CN112598428A - Transaction data processing method and device, computer equipment and storage medium - Google Patents
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Abstract
The application provides a transaction data processing method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring transaction data in a target time period; data extraction is carried out on the transaction data to obtain target data in the transaction data; constructing a fund going-and-going map between transaction data according to target data based on a knowledge map technology; and determining the abnormal condition of the transaction data according to the fund to-and-fro map. Clear fund round-trip relation between transaction objects is established in a map mode, data analysis difficulty is reduced, and transaction data abnormity detection efficiency is improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing transaction data, a computer device, and a storage medium.
Background
At present, some main bodies utilize the information asymmetric relation between the commodity flow and the fund flow to falsely trade the data of contract, receipt and the like under the background of no real commodity trade requirement, thereby causing serious financial safety hidden trouble to the market economy. In order to effectively prevent market risk, the regulatory authorities require the supervision of significant transaction data of enterprises.
In the related art, a supervision department supervises the major transaction records reported by banks, but the current false trade main body can utilize a plurality of enterprises to carry out fund operation among the banks, and the operation mode with extremely high concealment makes the supervision department difficult to analyze the false trade transactions from a large amount of transaction data. Therefore, the current transaction data supervision mode has the problem of data analysis difficulty.
Disclosure of Invention
An embodiment of the application aims to provide a transaction data processing method, a transaction data processing device, computer equipment and a storage medium, and aims to solve the problem that a data analysis is difficult in a current transaction data supervision mode.
In a first aspect, an embodiment of the present application provides a method for processing transaction data, including:
acquiring transaction data in a target time period;
data extraction is carried out on the transaction data to obtain target data in the transaction data;
constructing a fund going-and-going map between transaction data according to target data based on a knowledge map technology;
and determining the abnormal condition of the transaction data according to the fund to-and-fro map.
In the implementation process, the target data in the transaction data is obtained by performing data extraction on the transaction data within a period of time, so that key information such as a transaction object, a transaction amount, transaction time and the like is extracted. And then, a fund passing and returning map among transaction data is constructed according to the target data by a knowledge map technology, and clear fund passing and returning relations among transaction objects are established in a map mode, so that the data analysis difficulty is reduced. And finally, rapidly determining the abnormal condition of the transaction data based on the fund incoming and outgoing map, and improving the efficiency of abnormal detection of the transaction data.
Further, data extraction is performed on the transaction data to obtain target data in the transaction data, and the method comprises the following steps:
performing data cleaning on the transaction data to obtain first data corresponding to each transaction data;
for each piece of first data, inquiring second data corresponding to a preset index field in the first data according to the preset index field;
and converting the second data into target data according to the preset corresponding relation between the second data and the target data.
In the implementation process, the transaction data is cleaned to remove data such as incomplete invalid transactions and repeated transaction records in the transaction data, second data corresponding to index fields such as transaction amount, transaction time, transaction state and transaction object in the first data is inquired, and finally the second data is converted into target data to realize data standardization and facilitate subsequent processing of the target data.
Further, the target data comprises transaction objects, transaction time, transaction amount and transaction types, and a fund calling-and-going map between the transaction data is constructed according to the target data based on the knowledge map technology, and comprises the following steps:
establishing an ontology base of target data by taking a transaction object as a data entity, taking transaction time as a data attribute and taking a transaction amount and a transaction type as a data relation;
according to the data relation in the ontology base, carrying out entity linkage on the data entities to obtain an initial map;
and according to the data attributes in the ontology library, fusing the data attributes to the initial map to obtain a map of the capital from and the capital from.
In the implementation process, the transaction object, the transaction time, the transaction amount and the transaction type are used as identification parameters for identifying the false trade financing, and an ontology base is constructed on the basis of the identification parameters, so that classes used for generating a fund map between the transaction objects are conveniently stored.
Further, according to the data relationship in the ontology library, performing entity linking on the data entities, and before obtaining the initial map, the method further includes:
acquiring identification information of a transaction object;
determining whether the identification information between each transaction object is consistent;
and if the identification information is consistent, taking a plurality of transaction objects with consistent identification information as the same data entity.
In the implementation process, when the transaction object is used as a data entity, one transaction object may have multiple names, so as to avoid reducing the accuracy of the identification result by using the multiple names as multiple transaction objects, and determine whether the transaction objects are the same transaction object according to the identification information of the transaction object, thereby improving the accuracy of the identification result.
Further, determining abnormal conditions of the transaction data according to the fund-to-call map comprises the following steps:
inquiring a knowledge link which is in accordance with a preset index condition in a fund coming and going map according to the preset index condition;
determining whether data entities in the knowledge link meet preset transaction abnormal conditions or not;
and if the transaction abnormal conditions are met, determining that the transaction data between the data entities are abnormal data.
In the implementation process, the target knowledge link in the fund coming and going map is inquired through the index condition, so that the data to be checked by the user can be quickly inquired from a large amount of transaction data, whether the abnormal conditions such as virtual trade financing belong to or not is checked according to the knowledge link, and the checking efficiency is improved.
Further, after determining the abnormal condition of the transaction data according to the fund to-and-fro map, the method further comprises the following steps:
and if the transaction data are abnormal data, sending transaction abnormity prompt information to a preset terminal, wherein the transaction abnormity prompt information comprises a fund coming and going map containing the abnormal data.
In the implementation process, the abnormal prompt information is sent to the preset terminal, so that a user can conveniently do risk prevention and control work in time.
In a second aspect, an embodiment of the present application provides a transaction data processing apparatus, including:
the acquisition module is used for acquiring transaction data in a target time period;
the extraction module is used for carrying out data extraction on each transaction data to obtain target data in the transaction data;
the construction module is used for constructing a fund going-and-going map between transaction data based on a knowledge map technology and according to target data;
and the determining module is used for determining the abnormal condition of the transaction data according to the fund incoming and outgoing map.
Further, the target data includes a transaction object, a transaction time, a transaction amount and a transaction type, and the building module specifically includes:
the construction unit is used for constructing an ontology base of the target data by taking the transaction object as a data entity, the transaction time as a data attribute and the transaction amount and the transaction type as a data relation together;
the link unit is used for carrying out entity link on the data entities according to the data relation in the ontology library to obtain an initial map;
and the fusion unit is used for fusing the data attributes to the initial map according to the data attributes in the ontology library to obtain the map of the capital coming and going.
In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the computer device execute the transaction data processing method of any one of the above first aspects.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the method for processing transaction data of any one of the first aspect.
It is understood that the beneficial effects of the second to fourth aspects can be seen from the description of the first aspect, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart illustrating a transaction data processing method according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a transaction data processing apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
As described in the related art, the fraudulent trading principal can utilize multiple enterprises to perform highly concealed fund operation among multiple banks, so that it is difficult for the monitoring department to analyze the fraudulent trading financing from a large amount of transaction data.
In order to solve the problems in the prior art, the application provides a transaction data processing method, which obtains target data in transaction data by performing data extraction on the transaction data within a period of time, so as to extract key information such as a transaction object, a transaction amount, transaction time and the like. And then, a fund passing and returning map among transaction data is constructed according to the target data by a knowledge map technology, and clear fund passing and returning relations among transaction objects are established in a map mode, so that the data analysis difficulty is reduced. And finally, rapidly determining the abnormal condition of the transaction data based on the fund incoming and outgoing map, and improving the efficiency of abnormal detection of the transaction data.
Referring to fig. 1, fig. 1 is a schematic flowchart of a transaction data processing method provided in an embodiment of the present application. The transaction data processing method described in the embodiments of the present application may be applied to computer devices, including but not limited to smart phones, tablet computers, desktop computers, supercomputers, personal digital assistants, physical servers, cloud servers, and other devices. The transaction data processing method of the embodiment of the application includes steps S101 to S104, which are detailed as follows:
step S101, transaction data in a target time period are acquired.
In this embodiment, the transaction data is a user transaction record reported by a financial institution such as a bank required by a regulatory authority, for example, a transaction record of 1000w transferred from an enterprise a to an enterprise C through a bank B. Preferably, the transaction data is a transaction record with a transaction amount greater than a preset value, so as to better identify false trade transactions with a large amount. The target time period may be a time period within the past one month, within the past two months, within the past three months, or the like, and is not limited thereto.
And step S102, data extraction is carried out on the transaction data to obtain target data in the transaction data.
In this embodiment, since the transaction data contains a large amount of content, and the identification parameter for identifying the fraudulent trade transaction does not need all transaction content, the identification parameter only needed for identifying the fraudulent trade transaction is obtained by means of data extraction. The target data includes but is not limited to transaction objects (including both parties to a transaction), transaction amount, transaction time, transaction status (in or out), and the like. For example, when data extraction is performed on transaction data, an index field may be set, and according to the index field, data extraction is performed in rows and columns, and then data extraction is performed in rows and columns as an index condition, so as to obtain target data corresponding to the rows and columns.
In one embodiment, the data extraction of the transaction data to obtain the target data in the transaction data includes: performing data cleaning on the transaction data to obtain first data corresponding to each transaction data; for each piece of first data, inquiring second data corresponding to a preset index field in the first data according to the preset index field; and converting the second data into target data according to the preset corresponding relation between the second data and the target data.
In the above embodiment, since the transaction data is reported by a plurality of financial institutions, the transaction data is subjected to data cleansing. Optionally, the numerical apertures of the transaction data are unified, for example, some transaction data adopt transaction amount, some transaction data adopt transfer amount, so that the transaction amount and the transfer amount need to be unified into the same name; filling the transaction data with the unified numerical aperture into a preset standardized template to obtain a transaction data table; in order to avoid errors in subsequent processing, the transaction data table is backed up; and after backup, deleting redundant rows or columns in the transaction data table to obtain first data.
The preset index field is a field for filtering out target data, such as transaction amount and transaction time. Optionally, the time is used as a preset index field, the target data corresponding to the time in the first data may be queried, for example, the second data is transfer time: 2000.01.01, the transfer time is a field that matches the predetermined index field. Further, in order to meet the requirement of constructing the knowledge graph, the second data is converted into target data which accords with the data format of the knowledge graph.
In the implementation process, the transaction data is cleaned to remove data such as incomplete invalid transactions and repeated transaction records in the transaction data, second data corresponding to index fields such as transaction amount, transaction time, transaction state and transaction object in the first data is inquired, and finally the second data is converted into target data to realize data standardization and facilitate subsequent processing of the target data.
And step S103, constructing a fund going-going map between transaction data according to the target data based on the knowledge map technology.
In the embodiment, the knowledge map is a knowledge carrier represented by a graph data structure and describes objects of the objective world and the relationship of the objects, wherein the nodes represent the objects of the objective world and the edges represent the relationship between the objects. In this embodiment, the nodes of the data map are enterprise entities, and the edges are the fund exchange relationship between enterprises, that is, the transaction object is used as a data entity, the transaction time is used as a data attribute, the transaction amount and the transaction type are used together as a data relationship, the data relationship is adopted to link the data entities, and the data attribute is adopted to describe the data relationship. In order to improve the map recognition efficiency, a validity period, such as 2 months, can be established in the limited map. By means of the knowledge graph technology, a fund coming and going graph among transaction data is constructed according to target data, clear fund coming and going relations among transaction objects are established in a graph mode, and data analysis difficulty is reduced.
In one embodiment, the target data comprises transaction objects, transaction time, transaction amount and transaction types, and the fund calling and going graph between the transaction data is constructed according to the target data based on the knowledge graph technology, and comprises the following steps: establishing an ontology base of target data by taking a transaction object as a data entity, taking transaction time as a data attribute and taking a transaction amount and a transaction type as a data relation; according to the data relation in the ontology base, carrying out entity linkage on the data entities to obtain an initial map; and according to the data attributes in the ontology library, fusing the data attributes to the initial map to obtain a map of the capital from and the capital from.
In this embodiment, Ontology (Ontology) is a conceptual model modeling tool that can describe information systems at semantic and knowledge levels. The knowledge graph is divided into a data layer and a mode layer, wherein the data layer is formed by a series of fact data, the mode layer is used for constructing entities, attributes and relations from the data and is the core of the knowledge graph, and the ontology base is widely used for constructing the knowledge graph data layer. Illustratively, by using the transaction amount and the transaction type as data relations to link data entities, for example, the transaction amount is 1000 ten thousand yuan, the transaction type is roll-out, the transaction objects are enterprise a and enterprise B, then enterprise a and enterprise B may be linked in a graph by a connecting line, and according to the transaction type is roll-out, the arrow direction of the connecting line is marked as enterprise a to enterprise B, and "1000 ten thousand yuan" is marked on the connecting line, so as to obtain an initial graph. Further, since there may be a plurality of transaction records between every two transaction objects, that is, there are a plurality of connection lines between the two transaction objects, in order to distinguish the connection lines, the transaction data is used as the data attributes of the connection lines, so as to fuse the data attributes into the initial map, and obtain the map of the coming and going of the fund.
The transaction object, the transaction time, the transaction amount and the transaction type are used as identification parameters for identifying the false trade financing, and an ontology base is constructed on the basis of the identification parameters, so that classes used for generating fund calling and going maps among the transaction objects can be conveniently stored, and the fund calling and going maps with clear data structures and convenience in analysis can be constructed.
Further, according to the data relationship in the ontology library, performing entity linking on the data entities, and before obtaining the initial map, the method further includes: acquiring identification information of a transaction object; determining whether the identification information between each transaction object is consistent; and if the identification information is consistent, taking a plurality of transaction objects with consistent identification information as the same data entity.
When the transaction object is used as a data entity, the transaction object is an enterprise, and the names of the enterprises in different transaction records may have differences, so that whether the transaction object is the same data entity is determined in order to ensure that a data link of a fund map is more accurate and clear. Alternatively, the identification information may be an organizational code or other information representing a unique characteristic of the business. Specifically, consistency judgment of the identification information can be realized based on the OpenKE, so that data entity alignment is completed.
And step S104, determining the abnormal condition of the transaction data according to the fund calling and going map.
In this embodiment, the fund coming and going map can clearly and intuitively express the fund coming and going relationship among enterprises, so that the abnormal condition of the transaction data can be judged according to the judgment condition of the abnormal condition. The method and the device for detecting the abnormal condition of the transaction data rapidly determine the abnormal condition of the transaction data based on the fund incoming and outgoing map, and improve the efficiency of detecting the abnormal condition of the transaction data.
In one embodiment, determining anomalies in the transactional data from the fund-to-call graph comprises: inquiring a knowledge link which is in accordance with a preset index condition in a fund coming and going map according to the preset index condition; determining whether data entities in the knowledge link meet preset transaction abnormal conditions or not; and if the transaction abnormal conditions are met, determining that the transaction data between the data entities are abnormal data.
In the implementation process, the preset index condition may be that the transaction time occurs within a specified time period, a specified transaction object, and the like. The preset transaction exception condition may be that the knowledge link is a cyclic link, transaction amounts of the transactions in the knowledge link are consistent (transaction differences are considered to be consistent within a preset range), and transaction interval time meets a preset value, and the specific implementation manner of the transaction exception condition is not limited. Illustratively, a target knowledge link is inquired by taking a time period as a preset index condition, whether a cyclic link exists in the target knowledge link is judged, if yes, whether the transaction amount is consistent and the transaction interval time of both transaction parties is within a preset time range is judged, and if yes, the transaction data between data entities in the knowledge link can be determined to be abnormal data.
According to the embodiment, the target knowledge link in the fund coming and going map is inquired through the index condition, so that the data to be checked by the user can be quickly inquired from a large amount of transaction data, whether the abnormal conditions such as virtual trade financing belong to or not is checked according to the knowledge link, and the checking efficiency is improved.
In a possible implementation manner, after determining the abnormal condition of the transaction data according to the fund to-and-from map, the method further comprises the following steps: and if the transaction data are abnormal data, sending transaction abnormity prompt information to a preset terminal, wherein the transaction abnormity prompt information comprises a fund coming and going map containing the abnormal data.
In the implementation process, the abnormal prompt information is sent to the preset terminal, so that a user can conveniently do risk prevention and control work in time.
In order to execute the method corresponding to the above method embodiment to achieve the corresponding function and technical effect, the following provides a transaction data processing device. Referring to fig. 2, fig. 2 is a block diagram of a transaction data processing apparatus according to an embodiment of the present disclosure. The modules included in the apparatus in this embodiment are used for the steps in the embodiment corresponding to fig. 1, and refer to fig. 1 and the related description in the embodiment corresponding to fig. 1 specifically. For convenience of explanation, only a part related to the present embodiment is shown, and the transaction data processing apparatus provided in the embodiment of the present application includes:
an obtaining module 201, configured to obtain transaction data in a target time period;
the extracting module 202 is configured to perform data extraction on each transaction data to obtain target data in the transaction data;
the construction module 203 is used for constructing a fund map of the transaction data according to the target data based on the knowledge map technology;
and the determining module 204 is used for determining the abnormal condition of the transaction data according to the fund flow-to-flow map.
As an optional implementation manner, the extracting module 202 specifically includes:
the cleaning unit is used for carrying out data cleaning on the transaction data to obtain first data corresponding to each transaction data;
the first query unit is used for querying second data corresponding to a preset index field in the first data according to the preset index field aiming at each first data;
and the conversion unit is used for converting the second data into the target data according to the preset corresponding relation between the second data and the target data.
As an optional implementation manner, the target data includes a transaction object, a transaction time, a transaction amount, and a transaction type, and the building module 203 specifically includes:
the construction unit is used for constructing an ontology base of the target data by taking the transaction object as a data entity, the transaction time as a data attribute and the transaction amount and the transaction type as a data relation together;
the link unit is used for carrying out entity link on the data entities according to the data relation in the ontology library to obtain an initial map;
and the fusion unit is used for fusing the data attributes to the initial map according to the data attributes in the ontology library to obtain the map of the capital coming and going.
Optionally, the building module 203 further includes:
the acquisition unit is used for acquiring the identification information of the transaction object;
the first judging unit is used for determining whether the identification information between each trading object is consistent;
and the first judgment unit is used for taking a plurality of transaction objects with consistent identification information as the same data entity if the identification information is consistent.
As an optional implementation manner, the determining module 204 specifically includes:
the second query unit is used for querying a knowledge link which is in accordance with the preset index condition in the fund incoming and outgoing map according to the preset index condition;
the second judgment unit is used for determining whether the data entities in the knowledge link meet the preset transaction abnormal conditions or not;
and the second judgment unit is used for determining the transaction data between the data entities as abnormal data if the transaction abnormal conditions are met.
As an optional implementation, the apparatus further includes:
and the sending module is used for sending transaction abnormity prompt information to the preset terminal if the transaction data are abnormal data, wherein the transaction abnormity prompt information comprises a fund incoming and outgoing map containing the abnormal data.
The transaction data processing device can implement the transaction data processing method of the method embodiment. The alternatives in the above-described method embodiments are also applicable to this embodiment and will not be described in detail here. The rest of the embodiments of the present application may refer to the contents of the above method embodiments, and in this embodiment, details are not described again.
Fig. 3 is a schematic structural diagram of a computer device according to an embodiment of the present application. As shown in fig. 3, the computer device 3 of this embodiment includes: at least one processor 30 (only one shown in fig. 3), a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30, the processor 30 implementing the steps of any of the above-described method embodiments when executing the computer program 32.
The computer device 3 may be a computing device such as a smart phone, a tablet computer, a desktop computer, a supercomputer, a personal digital assistant, a physical server, and a cloud server. The computer device may include, but is not limited to, a processor 30, a memory 31. Those skilled in the art will appreciate that fig. 3 is merely an example of the computer device 3, and does not constitute a limitation of the computer device 3, and may include more or less components than those shown, or combine some of the components, or different components, such as input output devices, network access devices, etc.
The Processor 30 may be a Central Processing Unit (CPU), and the Processor 30 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may in some embodiments be an internal storage unit of the computer device 3, such as a hard disk or a memory of the computer device 3. The memory 31 may also be an external storage device of the computer device 3 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the computer device 3. The memory 31 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 31 may also be used to temporarily store data that has been output or is to be output.
In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in any of the method embodiments described above.
The embodiments of the present application provide a computer program product, which when running on an electronic device, enables the electronic device to implement the steps in the above method embodiments when executed.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. 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 and/or flowchart illustration, and combinations of blocks in the block diagrams and/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.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method of processing transaction data, comprising:
acquiring transaction data in a target time period;
performing data extraction on the transaction data to obtain target data in the transaction data;
constructing a fund going-going map between the transaction data according to the target data based on a knowledge map technology;
and determining the abnormal condition of the transaction data according to the fund incoming and outgoing map.
2. The method for processing transaction data according to claim 1, wherein the performing data extraction on the transaction data to obtain target data in the transaction data includes:
performing data cleaning on the transaction data to obtain first data corresponding to each transaction data;
for each piece of first data, inquiring second data corresponding to a preset index field in the first data according to the preset index field;
and converting the second data into the target data according to the preset corresponding relation between the second data and the target data.
3. The method for processing transaction data according to claim 1, wherein the target data comprises transaction objects, transaction time, transaction amount and transaction types, and the construction of the fund-to-call map between the transaction data according to the target data based on the knowledge map technology comprises:
establishing an ontology base of the target data by taking the transaction object as a data entity, the transaction time as a data attribute and the transaction amount and the transaction type as a data relation;
according to the data relation in the ontology base, performing entity linkage on the data entities to obtain an initial map;
and according to the data attributes in the ontology base, fusing the data attributes to the initial map to obtain the fund coming and going map.
4. The method of claim 3, wherein before the entity linking the data entities according to the data relationship in the ontology library to obtain the initial map, the method further comprises:
acquiring identification information of the transaction object;
determining whether the identification information between each of the transaction objects is consistent;
and if the identification information is consistent, taking a plurality of transaction objects with consistent identification information as the same data entity.
5. The method of processing transaction data according to claim 1, wherein said determining anomalies in the transaction data from the fund-to-call map comprises:
inquiring a knowledge link in the fund coming and going graph, which is in accordance with a preset index condition, according to the preset index condition;
determining whether the data entities in the knowledge link meet preset transaction abnormal conditions or not;
and if the transaction abnormal conditions are met, determining that the transaction data among the data entities are abnormal data.
6. The method for processing transaction data according to claim 1, wherein after determining the abnormal condition of the transaction data according to the fund flow-to-flow map, the method further comprises:
and if the transaction data are abnormal data, sending transaction abnormity prompt information to a preset terminal, wherein the transaction abnormity prompt information comprises a fund incoming and outgoing map containing the abnormal data.
7. An apparatus for processing transaction data, comprising:
the acquisition module is used for acquiring transaction data in a target time period;
the extraction module is used for carrying out data extraction on each transaction data to obtain target data in the transaction data;
the construction module is used for constructing a fund calling and going map between the transaction data based on knowledge map technology according to the target data;
and the determining module is used for determining the abnormal condition of the transaction data according to the fund calling and going map.
8. The device for processing transaction data according to claim 7, wherein the target data includes a transaction object, a transaction time, a transaction amount, and a transaction type, and the building module specifically includes:
the construction unit is used for constructing an ontology base of the target data by taking the transaction object as a data entity, the transaction time as a data attribute and the transaction amount and the transaction type as a data relation together;
the link unit is used for carrying out entity link on the data entities according to the data relation in the ontology library to obtain an initial map;
and the fusion unit is used for fusing the data attributes to the initial map according to the data attributes in the ontology base to obtain the fund coming and going map.
9. A computer device comprising a memory for storing a computer program and a processor for executing the computer program to cause the computer device to perform the method of processing transaction data of any of claims 1 to 6.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements the method of processing transaction data of any of claims 1 to 6.
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