CN113934760A - Financial data identification and transmission system and method based on artificial intelligence model - Google Patents
Financial data identification and transmission system and method based on artificial intelligence model Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/24—Querying
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Abstract
The invention provides a financial data identification and transmission system based on an artificial intelligence model, which comprises a multi-source terminal access subsystem, a terminal request identification subsystem, a read-write separation subsystem, a data association subsystem and a data transmission subsystem; the data association subsystem configures an artificial intelligence data model; the multi-source terminal access subsystem receives a plurality of financial data access requests sent by a plurality of multi-source heterogeneous terminals; the terminal request identification subsystem obtains a financial data access request group; the read-write separation subsystem executes read-write separation operation on the financial data access request; the data association subsystem performs data association to obtain a financial data object to be transmitted; and sending the financial data object to be transmitted to a remote financial data processing center for processing through the data transmission subsystem. The invention also discloses a financial data identification and transmission method based on the artificial intelligence model and electronic equipment for realizing the method. The invention can realize the identification and transmission of financial data based on an artificial intelligence model.
Description
Technical Field
The invention belongs to the technical field of financial data processing and acquisition, and particularly relates to a financial data identification and transmission system and method based on an artificial intelligence model, and electronic equipment for realizing the method.
Background
The financial data is comprehensive data taking payment, credit, bank financing, trust, public fund, private fund and other resource management products as core modules. Financial data is generally generated through various financial terminals, and is analyzed and processed through a background big data model so as to give targeted response and feedback.
The financial data processing refers to a process of processing collected data into data meeting target requirements by adopting a certain means according to a certain program and requirements. Financial data has some characteristics of its own, in addition to the general characteristics of data: universality, comprehensiveness, reliability, continuity; the particularity of the financial data causes the financial data to be processed in special places and have special requirements, and the financial data processing system has stricter input and verification, larger storage capacity, wider network transmission and more frequent data maintenance.
Korean patent application KR1020200027090A proposes a method based on artificial intelligence technology for performing an interactive session with a customer from a voice session or a session window based on the characteristics of the customer for processing an interactive financial transaction of a customer service request.
The chinese patent CN111861748B of the invention proposes an artificial intelligence-based financial big data analysis platform, which includes an identity information acquisition module, an identity verification module, an initial database, a financial data extraction module, a financial data classification module, a financial data analysis module, and a financial data analysis result storage module, where after the identity verification module passes the identity verification of a financial big data analysis initiator, the financial data classification module classifies the financial data extracted by the financial data extraction module, and the financial data analysis module analyzes the classified financial data set, and stores the analysis result in a corresponding financial data analysis result storage unit in the financial data analysis result storage module. The financial big data analysis platform based on artificial intelligence is regular in analysis process of financial big data, the analysis process of each type of financial data is not interfered with each other, and analysis results are not interfered with each other, so that the efficiency and the reliability of financial data analysis are improved.
However, the existing artificial intelligence processing on financial data is based on the characteristics of big data, and does not consider the individual reading and writing characteristics and scene relevance of financial data requests, so that all financial data processing under big data occupies a large amount of system resources for a long time, data storm can be caused, and processing efficiency and response timeliness are reduced.
Disclosure of Invention
In order to solve the technical problems, the invention provides a financial data identification and transmission system based on an artificial intelligence model, a method thereof and a visual electronic terminal device for realizing the method.
In a first aspect of the invention, a financial data identification and transmission system based on an artificial intelligence model is provided, wherein the system comprises a multi-source terminal access subsystem, a terminal request identification subsystem, a read-write separation subsystem, a data association subsystem and a data transmission subsystem;
as an improvement, the data association subsystem configures an updatable artificial intelligence data model;
the multi-source terminal access subsystem is used for receiving a plurality of financial data access requests sent by a plurality of multi-source heterogeneous terminals and sending the financial data access requests to the terminal request identification subsystem;
the terminal request identification subsystem identifies the attribute of each financial data access request, and groups all financial data access requests based on the attribute to obtain a plurality of financial data access request groups;
as an improvement, the read-write separation subsystem comprises a plurality of data channels, and each data channel corresponds to at least one financial data access request group; the read-write separation subsystem executes read-write separation operation on the financial data access request to obtain a financial data read request and a financial data write request;
the data association subsystem is used for performing data association on a plurality of financial data reading requests and/or a plurality of financial data writing requests to obtain financial data objects to be transmitted;
and sending the financial data object to be transmitted to a remote financial data processing center for processing through the data transmission subsystem.
As a more specific refinement, the artificial intelligence data model includes a financial scenario recognition engine and a read request guidance engine; each data channel of the read-write separation subsystem comprises a cache stack corresponding to the type of the financial data access request group.
And when the cache stack is full, performing read-write separation operation on the financial data access request group data stored in the full cache stack to obtain a financial data read request and a financial data write request.
In a second aspect of the present invention, a method for identifying and transmitting financial data based on an artificial intelligence model is provided, the method comprising the following steps:
s701: receiving a plurality of financial data access requests in parallel through a multi-process channel;
s702: identifying source terminal information of each path of financial data access request, and distributing each path of financial data access request to a corresponding financial data access request group based on the source terminal information;
s703: sending the financial data access request data of each financial data access request group to a corresponding cache stack;
s704: judging whether the cache stack is full, if so, entering the next step;
otherwise, returning to the step S701;
s705: performing read-write separation operation on financial data access request data stored in a full cache stack to obtain a financial data read request and a financial data write request;
s706: performing data association on a plurality of financial data reading requests and/or a plurality of financial data writing requests to obtain a financial data object to be transmitted;
s707: and sending the financial data object to be transmitted.
As a further improvement, before the step S701, the method further includes:
pre-training an artificial intelligence data model comprising a financial scene recognition engine and a read request guide engine;
the financial scene recognition engine is used for recognizing a financial scene of a financial data writing request;
the read request guide engine is used for estimating a target source database of the financial data degree request.
And updating the financial scenario recognition engine based on the recognition result and updating the read request guidance engine based on the estimation result according to user feedback data.
The method of the second aspect may be performed automatically by an electronic device comprising a processor and a memory, especially a visual image processing terminal device, including a mobile terminal, a desktop terminal, a server cluster, and the like, through program instructions, and thus, in a third aspect of the present invention, there is also provided a visual terminal device comprising a computer readable storage medium, the visual terminal comprising a computer readable storage medium having computer program instructions stored thereon or an electronic device comprising a processor, a memory, a communication interface, through an image processing terminal device comprising a processor and a memory and an electronic device executing the program instructions for implementing all or part of the steps of the method. The processor and the memory are connected through a bus to form internal communication of the terminal equipment.
According to the technical scheme, the financial scene recognition engine or the read request guide of the financial data access request is carried out through the updatable artificial intelligence data model, so that the data can be intensively and pertinently associated to a processing source before being sent to a remote financial data processing center for processing, and the data processing efficiency is improved; meanwhile, the read-write separation subsystem is adopted to perform read-write separation operation on the financial data access request and then perform subsequent processing, so that the waste of system resources can be reduced, and the utilization rate of the system resources can be improved; and finally, the impact of the data stream can be prevented by a processing mode of sending data in a full stack.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a block diagram of the sub-systems of an artificial intelligence model-based financial data recognition and transmission system, according to an embodiment of the present invention
FIG. 2 is a schematic diagram of type architecture of multiple multi-source heterogeneous terminals for generating financial data in the embodiment of FIG. 1
FIG. 3 is a schematic diagram of the subsystem connections and function execution of the system of FIG. 1
FIG. 4 is a schematic data processing flow diagram based on an artificial intelligence data model used by the system of FIG. 1
FIG. 5 is a flowchart of a method for identifying and transmitting financial data based on an artificial intelligence model implemented by the system of FIG. 1
FIG. 6 is a schematic diagram of a financial scenario involving a financial data write request in the embodiment described in FIG. 1 or FIG. 5
FIG. 7 is a schematic diagram of a partial premise implementation of a more preferred embodiment of the method of FIG. 5. FIG. 8 is a schematic diagram of a storage medium and a visual electronic device terminal implementing the method of FIG. 5
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Referring to fig. 1, a subsystem composition diagram of a financial data identification and transmission system based on an artificial intelligence model according to an embodiment of the present invention is shown.
In fig. 1, the system includes a multi-source terminal access subsystem, a terminal request identification subsystem, a read-write separation subsystem, a data association subsystem, and a data transmission subsystem, where the data transmission subsystem sends the final data to be processed to a remote financial data processing center.
Next, the functions of the subsystems are specifically described:
as one of the most important improvements of the present invention, in FIG. 1, the data association subsystem configures an updatable artificial intelligence data model.
The artificial intelligence data model comprises a financial scene recognition engine and a read request guide engine;
the financial scenario identification engine may identify a financial scenario for each financial data write request, and the read request steering engine may identify a target source database for each financial data read request.
The multi-source terminal access subsystem is used for receiving a plurality of financial data access requests sent by a plurality of multi-source heterogeneous terminals and sending the financial data access requests to the terminal request identification subsystem.
The multi-source heterogeneous terminals are a plurality of financial data terminals arranged in a preset geographic position range.
It should be noted that, in order to ensure the identity of data sources, a plurality of multi-source heterogeneous terminals are arranged in a predetermined geographical location range, for example, in each website range described below for the same branch of a certain bank.
The terminal request identification subsystem identifies the attribute of each financial data access request, and groups all financial data access requests based on the attribute to obtain a plurality of financial data access request groups;
the read-write separation subsystem comprises a plurality of data channels, and each data channel corresponds to at least one financial data access request group; the read-write separation subsystem executes read-write separation operation on the financial data access request to obtain a financial data read request and a financial data write request;
the data association subsystem is used for performing data association on a plurality of financial data reading requests and/or a plurality of financial data writing requests to obtain financial data objects to be transmitted;
and sending the financial data object to be transmitted to a remote financial data processing center for processing through the data transmission subsystem.
Fig. 2 shows a schematic diagram of the type of a plurality of financial data terminals arranged within a predetermined geographical location.
In fig. 2, the multi-source heterogeneous terminals are a plurality of financial data terminals arranged in a predetermined geographical location range, and the financial data terminals include an interactive counter terminal, a user handheld terminal and a desktop terminal arranged on a financial institution site.
The interactive counter machine terminal can be an interactive counter machine terminal arranged on the site of branch lines and subordinate network points thereof, and comprises an ATM (automatic Teller machine), an automatic intelligent access terminal, a face recognition terminal and the like;
the user handheld terminal comprises a mobile terminal, a mobile phone, a portable panel, a PDA and other equipment;
the desktop terminal comprises a desktop computer, an operation terminal and the like.
The multi-source heterogeneous terminals are in data communication with the multi-source terminal access subsystem through a communication network, particularly a 5G communication network, and specifically, the multi-source heterogeneous terminals send a plurality of financial data access requests to the multi-source terminal access subsystem.
Referring next to fig. 3, fig. 3 basically explains the specific workflow of the system of fig. 1 as follows:
the system comprises a multi-source terminal access subsystem, a terminal request identification subsystem and a terminal request identification subsystem, wherein the multi-source terminal access subsystem is used for receiving a plurality of financial data access requests sent by a plurality of multi-source heterogeneous terminals, sending the financial data access requests to the terminal request identification subsystem 2, the terminal request identification subsystem identifies the attribute of each financial data access request, and all financial data access requests are grouped based on the attribute to obtain a plurality of financial data access request groups;
more specifically, the attribute of the financial data access request comprises source terminal information of financial data, the source terminal information comprises a financial data terminal type for generating the financial data, and the type comprises a counter machine, a mobile terminal and a desktop terminal;
and the terminal request identification subsystem divides all financial data access requests into a counter financial data access request group, a mobile terminal financial data access request group and a desktop terminal financial data access request group based on the attributes.
In fig. 3, it is assumed that a financial data access request group a and a financial data access request group B are generated;
3, the read-write separation subsystem comprises a plurality of data channels, and each data channel corresponds to at least one financial data access request group; the read-write separation subsystem executes read-write separation operation on the financial data access request group A and the financial data access request group B to obtain a financial data read request and a financial data write request;
4, the data association subsystem is used for performing data association on the plurality of financial data reading requests and/or the plurality of financial data writing requests to obtain financial data objects to be transmitted;
and 5, the data association subsystem transmits the financial data object to be transmitted to a remote financial data processing center for processing through the data transmission subsystem.
A further embodiment can be seen in fig. 4.
In fig. 4, each data channel of the read-write separation subsystem includes a cache stack corresponding to the type of the financial data access request group; the cache stack is used for storing financial data access request group data of corresponding types;
and when the cache stack is full, performing read-write separation operation on the financial data access request group data stored in the full cache stack to obtain a financial data read request and a financial data write request.
The artificial intelligence data model comprises a financial scene recognition engine and a read request guide engine;
the financial scenario identification engine may identify a financial scenario for each financial data write request, and the read request steering engine may identify a target source database for each financial data read request.
The data association subsystem identifies a financial scene of each financial data writing request based on the financial scene identification engine, and judges whether the current financial data writing request is a real-time writing request or a non-real-time writing request based on the financial scene;
and associating the real-time writing requests with the same financial scene to obtain the financial data object to be transmitted.
The data association subsystem identifies a target source database of each financial data read request based on the read request guide engine;
and associating the real-time reading requests identical to all the target source databases to obtain the financial data object to be transmitted.
FIG. 5 shows a financial data identification and transmission method based on an artificial intelligence model, which includes a loop iteration judgment flow formed by steps S701-S707 in FIG. 5.
Specifically, the steps are implemented as follows:
s701: receiving a plurality of financial data access requests in parallel through a multi-process channel;
s702: identifying source terminal information of each path of financial data access request, and distributing each path of financial data access request to a corresponding financial data access request group based on the source terminal information;
s703: sending the financial data access request data of each financial data access request group to a corresponding cache stack;
s704: judging whether the cache stack is full, if so, entering the next step;
otherwise, returning to the step S701;
s705: performing read-write separation operation on financial data access request data stored in a full cache stack to obtain a financial data read request and a financial data write request;
s706: performing data association on a plurality of financial data reading requests and/or a plurality of financial data writing requests to obtain a financial data object to be transmitted;
s707: and sending the financial data object to be transmitted.
Wherein, before the step S701, the method further comprises:
pre-training an artificial intelligence data model comprising a financial scene recognition engine and a read request guide engine;
the financial scene recognition engine is used for recognizing a financial scene of a financial data writing request; the read request guide engine is used for estimating a target source database of the financial data degree request.
The financial scenario refers to a scenario related to financial data reading, writing and the like, and fig. 6 shows a schematic example of a part of scenarios, which includes a billing scenario, a payment scenario, a currency conversion scenario, an identity authentication scenario, a financial cash access scenario and the like, where each different scenario corresponds to a different write request, and of course, there may also be a read request and a data refresh request at the same time, and each scenario corresponds to a financial database of a different classification source. This is common knowledge generated by financial data processing in the art and is not specifically developed by the present invention.
FIG. 7 illustrates an embodiment of a more optimal preconditions for implementing the method described in FIG. 5, including:
pre-training a financial scene recognition engine/read request guide engine;
the method comprises the steps that a plurality of financial data access request groups and cache stacks corresponding to the financial data access request groups are established in advance; then executing the financial data identification and transmission method;
after this, the cache stack and the financial scenario recognition engine/read request boot engine may be updated.
All or part of the steps of the method of fig. 5 may be automatically executed by a visualization terminal device including a processor and a memory, especially an image processing terminal device, including a mobile terminal, a desktop terminal, a server cluster, and the like, through program instructions.
Thus, referring to fig. 8, the present embodiment also provides a visualization terminal comprising a computer readable storage medium having stored thereon computer program instructions; the program instructions are executed by an image terminal processing device comprising a processor and a memory for implementing all or part of the steps of the method. The processor and the memory are connected through a bus to form internal communication of the terminal equipment.
The advantages of the invention at least include:
1) the financial scene recognition engine or the read request guidance of the financial data access request is carried out through the updatable artificial intelligence data model, so that the data can be intensively and pertinently associated to a processing source before being sent to a remote financial data processing center for processing, and the data processing efficiency is improved;
2) meanwhile, the read-write separation subsystem is adopted to perform read-write separation operation on the financial data access request and then perform subsequent processing, so that the waste of system resources can be reduced, and the utilization rate of the system resources can be improved;
3) the data stream can be prevented from being impacted by a processing mode of sending data through a full stack.
One or more embodiments of the invention may achieve one or more of the above technical effects, but it is not required that each embodiment achieve all of the above effects simultaneously.
The present invention is not limited to the specific module structure described in the prior art. The prior art mentioned in the background section can be used as part of the invention to understand the meaning of some technical features or parameters. The scope of the present invention is defined by the claims.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. A financial data identification and transmission system based on artificial intelligence model comprises a multi-source terminal access subsystem, a terminal request identification subsystem, a read-write separation subsystem, a data association subsystem and a data transmission subsystem;
the method is characterized in that:
the data association subsystem configures an updatable artificial intelligence data model;
the multi-source terminal access subsystem is used for receiving a plurality of financial data access requests sent by a plurality of multi-source heterogeneous terminals and sending the financial data access requests to the terminal request identification subsystem;
the terminal request identification subsystem identifies the attribute of each financial data access request, and groups all financial data access requests based on the attribute to obtain a plurality of financial data access request groups;
the read-write separation subsystem comprises a plurality of data channels, and each data channel corresponds to at least one financial data access request group; the read-write separation subsystem executes read-write separation operation on the financial data access request to obtain a financial data read request and a financial data write request;
the data association subsystem is used for performing data association on a plurality of financial data reading requests and/or a plurality of financial data writing requests to obtain financial data objects to be transmitted;
and sending the financial data object to be transmitted to a remote financial data processing center for processing through the data transmission subsystem.
2. The artificial intelligence model-based financial data identification and transmission system of claim 1, wherein:
the multi-source heterogeneous terminals are a plurality of financial data terminals arranged in a preset geographic position range, and the financial data terminals comprise interactive counter machine terminals, user handheld terminals and desktop terminals arranged on the site of a financial institution.
3. The artificial intelligence model-based financial data identification and transmission system of claim 1, wherein:
the attribute of the financial data access request comprises source terminal information of financial data, the source terminal information comprises a financial data terminal type for generating the financial data, and the type comprises a counter machine, a mobile terminal and a desktop terminal;
and the terminal request identification subsystem divides all financial data access requests into a counter financial data access request group, a mobile terminal financial data access request group and a desktop terminal financial data access request group based on the attributes.
4. The artificial intelligence model-based financial data identification and transmission system of claim 1, wherein:
each data channel of the read-write separation subsystem comprises a cache stack corresponding to the type of the financial data access request group;
the cache stack is used for storing financial data access request group data of corresponding types;
and when the cache stack is full, performing read-write separation operation on the financial data access request group data stored in the full cache stack to obtain a financial data read request and a financial data write request.
5. The artificial intelligence model-based financial data identification and transmission system of claim 1, wherein:
the artificial intelligence data model comprises a financial scenario recognition engine;
the data association subsystem identifies a financial scene of each financial data writing request based on the financial scene identification engine, and judges whether the current financial data writing request is a real-time writing request or a non-real-time writing request based on the financial scene;
and associating the real-time writing requests with the same financial scene to obtain the financial data object to be transmitted.
6. The artificial intelligence model-based financial data identification and transmission system of claim 1 or 5, wherein:
the artificial intelligence data model comprises a read request guidance engine;
the data association subsystem identifies a target source database of each financial data read request based on the read request guide engine;
and associating the real-time reading requests identical to all the target source databases to obtain the financial data object to be transmitted.
7. A financial data identification and transmission method based on an artificial intelligence model is characterized by comprising the following steps:
s701: receiving a plurality of financial data access requests in parallel through a multi-process channel;
s702: identifying source terminal information of each path of financial data access request, and distributing each path of financial data access request to a corresponding financial data access request group based on the source terminal information;
s703: sending the financial data access request data of each financial data access request group to a corresponding cache stack;
s704: judging whether the cache stack is full, if so, entering the next step;
otherwise, returning to the step S701;
s705: performing read-write separation operation on financial data access request data stored in a full cache stack to obtain a financial data read request and a financial data write request;
s706: performing data association on a plurality of financial data reading requests and/or a plurality of financial data writing requests to obtain a financial data object to be transmitted;
s707: and sending the financial data object to be transmitted.
8. The artificial intelligence model-based financial data identification and transmission method of claim 7, wherein:
before the step S701, the method further includes:
s700: the method comprises the steps of establishing a plurality of financial data access request groups and cache stacks corresponding to the financial data access request groups in advance.
9. The artificial intelligence model-based financial data identification and transmission method of claim 7, wherein:
before the step S701, the method further includes:
pre-training an artificial intelligence data model comprising a financial scene recognition engine and a read request guide engine;
the financial scene recognition engine is used for recognizing a financial scene of a financial data writing request;
the read request guide engine is used for estimating a target source database of the financial data degree request.
10. An electronic device comprising a memory and a processor, the memory comprising a computer readable storage medium having computer program instructions stored thereon for execution by the processor to perform all or part of the steps of the method of any of claims 7 to 9.
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