CN118115185A - Knowledge graph-based client mining method and device - Google Patents
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Abstract
The embodiment of the application provides a client mining method and device based on a knowledge graph, wherein the method comprises the following steps: constructing a knowledge graph according to target account information, target transaction information and target behavior information of a target account, wherein the target account information and the target transaction information are acquired from a banking system, and the target behavior information is acquired from a target application logged in by the target account; obtaining N clients transacting with a target account and transaction information transacting with each client from a knowledge graph, wherein the transaction information comprises transaction amount, transaction flow direction and transaction times, the N clients comprise individual clients and enterprise clients in a banking system, and N is a natural number larger than 1; the target client is mined from the N clients based on the transaction information. The method solves the problem that the potential customers cannot be accurately excavated in the related technology, and further achieves the effect of accurately excavating the potential customers.
Description
Technical Field
The embodiment of the application relates to the field of computers, in particular to a knowledge-graph-based client mining method and device, a storage medium and electronic equipment.
Background
At present, under the background of comprehensive development of the Internet, a financial business is almost completely fight in different parts on line based on an off-line marketing mode, and in on-line marketing of a rubbing user, effective tools for deep accurate mining of potential customers are lacking, so that a major marketing business opportunity is easily missed, and accurate marketing is not facilitated.
Disclosure of Invention
The embodiment of the application provides a knowledge-graph-based client mining method and device, a storage medium and electronic equipment, which are used for at least solving the problem that potential clients cannot be accurately mined in the related technology.
According to an embodiment of the present application, there is provided a knowledge-graph-based client mining method, including: constructing a knowledge graph according to target account information, target transaction information and target behavior information of a target account, wherein the target account information and the target transaction information are acquired from a bank system, and the target behavior information is acquired from a target application logged in by the target account; obtaining N customers transacting with the target account and transaction information transacting with each customer from the knowledge graph, wherein the transaction information comprises transaction amount, transaction flow direction and transaction times, the N customers comprise individual customers and enterprise customers in the banking system, and the N is a natural number larger than 1; and mining target clients from N clients based on the transaction information.
In one exemplary embodiment, constructing a knowledge graph from target account information, target transaction information, and target behavior information of a target account includes: determining the target account and N clients as nodes in the knowledge graph; determining the transaction connection relation between the target account and N clients according to the target transaction information and the target behavior information; determining the transaction connection relation as the edge of the knowledge graph; and marking a legend of each node, the transaction connection relation and the transaction amount in the knowledge graph.
In an exemplary embodiment, obtaining N customers transacting with the target account and transaction information transacting with each of the customers from the knowledge graph includes: screening N clients from the knowledge graph according to a plurality of transaction scenes and fund flows; and extracting transaction nodes which are transacted with each client from the knowledge graph, and constructing a fund transaction visualization relation graph of the target account and N clients and each client and the corresponding transaction node in a preset time period by utilizing transaction information between the target account and each client.
In one exemplary embodiment, mining a target client from N clients based on the transaction information includes: and extracting a target client from the fund transaction visual relation diagram, wherein the transaction amount between the target client and the target account is larger than the preset amount, and the transaction number between the target client and the target account is larger than the preset transaction number.
In an exemplary embodiment, after mining the target client from the N clients based on the transaction information, the method further includes: when the target clients include a plurality of target clients, constructing a plurality of marketing knowledge maps of the target clients, wherein the marketing knowledge maps include client information of each target client, product information corresponding to each target client and marketing strategies corresponding to each target client, and the marketing knowledge maps are radar maps; and displaying the marketing knowledge graph on the target client.
In an exemplary embodiment, after the target client displays the marketing knowledge-graph, the method further includes: receiving marketing feedback information, wherein the marketing feedback information is used for representing feedback after each target client is marketing according to a marketing strategy corresponding to each target client; and adjusting the marketing strategy corresponding to each target client according to the marketing feedback information.
In an exemplary embodiment, after the knowledge graph is constructed according to the target account information, the target transaction information and the target behavior information of the target account, the method further includes: tracking the fund flow direction of the target account in the knowledge graph to obtain tracking information, wherein the fund flow direction comprises the time of passing by an upstream node, a downstream node and fund passing by in the fund circulation process; and marking the tracking information in the knowledge graph.
According to another embodiment of the present application, there is provided a knowledge-graph-based customer mining apparatus including: the first construction module is used for constructing a knowledge graph according to target account information, target transaction information and target behavior information of a target account, wherein the target account information and the target transaction information are acquired from a banking system, and the target behavior information is acquired from a target application logged in by the target account; the first acquisition module is used for acquiring N clients transacting with the target account and transaction information transacting with each client from the knowledge graph, wherein the transaction information comprises transaction amount, transaction flow direction and transaction times, the N clients comprise individual clients and enterprise clients in the banking system, and the N is a natural number larger than 1; and the first mining module is used for mining target clients from N clients based on the transaction information.
In an exemplary embodiment, the first building block includes: the first determining submodule is used for determining the target account and N clients as nodes in the knowledge graph; the second determining submodule is used for determining the transaction connection relation between the target account and N clients according to the target transaction information and the target behavior information; the third determining submodule is used for determining the transaction connection relation as the edge of the knowledge graph; and the first marking sub-module is used for marking the legend of each node, the transaction connection relation and the transaction amount in the knowledge graph.
In an exemplary embodiment, the first obtaining module includes: the first screening submodule is used for screening N clients from the knowledge graph according to a plurality of transaction scenes and fund flow directions; the first construction submodule is used for extracting transaction nodes which are transacted with each client from the knowledge graph, constructing a fund transaction visualization relation graph of the target account and N clients and funds transaction visualization relation graphs of each client and the corresponding transaction nodes in a preset time period by utilizing transaction information between the target account and each client.
In an exemplary embodiment, the first excavating module includes: and the first extraction sub-module is used for extracting the target client from the fund transaction visual relation diagram, wherein the transaction amount between the target client and the target account is larger than the preset amount, and the transaction times between the target client and the target account are larger than the preset transaction times.
In an exemplary embodiment, the above apparatus further includes: the first extraction module is configured to, after mining target clients from N clients based on the transaction information, construct a plurality of marketing knowledge graphs of the target clients when the target clients include a plurality of target clients, where the marketing knowledge graphs include client information of each target client, product information corresponding to each target client, and a marketing strategy corresponding to each target client, and the marketing knowledge graphs are radar graphs; and the first display module is used for displaying the marketing knowledge graph on the target client.
In an exemplary embodiment, the above apparatus further includes: the first receiving module is used for receiving marketing feedback information after the target clients display the marketing knowledge graph, wherein the marketing feedback information is used for representing feedback after each target client is marketing according to a marketing strategy corresponding to each target client; and the first adjustment module is used for adjusting the marketing strategy corresponding to each target client according to the marketing feedback information.
In an exemplary embodiment, the above apparatus further includes: the first tracking module is used for tracking the fund flow direction of the target account in the knowledge graph to obtain tracking information after the knowledge graph is constructed according to the target account information, the target transaction information and the target behavior information of the target account, wherein the fund flow direction comprises an upstream node, a downstream node and the time of fund passing in the fund circulation process; and the first marking module is used for marking the tracking information in the knowledge graph.
According to a further embodiment of the application, there is also provided a computer readable storage medium having stored therein a computer program, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
According to a further embodiment of the application there is also provided an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
According to the application, as the knowledge graph of the target account information, the target transaction information and the target behavior information is established, the client transacting with the target account and the corresponding transaction information can be acquired from the knowledge graph, and the target client can be mined from the clients based on the transaction information. Therefore, the problem that the related technology cannot accurately excavate the potential customers can be solved, and the effect of accurately excavating the potential customers is achieved.
Drawings
Fig. 1 is a hardware structure block diagram of a mobile terminal of a knowledge-graph-based client mining method according to an embodiment of the present application;
FIG. 2 is a flow chart of a knowledge-graph-based customer mining method, in accordance with an embodiment of the present application;
FIG. 3 is a flow chart of mining potential customers based on knowledge-graph according to the present embodiment;
FIG. 4 is a knowledge graph spectrum according to the present embodiment;
fig. 5 is a block diagram of a knowledge-graph-based customer mining apparatus, in accordance with an embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in detail below with reference to the accompanying drawings in conjunction with the embodiments.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the mobile terminal as an example, fig. 1 is a block diagram of a hardware structure of the mobile terminal according to the client mining method based on the knowledge graph in the embodiment of the present application. As shown in fig. 1, a mobile terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting of the structure of the mobile terminal described above. For example, the mobile terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.
The memory 104 may be used to store computer programs, such as software programs and modules of application software, such as computer programs corresponding to the knowledge-graph-based client mining method in the embodiment of the present application, and the processor 102 executes the computer programs stored in the memory 104 to perform various functional applications and data processing, that is, implement the above-mentioned method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as a NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.
In this embodiment, a knowledge-graph-based client mining method running on the mobile terminal is provided, and fig. 2 is a flowchart of the knowledge-graph-based client mining method according to an embodiment of the present application, as shown in fig. 2, where the flowchart includes the following steps:
Step S202, a knowledge graph is constructed according to target account information, target transaction information and target behavior information of a target account, wherein the target account information and the target transaction information are acquired from a bank system, and the target behavior information is acquired from a target application logged in by the target account;
step S204, N clients transacting with the target account and transaction information transacting with each client are obtained from the knowledge graph, wherein the transaction information comprises transaction amount, transaction flow direction and transaction times, the N clients comprise individual clients and enterprise clients in a banking system, and N is a natural number larger than 1;
Step S206, mining target clients from N clients based on the transaction information.
The application scenarios of the knowledge-graph-based client mining method in this embodiment include, but are not limited to, banks and financial institutions, telecom operators, insurance companies, and the like. For example, banks and financial institutions may utilize customer transaction information to build a knowledge graph from which potential customers are mined to conduct accurate promotion of loans, credit cards, investment products, and the like; the insurance company can establish a knowledge graph according to the customer insurance policy and the settlement record, and excavate potential customers from the knowledge graph so as to accurately promote new insurance products or upgrade the existing insurance policy.
The main body of execution of the above steps may be a specific processor set in a terminal, a server, a terminal or a server, or a processor or a processing device set relatively independently from the terminal or the server, but is not limited thereto.
Optionally, the target account in this embodiment includes, but is not limited to, a bank account, an investment account, or other financial account of an individual or business. The target account information includes, but is not limited to, the account holder's name, contact information, account number, account type (e.g., savings account, checking account, investment account, etc.), date of opening the account, etc.
Alternatively, the target transaction information in this embodiment includes, but is not limited to, transaction date, transaction amount, transaction type (deposit, withdrawal, transfer, etc.), transaction location (ATM, counter, online banking, etc.), counterpart account information (transfer-in/out account), account balance, transaction status (success, failure, freezing, etc.), transaction commission, transaction currency, etc.
Optionally, the target behavior information in this embodiment includes, but is not limited to, checking account balances and transaction records, transferring accounts, paying accounts, applying loans, paying credit cards, making regular deposits or financial products, conducting investment transactions or stock exchanges, and the like.
Through the steps, as the knowledge graph of the target account information, the target transaction information and the target behavior information is established, the client transacting with the target account and the corresponding transaction information can be acquired from the knowledge graph, and the target client can be mined from the clients based on the transaction information. Therefore, the problem that the related technology cannot accurately excavate the potential customers can be solved, and the effect of accurately excavating the potential customers is achieved.
In one exemplary embodiment, constructing a knowledge graph from target account information, target transaction information, and target behavior information of a target account includes: determining the target account and N clients as nodes in the knowledge graph; determining the transaction connection relation between the target account and N clients according to the target transaction information and the target behavior information; determining the transaction connection relation as the edge of the knowledge graph; and marking a legend of each node, the transaction connection relation and the transaction amount in the knowledge graph.
Alternatively, customers in the present embodiment include, but are not limited to, internal to a bank to a private customer, internal to a public customer, external to a private customer, external to a public customer, and the like. The form of the legend corresponding to each customer is not limited, and is mainly used for identifying the customer in the knowledge graph. As shown in fig. 4, different customers are identified with different colors. According to the method and the system, the knowledge graph is built through the transaction connection relation among different clients, and the purpose of clearly displaying the interaction relation between the target account and the clients is achieved.
In an exemplary embodiment, obtaining N customers transacting with the target account and transaction information transacting with each of the customers from the knowledge graph includes: screening N clients from the knowledge graph according to a plurality of transaction scenes and fund flows; and extracting transaction nodes which are transacted with each client from the knowledge graph, and constructing a fund transaction visualization relation graph of the target account and N clients and each client and the corresponding transaction node in a preset time period by utilizing transaction information between the target account and each client.
Optionally, the transaction scenario in the present embodiment includes, but is not limited to, payment transactions, financial investment transactions, personal transfers, financial financing. The flow direction of funds includes two main categories, inflow and outflow. The units of the preset time period include, but are not limited to, days, weeks, months, and the like. According to the embodiment, the target customer is accurately positioned by constructing the refined fund transaction visualization relation diagram related to the target account, the customer and the transaction information based on the comprehensive knowledge graph.
In one exemplary embodiment, mining a target client from N clients based on the transaction information includes: and extracting a target client from the fund transaction visual relation diagram, wherein the transaction amount between the target client and the target account is larger than the preset amount, and the transaction number between the target client and the target account is larger than the preset transaction number.
Alternatively, the preset amount and the number of transactions of the present embodiment may be defined according to a specific mining purpose. For example, to market small-cost financial products for customers, the preset amount and preset number of transactions are not very high; to market loan-type products for customers, the preset amount may be set high and the preset number of transactions may be set low. According to the method and the device, the target client is extracted according to the transaction amount and the transaction times, and the purpose of efficiently positioning the target client is achieved.
In an exemplary embodiment, after mining the target client from the N clients based on the transaction information, the method further includes: when the target clients include a plurality of target clients, constructing a plurality of marketing knowledge maps of the target clients, wherein the marketing knowledge maps include client information of each target client, product information corresponding to each target client and marketing strategies corresponding to each target client, and the marketing knowledge maps are radar maps; and displaying the marketing knowledge graph on the target client.
Optionally, in the present embodiment, the target client includes, but is not limited to, an application, a web page, an applet, and the like. Radar maps, also known as arachnids, are a common type of data visualization map, typically displaying a plurality of data variables in a radial fashion starting from a central point, thus forming a polygonal region. Such a map demonstrates the relative relationship between the different dimensions so that marketers or marketers can intuitively compare the data representations of the various dimensions. According to the embodiment, the characteristics and the demands of the target clients in different dimensions can be more clearly known by constructing the marketing knowledge graph of the target clients, so that the purposes of pertinently formulating marketing strategies and promoting activities and improving the marketing effect are achieved.
In an exemplary embodiment, after the target client displays the marketing knowledge-graph, the method further includes: receiving marketing feedback information, wherein the marketing feedback information is used for representing feedback after each target client is marketing according to a marketing strategy corresponding to each target client; and adjusting the marketing strategy corresponding to each target client according to the marketing feedback information.
Optionally, the marketing feedback information in the present embodiment includes, but is not limited to, product satisfaction surveys, service experience feedback, marketing campaign effect assessment, customer needs, and advice: customer feedback on the needs, desires, and advice of banking or financial institution products and services, including advice on new products, new functions, credit rating, risk identification, and the like. According to the embodiment, the marketing strategy is adjusted by receiving the marketing feedback information, so that the purposes of improving the customer satisfaction and the business effect are achieved.
In an exemplary embodiment, after the knowledge graph is constructed according to the target account information, the target transaction information and the target behavior information of the target account, the method further includes: tracking the fund flow direction of the target account in the knowledge graph to obtain tracking information, wherein the fund flow direction comprises the time of passing by an upstream node, a downstream node and fund passing by in the fund circulation process; and marking the tracking information in the knowledge graph.
Optionally, in this embodiment, the target account obtains funds from the upstream node, and the target account inputs funds to the downstream node. Upstream and downstream nodes include, but are not limited to, personal accounts, enterprise accounts, and the like. Tracking information includes, but is not limited to, upstream nodes, downstream nodes, time of funds transfer, amount of funds flow, purpose and goal of funds flow: the reason and purpose of the flow of funds, the end use goal of the flow of funds, information about the parties to the flow of funds, etc. The embodiment acquires the tracking information, can clearly present the complex relation of the fund flow direction, and helps related personnel to carry out data analysis and decision.
The invention is illustrated below with reference to specific examples:
Fig. 3 in this embodiment is a flowchart of mining potential customers based on knowledge graph according to this specific embodiment, as shown in fig. 3, including the following steps:
S302, starting;
s304, automatically performing preliminary exploration for each client by the system background of the bank or the financial institution according to the transaction scene and the fund flow direction;
S306, judging whether the client distributes business opportunities (corresponding to the marketing strategies), if yes, turning to S314, and if not, turning to S316;
s308, business opportunity distribution;
s310, business machine processing;
s312, searching funds in a designated time interval according to a selected client, visually displaying the funds, searching all users who have funds to and fro with the selected client through a 'funds transaction' knowledge graph, screening transaction type, number and amount information in the searching process, finally generating a funds transaction visual relation graph related to the client, associating the relationship graph with the internal client in the relation graph as a starting point, and expanding a transaction relation circle graph through continuous node searching;
s314, business machine feedback is carried out, a marketing knowledge graph of a plurality of target clients is constructed, wherein the marketing knowledge graph comprises client information of each target client, product information corresponding to each target client and a marketing strategy corresponding to each target client, and the marketing knowledge graph is a radar graph;
S316, marketing by a user;
S318, ending.
Fig. 4 in this embodiment is a knowledge graph spectrum according to this specific embodiment, as shown in fig. 4:
the nodes in the knowledge graph are a plurality of clients, the edges are transaction connection relations among the clients, wherein node categories comprise, but are not limited to, internal private clients, external private clients, internal public clients, external public clients and external home clients, and the edge categories comprise, but are not limited to, internal fund flows, external fund flows and transaction information flows. The marketer can search in the knowledge graph by taking a certain public/private client as a starting point, find all clients which have funds to come and go with, screen transaction category, number of strokes, stroke and the like information in the searching process, finally generate a fund transaction visual relation graph related to the client, and correlate the fund transaction visual relation graph by taking an internal client as the starting point, and expand a transaction relation circle chain graph through continuous node exploration.
From the description of the above embodiments, it will be clear to a person skilled in the art that the method according to the above embodiments may be implemented by means of software plus the necessary general hardware platform, but of course also by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the related art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiment also provides a knowledge-graph-based client mining device, which is used for realizing the above embodiment and the preferred implementation manner, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
Fig. 5 is a block diagram of a knowledge-graph-based customer mining apparatus according to an embodiment of the present application, as shown in fig. 5, including:
A first construction module 52, configured to construct a knowledge graph according to target account information, target transaction information, and target behavior information of a target account, where the target account information and the target transaction information are both obtained from a banking system, and the target behavior information is obtained from a target application registered by the target account;
A first obtaining module 54, configured to obtain, from the knowledge graph, N customers transacting with the target account and transaction information transacting with each of the customers, where the transaction information includes transaction amount, transaction flow direction, and transaction number, and the N customers include individual customers and enterprise customers in the banking system, and the N is a natural number greater than 1;
The first mining module 56 is configured to mine the target client from the N clients based on the transaction information.
In an exemplary embodiment, the first building block includes: the first determining submodule is used for determining the target account and N clients as nodes in the knowledge graph; the second determining submodule is used for determining the transaction connection relation between the target account and N clients according to the target transaction information and the target behavior information; the third determining submodule is used for determining the transaction connection relation as the edge of the knowledge graph; and the first marking sub-module is used for marking the legend of each node, the transaction connection relation and the transaction amount in the knowledge graph.
In an exemplary embodiment, the first obtaining module includes: the first screening submodule is used for screening N clients from the knowledge graph according to a plurality of transaction scenes and fund flow directions; the first construction submodule is used for extracting transaction nodes which are transacted with each client from the knowledge graph, constructing a fund transaction visualization relation graph of the target account and N clients and funds transaction visualization relation graphs of each client and the corresponding transaction nodes in a preset time period by utilizing transaction information between the target account and each client.
In an exemplary embodiment, the first excavating module includes: and the first extraction sub-module is used for extracting the target client from the fund transaction visual relation diagram, wherein the transaction amount between the target client and the target account is larger than the preset amount, and the transaction times between the target client and the target account are larger than the preset transaction times.
In an exemplary embodiment, the above apparatus further includes: the first extraction module is configured to, after mining target clients from N clients based on the transaction information, construct a plurality of marketing knowledge graphs of the target clients when the target clients include a plurality of target clients, where the marketing knowledge graphs include client information of each target client, product information corresponding to each target client, and a marketing strategy corresponding to each target client, and the marketing knowledge graphs are radar graphs; and the first display module is used for displaying the marketing knowledge graph on the target client.
In an exemplary embodiment, the above apparatus further includes: the first receiving module is used for receiving marketing feedback information after the target clients display the marketing knowledge graph, wherein the marketing feedback information is used for representing feedback after each target client is marketing according to a marketing strategy corresponding to each target client; and the first adjustment module is used for adjusting the marketing strategy corresponding to each target client according to the marketing feedback information.
In an exemplary embodiment, the above apparatus further includes: the first tracking module is used for tracking the fund flow direction of the target account in the knowledge graph to obtain tracking information after the knowledge graph is constructed according to the target account information, the target transaction information and the target behavior information of the target account, wherein the fund flow direction comprises an upstream node, a downstream node and the time of fund passing in the fund circulation process; and the first marking module is used for marking the tracking information in the knowledge graph.
It should be noted that each of the above modules may be implemented by software or hardware, and for the latter, it may be implemented by, but not limited to: the modules are all located in the same processor; or the above modules may be located in different processors in any combination.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the method embodiments described above when run.
In one exemplary embodiment, the computer readable storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
An embodiment of the application also provides an electronic device comprising a memory having stored therein a computer program and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
In an exemplary embodiment, the electronic device may further include a transmission device connected to the processor, and an input/output device connected to the processor.
Specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the exemplary implementation, and this embodiment is not described herein.
It will be appreciated by those skilled in the art that the modules or steps of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the principle of the present application should be included in the protection scope of the present application.
Claims (10)
1. The client mining method based on the knowledge graph is characterized by comprising the following steps of:
constructing a knowledge graph according to target account information, target transaction information and target behavior information of a target account, wherein the target account information and the target transaction information are acquired from a bank system, and the target behavior information is acquired from a target application logged in by the target account;
Obtaining N clients transacting with the target account and transaction information transacting with each client from the knowledge graph, wherein the transaction information comprises transaction amount, transaction flow direction and transaction times, the N clients comprise individual clients and enterprise clients in the banking system, and the N is a natural number greater than 1;
And mining target clients from N clients based on the transaction information.
2. The method of claim 1, wherein constructing a knowledge graph from target account information, target transaction information, and target behavior information for a target account comprises:
determining the target account and N clients as nodes in the knowledge graph;
Determining the transaction connection relation between the target account and N clients according to the target transaction information and the target behavior information;
Determining the transaction connection relationship as an edge of the knowledge graph;
and marking a legend of each node, the transaction connection relation and the transaction amount in the knowledge graph.
3. The method of claim 1, wherein obtaining from the knowledge-graph N customers transacting with the target account and transaction information transacting with each of the customers, comprises:
According to a plurality of transaction scenes and fund flows, N clients are screened out from the knowledge graph;
And extracting transaction nodes which are transacted with each client from the knowledge graph, and constructing a fund transaction visualization relation graph of the target account and N clients and each client and the corresponding transaction node in a preset time period by utilizing transaction information between the target account and each client.
4. A method according to claim 3, wherein mining a target customer from N of said customers based on said transaction information comprises:
and extracting a target client from the fund transaction visual relation diagram, wherein the transaction amount between the target client and the target account is larger than a preset amount, and the transaction times between the target client and the target account are larger than the preset transaction times.
5. The method of claim 1, wherein after mining a target customer from the N customers based on the transaction information, the method further comprises:
Under the condition that a plurality of target clients are included, a plurality of marketing knowledge patterns of the target clients are constructed, wherein the marketing knowledge patterns comprise client information of each target client, product information corresponding to each target client and marketing strategies corresponding to each target client, and the marketing knowledge patterns are radar patterns;
And displaying the marketing knowledge graph on the target client.
6. The method of claim 5, wherein after the target client displays the marketing knowledge-graph, the method further comprises:
Receiving marketing feedback information, wherein the marketing feedback information is used for representing feedback after each target client is marketing according to a marketing strategy corresponding to each target client;
And adjusting the marketing strategy corresponding to each target client according to the marketing feedback information.
7. The method of claim 1, wherein after constructing the knowledge-graph from the target account information, the target transaction information, and the target behavior information of the target account, the method further comprises:
tracking the fund flow direction of the target account in the knowledge graph to obtain tracking information, wherein the fund flow direction comprises the time of passing by an upstream node, a downstream node and fund passing by in the fund circulation process;
and marking the tracking information in the knowledge graph.
8. A knowledge-graph-based customer mining apparatus, comprising:
The first construction module is used for constructing a knowledge graph according to target account information, target transaction information and target behavior information of a target account, wherein the target account information and the target transaction information are acquired from a banking system, and the target behavior information is acquired from a target application logged in by the target account;
The first acquisition module is used for acquiring N clients transacting with the target account and transaction information transacting with each client from the knowledge graph, wherein the transaction information comprises transaction amount, transaction flow direction and transaction times, the N clients comprise individual clients and enterprise clients in the banking system, and the N is a natural number larger than 1;
And the first mining module is used for mining target clients from N clients based on the transaction information.
9. A computer readable storage medium, characterized in that a computer program is stored in the computer readable storage medium, wherein the computer program, when being executed by a processor, implements the steps of the method according to any of the claims 1 to 7.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
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| CN202410014896.2A CN118115185A (en) | 2024-01-04 | 2024-01-04 | Knowledge graph-based client mining method and device |
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