CN110610365A - Method and device for identifying transaction request - Google Patents
Method and device for identifying transaction request Download PDFInfo
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- CN110610365A CN110610365A CN201910876011.9A CN201910876011A CN110610365A CN 110610365 A CN110610365 A CN 110610365A CN 201910876011 A CN201910876011 A CN 201910876011A CN 110610365 A CN110610365 A CN 110610365A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/9038—Presentation of query results
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q20/00—Payment architectures, schemes or protocols
- G06Q20/38—Payment protocols; Details thereof
- G06Q20/40—Authorisation, e.g. identification of payer or payee, verification of customer or shop credentials; Review and approval of payers, e.g. check credit lines or negative lists
- G06Q20/401—Transaction verification
- G06Q20/4016—Transaction verification involving fraud or risk level assessment in transaction processing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
Abstract
The invention discloses a method and a device for identifying a transaction request, and relates to the technical field of computers. One embodiment of the method comprises: receiving and analyzing a transaction request to obtain associated information between a first user and a second user; adding the transaction request to a sub-network-like graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users; and identifying the characteristic information of the first user based on the subnet graph corresponding to the first user, thereby identifying the transaction request. The embodiment can solve the technical problems that the recognition efficiency is low and the accuracy cannot be guaranteed.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for identifying a transaction request.
Background
Today, network transactions are getting larger and larger, and therefore it is important to identify whether a transaction is normal or not. For abnormal transactions (such as fraudulent transactions, transaction of swiping bills, malicious transactions and the like), not only the benefits of both parties of the transaction are damaged, but also the fairness and fairness of network transactions are influenced, so that the user experience is reduced.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
at present, whether the transaction is normal or not is mainly identified through manual inspection, so that the identification efficiency is low, and the identification accuracy cannot be guaranteed.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for identifying a transaction request, so as to solve the technical problems of low identification efficiency and incapability of ensuring accuracy.
To achieve the above object, according to an aspect of an embodiment of the present invention, there is provided a method of identifying a transaction request, including:
receiving and analyzing a transaction request to obtain associated information between a first user and a second user;
adding the transaction request to a sub-network-like graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users;
and identifying the characteristic information of the first user based on the subnet graph corresponding to the first user, thereby identifying the transaction request.
Optionally, adding the transaction request to the sub-network graph corresponding to the first user according to the association information between the first user and the second user includes:
adding the transaction request to a global mesh graph according to association information between the first user and a second user; in the global mesh graph, nodes represent users, and the weight of connecting lines among the nodes represents the transaction times among the users;
judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value or not according to the global mesh map;
and if so, adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
Optionally, the weight change rate of the first user is calculated by the following method:
and in a preset time period, increasing the weight of a connecting line connected with the first user node.
Optionally, identifying feature information of the first user based on the subnet graph corresponding to the first user, so as to identify the transaction request includes:
calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user;
judging whether the similarity is greater than or equal to a preset similarity threshold value or not;
if so, identifying the first user as a characteristic user, and identifying the transaction request as an abnormal transaction request;
if not, identifying the transaction request as a normal transaction request.
Optionally, calculating a similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the feature user includes:
generating the first vector Q according to the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn)；
Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn)；
Calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm;
wherein, CiRepresenting a user i, M corresponding to a node connected to a first user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
Further, according to another aspect of an embodiment of the present invention, there is provided an apparatus for identifying a transaction request, including:
the receiving module is used for receiving and analyzing the transaction request to obtain the association information between the first user and the second user;
the adding module is used for adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users;
and the identification module is used for identifying the characteristic information of the first user based on the subnet graph corresponding to the first user so as to identify the transaction request.
Optionally, the adding module is further configured to:
adding the transaction request to a global mesh graph according to association information between the first user and a second user; in the global mesh graph, nodes represent users, and the weight of connecting lines among the nodes represents the transaction times among the users;
judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value or not according to the global mesh map;
and if so, adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
Optionally, the weight change rate of the first user is calculated by the following method:
and in a preset time period, increasing the weight of a connecting line connected with the first user node.
Optionally, the identification module is further configured to:
calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user;
judging whether the similarity is greater than or equal to a preset similarity threshold value or not;
if so, identifying the first user as a characteristic user, and identifying the transaction request as an abnormal transaction request;
if not, identifying the transaction request as a normal transaction request.
Optionally, the identification module is further configured to:
generating the first vector Q according to the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn)；
Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn)；
Calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm;
wherein, CiRepresenting a user i, M corresponding to a node connected to a first user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any of the embodiments described above.
According to another aspect of the embodiments of the present invention, there is also provided a computer readable medium, on which a computer program is stored, which when executed by a processor implements the method of any of the above embodiments.
One embodiment of the above invention has the following advantages or benefits: because the association information between the first user and the second user is adopted, the transaction request is added to the sub-network graph corresponding to the first user, and the technical means for identifying the transaction request based on the sub-network graph is adopted, the technical problems that the identification efficiency is low and the accuracy cannot be guaranteed in the prior art are solved. According to the embodiment of the invention, the global mesh graph is constructed based on the transaction times, and then the sub-mesh graphs are constructed based on the weight change rate of the global mesh graph, so that the transaction request is identified by calculating the similarity between the sub-mesh graphs, and not only can the normal condition of the transaction request be rapidly identified, but also the identification accuracy can be improved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of a main flow of a method of identifying a transaction request according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a main flow of a method of identifying a transaction request according to one referential embodiment of the present invention;
FIG. 3 is a schematic diagram of a main flow of a method of identifying a transaction request according to another referenceable embodiment of the present invention;
FIG. 4 is a schematic diagram of the main modules of an apparatus for identifying a transaction request according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of the main flow of a method of identifying a transaction request according to an embodiment of the invention. As an embodiment of the present invention, as shown in fig. 1, the method for identifying a transaction request may include:
step 101, receiving and analyzing a transaction request to obtain associated information between a first user and a second user.
After a transaction request sent by a client is received, the transaction request is analyzed, and therefore association information between a first user and a second user is obtained. Optionally, the association information of the first user and the second user may be association information of both parties of the transaction, such as an association relationship between an account name of the first user and an account name of the second user. Optionally, the transaction request may be parsed to obtain transaction elements such as a transaction card number of the first user, a transaction card number of the second user, a transaction amount, a transaction address (such as an IP address of the transaction device), and a transaction time.
Step 102, adding the transaction request to a sub-network-like graph corresponding to the first user according to the association information between the first user and the second user.
After the association information of the first user and the second user is obtained through analysis, the transaction request is added to the sub-network-shaped graph corresponding to the first user according to the association information of the first user and the second user. In the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users. Therefore, in the sub-network-like graph corresponding to the first user, the first user node is connected with other user nodes, and the connection line of the first user node and any one user node indicates that the first user and any one user generate a transaction request, and the weight of the connection line indicates the number of times of the transaction request. It should be noted that, in the embodiment of the present invention, whether the transaction request is finally accepted or not is independent of the number of transaction requests, and even if the transaction request is finally rejected, the weight is still added on the sub-mesh graph.
Optionally, step 102 comprises: adding the transaction request to a global mesh graph according to association information between the first user and a second user; judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value or not according to the global mesh map; and if so, adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
In the global mesh graph, each node is regarded as a user, and the user may be a merchant (such as a supermarket, a hotel, an e-commerce, and the like) or a cardholder. If a transaction request is generated between the users, a connecting line is added between the two user nodes, the weight of the connecting line is marked as 1, and the weight of the connecting line is correspondingly increased by one when a transaction request is added subsequently. Thus, in the global mesh graph, nodes represent users and the weights of the links between nodes represent the number of transactions between users. Therefore, in the global mesh graph, any user node is connected with other user nodes, the connection line of the user node and any user node indicates that the user and any other user generate transaction requests, and the weight of the connection line indicates the number of transaction requests. It should be noted that, in the embodiment of the present invention, whether the transaction request is eventually accepted or not is independent of the number of transaction requests, and even if the transaction request is eventually rejected to be accepted, the weight is still added on the global mesh map.
Whenever a transaction is made between the merchant and the cardholder, the weight between the merchant node and the cardholder node is increased by one. Thus, a global mesh graph is a many-to-many graph formed between all cardholders and all merchants, while a subnetwork graph is a one-to-many graph formed between one merchant and all cardholders that have transactions with that merchant.
Optionally, the weight change rate of the first user is calculated by the following method: and in a preset time period, increasing the weight of a connecting line connected with the first user node. In an embodiment of the present invention, the weight increase amount of each connection connected to the first user node in the last period of time (e.g., the last day, the last week, the last half month, and the last month) is calculated, so as to obtain the weight change rate of the first user. If the weight change rate of the first user is greater than or equal to a preset change rate threshold, it is indicated that the transaction amount of the first user is suddenly increased and a suspected risk exists, the transaction is regarded as a suspected risk transaction, and the transaction request is added to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user (for calculating the similarity in step 103 to further determine whether the transaction is normal). If the weight change rate of the first user is smaller than a preset change rate threshold value, the transaction is normal, and the transaction can be processed normally. Whether the merchant has a large number of scenes of consumption of the cardholder can be detected through the weight change rate, the general transaction amount is increased sharply, and except for normal marketing activities, abnormal transactions such as fraud and bill swiping are probably detected.
Step 103, identifying characteristic information of the first user based on the subnet graph corresponding to the first user, so as to identify the transaction request.
Before step 103, feature users that have been confirmed as merchants with abnormal transactions may be screened in advance, so that whether the first user is a merchant with abnormal transactions is determined through the screened feature users. Optionally, step 103 comprises: calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user; judging whether the similarity is greater than or equal to a preset similarity threshold value or not; if so, identifying the first user as a characteristic user, and identifying the transaction request as an abnormal transaction request; if not, identifying the transaction request as a normal transaction request.
Optionally, calculating a similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the feature user includes: generating the first vector Q according to the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn) (ii) a Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn) (ii) a And calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm. Wherein, CiRepresenting a user i, M corresponding to a node connected to a first user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
In the embodiment of the invention, the incidence relation between the first user and the characteristic user and each card holder is respectively expressed by the vectors, and then the similarity between the two vectors is calculated by a cosine similarity algorithm, so that the similarity between the first user and the characteristic user can be accurately obtained. It is noted that if no transaction has occurred between the user and a cardholder, the weight for that cardholder may be set to zero.
Alternatively, the similarity may be calculated using the following formula:
if two vectors form an angle QMNIf the similarity between the first user and the characteristic user is higher than the preset included angle threshold value, the transaction request is judged to be an abnormal transaction request. If the transaction request is a normal transaction request, the transaction is processed normally, and if the transaction request is an abnormal transaction request, the transaction is rejected.
According to the various embodiments described above, it can be seen that the technical means for identifying the transaction request based on the sub-mesh graph by adding the transaction request to the sub-mesh graph corresponding to the first user through the association information between the first user and the second user in the present invention solves the technical problems of low identification efficiency and incapability of ensuring accuracy in the prior art. According to the embodiment of the invention, the global mesh graph is constructed based on the transaction times, and then the sub-mesh graphs are constructed based on the weight change rate of the global mesh graph, so that the transaction request is identified by calculating the similarity between the sub-mesh graphs, and not only can the normal condition of the transaction request be rapidly identified, but also the identification accuracy can be improved.
Fig. 2 is a schematic diagram of a main flow of a method of identifying a transaction request according to one referential embodiment of the present invention.
Step 201, receiving and analyzing the transaction request to obtain the association information between the first user and the second user.
After a transaction request sent by a client is received, the transaction request is analyzed, and therefore association information between a first user and a second user is obtained.
Step 202, adding the transaction request to a global mesh graph according to the association information between the first user and the second user.
And after the association information of the first user and the second user is obtained through analysis, adding the transaction request to a global mesh graph according to the association information of the first user and the second user.
Step 203, judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value according to the global mesh map; if yes, go to step 204; if not, the process is ended.
Optionally, the weight change rate of the first user is calculated by the following method: and in a preset time period, increasing the weight of a connecting line connected with the first user node.
Step 204, adding the transaction request to the sub-network graph corresponding to the first user according to the association information between the first user and the second user.
And if the weight change rate of the first user is larger than or equal to a preset change rate threshold, the fact that the transaction amount of the first user is suddenly increased and suspected risk exists is indicated, the transaction is regarded as suspected risk transaction, and the transaction request is added to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
Step 205, identifying characteristic information of the first user based on the subnet graph corresponding to the first user, thereby identifying the transaction request.
Before step 205, feature users that have been confirmed as merchants with abnormal transactions may be screened in advance, so that whether the first user is a merchant with abnormal transactions is determined through the screened feature users.
In addition, in a reference embodiment of the present invention, the detailed implementation of the method for identifying a transaction request is described in detail in the above-mentioned method for identifying a transaction request, and therefore the repeated content will not be described again.
Fig. 3 is a schematic view of a main flow of a method of identifying a transaction request according to another referential embodiment of the present invention.
Step 301, receiving and analyzing the transaction request to obtain the association information between the first user and the second user.
Step 302, adding the transaction request to a global mesh graph according to the association information between the first user and the second user.
Step 303, judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value according to the global mesh map; if yes, go to step 204; if not, go to step 308.
Step 304, adding the transaction request to the sub-network graph corresponding to the first user according to the association information between the first user and the second user.
Step 305, calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user.
Optionally, the first vector Q is generated according to the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn) (ii) a Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn) (ii) a And calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm.
Wherein, CiRepresenting a user i, M corresponding to a node connected to a first user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
Step 306, judging whether the similarity is greater than or equal to a preset similarity threshold value; if yes, go to step 307; if not, go to step 308.
Step 307, identifying the first user as a characteristic user, identifying the transaction request as an abnormal transaction request, and refusing to process the transaction.
And if the similarity is greater than or equal to a preset similarity threshold, which indicates that the similarity between the first user and the characteristic user is higher, determining that the transaction request is an abnormal transaction request.
And step 308, identifying the transaction request as a normal transaction request, and processing the transaction normally.
And if the similarity is smaller than a preset similarity threshold, which indicates that the similarity between the first user and the characteristic user is not high, determining that the transaction request is a normal transaction request.
In addition, in another embodiment of the present invention, the detailed implementation of the method for identifying a transaction request is described in detail in the above-mentioned method for identifying a transaction request, and therefore the repeated content will not be described again.
Fig. 4 is a schematic diagram of main modules of an apparatus for identifying a transaction request according to an embodiment of the present invention, and as shown in fig. 4, the apparatus 400 for identifying a transaction request includes a receiving module 401, an adding module 402, and an identifying module 403. The receiving module 401 is configured to receive and analyze a transaction request to obtain association information between a first user and a second user; the adding module 402 is configured to add the transaction request to a sub-network-like graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users; the identifying module 403 is configured to identify feature information of the first user based on the subnet graph corresponding to the first user, so as to identify the transaction request.
Optionally, the adding module 402 is further configured to:
adding the transaction request to a global mesh graph according to association information between the first user and a second user; in the global mesh graph, nodes represent users, and the weight of connecting lines among the nodes represents the transaction times among the users;
judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value or not according to the global mesh map;
and if so, adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
Optionally, the weight change rate of the first user is calculated by the following method:
and in a preset time period, increasing the weight of a connecting line connected with the first user node.
Optionally, the identifying module 403 is further configured to:
calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user;
judging whether the similarity is greater than or equal to a preset similarity threshold value or not;
if so, identifying the first user as a characteristic user, and identifying the transaction request as an abnormal transaction request;
if not, identifying the transaction request as a normal transaction request.
Optionally, the identifying module 403 is further configured to:
generating the first vector Q according to the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn)；
Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn)；
Calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm;
wherein, CiRepresenting a user i, M corresponding to a node connected to a first user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
According to the various embodiments described above, it can be seen that the technical means for identifying the transaction request based on the sub-mesh graph by adding the transaction request to the sub-mesh graph corresponding to the first user through the association information between the first user and the second user in the present invention solves the technical problems of low identification efficiency and incapability of ensuring accuracy in the prior art. According to the embodiment of the invention, the global mesh graph is constructed based on the transaction times, and then the sub-mesh graphs are constructed based on the weight change rate of the global mesh graph, so that the transaction request is identified by calculating the similarity between the sub-mesh graphs, and not only can the normal condition of the transaction request be rapidly identified, but also the identification accuracy can be improved.
It should be noted that, in the implementation of the apparatus for identifying a transaction request according to the present invention, the above method for identifying a transaction request has been described in detail, and therefore, the repeated content is not described herein.
Fig. 5 illustrates an exemplary system architecture 500 of a method of identifying a transaction request or a device identifying a transaction request to which embodiments of the invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The background management server may analyze and process the received data such as the transaction request, and feed back a processing result (e.g., a transaction result — just an example) to the terminal device.
It should be noted that the method for identifying a transaction request provided by the embodiment of the present invention is generally performed by the server 505, and accordingly, the apparatus for identifying a transaction request is generally disposed in the server 505. The method for identifying the transaction request provided by the embodiment of the present invention may also be executed by the terminal devices 501, 502, 503, and accordingly, the apparatus for identifying the transaction request may be disposed in the terminal devices 501, 502, 503.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer programs according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes a receiving module, an adding module, and an identifying module, where the names of the modules do not in some cases constitute a limitation on the modules themselves.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to comprise: receiving and analyzing a transaction request to obtain associated information between a first user and a second user; adding the transaction request to a sub-network-like graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users; and identifying the characteristic information of the first user based on the subnet graph corresponding to the first user, thereby identifying the transaction request.
According to the technical scheme of the embodiment of the invention, the association information between the first user and the second user is adopted, and the transaction request is added to the sub-network graph corresponding to the first user, so that the technical means for identifying the transaction request based on the sub-network graph is adopted, and the technical problems of low identification efficiency and incapability of ensuring accuracy in the prior art are solved. According to the embodiment of the invention, the global mesh graph is constructed based on the transaction times, and then the sub-mesh graphs are constructed based on the weight change rate of the global mesh graph, so that the transaction request is identified by calculating the similarity between the sub-mesh graphs, and not only can the normal condition of the transaction request be rapidly identified, but also the identification accuracy can be improved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. A method of identifying a transaction request, comprising:
receiving and analyzing a transaction request to obtain associated information between a first user and a second user;
adding the transaction request to a sub-network-like graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users;
and identifying the characteristic information of the first user based on the subnet graph corresponding to the first user, thereby identifying the transaction request.
2. The method of claim 1, wherein adding the transaction request to a subnet graph corresponding to the first user according to the association information between the first user and the second user comprises:
adding the transaction request to a global mesh graph according to association information between the first user and a second user; in the global mesh graph, nodes represent users, and the weight of connecting lines among the nodes represents the transaction times among the users;
judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value or not according to the global mesh map;
and if so, adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
3. The method of claim 2, wherein the weight change rate of the first user is calculated by:
and in a preset time period, increasing the weight of a connecting line connected with the first user node.
4. The method of claim 1, wherein identifying the transaction request by identifying characteristic information of the first user based on a subnet graph corresponding to the first user comprises:
calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user;
judging whether the similarity is greater than or equal to a preset similarity threshold value or not;
if so, identifying the first user as a characteristic user, and identifying the transaction request as an abnormal transaction request;
if not, identifying the transaction request as a normal transaction request.
5. The method of claim 4, wherein calculating the similarity between the sub-network-graph corresponding to the first user and the sub-network-graph corresponding to the feature user comprises:
according toGenerating the first vector Q by the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn)；
Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn)；
Calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm;
wherein, CiRepresenting a user i, M corresponding to a node connected to a first user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
6. An apparatus for identifying a transaction request, comprising:
the receiving module is used for receiving and analyzing the transaction request to obtain the association information between the first user and the second user;
the adding module is used for adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user; in the sub-mesh graph, nodes represent users, and the weight of connecting lines between the nodes represents the transaction times between the users;
and the identification module is used for identifying the characteristic information of the first user based on the subnet graph corresponding to the first user so as to identify the transaction request.
7. The apparatus of claim 1, wherein the adding module is further configured to:
adding the transaction request to a global mesh graph according to association information between the first user and a second user; in the global mesh graph, nodes represent users, and the weight of connecting lines among the nodes represents the transaction times among the users;
judging whether the weight change rate of the first user is greater than or equal to a preset change rate threshold value or not according to the global mesh map;
and if so, adding the transaction request to the sub-network-shaped graph corresponding to the first user according to the association information between the first user and the second user.
8. The apparatus of claim 7, wherein the weight change rate of the first user is calculated by:
and in a preset time period, increasing the weight of a connecting line connected with the first user node.
9. The apparatus of claim 1, wherein the identification module is further configured to:
calculating the similarity between the sub-network-like graph corresponding to the first user and the sub-network-like graph corresponding to the characteristic user;
judging whether the similarity is greater than or equal to a preset similarity threshold value or not;
if so, identifying the first user as a characteristic user, and identifying the transaction request as an abnormal transaction request;
if not, identifying the transaction request as a normal transaction request.
10. The apparatus of claim 1, wherein the identification module is further configured to:
generating the first vector Q according to the sub-network graph corresponding to the first userM＝(M1C1，M2C2，…，MnCn)；
Generating a second vector Q according to the sub-network-like graph corresponding to the characteristic userN＝(N1C1，N2C2，…，NnCn)；
Calculating the similarity between the first vector and the second vector by adopting a cosine similarity algorithm;
wherein, CiIs shown withUser i, M corresponding to a node connected by a user nodeiWeight, N, representing a connection to a first user nodeiA weight representing a link connected to the characteristic user node; i is 1, …, n.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-5.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639944A (en) * | 2020-06-08 | 2020-09-08 | 杭州复杂美科技有限公司 | Transaction broadcasting method, apparatus and storage medium |
| CN113935738A (en) * | 2020-06-29 | 2022-01-14 | 腾讯科技（深圳）有限公司 | Transaction data processing method, device, storage medium and equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107146089A (en) * | 2017-03-29 | 2017-09-08 | 北京三快在线科技有限公司 | The single recognition methods of one kind brush and device, electronic equipment |
| CN107305665A (en) * | 2016-04-19 | 2017-10-31 | 李辉 | It is a kind of to differentiate wash sale, prevent the single method and device of brush |
| CN108550052A (en) * | 2018-04-03 | 2018-09-18 | 杭州呯嘭智能技术有限公司 | Brush list detection method and system based on user behavior data feature |
| CN108564371A (en) * | 2017-12-27 | 2018-09-21 | 中国银联股份有限公司 | A kind of transaction verification method and device |
| CN109598563A (en) * | 2019-01-24 | 2019-04-09 | 北京三快在线科技有限公司 | Brush single detection method, device, storage medium and electronic equipment |
| CN110033170A (en) * | 2019-03-14 | 2019-07-19 | 阿里巴巴集团控股有限公司 | Identify the method and device of risk businessman |
-
2019
- 2019-09-17 CN CN201910876011.9A patent/CN110610365A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107305665A (en) * | 2016-04-19 | 2017-10-31 | 李辉 | It is a kind of to differentiate wash sale, prevent the single method and device of brush |
| CN107146089A (en) * | 2017-03-29 | 2017-09-08 | 北京三快在线科技有限公司 | The single recognition methods of one kind brush and device, electronic equipment |
| CN108564371A (en) * | 2017-12-27 | 2018-09-21 | 中国银联股份有限公司 | A kind of transaction verification method and device |
| CN108550052A (en) * | 2018-04-03 | 2018-09-18 | 杭州呯嘭智能技术有限公司 | Brush list detection method and system based on user behavior data feature |
| CN109598563A (en) * | 2019-01-24 | 2019-04-09 | 北京三快在线科技有限公司 | Brush single detection method, device, storage medium and electronic equipment |
| CN110033170A (en) * | 2019-03-14 | 2019-07-19 | 阿里巴巴集团控股有限公司 | Identify the method and device of risk businessman |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111639944A (en) * | 2020-06-08 | 2020-09-08 | 杭州复杂美科技有限公司 | Transaction broadcasting method, apparatus and storage medium |
| CN111639944B (en) * | 2020-06-08 | 2023-06-02 | 杭州复杂美科技有限公司 | Transaction broadcasting method, apparatus and storage medium |
| CN113935738A (en) * | 2020-06-29 | 2022-01-14 | 腾讯科技（深圳）有限公司 | Transaction data processing method, device, storage medium and equipment |
| CN113935738B (en) * | 2020-06-29 | 2024-04-19 | 腾讯科技（深圳）有限公司 | Transaction data processing method, device, storage medium and equipment |
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