicators, the abnormal transaction groups can be identified based on the basic user information and transaction data of the users corresponding to the abnormal transactions.

2. The method according to claim 1, characterized in that, The user transaction data includes: daily user transaction data, account recharge data, game property transaction data, game equipment transaction data, and account login data.

3. The method according to claim 1, characterized in that, The user association metrics include identity association metrics and transaction association metrics.

4. The method according to claim 3, characterized in that, The method of identifying abnormal trading groups by utilizing preset user association indicators and based on the basic user information and transaction data of the users corresponding to the abnormal transactions includes: Using the identity association index among the preset user association indicators, the basic information of the users corresponding to the abnormal transactions is matched to identify the abnormal transaction group.

5. The method according to claim 3, characterized in that, The step of identifying abnormal trading groups based on preset user association indicators and the basic user information and transaction data of the users with abnormal transactions also includes: By using the transaction association indicators in the preset user association indicators, the user transaction data corresponding to the abnormal transaction users are matched to identify the abnormal transaction group.

6. An abnormal transaction processing device for an online game, characterized in that, The device includes: The user information module is used to obtain user basic information authorized by the user, and based on the user basic information, to obtain information on multiple user accounts belonging to the same operator authorized by the user. The transaction data module is used to obtain user transaction data that is authorized by the user and corresponds one-to-one with the user account information, based on the user account information; The abnormal transaction module is used to identify users with abnormal transactions based on preset game transaction rules and the user transaction data. The preset game transaction rules include: transaction frequency threshold, transaction amount threshold, game property outflow ratio threshold, and account login frequency threshold. The abnormal transaction module includes: a transaction data unit, used to determine the transaction frequency, transaction amount, and game property outflow ratio based on the user's daily transaction data, account recharge data, game property transaction data, and game equipment transaction data in the user transaction data; and a first abnormal transaction unit, used to determine abnormal transaction users based on preset game transaction rules, the transaction frequency, the transaction amount, and the game property outflow ratio. The abnormal transaction module further includes: an account login unit, used to determine the account login frequency based on the account login data in the user transaction data; and a second abnormal transaction unit, used to determine abnormal transaction users based on preset game transaction rules and the account login frequency. The user association module is used to identify abnormal trading groups based on the user's basic information and transaction data corresponding to the abnormal trading user by using preset user association indicators.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that enables a computer to execute the method according to any one of claims 1 to 5.

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