CN116843486A - Financial tax data processing method, system, equipment and storage medium - Google Patents
Financial tax data processing method, system, equipment and storage medium Download PDFInfo
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Abstract
The embodiment of the invention discloses a financial data processing method, a system, equipment and a storage medium. The method comprises the following steps: acquiring original financial tax data; inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data, and dividing the original financial data into theme domains; performing dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table; and carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results. According to the embodiment of the invention, a series of aggregation data tables are formed in the modeling process, the reusability of public indexes is improved by the aggregation data tables, the repeated processing work can be reduced when the data personalized statistics is finally carried out, the user side is directly queried, the query response time is greatly shortened, and the data use efficiency is higher.
Description
Technical Field
The present invention relates to the field of software technologies, and in particular, to a financial tax data processing method, system, device, and storage medium.
Background
Under the background of new economic normalcy, technological innovation and intellectualization of finance and tax gradually become new challenges of industry consensus and enterprises. The back of the method needs a large amount of reasonable structured data as the support of data analysis, and further tight combination of financial tax and big data promotes the pace of intelligent tax reform.
At present, small and medium-sized enterprises use billing systems in various types, financial data of the same dimension are called inconsistent, semi-structured/unstructured data of the small and medium-sized enterprises are not fully analyzed, data are defined, data are classified, data subject domains are divided, a data model is maintained, and a unified global view is lacking, so that a client cannot quickly search data required by the client.
Disclosure of Invention
In view of the above, the embodiments of the present invention provide a financial tax data processing method, system, device and storage medium, so as to provide a convenient and unified financial tax data processing means, and facilitate financial tax data analysis by small and medium-sized enterprises at low cost.
In a first aspect, an embodiment of the present invention provides a financial tax data processing method, including:
acquiring original financial tax data;
inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data, and dividing the original financial data into the theme domains;
performing dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table;
and carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results.
Optionally, in some embodiments, the acquiring the original financial data includes:
the original financial data of the source system is obtained through a preset data interface, or the paper financial report is identified through an OCR (optical character recognition) model to obtain the original financial data.
Optionally, in some embodiments, the inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data includes:
pairing the original financial tax data with a preset theme to calculate the theme correlation;
if the topic relevance meets the preset requirement, taking the preset topic as a topic domain;
and if the topic relevance does not meet the preset requirement, carrying out synthesis, classification and analysis according to the original financial data and generating at least one topic domain.
Optionally, in some embodiments, before performing dimension modeling, service modeling and topic modeling on the original financial data in each topic domain to obtain an aggregate data table, the method further includes:
and cleaning abnormal data of the original financial data, and carrying out data normalized storage.
Optionally, in some embodiments, the dimension modeling includes:
based on the original financial data, obtaining dimension attributes through logic processing, obtaining dimension attributes through multi-table association, obtaining dimension attributes through mixing processing of different fields of a single table, and obtaining dimension attributes through analyzing designated fields of the single table;
and constructing a dimension table according to the dimension attribute, and sorting the original financial data into the dimension table.
Optionally, in some embodiments, after generating the personalized statistical result according to the personalized data statistics of the aggregated data table, the method further includes:
matching the personalized statistical result with the subject domain to calculate the correlation degree;
and reversely adjusting the theme zone induction model according to the relevance.
In a second aspect, an embodiment of the present invention further provides a financial tax data processing system, including:
the data acquisition module is used for acquiring original financial data;
the theme domain division module is used for inputting the original financial data into a preset theme domain induction model so as to generate at least one theme domain according to the original financial data and divide the original financial data into the theme domains;
the data aggregation module is used for carrying out dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table;
and the personalized statistics module is used for carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program that can be executed by the processor, and the processor implements the foregoing financial tax data processing method when executing the computer program.
In a fourth aspect, embodiments of the present invention provide a computer readable storage medium storing a computer program comprising program instructions which, when executed, implement the foregoing financial data processing method.
According to the technical scheme provided by the embodiment of the invention, the acquired original financial data is subjected to data warehouse layering, the theme domain is divided by means of the theme domain induction model, the theme domain is based on service planning, so that data required by financial tax related services are quickly cleaned from huge financial tax data, dimension modeling, service modeling and theme modeling are performed on the cleaned financial tax data, a series of aggregated data tables are formed in the modeling process, the reusability of public indexes is improved by the aggregated data tables, the repeated processing work can be reduced when the personalized data statistics is performed at last, the inquiry is directly performed on a user side, the inquiry response time is greatly shortened, and the data use efficiency is higher.
Drawings
FIG. 1 is a flow chart of a financial tax data processing method according to a first embodiment of the invention;
FIG. 2 is a sub-flowchart of a financial data processing method according to a first embodiment of the present invention;
FIG. 3 is a sub-flowchart of a financial data processing method according to a first embodiment of the present invention;
FIG. 4 is a flowchart of another financial data processing method according to the first embodiment of the present invention;
FIG. 5 is a diagram of a financial tax data processing system in a second embodiment of the present invention;
fig. 6 is a schematic diagram of an electronic device according to a third 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.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Furthermore, the terms "first," "second," and the like, may be used herein to describe various directions, acts, steps, or elements, etc., but these directions, acts, steps, or elements are not limited by these terms. These terms are only used to distinguish one direction, action, step or element from another direction, action, step or element. For example, the first speed difference may be referred to as a second speed difference, and similarly, the second speed difference may be referred to as the first speed difference, without departing from the scope of the present invention. Both the first application and the second application are applications, but they are not the same application. The terms "first," "second," and the like, are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. It should be noted that when a portion is referred to as being "fixed to" another portion, it may be directly on the other portion or there may be a portion in the middle. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only and do not represent the only embodiment.
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts steps as a sequential process, many of the steps may be implemented in parallel, concurrently, or with other steps. Furthermore, the order of the steps may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
Example 1
Fig. 1 is a flowchart of a financial tax data processing method according to an embodiment of the present invention, where the method may be executed by a terminal or a server, or may be completed through interaction between the terminal and the server. The present embodiment will be described by taking the application of the financial tax data processing method to a server as an example. Specifically, as shown in fig. 1, the method comprises the following steps:
s110, acquiring original financial tax data.
The original financial data has different acquisition calibers, a large amount of semi-structured/unstructured financial data exists in small, medium and micro enterprises at present, billing and accounting systems (called source systems, such as brute force financial, strapdown, brute force fusion and the like) adopted by different enterprises are also different, and tax data interfaces provided by related institutions and departments are also not uniform, such as government big data, customs, tax interfaces and the like. Therefore, in this embodiment, the acquiring of the original financial tax data needs to be applied to a different type of data interface, and even needs to perform data acquisition on a part of paper financial tax report, specifically, in this embodiment, step 110 specifically includes: the original financial data of the source system is obtained through a preset data interface (comprising interfaces aiming at different source systems), or the original financial data is obtained through the recognition of paper financial reports by an OCR recognition model.
OCR (Optical Character Recognition ) refers to a process of analyzing, recognizing and processing an input image to obtain Chinese character information in the image, and has wide application fields, such as scene image character recognition, document image recognition, card recognition (such as identity card, bank card and social security card), bill recognition and the like, and the main recognition modes include three types: 1) A connected domain-based method; 2) A sliding window based approach; 3) A deep learning-based method. Considering that the paper financial tax report always has partial handwritten information, the difference is large, the accuracy of the recognition standard fonts is high based on the connected domain and the sliding window mode, but the accuracy of the recognition of the handwritten characters is low, the embodiment preferably adopts an OCR recognition mode based on deep learning, specifically, an OCR recognition model based on a neural network is built in advance, partial paper financial tax report image data is obtained, the image data is marked, the marked image data is used for training the OCR recognition model, when the recognition accuracy of the OCR recognition model meets the ending condition, the trained OCR recognition model is obtained, and the trained OCR recognition model is used for collecting the original financial tax data of the paper financial tax report. It should be understood that the OCR recognition model is not always unchanged, and is optimized in continuous training in the use process, the recognition method based on deep learning uses high-level semantic features with more robust effects, utilizes more data to fit a model with more complexity and stronger generalization capability, can be continuously evolved according to actual requirements to adapt to more complex scenes, and has higher accuracy.
S120, inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data, and dividing the original financial data into the theme domains.
The division of the topic domains is an abstract concept for integrating, classifying and analyzing and utilizing data in an enterprise information system at a higher level, the topic domains are usually a set of data topics which are closely related, the abstract classification is carried out according to the view angle of business requirement analysis, and each topic basically corresponds to a macroscopic analysis field.
Optionally, in some embodiments, step 120 specifically includes steps 121-123 as shown in FIG. 2:
s121, matching the original financial data with a preset theme to calculate the theme correlation.
S122, if the topic relevance meets the preset requirement, taking the preset topic as a topic domain.
And S123, if the topic relativity does not meet the preset requirement, carrying out synthesis, classification and analysis according to the original financial tax data and generating at least one topic domain.
The preset theme zone with the pre-classified theme comprises the following steps: the basic theme is used for storing data such as basic business information, legal information and the like of enterprises; tax theme, storing tax interface data of a company; financial topics storing business accounting service data such as asset liabilities sheets, profit sheets, subject balance sheets, etc.; business theme, store sales loan order, customer information, etc. of enterprises. Of course, the above-mentioned several theme domains also have the situation that all demands are not met, for example, in some enterprises, the business blocks are numerous, and the financial data stored in a single theme domain is too much, so that the theme domains can be further divided, for example, in some taxi taking software, the theme domains can be divided into a personal moving theme domain and an enterprise goods moving theme domain, and when a large number of financial and tax data do not match with the theme domains, the theme domains can be dynamically analyzed and generalized according to the original financial and tax data, and automatically generated, and the process can be to analyze business nodes of the enterprises, cluster the financial and tax data corresponding to the business with a plurality of common nodes, and abstract and describe the clustered financial and tax data to obtain a theme domain corresponding to the financial and tax data, which is not repeated here.
S130, carrying out dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table.
The aggregate data table comprises a dimension table, a service table and a theme table. The DIM dimension table is based on the dimension modeling idea, the consistency dimension of the whole enterprise is established, and the risk of non-unification of the data calculation caliber and the algorithm is reduced. The DWD business table takes the business process as modeling drive, builds the detail layer fact table with the finest granularity based on the characteristics of each specific business process, and can combine the data use characteristics of enterprises to make proper redundancy on some important dimension attribute fields of the detail fact table, namely, wide surfacing. The DWS topic table takes the analyzed topic object as modeling drive, builds a summary index fact table with common granularity based on the index requirements of the upper application and the product, and visualizes the model by a broad-representation means. Step 130 is aimed at constructing statistical indexes with consistent naming standards and calibers, providing public indexes for upper layers, and establishing a summary broad table and a detail fact table.
For easy understanding, taking a dimension table as an example to further illustrate the implementation principle of the aggregate data table in this embodiment, as shown in fig. 3, step 130 specifically includes steps 131-132:
s131, based on the original financial data, obtaining the dimension attribute through logic processing, obtaining the dimension attribute through multi-table association, obtaining the dimension attribute through different field mixing processing of the single table, and obtaining the dimension attribute through analyzing the appointed field of the single table.
S132, constructing a dimension table according to the dimension attribute, and sorting the original financial data into the dimension table.
Dimension is a measured environment used for reflecting a class of attributes of a service, and a set of such attributes forms a dimension, which can also be called an entity object, and the dimension belongs to a data domain, such as a geographic dimension (including content of the levels of country, region, province, city, etc.), and a time dimension (including content of the levels of year, season, month, week, day, etc.).
And S140, carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results.
The personalized statistics of the data is to develop data items, such as KPI report, enterprise portrait, risk prevention and control, financial tax pre-warning, etc., on the aggregated data.
The embodiment of the invention provides a financial data processing method, which is characterized in that data warehouse layering is carried out on acquired original financial data, firstly, theme domain division is carried out by means of a theme domain induction model, the theme domain is based on service planning, thus data required by financial related services are rapidly cleaned from huge financial data, dimensional modeling, service modeling and theme modeling are carried out on the cleaned financial data, a series of aggregated data tables are formed in the modeling process, the reusability of public indexes is improved by the aggregated data tables, repeated processing work can be reduced when data individuation statistics is carried out finally, inquiry is directly carried out on a user side, inquiry response time is greatly shortened, and data use efficiency is higher.
More specifically, in some embodiments, step 100 (not shown) is further included prior to step 130:
s100, cleaning abnormal data of the original financial data, and performing data normalized storage.
Before data aggregation analysis is performed, exception handling is required, for data with special meaning, exception characters are removed or replaced, after extraction, data accuracy, processing performance and service expansion are designed through a standard data format, and in terms of data accuracy, original data often have some exception characters such as "", blank spaces and the like in Chinese fields such as names, short names and the like due to input errors. In terms of performance optimization, in order to improve access efficiency, invoice data and financial data are stored in a partitioning mode by taking time as a partitioning key, and indexes are built on relevant corresponding fields.
Optionally, in some embodiments, as shown in fig. 4, after step 140, steps 150-160 are further included:
and S150, matching the personalized statistical result with the subject domain to calculate the correlation degree.
And S160, reversely adjusting the theme zone induction model according to the relevance.
The relevance is actually whether the division of the calculated topic domains is reasonable or not, if the division of the current topic domains is reasonably described to be beneficial to the personalized statistics of the users, the data analysis efficiency and the accuracy are both influenced well, otherwise, the division of the unreasonable topic domains is described to obstruct the personalized statistics of the data of the users, and the personalized statistics results need to be utilized to reversely influence the topic domain induction model at the moment, so that the divided topic domains are more reasonably attached to the actual requirements of the users, for example, the users need to carry out personalized statistics around the KPI report, and the topic domains divided in the step 120 are all around the basic topic, so that the adjustment to be in the form of business topic and core is necessarily unreasonable.
Example two
Fig. 5 is a schematic structural diagram of a financial data processing system 300 according to a second embodiment of the present invention, where the specific structure of the system includes:
a data acquisition module 310, configured to acquire original financial data;
the theme zone division module 320 is configured to input the original financial data into a preset theme zone induction model, generate at least one theme zone according to the original financial data, and divide the original financial data into the theme zones;
the data aggregation module 330 is configured to perform dimension modeling, service modeling and topic modeling on the original financial data in each topic domain to obtain an aggregate data table;
and the personalized statistics module 340 is configured to perform personalized statistics on data according to the aggregated data table to generate a personalized statistics result.
Optionally, in some embodiments, the system further comprises:
and the model adjustment module is used for carrying out matching calculation on the personalized statistical result and the topic domain, and reversely adjusting the topic domain induction model according to the correlation.
Optionally, in some embodiments:
the data obtaining module 310 is specifically configured to obtain the original financial tax data of the source system through a preset data interface, or obtain the original financial tax data by recognizing the paper financial tax report through an OCR recognition model.
Optionally, in some embodiments, the topic domain partitioning module 320 is specifically configured to:
pairing the original financial tax data with a preset theme to calculate the theme correlation;
if the topic relevance meets the preset requirement, taking the preset topic as a topic domain;
and if the topic relevance does not meet the preset requirement, carrying out synthesis, classification and analysis according to the original financial data and generating at least one topic domain.
Optionally, in some embodiments, the data aggregation module 330 is specifically configured to:
based on the original financial data, obtaining dimension attributes through logic processing, obtaining dimension attributes through multi-table association, obtaining dimension attributes through mixing processing of different fields of a single table, and obtaining dimension attributes through analyzing designated fields of the single table;
and constructing a dimension table according to the dimension attribute, and sorting the original financial data into the dimension table.
The embodiment further provides a financial data processing system, which is characterized in that data warehouse layering is carried out on the acquired original financial data, firstly, the topic domain is divided by means of a topic domain induction model, and the topic domain is based on service planning, so that data required by financial related services are quickly cleaned from huge financial data, then dimensional modeling, service modeling and topic modeling are carried out on the cleaned financial data, a series of aggregated data tables are formed in the modeling process, the reusability of public indexes is improved by the aggregated data tables, the repeated processing work can be reduced when the data individuation statistics is carried out finally, the inquiry is directly carried out on a user terminal, the inquiry response time is greatly shortened, and the data use efficiency is higher.
The financial data processing system provided by the embodiment of the invention can execute any financial data processing method provided by the previous embodiment of the invention, and has the corresponding functional modules and beneficial effects of the executing method.
Example III
Fig. 6 is a schematic structural diagram of an electronic device 400 according to a third embodiment of the present invention, where, as shown in fig. 6, the terminal includes a memory 410 and a processor 420, and the number of the processors 420 in the terminal may be one or more, and in fig. 6, one processor 420 is taken as an example. The memory 410, processor 420 in the terminal may be connected by a bus or other means, for example in fig. 6.
The memory 40 is used as a computer readable storage medium for storing software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the financial data processing method in the embodiment of the present invention (e.g., the data acquisition module 310, the topic domain division module 320, the data aggregation module 330, and the personalized statistics module 340 in the financial data processing system). The processor 420 executes various functional applications of the terminal and data processing, i.e., implements the financial data processing method described above, by running software programs, instructions and modules stored in the memory 410.
Wherein the processor 420 is configured to execute a computer executable program stored in the memory 410 to implement the following steps: step 110, acquiring original financial tax data; step 120, inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data, and dividing the original financial data into the theme domains; 130, performing dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregate data table; and 140, performing data personalized statistics according to the aggregation data table to generate personalized statistics results.
Of course, the electronic device provided by the embodiment of the present invention is not limited to the above-mentioned method operations, and may also perform the related operations in the financial tax data processing method provided by any embodiment of the present invention.
The memory 410 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for functions; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 410 may include high-speed random access memory, and may also include non-volatile memory, such as magnetic disk storage devices, flash memory devices, or other non-volatile solid state storage devices. In some examples, memory 410 may further include memory located remotely from processor 420, which may be connected to the 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 device can execute the method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.
Example IV
A fourth embodiment of the present invention also provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are for performing a financial data processing method comprising:
acquiring original financial tax data;
inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data, and dividing the original financial data into the theme domains;
performing dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table;
and carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results.
Of course, the storage medium containing the computer executable instructions provided in the embodiments of the present invention is not limited to the method operations described above, and may also perform the related operations in the financial data processing method provided in any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a terminal, a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the foregoing embodiment of the financial data processing system, each unit and module included are only divided according to the functional logic, but are not limited to the above division, so long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
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 tax data processing method, comprising:
acquiring original financial tax data;
inputting the original financial data into a preset theme domain induction model to determine at least one theme domain according to the original financial data, and dividing the original financial data into the theme domains;
performing dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table;
and carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results.
2. The financial data processing method according to claim 1, wherein the acquiring the original financial data comprises:
the original financial data of the source system is obtained through a preset data interface, or the paper financial report is identified through an OCR (optical character recognition) model to obtain the original financial data.
3. The financial data processing method according to claim 1, wherein said inputting the raw financial data into a predetermined subject domain induction model to determine at least one subject domain from the raw financial data comprises:
pairing the original financial tax data with a preset theme to calculate the theme correlation;
if the topic relevance meets the preset requirement, taking the preset topic as a topic domain;
and if the topic relevance does not meet the preset requirement, carrying out synthesis, classification and analysis according to the original financial data and generating at least one topic domain.
4. The financial data processing method according to claim 1, wherein before performing dimension modeling, service modeling and theme modeling on the raw financial data in each theme zone to obtain an aggregate data table, the method further comprises:
and cleaning abnormal data of the original financial data, and carrying out data normalized storage.
5. The financial data processing method of claim 1 wherein the dimension modeling comprises:
based on the original financial data, obtaining dimension attributes through logic processing, obtaining dimension attributes through multi-table association, obtaining dimension attributes through mixing processing of different fields of a single table, and obtaining dimension attributes through analyzing designated fields of the single table;
and constructing a dimension table according to the dimension attribute, and sorting the original financial data into the dimension table.
6. The financial data processing method according to claim 1, wherein after generating personalized statistics according to the personalized statistics of the aggregated data table, further comprising:
matching the personalized statistical result with the subject domain to calculate the correlation degree;
and reversely adjusting the theme zone induction model according to the relevance.
7. A financial data processing system, comprising:
the data acquisition module is used for acquiring original financial data;
the theme domain division module is used for inputting the original financial data into a preset theme domain induction model so as to generate at least one theme domain according to the original financial data and divide the original financial data into the theme domains;
the data aggregation module is used for carrying out dimension modeling, service modeling and theme modeling on the original financial data in each theme domain to obtain an aggregation data table;
and the personalized statistics module is used for carrying out data personalized statistics according to the aggregation data table to generate personalized statistics results.
8. The financial data processing system of claim 7 further comprising:
and the model adjustment module is used for carrying out matching calculation on the personalized statistical result and the topic domain, and reversely adjusting the topic domain induction model according to the correlation.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program executable by the processor, the processor implementing the financial data processing method according to claims 1-6 when the computer program is executed.
10. A computer readable storage medium, wherein the storage medium stores a computer program comprising program instructions which, when executed, implement the financial data processing method of any one of claims 1 to 6.
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| CN117408655A (en) * | 2023-12-13 | 2024-01-16 | 国网浙江省电力有限公司金华供电公司 | Financial tax data management method and platform based on full-service view angle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117408655A (en) * | 2023-12-13 | 2024-01-16 | 国网浙江省电力有限公司金华供电公司 | Financial tax data management method and platform based on full-service view angle |
| CN117408655B (en) * | 2023-12-13 | 2024-03-05 | 国网浙江省电力有限公司金华供电公司 | Financial tax data management method and platform based on full-service view angle |
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