CN117493420A - Financial cloud data processing method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the invention discloses a financial cloud data processing method, a device, equipment and a medium, wherein the method comprises the following steps: according to a preset data analysis period, carrying out statistical analysis on target business data of target financial business to obtain a current data analysis result corresponding to the current data analysis period; when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference business data of a plurality of related financial businesses related to the target financial business in the current data analysis period and the historical data analysis period; and comparing and analyzing the target service data with the reference service data, and displaying an analysis result. The technical scheme of the embodiment of the invention solves the problem of low timeliness of mining and analyzing service data, can timely discover the change of the service state of the target financial service, and acquire more service related data for analysis so as to be convenient for managing the development of a certain service.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a financial cloud data processing method, a device, equipment and a medium.
Background
At present, when the service body manages service data, data storage management and data mining are carried out through a cloud data platform. The corresponding service state is evaluated by mining and analyzing a large amount of service data in a service.
However, when analyzing a large amount of service data, there are certain time delays and limitations, and it is impossible to perform multidimensional and timely analysis on data of a certain service.
Disclosure of Invention
The embodiment of the invention provides a financial cloud data processing method, a device, equipment and a medium, which can timely find the change of the business state of a target financial business, acquire more business-related data and analyze the business-related data so as to be convenient for managing the development of a certain business.
In a first aspect, an embodiment of the present invention provides a financial cloud data processing method, including:
according to a preset data analysis period, carrying out statistical analysis on target business data of target financial business to obtain a current data analysis result corresponding to the current data analysis period;
when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period;
and comparing and analyzing the target service data with the reference service data, and displaying an analysis result.
In a second aspect, an embodiment of the present invention provides a financial cloud data processing apparatus, including:
the first data acquisition module is used for carrying out statistical analysis on target business data of target financial business according to a preset data analysis period to obtain a current data analysis result corresponding to the current data analysis period;
a second data obtaining module, configured to obtain reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period;
and the data analysis module is used for comparing and analyzing the target service data with the reference service data and displaying an analysis result.
In a third aspect, an embodiment of the present invention further provides a computer apparatus, including:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the financial cloud data processing method as provided by any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a financial cloud data processing method as provided by any embodiment of the present invention.
According to the embodiment of the invention, the target business data of the target financial business is subjected to statistical analysis according to the preset data analysis period to obtain the current data analysis result corresponding to the current data analysis period; when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference business data of a plurality of related financial businesses related to the target financial business in the current data analysis period and the historical data analysis period; firstly, evaluating the service development state of a target financial service through analysis of a small amount of data in a small range, and timely finding out service development problems; when the service data change is abnormal, the target service data and the reference service data are compared and analyzed, and the analysis result is displayed, so that the service data can be further mined in a targeted manner. The technical scheme of the embodiment of the invention solves the problem of low timeliness of mining and analyzing service data, can timely find the change of the service state of the target financial service, and acquire more service related data for analysis, so as to conveniently and efficiently mine the service data in time and facilitate management of development of a certain service.
Drawings
Fig. 1 is a flowchart of a financial cloud data processing method provided in an embodiment of the present invention;
FIG. 2 is a flowchart of a financial cloud data processing method according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a financial cloud data processing device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Fig. 1 is a flowchart of a financial cloud data processing method according to an embodiment of the present invention, where the embodiment is applicable to a scenario of financial cloud data analysis and management. The method can be executed by a cloud data processing device, and the device can be realized by software and/or hardware and is integrated into computer equipment with application development function.
As shown in fig. 1, the method for processing financial cloud data according to the present embodiment includes the following steps:
s110, carrying out statistical analysis on target business data of target financial businesses according to a preset data analysis period to obtain a current data analysis result corresponding to the current data analysis period.
The preset data analysis period may be a period preset in the cloud data platform for automatically analyzing a service state corresponding to service data of the target financial service, and may be a time period counted in units of hours, days or weeks.
The target financial business may be a business related to finance, such as deposit, finance, insurance or investment, which can be provided by a business entity (bank, financial service company, etc.).
The business data may include data that can be recorded and quantized during the progress of the financial business, such as the number of target financial business strokes, the running amount, the business consultation amount, the bad account amount, etc., in one data analysis period.
The statistical analysis of the target financial business data may be a mathematical statistical analysis of the recorded and quantized data. The statistical analysis mode may be at least one of data calculation methods such as accumulation, average value, standard deviation, variance, fluctuation curve and the like. Further, the statistical analysis result obtained by calculation based on the at least one statistical analysis mode is the current data analysis result corresponding to the business data of the target financial business in the current data analysis period.
The current data analysis period may be the latest data analysis period in the data analysis process according to the preset data analysis period.
S120, when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period.
Wherein the adjacent historical data analysis period may be at least one historical data analysis period adjacent to and prior to the current data analysis period. In this step, the current data analysis result is compared longitudinally with at least one historical data analysis result to determine a variation in the data analysis result in the time dimension.
The abnormal condition of the data fluctuation determined after the comparison of the current data analysis result and the historical data analysis result can be determined according to the service attribute characteristics of the target financial service and can be judged according to the data analysis result. If the amount of funds corresponding to a financial product service increases exponentially during the time of a data analysis period, the amount of funds does not fluctuate in correlation with the corresponding number of people transacting the service.
And when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference business data of a plurality of related financial businesses associated with the target financial business in the current data analysis period and the historical data analysis period. And further data analysis can be performed specifically for abnormal situations of data fluctuation, so that abnormal situations in the target financial business can be found and processed in time.
It will be appreciated that, in general, the financial business data will be stored in the cloud data platform after being generated, and the data mining analysis is performed on the data after a long period of accumulation, so that the analysis of the financial business data generated by flow has a certain hysteresis. In this embodiment, the preset data analysis period may be set to a shorter time period, and a relatively small amount of data is analyzed in a shorter time period, so as to timely find an abnormal situation in the target financial service.
The plurality of related financial transactions associated with the target financial transaction may be related financial transactions of the same type as the target financial transaction or related financial transactions in competing relationship with the target financial transaction.
S130, comparing and analyzing the target service data with the reference service data, and displaying an analysis result.
The objective business data is compared with the reference business data for analysis to find the reasons for the abnormal situation of the data fluctuation, and whether the abnormal situation of the data fluctuation is related to other financial business situations or not is determined. The analysis result may be an analysis of a fluctuation curve of the financial business data.
Further, encryption processing can be carried out on the analysis result in the process of displaying the analysis result, and a data display permission verification instruction is set; and when the right checking instruction is checked and the key checking is passed, displaying the analysis junction so as to increase the safety performance of the data. In order to protect data security, data security level labels are respectively set for the target service data, the reference service data and the data analysis result. And if the user wants to further view the original data, displaying the corresponding data according to the data security level corresponding to the permission verification instruction.
According to the technical scheme of the embodiment, the target business data of the target financial business are subjected to statistical analysis according to the preset data analysis period, so that a current data analysis result corresponding to the current data analysis period is obtained; when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference business data of a plurality of related financial businesses related to the target financial business in the current data analysis period and the historical data analysis period; firstly, evaluating the service development state of a target financial service through analysis of a small amount of data in a small range, and timely finding out service development problems; when the service data change is abnormal, the target service data and the reference service data are compared and analyzed, and the analysis result is displayed, so that the service data can be further mined in a targeted manner. The technical scheme of the embodiment of the invention solves the problem of low timeliness of mining and analyzing service data, can timely find the change of the service state of the target financial service, and acquire more service related data for analysis, so as to conveniently and efficiently mine the service data in time and facilitate management of development of a certain service.
Fig. 2 is a flowchart of a financial cloud data processing method according to an embodiment of the present invention, where the financial cloud data processing method in the embodiment and the foregoing embodiment belong to the same inventive concept. The method can be executed by a financial cloud data processing device, and the device can be realized by software and/or hardware and is integrated into a computer device with an application development function.
As shown in fig. 2, the financial cloud data processing method of the present embodiment includes the following steps:
s210, carrying out statistical analysis on target business data of target financial businesses according to a preset data analysis period to obtain a current data analysis result corresponding to the current data analysis period.
S220, when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring the reference business data of the associated business with the same business attribute of the target financial business as a first group of analysis reference data, and acquiring the reference business data of the associated business which belongs to an upstream-downstream relation with the target financial business as a second group of analysis reference data.
S230, comparing and analyzing the change trend of each same data item in the target business data, the first group of analysis reference data and the second group of analysis reference data according to the data generation time, and displaying a data trend line.
In this step, the change trend of each identical data item in the target service data, the first set of analysis reference data and the second set of analysis reference data may be compared and analyzed, that is, the data change of the identical data item is determined, and the data fluctuation degree of the identical data item of different financial services may be further analyzed, so as to further mine the relevance of the data fluctuation between different financial services.
S240, extracting a plurality of preset data features in the target business data, the first set of analysis reference data and the second set of analysis reference data.
The multiple preset data features extracted from the target service data, the first set of analysis reference data and the second set of analysis reference data can be data features such as average value, difference value, ring ratio, homonymy ratio, fixed base ratio and the like of each data of each financial service in multiple data analysis periods, and can also comprise data features such as conversion rate and the like corresponding to upstream and downstream services.
S250, inputting the preset data characteristics into a pre-trained target business prediction model to obtain a business data prediction result of the target financial business in the next data analysis period, and displaying the business data prediction result and the data trend line together.
The target business prediction model is a neural network model obtained by training according to a model training sample comprising the data characteristics extracted in the steps, and can predict business data in the next data analysis period of the target financial business. The next data analysis period refers to a next data analysis period adjacent to the current data analysis period.
The business data prediction result of the target financial business in the next data analysis period is displayed together with the data trend line determined in the step S230, so that whether the prediction result can be matched with the corresponding data trend line can be determined more intuitively. If the two do not match, the data representing the target financial transaction may still have an abnormal state or be gradually restored from the abnormal state.
According to the technical scheme, statistical analysis is performed on target business data of target financial businesses according to a preset data analysis period, so that a current data analysis result corresponding to a current data analysis period is obtained; when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference service data of the related service which is the same as the service attribute of the target financial service in the current data analysis period and the historical data analysis period as first group analysis reference data, and acquiring reference service data of the related service which is in an upstream-downstream relation with the target financial service in the current data analysis period and the historical data analysis period as second group analysis reference data; according to the data generation time, comparing and analyzing the change trend of each same data item in the target business data, the first group of analysis reference data and the second group of analysis reference data, and displaying a data trend line; extracting a plurality of preset data features in the target service data, the first group of analysis reference data and the second group of analysis reference data; and inputting the preset data characteristics into a pre-trained target service prediction model to obtain a service data prediction result of the target financial service in the next data analysis period, and displaying the service data prediction result and the data trend line together. The technical scheme of the embodiment of the invention solves the problem of low timeliness of mining and analyzing service data, can timely find the change of the service state of the target financial service, and acquire more service related data for analysis, so as to conveniently and efficiently mine the service data in time and facilitate management of development of a certain service.
Fig. 3 is a schematic structural diagram of a financial cloud data processing device provided by the embodiment of the present invention, where the embodiment is applicable to a scenario of business data management analysis, and the financial cloud data processing device may be implemented by software and/or hardware, and integrated into a computer terminal device with an application development function.
As shown in fig. 3, the financial cloud data processing apparatus includes: the first data acquisition module 310, the second data acquisition module 320, and the data analysis module 330.
The first data obtaining module 310 is configured to perform statistical analysis on target service data of a target financial service according to a preset data analysis period, so as to obtain a current data analysis result corresponding to the current data analysis period; a second data obtaining module 320, configured to obtain reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period; and the data analysis module 330 is configured to compare and analyze the target service data with the reference service data, and display an analysis result.
According to the technical scheme of the embodiment, the target business data of the target financial business are subjected to statistical analysis according to the preset data analysis period, so that a current data analysis result corresponding to the current data analysis period is obtained; when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference business data of a plurality of related financial businesses related to the target financial business in the current data analysis period and the historical data analysis period; firstly, evaluating the service development state of a target financial service through analysis of a small amount of data in a small range, and timely finding out service development problems; when the service data change is abnormal, the target service data and the reference service data are compared and analyzed, and the analysis result is displayed, so that the service data can be further mined in a targeted manner. The technical scheme of the embodiment of the invention solves the problem of low timeliness of mining and analyzing service data, can timely find the change of the service state of the target financial service, and acquire more service related data for analysis, so as to conveniently and efficiently mine the service data in time and facilitate management of development of a certain service.
In an alternative embodiment, the second data acquisition module 320 is specifically configured to:
acquiring reference business data of the associated business with the same business attribute of the target financial business in the current data analysis period and the historical data analysis period as first group analysis reference data; the method comprises the steps of,
and acquiring reference business data of the associated business belonging to the upstream-downstream relation with the target financial business in the current data analysis period and the historical data analysis period as second group analysis reference data.
In an alternative embodiment, the data analysis module 330 is specifically configured to:
and comparing and analyzing the change trend of each same data item in the target business data, the first group of analysis reference data and the second group of analysis reference data according to the data generation time, and displaying a data trend line.
In an alternative embodiment, the data analysis module 330 may be further configured to:
extracting a plurality of preset data features in the target service data, the first group of analysis reference data and the second group of analysis reference data;
and inputting the preset data characteristics into a pre-trained target business prediction model to obtain a business data prediction result of the target financial business in the next data analysis period.
In an alternative embodiment, the data analysis module 330 may be further configured to:
performing data association relation fitting analysis on the target business data and target data items in the second group of analysis reference data to obtain fitting analysis results;
and carrying out matching analysis on the fitting analysis result and a reference data model of preset standard linkage data.
In an alternative embodiment, the data analysis module 330 may be further configured to:
encrypting the analysis result and setting a data display authority check instruction;
and when the authority verification instruction is verified and the key verification is passed, displaying the analysis result.
In an alternative embodiment, the data analysis module 330 may be further configured to:
respectively setting data security level labels for the target service data, the reference service data and the data analysis result;
and displaying corresponding data according to the data security level corresponding to the permission verification instruction.
The financial cloud data processing device provided by the embodiment of the invention can execute the financial cloud data processing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present invention. Fig. 4 illustrates a block diagram of an exemplary computer device 12 suitable for use in implementing embodiments of the present invention. The computer device 12 shown in fig. 4 is merely an example and should not be construed as limiting the functionality and scope of use of embodiments of the present invention. The computer device 12 may be any terminal device with computing power, such as an intelligent controller, a server, a mobile phone, and the like.
As shown in FIG. 4, the computer device 12 is in the form of a general purpose computing device. Components of computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, commonly referred to as a "hard disk drive"). Although not shown in fig. 4, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through network adapter 20. As shown, network adapter 20 communicates with other modules of computer device 12 via bus 18. It should be appreciated that although not shown in fig. 4, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing a financial cloud data processing method provided by the present embodiment, the method including:
according to a preset data analysis period, carrying out statistical analysis on target business data of target financial business to obtain a current data analysis result corresponding to the current data analysis period;
when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period;
and comparing and analyzing the target service data with the reference service data, and displaying an analysis result. The embodiment of the invention also provides a computer readable storage medium, on which a computer program is stored, the program when executed by a processor implementing the financial cloud data processing method according to any embodiment of the invention, the method comprising:
according to a preset data analysis period, carrying out statistical analysis on target business data of target financial business to obtain a current data analysis result corresponding to the current data analysis period;
when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period;
and comparing and analyzing the target service data with the reference service data, and displaying an analysis result. The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It will be appreciated by those of ordinary skill in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A financial cloud data processing method, comprising:
according to a preset data analysis period, carrying out statistical analysis on target business data of target financial business to obtain a current data analysis result corresponding to the current data analysis period;
when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period, acquiring reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period;
and comparing and analyzing the target service data with the reference service data, and displaying an analysis result.
2. The method of claim 1, wherein obtaining reference transaction data for a plurality of related financial transactions associated with the target financial transaction during the current data analysis period and the historical data analysis period comprises:
acquiring reference business data of the associated business with the same business attribute of the target financial business in the current data analysis period and the historical data analysis period as first group analysis reference data; the method comprises the steps of,
and acquiring reference business data of the associated business belonging to the upstream-downstream relation with the target financial business in the current data analysis period and the historical data analysis period as second group analysis reference data.
3. The method of claim 2, wherein comparing the target service data with the reference service data and displaying the analysis results, further comprises:
and comparing and analyzing the change trend of each same data item in the target business data, the first group of analysis reference data and the second group of analysis reference data according to the data generation time, and displaying a data trend line.
4. The method of claim 2, wherein comparing the target traffic data with the reference traffic data for analysis comprises:
extracting a plurality of preset data features in the target service data, the first group of analysis reference data and the second group of analysis reference data;
and inputting the preset data characteristics into a pre-trained target business prediction model to obtain a business data prediction result of the target financial business in the next data analysis period.
5. The method of claim 2, wherein comparing the target traffic data with the reference traffic data for analysis further comprises:
performing data association relation fitting analysis on the target business data and target data items in the second group of analysis reference data to obtain fitting analysis results;
and carrying out matching analysis on the fitting analysis result and a reference data model of preset standard linkage data.
6. The method of any one of claims 1-5, wherein the presenting the analysis results comprises:
encrypting the analysis result and setting a data display authority check instruction;
and when the authority verification instruction is verified and the key verification is passed, displaying the analysis result.
7. The method of claim 6, wherein the method further comprises:
respectively setting data security level labels for the target service data, the reference service data and the data analysis result;
and displaying corresponding data according to the data security level corresponding to the permission verification instruction.
8. A financial cloud data processing apparatus, comprising:
the first data acquisition module is used for carrying out statistical analysis on target business data of target financial business according to a preset data analysis period to obtain a current data analysis result corresponding to the current data analysis period;
a second data obtaining module, configured to obtain reference service data of a plurality of related financial services associated with the target financial service in the current data analysis period and the historical data analysis period when the current data analysis result is abnormal compared with the historical data analysis result of the adjacent historical data analysis period;
and the data analysis module is used for comparing and analyzing the target service data with the reference service data and displaying an analysis result.
9. A computer device, the computer device comprising:
one or more processors;
a memory for storing one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the financial cloud data processing method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the financial cloud data processing method as claimed in any one of claims 1 to 7.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311675703.XA CN117493420A (en) | 2023-12-07 | 2023-12-07 | Financial cloud data processing method, device, equipment and medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311675703.XA CN117493420A (en) | 2023-12-07 | 2023-12-07 | Financial cloud data processing method, device, equipment and medium |
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| CN117493420A true CN117493420A (en) | 2024-02-02 |
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| CN202311675703.XA Pending CN117493420A (en) | 2023-12-07 | 2023-12-07 | Financial cloud data processing method, device, equipment and medium |
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