CN117172677A - Automatic financial analysis report processing method, system, device and medium based on natural language processing - Google Patents
Automatic financial analysis report processing method, system, device and medium based on natural language processing Download PDFInfo
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Abstract
Embodiments of the present disclosure provide a method, system, apparatus, and medium for automatically processing a financial analysis report based on natural language processing, where the method includes: acquiring a financial statement; acquiring a financial statement; generating an index database based on the financial statement; generating analysis report graphics based on the index database; generating a report page based on the analysis report graph and text; and downloading a report page.
Description
Technical Field
The present disclosure relates to the field of recognition technologies, and in particular, to a method, a system, a device, and a medium for automatically processing a financial analysis report based on natural language processing.
Background
The financial statement may reflect the status of the enterprise or budget unit for a period of funds, profit. However, at present, partial financial report automation systems only rely on crawlers to manually crawl information because professional and comprehensive data sources are not adopted, and partial key indexes are lacking, so that the problems of rough numerical indexes of financial analysis reports and single presentation result exist in calculation. Meanwhile, particularly financial change trend, risk preference and the like of the marketing company cannot be effectively predicted and accurately controlled. Therefore, a more scientific and comprehensive investment reference cannot be provided for the user, and the diversified scene requirements of the user cannot be met.
In addition, as the financial statement involves more numbers and calculation formulas, the processing speed of the processing system is low, and the real-time response is not achieved. Particularly, in part of the financial report automation system, the adopted processing model is old, and the calculation steps are complicated. Thus, the processing system has poor computing power and does not perform real-time display data update, and the processing speed fails to reach expectations.
Finally, the currently used financial statement only displays a large amount of processed financial text data in a system interface in a direct laying way without data integration. The user needs to analyze and compare by himself according to the complicated redundant form data, which is time-consuming, labor-consuming, error-prone, low in user interface friendliness and lack of rich chart display
It is therefore desirable to provide an automated processing method for financial analysis reports based on natural language processing that can improve data processing capacity and display items of financial analysis data more intuitively through rich charts.
Disclosure of Invention
The automatic processing method for the financial analysis report based on natural language processing can improve data processing capacity and can intuitively display various financial analysis data through rich charts.
One or more embodiments of the present specification provide a financial analysis report automation processing method, system, apparatus, and medium based on natural language processing, the method comprising: acquiring a financial statement; acquiring a financial statement; generating an index database based on the financial statement; generating analysis report graphics based on the index database; generating a report page based on the analysis report graph and text; and downloading a report page.
One or more embodiments of the present specification provide a financial analysis report automation processing system based on natural language processing, wherein the system includes: the acquisition module is used for acquiring a financial statement; the database generation module is used for generating an index database based on the financial report; the image-text generation module is used for generating an index database based on the financial statement; the report generation module is used for generating a report page based on the analysis report graph and text; and the downloading module is used for downloading the report page.
One or more embodiments of the present specification provide an automated natural language processing based financial analysis report processing apparatus comprising at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the natural language processing based financial analysis report automation processing method as described above.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when read by a computer, perform a financial analysis report automation processing method based on natural language processing as described above.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is a schematic illustration of an application scenario of a natural language processing based financial analysis report automation processing system according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a financial analysis report automation process method shown in accordance with some embodiments of the present description;
FIG. 3 is an exemplary diagram of a generated metrics database shown in accordance with some embodiments of the present disclosure;
FIG. 4 is an exemplary diagram of a generated metrics database shown in accordance with some embodiments of the present disclosure;
FIG. 5 is an exemplary diagram of a report page generated as shown in accordance with some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
FIG. 1 is a schematic illustration of an application scenario for a natural language processing based financial analysis report automation processing system according to some embodiments of the present description.
The natural language processing-based financial analysis report automation processing system in some embodiments of the present description can be applied to the extraction of various financial reports, and can further calculate and combine based on the extracted contents to generate formats and contents meeting the requirements of users.
As shown in fig. 1, an application scenario 100 of a natural language processing-based financial analysis report automation processing system according to an embodiment of the present disclosure may include a processor 110, a user terminal 120, a memory 130, and a network 140.
Processor 110 may refer to a system having computing capabilities and may include various computers such as processors and personal computers, or a computing platform comprising multiple computers connected in various configurations. In some embodiments, the processor 110 may be implemented on a cloud platform. For example, the cloud platform may include one or a combination of several of private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, and the like.
Processor 110 may execute program instructions. The processor may include various common general purpose central processing units, graphics processors, microprocessors, application specific integrated circuits, or other types of integrated circuits. The storage medium may include mass memory, removable memory, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof.
In some embodiments, the processor 110 may obtain the financial statement and generate an index database based on the financial statement, further generate an analysis report graphic based on the automated report icon, generate a report page based on the analysis report graphic, and finally download the report page.
The user terminal 120 may provide a channel for the processor to interact with the user. In some embodiments, the user terminal 120 may be one or any combination of a mobile device, tablet computer, laptop computer, desktop computer, or other input and/or output enabled device. In some embodiments, the user may enter an automated financial analysis requirements document through the user terminal 120. In some embodiments, the user terminal 120 may transmit related information and data through the network 140 and other components (e.g., the processor 110, the memory 130) in the application scenario 100 of the natural language processing based financial analysis reporting automation processing system.
Memory 130 may store data, instructions, and/or any other information. In some embodiments, memory 130 may store data obtained from processor 110. For example, the memory may store obtained financial statements, index databases, analysis report graphics, report pages, and the like. In some embodiments, memory 130 may store data and/or instructions that may be executed or used by processor 110 to perform the exemplary methods described in this specification. In some embodiments, memory 130 may include one or a combination of a large capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), and the like. In some embodiments, memory 130 may be implemented by a cloud platform. For example, the cloud platform may include one or a combination of several of private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, cross-cloud, multi-cloud, and the like. In some embodiments, memory 130 may be part of processor 110 or may be separate and coupled directly or indirectly to processor 110.
Network 140 may include any suitable network capable of facilitating the exchange of information and/or data of components in natural language processing based financial analysis report automation processing scenario 100. In some embodiments, the natural language processing based financial analysis reports one or more components (e.g., processor 110, memory 130, etc.) in the application scenario 100 of the automated processing systemInformation and/or data may be exchanged therebetween via network 140. Network 140 may include one or a combination of public networks (e.g., the internet), private networks (e.g., local area networks, wide area networks, etc.), and the like. For example, the network 140 may include a wired network, a fiber optic network, a telecommunications network, a local area network, a wireless local area network, a ZigBe TM A network, a Near Field Communication (NFC) network, or the like. In some embodiments, network 140 may include one or more network access points. For example, the network 140 may include wired and/or wireless network access points, such as base stations and/or internet switching points, through which one or more components in the application scenario 100 of the natural language processing based financial analysis reporting automation processing system may connect to the network 140 to exchange data and/or information.
It should be noted that the application scenario 100 of the natural language processing based financial analysis reporting automation system is provided for illustrative purposes only and is not intended to limit the scope of the present application. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario 100 of the natural language processing based financial analysis report automation processing system may implement similar or different functionality on other devices. However, such changes and modifications do not depart from the scope of the present application.
{ { FIG. 2}
FIG. 2 is an exemplary flow chart of a financial analysis report automation process method according to some embodiments of the present description. In some embodiments, the process 200 may be performed by a processor. The process 200 includes the steps of:
and 210, acquiring a financial statement.
Financial statements may refer to statements in a company that relate to financial conditions. For example, the financial statement may include daily, weekly, monthly, quarterly, annual, and the like. The financial statement may include an equity statement, profit statement, cash flow statement, owner equity variation statement, and the like.
In some embodiments, the processor may retrieve the financial statement from a memory or platform.
In some embodiments, the processor may obtain the financial statement at the time of acquisition.
In some embodiments, the acquisition time may refer to a point in time or a time interval. In some embodiments, the acquisition time for acquiring the financial statement may be determined based on the size of the company and the stage at which the company is located. The size of a company may be determined by comparing an index of aspects of the company to a corresponding threshold, e.g., a total asset less than a total asset threshold, for a small and medium-sized company. The corresponding threshold may be set manually. The stages at which the company is located may include a start-up stage, a steady development stage, an expansion stage, and the like. Illustratively, the processor may obtain the correspondence of the at least one of the company size and the stage at which the company is located to the acquisition time by multiple linear regression fitting; the current company acquisition time is determined based on at least one of the company scale of the current company and the stage in which the company is located through the correspondence.
In some embodiments, the processor may select the financial statement.
And 220, generating an index database based on the financial report.
The index database may refer to a graph generated by generalizing the computational classifications of information in financial statements. And an index database.
In some embodiments, the processor may convert the financial statement into a financial statement image file; carrying out partition identification on the financial statement image file to generate original financial text data; generating an index data table based on the original financial text data; and establishing an index database based on the index data table. Additional details regarding generating an index database based on financial statements may be found in FIG. 3 and its associated description.
And 230, generating an analysis report graph based on the index database.
In some embodiments, the processor may generate a target text template based on the automated financial analysis requirements document, determine a record form template based on the target text template; generating a target record table based on the index database; and generating analysis report graphics based on the target record form and the target text template. For details on generating an analysis report graph based on an index database, see fig. 4 and its associated description.
Step 240, generating a report page based on the analysis report graph.
In some embodiments, the processor may generate corresponding Word code and HTML code via Python and Openxml specifications based on the analysis report text; further, storing HTML codes corresponding to the analysis report graph text into a database, and generating pages of the HTML codes and corresponding interfaces; and accessing the interface to generate a report page. Details regarding the generation of report pages based on analysis report graphics may be found in fig. 5 and its associated description.
Step 250, download report page.
In some embodiments, the automated word version report file generated by the processor is stored on the ali cloud OSS storage processor, and access rights management is performed through the STS temporary access credentials. Further, an automatic report downloading interface is called, so that the financial strict automatic report can be downloaded to the local, and the downloaded files are word files.
In some embodiments of the present description, by using OSS storage space technology, a function of supporting massive concurrent access of users is implemented through a distributed object storage service.
Through some embodiments of this specification, the problems of low coverage rate of financial indexes, single data source, low response speed of the system and low interface friendliness of the traditional financial analysis text automation system are solved. And a more comprehensive financial data index is obtained by adopting an OCR optical recognition company financial statement mode, so that the problem of insufficient data is effectively solved. Compared with the prior art, the multifunctional device has stronger functionality and wider application range. Through all-round, multidimensional financial index and abundant chart for company's financial analysis report can be presented in effective audio-visual form, very big convenience is brought to user's consulting and downloading, is a convenient and directly perceived financial analysis text automation system of high efficiency.
{ { FIG. 3 })
FIG. 3 is an exemplary schematic diagram of a generated metrics database shown in accordance with some embodiments of the present description.
At step 310, the financial statement is converted into a financial statement image file.
The financial statement image file may refer to an image file corresponding to a financial statement. The image file may include a picture or video.
In some embodiments, the processor may convert the financial statement into a financial statement image file via a file format conversion model. The file format conversion model can refer to inputting, outputting and training financial reports
And 320, carrying out partition identification on the financial statement image file to generate original financial text data.
In some embodiments, the processor may divide the financial statement image file into at least one region through the layout analysis model, and identify the at least one region respectively through the identification module, generating the original financial text data.
At least one region may refer to a region in a financial statement. The type of at least one region may include a text region, a form region, an image region, and the like. For example, the header portion of the financial statement image file (e.g., name of the table, time, etc.) may be a text region.
The layout analysis model is input into a financial statement image and output into images of different types of areas. The layout analysis model may be an image segmentation model.
The identification module may be configured to identify at least one region image to generate corresponding raw financial text data. The recognition module may include a form recognition module, a text recognition module, an image recognition module, and the like recognition sub-modules. The text recognition module may be an OCR recognition technique. Each identification module corresponds to the type of at least one area respectively, and corresponding original financial text data is generated. For example, the form region is input to the form recognition module for structural recognition, the text region is input to the text recognition module for text recognition, and the like, thereby obtaining the original financial text data.
Step 330, an index data table is generated based on the raw financial text data.
In some embodiments, the original financial text data is processed by key information extraction, calculation, grouping, conversion and the like, and is stored in a data index database according to the data index. The data index may be determined according to the user's needs. For example, the metrics may include basic information, daily data, and the like.
In some embodiments, the processor may generate the index data table by computing in accordance with the financial index through a basic feature model matrix. The index data table may refer to a graph containing at least one index. For example, the index data table may include a corporate financial data table, a basic information table, a daily market table, an industry data table, and the like.
In some embodiments of the present disclosure, the function of performing a fast financial index calculation on a time-series two-dimensional array of different reporting periods of a company according to a financial formula of the latest accounting criteria is implemented by using a basic feature model matrix calculation according to the financial index.
Step 340, establishing an index database based on the index data table.
In some embodiments, the processor may generate the index data table from the index data table through the front end ECharts, canvas drawing frame model. Further, at least one part of the index data table is intercepted through a Selenium web automation test framework and stored in an index database according to the chart category number.
According to some embodiments of the present specification, the function of quickly and accurately acquiring financial related forms and text data from company financial quarter report, half-year report and annual report is realized through OCR optical recognition technology. According to some embodiments of the specification, the function of automatically intercepting and storing the financial data chart after being based on the index data table is realized through the Selenium Web automatic test framework, and related chart descriptions can be attached to the analysis report chart later, so that a user can more intuitively know the index description.
FIG. 4 is an exemplary schematic diagram of a generated metrics database shown in accordance with some embodiments of the present description.
Step 410, generating a target text template based on the automated financial analysis requirements document, and determining a record form template based on the target text template.
An automated financial analysis requirements document may refer to a document that includes target data metrics that meet a user's requirements. The automatic financial analysis requirement document can be an existing document or a customized document.
The target text template may refer to a text template that hides the portion of the automated financial analysis requirements document that needs to be filled in. The portion that needs to be filled in may be a digital portion. The hidden digital portion of the target text template may be converted into an insertable form of space, brackets, etc. In some embodiments, each space in the target text template corresponds to data of the target data indicator. In some embodiments, the spaces may be replaced with numerical numbers. The part to be filled in corresponds to a space name. For example, the space name is a company name, and the corresponding space number is 1. In some embodiments, space names may include financial indicators, financial scores, corporate base information, people average data, label automation text, risk details.
The record form template may refer to a space name, a space number, and a calculation method corresponding to the space in the target text template. In some embodiments, the automatic financial analysis requirement document is traversed, the number, the name and the calculation method of the corresponding space value of the required data are sequentially arranged, and the number, the name and the calculation method of the corresponding space value are recorded in the corresponding record form template. In some embodiments, the method of calculating the space value may include a variety of calculation methods.
Step 420, a target record table is generated based on the index database.
The target record form may refer to a record form template that replaces the space number with a corresponding value. In some embodiments, the processor may obtain the data to be calculated based on the index database through a table processing module of Python, and calculate a space value corresponding to the corresponding space name according to the record table template using a dynamic programming algorithm.
According to some embodiments of the present disclosure, by using a dynamic programming algorithm strategy, the calculation of the financial text values according to different calculation methods is achieved, so that the solution has optimal properties, and the calculation rate and accuracy reach expectations.
And step 430, generating analysis report graphics based on the target record form and the target text template.
In some embodiments, space values in the target record form are inserted into corresponding positions of the same space names in the target text template to generate the analysis report graph.
In some embodiments of the present description, the financial situation of the company can be more intuitively observed through analysis of the report text.
FIG. 5 is an exemplary diagram of a report page generated as shown in accordance with some embodiments of the present description.
Step 510, based on the analysis report graph, corresponding Word codes and HTML codes are generated through Python and Openxml specifications.
In some embodiments, the HTML code is optimized by JavaScript and CSS.
And step 520, storing the HTML code corresponding to the analysis report graph in a database, and generating a page of the HTML code and a corresponding interface.
Step 530, accessing the interface and generating a report page.
In some embodiments, the processor may access the interface through a STS (Security Token Service) temporary access rights management service.
In some embodiments of the present application, by using STS (Security Token Service) temporary access rights management services, the risk of disclosure of long-term access keys (accesskeys) of the system is reduced, the key validity period can be customized, the rights policy can be customized, and more flexible and fine cloud resource authorization can be provided.
It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present application.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing processor or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification, and thereby aid in understanding one or more embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of the preceding description of the embodiments of the present specification. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.
Claims (10)
1. A method for automatically processing a financial analysis report based on natural language processing, the method comprising:
acquiring a financial statement;
generating an index database based on the financial statement;
generating analysis report graphics based on the index database;
generating a report page based on the analysis report graph and text;
and downloading the report page.
2. A natural language processing based financial analysis report automation processing method in accordance with claim 1, wherein said generating an index database based on said financial statement comprises:
converting the financial statement into a financial statement image file;
carrying out partition identification on the financial statement image file to generate original financial text data;
generating an index data table based on the original financial text data;
and establishing an index database based on the index data table.
3. The automatic processing method for financial analysis report based on natural language processing according to claim 1, wherein the generating analysis report graph based on the index database comprises:
generating a target text template based on an automatic financial analysis demand document, and determining a record form template based on the target text template;
generating a target record table based on the index database;
and generating the analysis report graph based on the target record table and the target text template.
4. The automatic processing method for financial analysis report based on natural language processing according to claim 1, wherein the generating a report page based on the analysis report graph comprises:
based on the analysis report image-text, generating corresponding Word codes and HTML codes through Python and Openxml specifications;
storing the HTML codes corresponding to the analysis report text into a database, and generating pages and corresponding interfaces of the HTML codes;
and accessing the interface to generate the report page.
5. A natural language processing based financial analysis report automation processing system, the system comprising:
the acquisition module is used for acquiring a financial statement;
the database generation module is used for generating an index database based on the financial statement;
the image-text generating module is used for generating analysis report image-text based on the index database;
the report generation module is used for generating a report page based on the analysis report graph and text;
and the downloading module is used for downloading the report page.
6. The automated natural language processing based financial analysis report processing system of claim 5, wherein the database generation module comprises an identification module to:
converting the financial statement into a financial statement image file;
carrying out partition identification on the financial statement image file to generate original financial text data;
generating an index data table based on the original financial text data;
and establishing an index database based on the index data table.
7. The automated natural language processing based financial analysis report processing system of claim 5, wherein the teletext generation module comprises a template insertion module for:
generating a target text template based on an automatic financial analysis demand document, and determining a record form template based on the target text template;
generating a target record table based on the index database;
and generating the analysis report graph based on the target record table and the target text template.
8. The automated natural language processing based financial analysis report processing system of claim 5, wherein the report generation module comprises an access module to:
based on the analysis report image-text, generating corresponding Word codes and HTML codes through Python and Openxml specifications;
storing the HTML codes corresponding to the analysis report text into a database, and generating pages and corresponding interfaces of the HTML codes;
and accessing the interface to generate the report page.
9. A natural language processing based financial analysis report automation processing device, the device comprising at least one processor and at least one memory;
the at least one memory is configured to store computer instructions;
the at least one processor is configured to execute at least some of the computer instructions to implement the natural language processing based financial analysis report automation processing method of any one of claims 1 to 5.
10. A computer-readable storage medium storing computer instructions that, when read by a computer, perform the natural language processing based financial analysis report automation processing method of any one of claims 1 to 4.
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