WO2022011947A1 - 一种交易数据的处理方法、装置、计算机设备及存储介质 - Google Patents

一种交易数据的处理方法、装置、计算机设备及存储介质 Download PDF

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Publication number
WO2022011947A1
WO2022011947A1 PCT/CN2020/135606 CN2020135606W WO2022011947A1 WO 2022011947 A1 WO2022011947 A1 WO 2022011947A1 CN 2020135606 W CN2020135606 W CN 2020135606W WO 2022011947 A1 WO2022011947 A1 WO 2022011947A1
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Prior art keywords
transaction
transaction object
additional
main
objects
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English (en)
French (fr)
Inventor
任杰
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation

Definitions

  • the present application relates to the technical field of big data, and in particular, to a transaction data processing method, device, computer equipment and storage medium.
  • the current online transaction generates a large amount of transaction data.
  • the inventor found that some of the transaction data is generated by the transaction initiated by the transaction party using improper means, such as malicious brushing after combining multiple transaction objects.
  • improper means such as malicious brushing after combining multiple transaction objects.
  • the embodiments of the present application propose a transaction data processing method, device, computer equipment and storage medium, so as to solve the problem that the prior art cannot effectively identify abnormal transaction data to realize risk control, and cannot quickly mine effective information from transaction data for information push problem.
  • the embodiments of the present application provide a method for processing transaction data, which adopts the following technical embodiments:
  • a method for processing transaction data comprising the following steps:
  • Acquire multiple transaction data in real time determine the included transaction objects according to the multiple transaction data, determine the main transaction object and the additional transaction object according to the transaction object, and construct a directed graph based on the main transaction object and the additional transaction object ;
  • the strongly connected components of the directed graph are obtained, multiple additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are combined with each of the main transaction objects in turn, respectively, Get multiple transaction object combinations;
  • each of the transaction object combinations satisfies a preset condition, and if so, a data operation corresponding to the preset condition is performed based on the preset condition, and the preset condition includes a risk control condition and/or an information push condition.
  • the determining of the main transaction object and the additional transaction object according to the transaction object includes:
  • one transaction object is included, it is determined as the main transaction object; if at least two transaction objects are included, the main transaction object and the additional transaction object are determined according to the attribute information and/or transaction frequency of the transaction objects.
  • constructing a directed graph based on the main transaction object and the additional transaction object includes:
  • each additional transaction object points to at least one of the main transaction objects, and when at least two of the additional transaction objects are included in the same item of the transaction data, each additional transaction object in the same item of the transaction data Build a two-way connection between them.
  • the combination of a plurality of the additional transaction object sets and each of the main transaction objects in turn, to obtain a plurality of transaction object combinations includes:
  • the method further includes:
  • the method further includes: archiving and storing the finally obtained transaction object combination formed by the additional transaction object and the main transaction object.
  • the method further includes: generating a main-attached transaction object category table according to the transaction object combination, converting the main-attached transaction object category table into a label, and converting all the transaction objects into a label.
  • the tag is pushed to the target object.
  • the embodiments of the present application also provide a transaction data processing device, which adopts the following technical embodiments:
  • a device for processing transaction data comprising:
  • a building module for acquiring multiple transaction data in real time, determining the included transaction objects according to the multiple transaction data, determining a main transaction object and an additional transaction object according to the transaction object, and based on the main transaction object and the additional transaction Objects build directed graphs;
  • the object combination generation module is used to obtain the strongly connected components of the directed graph based on the TARJAN algorithm, obtain a plurality of additional transaction object sets according to the strongly connected components, and respectively associate the multiple additional transaction object sets with each of the The main transaction objects are combined in sequence to obtain a combination of multiple transaction objects;
  • the judgment processing module is used for judging whether each of the transaction object combinations satisfies a preset condition, and if so, executes a data operation corresponding to the preset condition based on the preset condition, and the preset condition includes risk control conditions and / or information push conditions.
  • the embodiments of the present application also provide a computer device, which adopts the following technical embodiments:
  • a computer device comprising a memory and a processor, wherein computer-readable instructions are stored in the memory, and the processor implements the following steps when executing the computer-readable instructions:
  • Acquire multiple transaction data in real time determine the included transaction objects according to the multiple transaction data, determine the main transaction object and the additional transaction object according to the transaction object, and construct a directed graph based on the main transaction object and the additional transaction object ;
  • the strongly connected components of the directed graph are obtained, multiple additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are combined with each of the main transaction objects in turn, respectively, Get multiple transaction object combinations;
  • each of the transaction object combinations satisfies a preset condition, and if so, a data operation corresponding to the preset condition is performed based on the preset condition, and the preset condition includes a risk control condition and/or an information push condition.
  • the embodiments of the present application also provide a computer-readable storage medium, which adopts the following technical embodiments:
  • a computer-readable storage medium where computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the processor is caused to perform the following steps:
  • Acquire multiple transaction data in real time determine the included transaction objects according to the multiple transaction data, determine the main transaction object and the additional transaction object according to the transaction object, and construct a directed graph based on the main transaction object and the additional transaction object ;
  • the strongly connected components of the directed graph are obtained, multiple additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are combined with each of the main transaction objects in turn, respectively, Get multiple transaction object combinations;
  • each of the transaction object combinations satisfies a preset condition, and if so, a data operation corresponding to the preset condition is performed based on the preset condition, and the preset condition includes a risk control condition and/or an information push condition.
  • the transaction data processing method, device, computer equipment and storage medium provided by the embodiments of the present application mainly have the following beneficial effects:
  • a directed graph is constructed based on the main transaction object and the additional transaction object, so as to obtain a plurality of additional transaction object sets, and then each additional transaction object set is respectively associated with
  • the main transaction objects are combined in sequence to obtain multiple transaction object combinations. According to whether the transaction object combination meets the preset conditions, it is judged whether the transaction object combination will lead to a risky transaction, or whether it can be used as a better transaction object combination. Transaction risks can be controlled, and effective information can be quickly mined from transaction data to push to users.
  • FIG. 1 is an exemplary system architecture diagram to which the present application can be applied;
  • FIG. 2 is a flowchart of an embodiment of a method for processing transaction data according to the present application
  • FIG. 4 is a schematic structural diagram of an embodiment of an apparatus for processing transaction data according to the present application.
  • FIG. 5 is a schematic structural diagram of an embodiment of a computer device according to the present application.
  • the transaction data processing method includes the following steps:
  • S201 Acquire multiple transaction data in real time, determine the included transaction objects according to the multiple transaction data, determine a main transaction object and an additional transaction object according to the transaction objects, and construct a network based on the main transaction object and the additional transaction object. to graph;
  • the strongly connected components of the directed graph are obtained, a plurality of additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are sequentially performed with each of the main transaction objects. Combination to get a combination of multiple transaction objects;
  • S203 Determine whether each of the transaction object combinations satisfies a preset condition, and execute a data operation corresponding to the preset condition based on the preset condition, where the preset condition includes risk control conditions and/or information push condition.
  • the present application also provides an embodiment of an apparatus for processing transaction data.
  • the apparatus for processing transaction data includes: a construction module 401 , an object combination generation module 402 , and a judgment processing module 403 .
  • the construction module 401 is used to acquire multiple transaction data in real time, determine the included transaction objects according to the multiple transaction data, determine the main transaction object and additional transaction objects according to the transaction objects, and determine the main transaction object and additional transaction objects based on the main transaction object and the transaction object.
  • the additional transaction object constructs a directed graph;
  • the object combination generation module 402 is used to obtain the strongly connected component of the directed graph based on the TARJAN algorithm, obtain a plurality of additional transaction object sets according to the strongly connected component, and combine the multiple
  • Each of the additional transaction object sets is sequentially combined with each of the main transaction objects to obtain a plurality of transaction object combinations;
  • the judgment processing module 403 is used to determine whether each of the transaction object combinations satisfies the preset conditions, and then based on The preset conditions execute data operations corresponding to the preset conditions, and the preset conditions include risk control conditions and/or information push conditions.
  • the embodiment of the present application also provides an embodiment of a computer device, please refer to the basic structural block diagram of the computer device shown in FIG. 5 .
  • the computer device 5 includes a memory 51, a processor 52, and a network interface 53 that communicate with each other through a system bus.
  • the memory 51 stores computer-readable instructions
  • the processor 52 implements the following when executing the computer-readable instructions. step:
  • Acquire multiple transaction data in real time determine the included transaction objects according to the multiple transaction data, determine the main transaction object and the additional transaction object according to the transaction object, and construct a directed graph based on the main transaction object and the additional transaction object ;
  • the strongly connected components of the directed graph are obtained, multiple additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are combined with each of the main transaction objects in turn, respectively, Get multiple transaction object combinations;
  • each of the transaction object combinations satisfies a preset condition, and if so, a data operation corresponding to the preset condition is performed based on the preset condition, and the preset condition includes a risk control condition and/or an information push condition.
  • This embodiment of the present application further provides an embodiment of a computer-readable storage medium, where computer-readable instructions are stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the processor causes the processor to Perform the following steps:
  • Acquire multiple transaction data in real time determine the included transaction objects according to the multiple transaction data, determine the main transaction object and the additional transaction object according to the transaction object, and construct a directed graph based on the main transaction object and the additional transaction object ;
  • the strongly connected components of the directed graph are obtained, multiple additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are combined with each of the main transaction objects in turn, respectively, Get multiple transaction object combinations;
  • each of the transaction object combinations satisfies a preset condition, and if so, a data operation corresponding to the preset condition is performed based on the preset condition, and the preset condition includes a risk control condition and/or an information push condition.
  • the system architecture 100 may include terminal devices 101 , 102 , and 103 , a network 104 and a server 105 .
  • the network 104 is a medium used to provide a communication link between the terminal devices 101 , 102 , 103 and the server 105 .
  • the network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
  • the user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like.
  • Various communication client applications may be installed on the terminal devices 101 , 102 and 103 , such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, and the like.
  • the terminal devices 101, 102, and 103 may be various electronic devices that have a display screen and support web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic Video Expert Compression Standard Audio Layer 3), MP4 (Moving Picture Experts Group Audio Layer IV, Motion Picture Experts Compression Standard Audio Layer 4) Players, Laptops and Desktops, etc.
  • MP3 players Moving Picture Experts Group Audio Layer III, dynamic Video Expert Compression Standard Audio Layer 3
  • MP4 Motion Picture Experts Compression Standard Audio Layer 4
  • the server 105 may be a server that provides various services, such as a background server that provides support for the pages displayed on the terminal devices 101 , 102 , and 103 .
  • transaction data processing method provided by the embodiments of the present application is generally performed by a server, and accordingly, a transaction data processing apparatus is generally set in the server.
  • terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.
  • FIG. 2 shows a flowchart of one embodiment of a method for processing transaction data according to the present application.
  • the processing method of the transaction data includes the following steps:
  • S201 Acquire multiple transaction data in real time, determine the included transaction objects according to the multiple transaction data, determine a main transaction object and an additional transaction object according to the transaction objects, and construct a network based on the main transaction object and the additional transaction object. to graph;
  • the strongly connected components of the directed graph are obtained, a plurality of additional transaction object sets are obtained according to the strongly connected components, and the multiple additional transaction object sets are sequentially performed with each of the main transaction objects. Combination to get a combination of multiple transaction objects;
  • S203 Determine whether each of the transaction object combinations satisfies a preset condition, and execute a data operation corresponding to the preset condition based on the preset condition, where the preset condition includes risk control conditions and/or information push condition.
  • a transaction data refers to a transaction order
  • the transaction object is the products or services involved in the transaction order, such as electronic products or warranty services, insurance products, etc.
  • the object may specifically refer to the product to be traded, and correspondingly, the transaction object combination may specifically be a product combination.
  • the transaction object combination may specifically be a product combination.
  • a large amount of real-time transaction data will be generated. Obtain transaction data that has occurred for subsequent data processing operations, such as preprocessing to form structured data.
  • the determining the main transaction object and the additional transaction object according to the transaction object includes: determining the number of transaction objects included in each item of the transaction data; if one transaction object is included, determining it as the main transaction If the object includes at least two transaction objects, the main transaction object and the additional transaction object are determined according to the attribute information and/or transaction frequency of the transaction objects.
  • the transaction object can be directly determined as the main transaction object. object.
  • a transaction data corresponds to at least two transaction objects, in some embodiments, it can be determined by acquiring attribute information of the transaction objects. If the insurance policy includes the main insurance and the additional insurance, it can be directly determined that the main insurance is the main transaction object, and the additional insurance is the additional transaction object. For the information identifying the main category of the product, it means that the transactions of some transaction objects depend on other transaction objects. For example, the transaction of the warranty service of electronic products depends on the transaction of electronic products; in other embodiments, determining the main transaction object and the additional transaction object according to the transaction object can be calculated by the transaction frequency of the transaction objects of all transaction data. Specifically, the transaction object whose transaction frequency meets the preset threshold is used as the main transaction object, and the remaining transaction objects are used as additional transaction objects.
  • the main transaction object and the additional transaction object Transaction objects may be interchanged, that is, there are some transaction objects that are the main transaction object in a certain period or scene, and are additional transaction objects in another period or scene.
  • the above-mentioned ways of determining the main transaction object and the additional transaction object may be combined, which is not limited herein.
  • the constructing a directed graph based on the main transaction object and the additional transaction object includes: removing a plurality of transaction data including only a single transaction object in the transaction data, and sequentially processing the remaining transaction data constructing a directed graph in which the connection of the additional transaction objects points to the main transaction object; wherein each additional transaction object points to at least one of the main transaction objects, and at least two of the additional transaction data are included in the same item of transaction data When trading objects, a two-way connection is constructed between the additional transaction objects in the transaction data of the same item.
  • an additional transaction object may point to multiple main transaction objects, and multiple additional transaction objects may point to the same main transaction object. If the main transaction object and the additional transaction object correspond to the same transaction data, the additional transaction object is constructed to point to the main transaction.
  • the directed line segment of the object if the main transaction object and two or more additional transaction objects correspond to the same transaction data, while constructing the directed line segment of the additional transaction object pointing to the main transaction object, it also constructs the line segment between the two additional transaction objects. Bidirectional line segments, and so on until all transaction data are processed, that is, the construction of the directed graph is completed.
  • the constructed directed graph can be shown in Figure 3.
  • the "main 1", “main 2" etc. represent the main transaction object, and the "Attach 1", “Attach 2", “Attach 3" etc. indicated by the light colored dots represent additional transaction objects.
  • step S202 after the directed graph is constructed, additional transaction object combinations need to be extracted from it to obtain a category combination table of additional transaction objects.
  • the directed graph shown in FIG. 3 has a combination relationship of many transaction objects.
  • the additional transaction object combination with associated relationship is obtained through the TARJAN algorithm.
  • each dot in the directed graph is regarded as a vertex. If the two vertices in the directed graph communicate with each other, the two vertices are strongly connected. (Strongly Connected), if every two vertices of the directed graph are strongly connected, the directed graph is a strongly connected graph.
  • the directed graph will not be strongly connected to every two vertices, but some vertices exist in pairs.
  • the connected graph of these vertices (or the maximally strongly connected subgraph) is extracted as a whole to obtain the strongly connected component (Strongly Connected Graph). Connected Components).
  • an association list of additional transaction objects can be generated through the vertices corresponding to the strongly connected components, and a set of multiple additional transaction objects can be obtained.
  • the sets of additional transaction objects are ⁇ Attach 1, Attachment 4, Attachment 5 ⁇ , ⁇ Attachment 2 ⁇ , ⁇ Attachment 3 ⁇ and so on.
  • the step of sequentially combining multiple sets of additional transaction objects with each of the main transaction objects to obtain multiple combinations of transaction objects includes: traversing all sets of additional transaction objects, and traversing all sets of additional transaction objects.
  • the main transaction object if there is a connected relationship between the additional transaction object in the current additional transaction object set and one of the main transaction objects, the additional transaction object in the current additional transaction object set is retained, and the current additional transaction object is added.
  • the association relationship between the additional transaction object set and the currently traversed main transaction object is obtained, that is, a plurality of transaction object combinations are obtained. Specifically, referring to FIG.
  • the method further includes: judging each additional transaction in the current set of additional transaction objects Whether the association conditions are met between the objects, if so, add the association relationship between the current additional transaction object set and the currently traversed main transaction object, otherwise cancel the association.
  • the purchase of some additional insurances depends on the purchase share of another additional insurance reaching a specified amount. If the specified amount is not reached Associative combinations will not be possible, thereby avoiding meaningless combinations of transaction objects.
  • step S203 for the risk control conditions, it is mainly used to judge whether the combination of transaction objects will lead to malicious purchases by users.
  • the combination of trading objects requires risk management and control, and the corresponding data operations can adjust the trading price of each trading object so that it cannot form a combination that would lead to risky transactions.
  • the method further includes: generating a main-attached transaction object category table according to the transaction object combination, and converting the main-attached transaction object category table into a label , push the tag to the target object.
  • the method further includes: generating a main-attached transaction object category table according to the transaction object combination, and converting the main-attached transaction object category table into a label , push the tag to the target object.
  • the combined products related to this product are displayed on the user interface, and combined with the user's historical browsing information, the products that the user is interested in among the related combined products can be displayed first.
  • Marketers can choose to sort the actual product sales list in real time, prioritize high-yield sales mix marketing to promote sales, or limit abnormal sales mixes to reduce subsequent losses and achieve marketing product category management.
  • the main and attached transaction object category table can also be used as the output of other marketing models or embodiments to enumerate the combination situation online in real time.
  • the method further includes: archiving and storing the finally obtained transaction object combination formed by the additional transaction object and the main transaction object.
  • real-time archiving and sorting of online transaction data is supported, manpower input is reduced, and transaction losses caused by untimely analysis and processing of transaction data are avoided.
  • the transaction data processing method by classifying the transaction objects as the main transaction object and the additional transaction object, and constructing a directed graph based on the main transaction object and the additional transaction object, a plurality of additional transaction object sets are obtained, Then combine each additional transaction object set with each main transaction object in turn to obtain multiple transaction object combinations, and judge whether the transaction object combination will lead to a risky transaction or whether it can be used as a better transaction according to whether the transaction object combination satisfies the preset conditions.
  • the combination of transaction objects can control transaction risks, and at the same time, it can quickly mine effective information from transaction data to push it to users.
  • This application solution is suitable for statistical analysis of online transaction data for large-scale marketing scenarios, and can also be directly displayed on the data dashboard.
  • marketing effects can be sorted according to specific transaction data, which can help users truly understand the sales situation and avoid malicious intent. Risk of brushing orders, promote sales and improve performance.
  • the solution of this application can also be used as an artificial intelligence model to automatically archive categories that do not require high timeliness.
  • the transaction data described in the above-mentioned embodiment and the obtained related information of the transaction object combination may also be stored in a node of a blockchain.
  • the blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
  • the present application may be used in numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, including A distributed computing environment for any of the above systems or devices, and the like.
  • the application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer.
  • program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types.
  • the application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer storage media including storage devices.
  • the aforementioned computer-readable storage medium may be a non-volatile storage medium or a volatile storage medium, such as a magnetic disk, an optical disk, a read-only storage memory (Read-Only Memory) Memory, ROM) and other non-volatile storage media, or random storage memory (Random Access Memory, RAM), etc.
  • the present application provides an embodiment of an apparatus for processing transaction data.
  • the apparatus embodiment corresponds to the method embodiment shown in FIG. 2 .
  • the apparatus Specifically, it can be applied to various electronic devices.
  • the apparatus for processing transaction data in this embodiment includes: a construction module 401 , an object combination generation module 402 and a judgment processing module 403 .
  • the construction module 401 is used to acquire multiple transaction data in real time, determine the included transaction objects according to the multiple transaction data, determine the main transaction object and additional transaction objects according to the transaction objects, and determine the main transaction object and additional transaction objects based on the main transaction object and the transaction object.
  • the additional transaction object constructs a directed graph;
  • the object combination generation module 402 is used to obtain the strongly connected component of the directed graph based on the TARJAN algorithm, obtain a plurality of additional transaction object sets according to the strongly connected component, and combine the multiple
  • Each of the additional transaction object sets is sequentially combined with each of the main transaction objects to obtain a plurality of transaction object combinations;
  • the judgment processing module 403 is used to determine whether each of the transaction object combinations satisfies the preset conditions, and then based on The preset conditions execute data operations corresponding to the preset conditions, and the preset conditions include risk control conditions and/or information push conditions.
  • a piece of transaction data refers to a transaction order
  • the transaction object is the product or service involved in the transaction order, such as electronic products or warranty services, insurance products, etc.
  • the transaction object may specifically refer to
  • the combination of transaction objects may specifically be a combination of products.
  • the real-time transaction scenario for example, when an e-commerce trading platform conducts marketing activities, a large amount of real-time transaction data will be generated. Obtain the transaction data that has occurred for subsequent data processing operations.
  • the construction module 401 when determining the main transaction object and the additional transaction object according to the transaction object, is specifically used to: determine the number of transaction objects included in each item of the transaction data; if one transaction object is included, then It is determined as the main transaction object, and if at least two transaction objects are included, the main transaction object and the additional transaction object are determined according to the attribute information and/or transaction frequency of the transaction objects.
  • the construction module 401 when constructing a directed graph based on the main transaction object and the additional transaction object, is specifically configured to: exclude transaction data that only includes a single transaction object in the transaction data of a plurality of items , construct a directed graph for the remaining transaction data in turn, in which the additional transaction objects are connected to the main transaction object; wherein, each additional transaction object points to at least one of the main transaction objects, and in the same item of the transaction data When at least two of the additional transaction objects are included, a bidirectional connection is constructed between the additional transaction objects in the same item of the transaction data.
  • the above method embodiments which are not expanded here.
  • the construction module 401 constructs a directed graph, it needs to extract additional transaction object combinations from it to obtain a category combination table of additional transaction objects.
  • the directed graph shown in FIG. 3 contains many transaction objects.
  • Combination relationship in this embodiment, the TARJAN algorithm is used to obtain the combination of additional transaction objects with associated relationship, and the specific process can refer to the above method embodiment, which will not be expanded here.
  • the object combination generation module 402 sequentially combines a plurality of the additional transaction object sets with each of the main transaction objects, and when a plurality of transaction object combinations are obtained, it is specifically used for: traversing all the Additional transaction object set, and traverse all the main transaction objects, if there is a connection relationship between the additional transaction object in the current additional transaction object set and one of the main transaction objects, keep the current additional transaction object set. Additional transaction objects are added, and the association relationship between the current additional transaction object set and the currently traversed main transaction object is added, that is, a plurality of transaction object combinations are obtained.
  • the above method embodiments which are not expanded here.
  • the object combination generation module 402 when adding the association relationship between the current set of additional transaction objects and the currently traversed main transaction object, is further configured to: determine whether the current set of additional transaction objects is in the current set of additional transaction objects. Whether the association conditions are satisfied between each additional transaction object, if so, add the current association relationship between the additional transaction object set and the currently traversed main transaction object, otherwise cancel the association.
  • the risk control condition is mainly used to determine whether the combination of transaction objects will lead to malicious purchases by users.
  • Such a combination of transaction objects requires risk management and control, and the corresponding data operations can adjust the transaction price of each transaction object so that it cannot form a combination that would lead to risky transactions.
  • the judgment processing module 403 is further configured to: generate a main-attached transaction object category table according to the transaction object combination, and classify the main-attached transaction object category Tables are converted to tags, which are pushed to the target object.
  • the object combination generation module 402 is further configured to: combine the finally obtained additional transaction object with the main transaction object
  • the formed transaction object combination is archived and stored. In this embodiment, real-time archiving and sorting of online transaction data is supported, manpower input is reduced, and transaction losses caused by untimely analysis and processing of transaction data are avoided.
  • a directed graph is constructed based on the main transaction object and the additional transaction object, thereby obtaining a plurality of additional transaction object sets, Then combine each additional transaction object set with each main transaction object in turn to obtain multiple transaction object combinations, and judge whether the transaction object combination will lead to a risky transaction or whether it can be used as a better transaction according to whether the transaction object combination satisfies the preset conditions.
  • the combination of transaction objects can control transaction risks, and at the same time, it can quickly mine effective information from transaction data to push it to users.
  • This application solution is suitable for statistical analysis of online transaction data for large-scale marketing scenarios, and can also be directly displayed on the data dashboard.
  • marketing effects can be sorted according to specific transaction data, which can help users truly understand the sales situation and avoid malicious intent. Risk of brushing orders, promote sales and improve performance.
  • the solution of this application can also be used as an artificial intelligence model to automatically archive categories that do not require high timeliness.
  • FIG. 5 is a block diagram of a basic structure of a computer device according to this embodiment.
  • the computer device 5 includes a memory 51, a processor 52, and a network interface 53 that communicate with each other through a system bus.
  • the memory 51 stores computer-readable instructions
  • the processor 52 implements the above when executing the computer-readable instructions.
  • the steps of the transaction data processing method described in the method embodiment have beneficial effects corresponding to the transaction data processing method described above, which are not described here.
  • the computer device 5 having the memory 51, the processor 52 and the network interface 53 is shown in the figure, but it should be understood that it is not required to implement all the shown components, and more or more can be implemented instead. Fewer components.
  • the computer device here is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, special-purpose Integrated Circuit (Application Specific Integrated Circuit, ASIC), programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded devices, etc.
  • ASIC Application Specific Integrated Circuit
  • FPGA Field-Programmable Gate Array
  • DSP Digital Signal Processor
  • the computer equipment may be a desktop computer, a notebook computer, a palmtop computer, a cloud server and other computing equipment.
  • the computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad or a voice control device.
  • the memory 51 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (eg, SD or DX memory, etc.), random access Memory (RAM), Static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Magnetic Memory, Magnetic Disks, Optical Disks, etc.
  • the memory 51 may be an internal storage unit of the computer device 5 , such as a hard disk or a memory of the computer device 5 .
  • the memory 51 may also be an external storage device of the computer device 5 , for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (Flash Card), etc.
  • the memory 51 may also include both the internal storage unit of the computer device 5 and its external storage device.
  • the memory 51 is generally used to store the operating system and various application software installed on the computer device 5 , such as computer-readable instructions corresponding to the above-mentioned transaction data processing method.
  • the memory 51 can also be used to temporarily store various types of data that have been output or will be output.
  • the processor 52 may be a central processing unit (Central Processing Unit) in some embodiments. Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip. This processor 52 is typically used to control the overall operation of the computer device 5 . In this embodiment, the processor 52 is configured to execute computer-readable instructions or process data stored in the memory 51, for example, execute computer-readable instructions corresponding to the processing method of the transaction data.
  • CPU Central Processing Unit
  • controller controller
  • microcontroller microprocessor
  • microprocessor microprocessor
  • This processor 52 is typically used to control the overall operation of the computer device 5 .
  • the processor 52 is configured to execute computer-readable instructions or process data stored in the memory 51, for example, execute computer-readable instructions corresponding to the processing method of the transaction data.
  • the network interface 53 may include a wireless network interface or a wired network interface, and the network interface 53 is generally used to establish a communication connection between the computer device 5 and other electronic devices.
  • the present application also provides another embodiment, that is, to provide a computer-readable storage medium, where the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to The at least one processor is caused to execute the steps of the above-mentioned transaction data processing method, and has beneficial effects corresponding to the above-mentioned transaction data processing method, which is not described here.
  • the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation.
  • the technical embodiments of the present application can be embodied in the form of software products that are essentially or contribute to the prior art.
  • the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , CD-ROM), including several computer-readable instructions to enable a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present application.

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Abstract

一种交易数据的处理方法、装置、计算机设备及存储介质，方法包括：根据多项交易数据确定包含的交易对象，以确定主交易对象和附加交易对象并构建有向图；基于TARJAN算法求取有向图的强连通分量，以得到多个附加交易对象集合，将多个附加交易对象集合分别与各主交易对象依次进行组合，得到多个交易对象组合；判断各交易对象组合是否满足预设条件，满足则基于预设条件执行与预设条件相对应的数据操作，预设条件包括风险控制条件和/或信息推送条件。此外，方法还涉及区块链技术，交易数据可存储于区块链中。本方法可以实现交易风险控制以及实现交易对象组合的推荐。

Description

一种交易数据的处理方法、装置、计算机设备及存储介质
本申请要求于 2020 年 10 月 23 日提交中国专利局、申请号为 202011148653.6 ，发明名称为“一种交易数据的处理方法、装置、计算机设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及大数据技术领域，尤其涉及一种交易数据的处理方法、装置、计算机设备及存储介质。
 背景技术
当前线上交易产生大量的交易数据，发明人发现这些交易数据一方面存在部分交易数据由交易方采用不正当手段发起的交易产生，比如对多个交易对象组合后进行恶意刷单，需要对此类交易进行识别和规避，另一方面由于可用于组合的交易对象过多，无法快速确定何种组合能够实现效益的最大化，存在从交易数据中挖掘信息，得到最佳交易对象组合以推送给用户的需求。因此，如何识别异常交易数据以实现风险控制和从交易数据中挖掘有效信息以供信息推送成为亟待解决的问题。
 技术问题
本申请实施例提出一种交易数据的处理方法、装置、计算机设备及存储介质，以解决现有技术不能有效识别异常交易数据以实现风险控制，以及不能快速从交易数据中挖掘有效信息以供信息推送的问题。
 技术解决方案
为了解决上述技术问题，本申请实施例提供一种交易数据的处理方法，采用了如下所述的技术实施例：
一种交易数据的处理方法，包括下述步骤：
实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
进一步的，所述根据所述交易对象确定主交易对象和附加交易对象包括：
确定每项所述交易数据包含的交易对象的数量；
若包含一个交易对象，则将其确定为主交易对象，若包含至少两个交易对象，则根据交易对象的属性信息和/或交易频次来确定主交易对象和附加交易对象。
进一步的，所述基于所述主交易对象和所述附加交易对象构建有向图包括：
将多项所述交易数据中仅包含单个交易对象的交易数据剔除，依次对剩余的交易数据构建所述附加交易对象连线指向所述主交易对象的有向图；
其中，每个附加交易对象指向至少一个所述主交易对象，在同一项所述交易数据中包含至少两个所述附加交易对象时，在同一项所述交易数据中的各所述附加交易对象之间构建双向连线。
进一步的，所述将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合包括：
遍历所有的所述附加交易对象集合，并遍历所有所述主交易对象，若当前所述附加交易对象集合中的附加交易对象和其中一个所述主交易对象存在连通关系，则保留当前所述附加交易对象集合中的附加交易对象，并添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，即得到多个所述交易对象组合。
进一步的，在所述添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系时，所述方法还包括：
判断当前所述附加交易对象集合中各附加交易对象之间是否满足关联条件，若满足则添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，否则取消关联。
进一步的，在所述得到多个交易对象组合之后，所述方法还包括：将最终得到的由所述附加交易对象与所述主交易对象形成的交易对象组合进行归档存储。
进一步的，在满足所述信息推送条件时，所述方法还包括：根据所述交易对象组合生成主附交易对象类目表，并将所述主附交易对象类目表转化为标签，将所述标签推送至目标对象。
为了解决上述技术问题，本申请实施例还提供一种交易数据的处理装置，采用了如下所述的技术实施例：
一种交易数据的处理装置，包括：
构建模块，用于实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
对象组合生成模块，用于基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
判断处理模块，用于判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
为了解决上述技术问题，本申请实施例还提供一种计算机设备，采用了如下所述的技术实施例：
一种计算机设备，包括存储器和处理器，所述存储器中存储有计算机可读指令，所述处理器执行所述计算机可读指令时实现如下步骤：
实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
为了解决上述技术问题，本申请实施例还提供一种计算机可读存储介质，采用了如下所述的技术实施例：
一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时，使得所述处理器执行如下步骤：
实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
 有益效果
与现有技术相比，本申请实施例提供的交易数据的处理方法、装置、计算机设备及存储介质主要有以下有益效果：
本申请通过对交易对象进行主交易对象和附加交易对象的分类，基于主交易对象和附加交易对象构建有向图，以此来获得多个附加交易对象集合，再将各附加交易对象集合分别与各主交易对象依次进行组合，得到多个交易对象组合，根据交易对象组合是否满足预设条件来判断交易对象组合是否会导致风险交易，或者是否可以作为较佳的交易对象组合，由此可以对交易风险进行控制，同时也可快速从交易数据中挖掘出有效信息以向用户推送。
 附图说明
为了更清楚地说明本申请中的实施例，下面将对本申请实施例描述中所需要使用的附图作一个简单介绍，下面描述中的附图对应于本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请可以应用于其中的示例性系统架构图；
图2是根据本申请的交易数据的处理方法的一个实施例的流程图；
图3是根据本申请的交易数据的处理方法构建的有向图的示例；
图4是根据本申请的交易数据的处理装置的一个实施例的结构示意图；
图5是根据本申请的计算机设备的一个实施例的结构示意图。
 本发明的最佳实施方式
本申请提供交易数据的处理方法的一个实施例，参考图2，所述的交易数据的处理方法包括以下步骤：
S201，实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
S202，基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
S203，判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
本申请还提供一种交易数据的处理装置的一个实施例，参考图4，所述的交易数据的处理装置包括：构建模块401、对象组合生成模块402以及判断处理模块403。
其中，所述构建模块401用于实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；所述对象组合生成模块402用于基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；所述判断处理模块403用于判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
本申请实施例还提供计算机设备的一个实施例，参阅图5所示的计算机设备基本结构框图。所述计算机设备5包括通过系统总线相互通信连接存储器51、处理器52、网络接口53，所述存储器51中存储有计算机可读指令，所述处理器52执行所述计算机可读指令时实现如下步骤：
实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
本申请实施例还提供一种计算机可读存储介质的一个实施例，所述计算机可读存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时，使得所述处理器执行如下步骤：
实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
 本发明的实施方式
除非另有定义，本文所使用的所有的技术和科学术语与属于本申请的技术领域的技术人员通常理解的含义相同；本文中在申请的说明书中所使用的术语只是为了描述具体的实施例的目的，不是旨在于限制本申请；本申请的说明书和权利要求书及上述附图说明中的术语“包括”和“具有”以及它们的任何变形，意图在于覆盖不排他的包含。本申请说明书和权利要求书或上述附图中的术语“第一”、“第二”等是用于区别不同对象，而不是用于描述特定顺序。
在本文中提及“实施例”意味着，结合实施例描述的特定特征、结构或特性可以包含在本申请的至少一个实施例中。在说明书中的各个位置出现该短语并不一定均是指相同的实施例，也不是与其它实施例互斥的独立的或备选的实施例。本领域技术人员显式地和隐式地理解的是，本文所描述的实施例可以与其它实施例相结合。
为了使本技术领域的人员更好地理解本申请实施例，下面将结合附图，对本申请实施例中的技术实施例进行清楚、完整地描述。
如图1所示，系统架构100可以包括终端设备101、102、103，网络104和服务器105。网络104用以在终端设备101、102、103和服务器105之间提供通信链路的介质。网络104可以包括各种连接类型，例如有线、无线通信链路或者光纤电缆等等。
用户可以使用终端设备101、102、103通过网络104与服务器105交互，以接收或发送消息等。终端设备101、102、103上可以安装有各种通讯客户端应用，例如网页浏览器应用、购物类应用、搜索类应用、即时通信工具、邮箱客户端、社交平台软件等。
终端设备101、102、103可以是具有显示屏并且支持网页浏览的各种电子设备，包括但不限于智能手机、平板电脑、电子书阅读器、MP3播放器( Moving Picture Experts Group Audio Layer III，动态影像专家压缩标准音频层面3 )、MP4( Moving Picture Experts Group Audio Layer IV，动态影像专家压缩标准音频层面4 )播放器、膝上型便携计算机和台式计算机等等。
服务器105可以是提供各种服务的服务器，例如对终端设备101、102、103上显示的页面提供支持的后台服务器。
需要说明的是，本申请实施例所提供的交易数据的处理方法一般由服务器执行，相应地，交易数据的处理装置一般设置于服务器中。
应该理解，图1中的终端设备、网络和服务器的数目仅仅是示意性的。根据实现需要，可以具有任意数目的终端设备、网络和服务器。
继续参考图2，其示出了根据本申请的交易数据的处理方法的一个实施例的流程图。所述的交易数据的处理方法包括以下步骤：
S201，实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
S202，基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
S203，判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
下面对上述步骤进行展开说明。
对于步骤S201，本实施例中，一项交易数据是指一笔交易订单，交易对象为交易订单中涉及的产品或服务，比如电子产品或者保修服务、保险产品等，在下文具体实施例中交易对象具体可指被交易的产品，相应的，交易对象组合具体可为产品组合。在本实施例中，实时交易的场景例如在电商交易平台开展营销活动时，将产生大量的实时交易数据，此时为了对营销时的组合产品的风险进行管控以及向用户推送产品组合，可对已发生的交易数据进行获取，以进行后续的数据处理操作，比如预处理后形成结构化数据。
在一些实施例中，所述根据所述交易对象确定主交易对象和附加交易对象包括：确定每项所述交易数据包含的交易对象的数量；若包含一个交易对象，则将其确定为主交易对象，若包含至少两个交易对象，则根据交易对象的属性信息和/或交易频次来确定主交易对象和附加交易对象。
具体的，对于根据交易对象确定主交易对象和附加交易对象的过程，由于一项交易数据可能对应一个或多个交易对象，若交易数据仅对应一个交易对象，可直接确定该交易对象为主交易对象。
若一项交易数据对应至少两个交易对象，在一些实施例中，具体可通过获取交易对象的属性信息来确定，属性信息具体可为产品信息或服务信息或者标识产品主附类别的信息，比如在保险单中包含主险和附加险，则可直接确定主险为主交易对象，附加险为附加交易对象，对于标识产品主附类别的信息是指一些交易对象的交易依附于另一些交易对象的信息，比如电子产品的保修服务的交易依附于电子产品的交易；在另一些实施例中，根据交易对象确定主交易对象和附加交易对象可通过对所有交易数据的交易对象的交易频次进行统计排序来确定，具体将交易频次满足预设阈值的交易对象作为主交易对象，剩余的交易对象作为附加交易对象，随着交易数据的增加，在对交易对象组合进行更新时，主交易对象和附加交易对象可能互换，即存在一些交易对象在某个时期或场景下为主交易对象，在另一时期或场景下为附加交易对象。
在具体实施例中可将上述各确定主交易对象和附加交易对象的方式结合，在此不做限定。
在一些实施例中，所述基于所述主交易对象和所述附加交易对象构建有向图包括：将多项所述交易数据中仅包含单个交易对象的交易数据剔除，依次对剩余的交易数据构建所述附加交易对象连线指向所述主交易对象的有向图；其中，每个附加交易对象指向至少一个所述主交易对象，在同一项所述交易数据中包含至少两个所述附加交易对象时，在同一项所述交易数据中的各所述附加交易对象之间构建双向连线。
具体的，一个附加交易对象可能指向多个主交易对象，多个附加交易对象可能指向同一个主交易对象，如果主交易对象和附加交易对象对应同一项交易数据，则构建附加交易对象指向主交易对象的有向线段，如果主交易对象和两个及以上附加交易对象对应同一项交易数据，在构建附加交易对象指向主交易对象的有向线段的同时，还构建两个附加交易对象之间的双向线段，以此类推直到处理完所有交易数据，即完成有向图的构建，构建的有向图可如图3所示，图中深色圆点中所示的“主1”、“主2”等表示主交易对象，浅色圆点所示的“附1”、“附2”、“附3”等表示附加交易对象。
对于步骤S202，在构建有向图后，需要从中提取附加交易对象组合，以得到附加交易对象的类目组合表，图3所示的有向图中存在众多交易对象的组合关系，本实施例通过TARJAN算法求取有关联关系的附加交易对象组合。在根据通过TARJAN算法求取有关联关系的附加交易对象组合的过程中，将有向图中的每个圆点作为一个顶点，如果有向图中两个顶点相互通达，则两个顶点强连通(Strongly Connected)，如果有向图的每两个顶点都强连通，则有向图为强连通图，在本实施例场景下有向图不会每两个顶点都强连通，但存在部分顶点两两之间强连通的情形，将这些顶点的连通图（或称极大强连通子图）作为整体提取出来即得到强连通分量(Strongly Connected Components)。本实施例得到强连通分量后，通过强连通分量对应的顶点即可生成附加交易对象的关联列表，得到多个附加交易对象的集合，例如上图3中根据附加交易对象的强连通分量可得到附加交易对象的集合分别为{附1，附4，附5}，{附2}，{附3}等。
在一些实施例中，所述将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合包括：遍历所有的所述附加交易对象集合，并遍历所有所述主交易对象，若当前所述附加交易对象集合中的附加交易对象和其中一个所述主交易对象存在连通关系，则保留当前所述附加交易对象集合中的附加交易对象，并添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，即得到多个所述交易对象组合。具体的，结合图3，遍历根据图示得到的所有的附加交易对象集合，并遍历所有主交易对象，将附加交易对象集合中的附加交易对象和主交易对象存在连通关系的保留，然后添加附加交易对象集合和当前遍历到的主交易对象的关联关系，生成主交易对象和附加交易对象的关联关系清单（附加交易对象可多选），即可得到多个交易对象组合如下：
{主1+附1/附4}，
{主1+附2}，
{主2+附1/附4/附5}，
{主2+附3}。
在一些实施例中，在所述添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系时，所述方法还包括：判断当前所述附加交易对象集合中各附加交易对象之间是否满足关联条件，若满足则添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，否则取消关联。具体的，以保险单为例，在将主险和多个附加险进行关联得到交易对象组合时，某些附加险的购买依赖于另一附加险的购买份额达到指定数量，如果未达到指定数量将无法实现关联组合，由此可以避免得到无意义的交易对象组合。
对于步骤S203，对于风险控制条件而言，主要用于判断交易对象组合是否会导致用户的恶意购买，比如在营销活动中，某些关联销售的促销产品被恶意组合，低价刷单，这样的交易对象组合需要进行风险管控，相应执行的数据操作可为调整各交易对象的交易价格，使其无法形成会导致风险交易的组合。
而对于信息推送条件而言，主要用于将交易对象组合是否能够促进交易的发生或者交易是否能够提高交易额或者组合的期望受益数据是否满足要求。在一些实施例中，在满足所述信息推送条件时，所述方法还包括：根据所述交易对象组合生成主附交易对象类目表，并将所述主附交易对象类目表转化为标签，将所述标签推送至目标对象。具体的，比如用户浏览某个产品，将与这个产品相关的组合产品显示在用户界面，结合用户的历史浏览信息，可以将相关的组合产品中用户感兴趣的产品靠前显示。而营销人员可以选择将实际发生的产品销售列表实时统计排序，优先高收益的销售组合营销，促进销售，也可以对发生异常的销售组合加以限制，减少后续损失，实现营销产品类目管理。在本实施例中，主附交易对象类目表还可以作为其他营销模型或者实施例的输出，实时在线枚举组合情况。
在一些实施例中，在所述得到多个交易对象组合之后，所述方法还包括：将最终得到的由所述附加交易对象与所述主交易对象形成的交易对象组合进行归档存储。在本实施例中，支持在线交易数据的实时归档整理，降低人力投入，同时避免交易数据分析处理的不及时而造成的交易损失。
根据本申请提供的交易数据的处理方法，通过对交易对象进行主交易对象和附加交易对象的分类，基于主交易对象和附加交易对象构建有向图，以此来获得多个附加交易对象集合，再将各附加交易对象集合分别与各主交易对象依次进行组合，得到多个交易对象组合，根据交易对象组合是否满足预设条件来判断交易对象组合是否会导致风险交易，或者是否可以作为较佳的交易对象组合，由此可以对交易风险进行控制，同时也可快速从交易数据中挖掘出有效信息以向用户推送。本申请方案适合针对大规模的营销场景进行在线交易数据的统计分析，还可以直接展示在数据看板，同时可以按照具体交易数据对营销效果进行排序，可以帮助于用户真正的了解销售情况，规避恶意刷单风险，促进销售和提升业绩。此外本申请方案也可以作为一种人工智能的模式，对时效性要求不太高的类目进行自动化归档。
需要强调的是，为进一步保证信息的私密和安全性，上述实施例中所述的交易数据以及得到的交易对象组合的相关信息还可以存储于一区块链的节点中。本申请所指区块链是分布式数据存储、点对点传输、共识机制、加密算法等计算机技术的新型应用模式。区块链(Blockchain)，本质上是一个去中心化的数据库，是一串使用密码学方法相关联产生的数据块，每一个数据块中包含了一批次网络交易的信息，用于验证其信息的有效性(防伪)和生成下一个区块。区块链可以包括区块链底层平台、平台产品服务层以及应用服务层等。
本申请可用于众多通用或专用的计算机系统环境或配置中。例如：个人计算机、服务器计算机、手持设备或便携式设备、平板型设备、多处理器系统、基于微处理器的系统、置顶盒、可编程的消费电子设备、网络PC、小型计算机、大型计算机、包括以上任何系统或设备的分布式计算环境等等。本申请可以在由计算机执行的计算机可执行指令的一般上下文中描述，例如程序模块。一般地，程序模块包括执行特定任务或实现特定抽象数据类型的例程、程序、对象、组件、数据结构等等。也可以在分布式计算环境中实践本申请，在这些分布式计算环境中，由通过通信网络而被连接的远程处理设备来执行任务。在分布式计算环境中，程序模块可以位于包括存储设备在内的本地和远程计算机存储介质中。
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程，是可以通过计算机可读指令来指令相关的硬件来完成，该计算机可读指令可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施例的流程。其中，前述的所述计算机可读存储介质可以是非易失性存储介质，也可以是易失性存储介质，比如可为磁碟、光盘、只读存储记忆体（Read-Only Memory，ROM）等非易失性存储介质，或随机存储记忆体（Random Access Memory，RAM）等。
应该理解的是，虽然附图的流程图中的各个步骤按照箭头的指示依次显示，但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明，这些步骤的执行并没有严格的顺序限制，其可以以其他的顺序执行。而且，附图的流程图中的至少一部分步骤可以包括多个子步骤或者多个阶段，这些子步骤或者阶段并不必然是在同一时刻执行完成，而是可以在不同的时刻执行，其执行顺序也不必然是依次进行，而是可以与其他步骤或者其他步骤的子步骤或者阶段的至少一部分轮流或者交替地执行。
进一步参考图4，作为对上述图2所示方法的实现，本申请提供了一种交易数据的处理装置的一个实施例，该装置实施例与图2所示的方法实施例相对应，该装置具体可以应用于各种电子设备中。
如图4所示，本实施例所述的交易数据的处理装置包括：构建模块401、对象组合生成模块402以及判断处理模块403。
其中，所述构建模块401用于实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；所述对象组合生成模块402用于基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；所述判断处理模块403用于判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
本实施例中，一项交易数据是指一笔交易订单，交易对象为交易订单中涉及的产品或服务，比如电子产品或者保修服务、保险产品等，在下文具体实施例中交易对象具体可指被交易的产品，相应的，交易对象组合具体可为产品组合。在本实施例中，实时交易的场景例如在电商交易平台开展营销活动时，将产生大量的实时交易数据，此时为了对营销时的组合产品的风险进行管控以及向用户推送产品组合，可对已发生的交易数据进行获取，以进行后续的数据处理操作。
在一些实施例中，所述构建模块401根据所述交易对象确定主交易对象和附加交易对象时具体用于：确定每项所述交易数据包含的交易对象的数量；若包含一个交易对象，则将其确定为主交易对象，若包含至少两个交易对象，则根据交易对象的属性信息和/或交易频次来确定主交易对象和附加交易对象。对于根据交易对象确定主交易对象和附加交易对象的过程可参考上述方法实施例，在此不作展开。
在一些实施例中，所述构建模块401基于所述主交易对象和所述附加交易对象构建有向图时，具体用于：将多项所述交易数据中仅包含单个交易对象的交易数据剔除，依次对剩余的交易数据构建所述附加交易对象连线指向所述主交易对象的有向图；其中，每个附加交易对象指向至少一个所述主交易对象，在同一项所述交易数据中包含至少两个所述附加交易对象时，在同一项所述交易数据中的各所述附加交易对象之间构建双向连线。具体可参考上述方法实施例，在此不作展开。
在本实施例中，所述构建模块401构建有向图后，需要从中提取附加交易对象组合，以得到附加交易对象的类目组合表，图3所示的有向图中存在众多交易对象的组合关系，本实施例通过TARJAN算法求取有关联关系的附加交易对象组合，具体过程可参考上述方法实施例，在此不作展开。
在一些实施例中，所述对象组合生成模块402将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合时具体用于：遍历所有的所述附加交易对象集合，并遍历所有所述主交易对象，若当前所述附加交易对象集合中的附加交易对象和其中一个所述主交易对象存在连通关系，则保留当前所述附加交易对象集合中的附加交易对象，并添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，即得到多个所述交易对象组合。具体可参考上述方法实施例，在此不作展开。
在一些实施例中，所述对象组合生成模块402在添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系时，还用于：判断当前所述附加交易对象集合中各附加交易对象之间是否满足关联条件，若满足则添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，否则取消关联。具体可参考上述方法实施例，在此不作展开。
在本实施例中，对于风险控制条件而言，主要用于判断交易对象组合是否会导致用户的恶意购买，比如在营销活动中，某些关联销售的促销产品被恶意组合，低价刷单，这样的交易对象组合需要进行风险管控，相应执行的数据操作可为调整各交易对象的交易价格，使其无法形成会导致风险交易的组合。
而对于信息推送条件而言，主要用于将交易对象组合是否能够促进交易的发生或者交易是否能够提高交易额或者组合的期望受益数据是否满足要求。在一些实施例中，在满足所述信息推送条件时，所述判断处理模块403还用于：根据所述交易对象组合生成主附交易对象类目表，并将所述主附交易对象类目表转化为标签，将所述标签推送至目标对象。具体可参考上述方法实施例，在此不作展开。
在一些实施例中，在所述得对象组合生成模块402到多个交易对象组合之后，所述对象组合生成模块402还用于：将最终得到的由所述附加交易对象与所述主交易对象形成的交易对象组合进行归档存储。在本实施例中，支持在线交易数据的实时归档整理，降低人力投入，同时避免交易数据分析处理的不及时而造成的交易损失。
根据本申请提供的交易数据的处理装置，通过对交易对象进行主交易对象和附加交易对象的分类，基于主交易对象和附加交易对象构建有向图，以此来获得多个附加交易对象集合，再将各附加交易对象集合分别与各主交易对象依次进行组合，得到多个交易对象组合，根据交易对象组合是否满足预设条件来判断交易对象组合是否会导致风险交易，或者是否可以作为较佳的交易对象组合，由此可以对交易风险进行控制，同时也可快速从交易数据中挖掘出有效信息以向用户推送。本申请方案适合针对大规模的营销场景进行在线交易数据的统计分析，还可以直接展示在数据看板，同时可以按照具体交易数据对营销效果进行排序，可以帮助于用户真正的了解销售情况，规避恶意刷单风险，促进销售和提升业绩。此外本申请方案也可以作为一种人工智能的模式，对时效性要求不太高的类目进行自动化归档。
为解决上述技术问题，本申请实施例还提供计算机设备。具体请参阅图5，图5为本实施例计算机设备基本结构框图。所述计算机设备5包括通过系统总线相互通信连接存储器51、处理器52、网络接口53，所述存储器51中存储有计算机可读指令，所述处理器52执行所述计算机可读指令时实现上述方法实施例中所述的交易数据的处理方法的步骤，并具有与上述交易数据的处理方法相对应的有益效果，在此不作展开。
需要指出的是，图中仅示出了具有存储器51、处理器52、网络接口53的计算机设备5，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。其中，本技术领域技术人员可以理解，这里的计算机设备是一种能够按照事先设定或存储的指令，自动进行数值计算和/或信息处理的设备，其硬件包括但不限于微处理器、专用集成电路(Application Specific Integrated Circuit，ASIC)、可编程门阵列(Field－Programmable Gate Array，FPGA)、数字处理器 (Digital Signal Processor，DSP)、嵌入式设备等。
所述计算机设备可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。所述计算机设备可以与用户通过键盘、鼠标、遥控器、触摸板或声控设备等方式进行人机交互。
在本实施例中，所述存储器51至少包括一种类型的可读存储介质，所述可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器（例如，SD或DX存储器等）、随机访问存储器（RAM）、静态随机访问存储器（SRAM）、只读存储器（ROM）、电可擦除可编程只读存储器（EEPROM）、可编程只读存储器（PROM）、磁性存储器、磁盘、光盘等。在一些实施例中，所述存储器51可以是所述计算机设备5的内部存储单元，例如该计算机设备5的硬盘或内存。在另一些实施例中，所述存储器51也可以是所述计算机设备5的外部存储设备，例如该计算机设备5上配备的插接式硬盘，智能存储卡（Smart Media Card, SMC），安全数字（Secure Digital, SD）卡，闪存卡（Flash Card）等。当然，所述存储器51还可以既包括所述计算机设备5的内部存储单元也包括其外部存储设备。本实施例中，所述存储器51通常用于存储安装于所述计算机设备5的操作系统和各类应用软件，例如对应于上述交易数据的处理方法的计算机可读指令等。此外，所述存储器51还可以用于暂时地存储已经输出或者将要输出的各类数据。
所述处理器52在一些实施例中可以是中央处理器（Central Processing Unit，CPU）、控制器、微控制器、微处理器、或其他数据处理芯片。该处理器52通常用于控制所述计算机设备5的总体操作。本实施例中，所述处理器52用于运行所述存储器51中存储的计算机可读指令或者处理数据，例如运行对应于所述交易数据的处理方法的计算机可读指令。
所述网络接口53可包括无线网络接口或有线网络接口，该网络接口53通常用于在所述计算机设备5与其他电子设备之间建立通信连接。
本申请还提供了另一种实施方式，即提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，所述计算机可读指令可被至少一个处理器执行，以使所述至少一个处理器执行如上述的交易数据的处理方法的步骤，并具有与上述交易数据的处理方法相对应的有益效果，在此不作展开。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术实施例本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质（如ROM/RAM、磁碟、光盘）中，包括若干计算机可读指令用以使得一台终端设备（可以是手机，计算机，服务器，空调器，或者网络设备等）执行本申请各个实施例所述的方法。
显然，以上所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例，附图中给出了本申请的较佳实施例，但并不限制本申请的专利范围。本申请可以以许多不同的形式来实现，相反地，提供这些实施例的目的是使对本申请的公开内容的理解更加透彻全面。尽管参照前述实施例对本申请进行了详细的说明，对于本领域的技术人员来而言，其依然可以对前述各具体实施方式所记载的技术实施例进行修改，或者对其中部分技术特征进行等效替换。凡是利用本申请说明书及附图内容所做的等效结构，直接或间接运用在其他相关的技术领域，均同理在本申请专利保护范围之内。

Claims (20)

  1. 一种交易数据的处理方法，包括下述步骤：
    实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
    基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
    判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
  2. 根据权利要求1所述的交易数据的处理方法，其中，所述根据所述交易对象确定主交易对象和附加交易对象包括：
    确定每项所述交易数据包含的交易对象的数量；
    若包含一个交易对象，则将其确定为主交易对象，若包含至少两个交易对象，则根据交易对象的属性信息和/或交易频次来确定主交易对象和附加交易对象。
  3. 根据权利要求2所述的交易数据的处理方法，其中，所述基于所述主交易对象和所述附加交易对象构建有向图包括：
    将多项所述交易数据中仅包含单个交易对象的交易数据剔除，依次对剩余的交易数据构建所述附加交易对象连线指向所述主交易对象的有向图；
    其中，每个附加交易对象指向至少一个所述主交易对象，在同一项所述交易数据中包含至少两个所述附加交易对象时，在同一项所述交易数据中的各所述附加交易对象之间构建双向连线。
  4. 根据权利要求1至3任一项所述的交易数据的处理方法，其中，所述将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合包括：
    遍历所有的所述附加交易对象集合，并遍历所有所述主交易对象，若当前所述附加交易对象集合中的附加交易对象和其中一个所述主交易对象存在连通关系，则保留当前所述附加交易对象集合中的附加交易对象，并添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，即得到多个所述交易对象组合。
  5. 根据权利要求4所述的交易数据的处理方法，其中，在所述添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系时，所述方法还包括：
    判断当前所述附加交易对象集合中各附加交易对象之间是否满足关联条件，若满足则添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，否则取消关联。
  6. 根据权利要求1至3任一项所述的交易数据的处理方法，其中，在所述得到多个交易对象组合之后，所述方法还包括：将最终得到的由所述附加交易对象与所述主交易对象形成的交易对象组合进行归档存储。
  7. 根据权利要求1至3任一项所述的交易数据的处理方法，其中，在满足所述信息推送条件时，所述方法还包括：根据所述交易对象组合生成主附交易对象类目表，并将所述主附交易对象类目表转化为标签，将所述标签推送至目标对象。
  8. 一种交易数据的处理装置，包括：
    构建模块，用于实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
    对象组合生成模块，用于基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
    判断处理模块，用于判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
  9. 一种交易数据的处理装置，包括：
    构建模块，用于实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
    对象组合生成模块，用于基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
    判断处理模块，用于判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
  10. 根据权利要求9所述的计算机设备，其中，所述处理器执行所述计算机可读指令实现所述根据所述交易对象确定主交易对象和附加交易对象的步骤时，具体实现如下步骤：
    确定每项所述交易数据包含的交易对象的数量；
    若包含一个交易对象，则将其确定为主交易对象，若包含至少两个交易对象，则根据交易对象的属性信息和/或交易频次来确定主交易对象和附加交易对象。
  11. 根据权利要求10所述的计算机设备，其中，所述处理器执行所述计算机可读指令实现所述基于所述主交易对象和所述附加交易对象构建有向图的步骤时，具体实现如下步骤：
    将多项所述交易数据中仅包含单个交易对象的交易数据剔除，依次对剩余的交易数据构建所述附加交易对象连线指向所述主交易对象的有向图；
    其中，每个附加交易对象指向至少一个所述主交易对象，在同一项所述交易数据中包含至少两个所述附加交易对象时，在同一项所述交易数据中的各所述附加交易对象之间构建双向连线。
  12. 根据权利要求9至11任一项所述的计算机设备，其中，所述处理器执行所述计算机可读指令实现所述将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合的步骤时，具体实现如下步骤：
    遍历所有的所述附加交易对象集合，并遍历所有所述主交易对象，若当前所述附加交易对象集合中的附加交易对象和其中一个所述主交易对象存在连通关系，则保留当前所述附加交易对象集合中的附加交易对象，并添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，即得到多个所述交易对象组合。
  13. 根据权利要求12所述的计算机设备，其中，所述处理器在执行所述计算机可读指令实现所述添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系的步骤时，所述处理器执行所述计算机可读指令时还实现如下步骤：
    判断当前所述附加交易对象集合中各附加交易对象之间是否满足关联条件，若满足则添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，否则取消关联。
  14. 根据权利要求9至11任一项所述的计算机设备，其中，所述处理器在执行所述计算机可读指令实现所述得到多个交易对象组合的步骤之后，所述处理器执行所述计算机可读指令时还实现如下步骤：将最终得到的由所述附加交易对象与所述主交易对象形成的交易对象组合进行归档存储。
  15. 一种计算机可读存储介质，所述计算机可读存储介质上存储有计算机可读指令，所述计算机可读指令被处理器执行时，使得所述处理器执行如下步骤：
    实时获取多项交易数据，根据所述多项交易数据确定包含的交易对象，根据所述交易对象确定主交易对象和附加交易对象，基于所述主交易对象和所述附加交易对象构建有向图；
    基于TARJAN算法求取所述有向图的强连通分量，根据所述强连通分量得到多个附加交易对象集合，将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合；
    判断各所述交易对象组合是否满足预设条件，满足则基于所述预设条件执行与所述预设条件相对应的数据操作，所述预设条件包括风险控制条件和/或信息推送条件。
  16. 根据权利要求15所述的计算机可读存储介质，其中，所述计算机可读指令被所述处理器执行，使得所述处理器执行所述根据所述交易对象确定主交易对象和附加交易对象的步骤时，具体执行如下步骤：
    确定每项所述交易数据包含的交易对象的数量；
    若包含一个交易对象，则将其确定为主交易对象，若包含至少两个交易对象，则根据交易对象的属性信息和/或交易频次来确定主交易对象和附加交易对象。
  17. 根据权利要求16所述的计算机可读存储介质，其中，所述计算机可读指令被所述处理器执行，使得所述处理器执行所述基于所述主交易对象和所述附加交易对象构建有向图的步骤时，具体执行如下步骤：
    将多项所述交易数据中仅包含单个交易对象的交易数据剔除，依次对剩余的交易数据构建所述附加交易对象连线指向所述主交易对象的有向图；
    其中，每个附加交易对象指向至少一个所述主交易对象，在同一项所述交易数据中包含至少两个所述附加交易对象时，在同一项所述交易数据中的各所述附加交易对象之间构建双向连线。
  18. 根据权利要求15至17任一项所述的计算机可读存储介质，其中，所述计算机可读指令被所述处理器执行，使得所述处理器执行所述将多个所述附加交易对象集合分别与各所述主交易对象依次进行组合，得到多个交易对象组合的步骤时，具体执行如下步骤：
    遍历所有的所述附加交易对象集合，并遍历所有所述主交易对象，若当前所述附加交易对象集合中的附加交易对象和其中一个所述主交易对象存在连通关系，则保留当前所述附加交易对象集合中的附加交易对象，并添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，即得到多个所述交易对象组合。
  19. 根据权利要求18所述的计算机可读存储介质，其中，所述计算机可读指令被所述处理器执行，使得所述处理器在执行所述添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系的步骤时，还执行如下步骤：
    判断当前所述附加交易对象集合中各附加交易对象之间是否满足关联条件，若满足则添加当前所述附加交易对象集合和当前遍历到的所述主交易对象的关联关系，否则取消关联。
  20. 根据权利要求15至17任一项所述的计算机可读存储介质，其中，所述计算机可读指令被所述处理器执行，使得所述处理器在执行所述得到多个交易对象组合的步骤之后，还执行如下步骤：将最终得到的由所述附加交易对象与所述主交易对象形成的交易对象组合进行归档存储。
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CN115641185A (zh) * 2022-10-08 2023-01-24 北京健康之家科技有限公司 信息推荐方法及装置、存储介质、终端
CN118279060B (zh) * 2024-05-29 2024-09-27 华侨大学 基于并发时序环枚举的可疑资金回流检测方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105574649A (zh) * 2015-12-10 2016-05-11 西安交通大学 一种基于多阶段MapReduce模型的纳税人偷漏税嫌疑群组检测方法
CN107240005A (zh) * 2017-06-13 2017-10-10 携程旅游网络技术（上海）有限公司 机票附加产品的推荐系统及方法
CN110335121A (zh) * 2019-07-10 2019-10-15 中国民航信息网络股份有限公司 附加服务产品的销售方法及销售装置
CN111340578A (zh) * 2018-12-18 2020-06-26 北京京东尚科信息技术有限公司 一种商品关联关系的生成方法、装置、介质及电子设备
CN111612391A (zh) * 2020-04-02 2020-09-01 杭州电子科技大学 基于FP-growth的物流拣选设备商品摆放方法
CN111738786A (zh) * 2019-04-24 2020-10-02 北京京东尚科信息技术有限公司 构建商品组合的方法、系统、装置和可读存储介质

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19616712A1 (de) * 1996-04-26 1997-11-06 Philips Patentverwaltung Verfahren zur Planung von Netzwerken mit mehreren Knoten enthaltende Cluster
US20140143110A1 (en) * 2012-11-20 2014-05-22 Sap Ag Circular Transaction Path Detection
CN103236013B (zh) * 2013-05-08 2016-03-30 南京大学 一种基于关键股票集识别的股票市场大盘数据分析方法
US10410234B1 (en) * 2018-03-23 2019-09-10 The Bartley J. Madden Foundation Machine learning based systems and methods for optimizing search engine results
CN108766535B (zh) * 2018-05-28 2021-09-17 天津中德应用技术大学 一种智能化个人健康管理系统
CN109948704A (zh) * 2019-03-20 2019-06-28 中国银联股份有限公司 一种交易监测方法与装置
CN111476662A (zh) * 2020-04-13 2020-07-31 中国工商银行股份有限公司 反洗钱识别方法及装置

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105574649A (zh) * 2015-12-10 2016-05-11 西安交通大学 一种基于多阶段MapReduce模型的纳税人偷漏税嫌疑群组检测方法
CN107240005A (zh) * 2017-06-13 2017-10-10 携程旅游网络技术（上海）有限公司 机票附加产品的推荐系统及方法
CN111340578A (zh) * 2018-12-18 2020-06-26 北京京东尚科信息技术有限公司 一种商品关联关系的生成方法、装置、介质及电子设备
CN111738786A (zh) * 2019-04-24 2020-10-02 北京京东尚科信息技术有限公司 构建商品组合的方法、系统、装置和可读存储介质
CN110335121A (zh) * 2019-07-10 2019-10-15 中国民航信息网络股份有限公司 附加服务产品的销售方法及销售装置
CN111612391A (zh) * 2020-04-02 2020-09-01 杭州电子科技大学 基于FP-growth的物流拣选设备商品摆放方法

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