Intelligent financial data management system based on big data

Abstract

The invention relates to the technical field of data management, in particular to an intelligent financial data management system based on big data, which receives uploaded invoices through a financial data receiving module, compares identified financial data with manually input financial data through an invoice analysis module, and transmits the data to a server; the invoice is subjected to color region division based on a clustering color division algorithm, whether the invoice is stored or not is judged according to a comparison result and a chromatic value, and the financial data encryption module sets encryption passwords for the invoice and financial data of the invoice. The financial data classification unit records the number of checking times of the financial data and the password input error rate of the financial data and classifies the financial data, and the financial data encryption adjustment unit selects a specific financial data category for secondary encryption. The accuracy and integrity of information in the financial data management system are ensured, and the security of financial data is improved on the premise of ensuring that computing resources are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of data management, and particularly to an intelligent financial data management system based on big data. Background Art

[0002] With the increasing complexity of enterprise operations and the growing volume of data, traditional financial data management systems have become difficult to meet the needs of modern enterprises for efficient, accurate, and real-time financial management. The existing financial data management usually faces the following challenges: scattered and difficult-to-integrate data, low data processing efficiency, insufficient risk warning capabilities, and lack of intelligent decision-making support. In addition, the diversity and complexity of financial data also increase the management difficulty, especially in terms of data sharing, collaborative work, and real-time monitoring.

[0003] In the prior art, Chinese Patent Publication No.: CN113435986A discloses a financial data management method, belonging to the field of new material processing. The present invention includes the following steps: S1 financial data input, S2 financial data additional information marking, S3 financial data processing, S4 financial data analysis, S5 data comparison and additional marking, generating an additional marking list according to the inconsistency of the additional data, and S6 report generation. By establishing financial data additional information marking during the financial data entry process, the input financial data can be better verified with the voucher collection module and the bank statement storage module. If any inaccuracy is found, or if the data is entered first and the bill and actual expenses occur later, it can be better verified from the additional marking list, which can better avoid the occurrence of loopholes and reduce financial losses.

[0004] However, in the prior art, the quality of invoices is not comprehensively evaluated from multiple aspects, resulting in low efficiency of invoice management, low accuracy and integrity of information in the financial data management system, and the problem that the financial data management system consumes too much computing resources or has a low security level because the financial data is not classified and different encryption methods are not adopted for different categories of financial data. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent financial data management system based on big data, which can comprehensively evaluate the quality of invoices from multiple aspects, thereby improving the efficiency of invoice management, ensuring the accuracy and integrity of information in the financial data management system, and classifying the financial data and adopting different encryption methods for different categories of financial data to improve the security of the system without consuming too much computing resources.

[0006] The present invention provides an intelligent financial data management system based on big data, including: A financial data receiving module, which is used to receive uploaded invoices, identify the financial data in the invoices, and receive the financial data of the invoices manually input; An invoice analysis module, connected to the financial data receiving module, which is used to compare the identified financial data with the manually input financial data, divide the color regions of the invoice based on the clustering color segmentation algorithm, obtain the chromaticity values of several points in each color region, and determine whether to store the invoice according to the comparison result and the chromaticity values; A financial data storage module, connected to the invoice analysis module, which is used to store invoices and the financial data of the invoices; A financial data encryption module, connected to the financial data storage module, which is used to set encryption passwords for the invoices and the financial data of the invoices according to preset rules; A financial data monitoring module, connected to the financial data storage module, including a financial data classification unit and a financial data encryption adjustment unit, The financial data classification unit is used to record the viewing times of financial data and the password input error rate of financial data, and classify the financial data according to the viewing times of financial data and the password input error rate of financial data, The financial data encryption adjustment unit is used to select a specific financial data category and perform secondary encryption on the specific financial data category.

[0007] Further, the invoice analysis module is used to compare the identified financial data with the manually input financial data, including, Obtain the identified financial data of the invoice and the manually input financial data; Compare each manually input financial data with the corresponding identified financial data in turn; If the manually input financial data is the same as the corresponding identified financial data, it is determined that the financial data is accurately identified; If the manually input financial data is different from the corresponding identified financial data, it is determined that the financial data is not accurately identified; If there is no corresponding identified financial data for the manually input financial data, it is determined that the identified financial data is missing; Record the ratio of the number of inaccurately identified financial data to the number of manually input financial data as the financial identification inaccuracy rate; Record the ratio of the missing identified financial data to the number of manually input financial data as the financial identification missing rate.

[0008] Further, the invoice analysis module is used to divide the color regions of the invoice based on the clustering color segmentation algorithm, obtain the chromaticity values of several points in each color region, including, Use a clustering-based color segmentation algorithm to divide the invoice into color regions; Select any number of points within each color region; Obtain the chromaticity values of any number of points within each color region.

[0009] Furthermore, the invoice analysis module is also used to calculate an invoice storage tendency characterization value based on the comparison result and the chromaticity value, including, Compare the chromaticity values of any number of points within each color region with the corresponding preset chromaticity value threshold; Obtain the difference between the chromaticity values of any number of points within each color region and the corresponding preset chromaticity value threshold; Obtain the ratio of the absolute value of the difference to the corresponding preset chromaticity value threshold, denoted as the chromaticity value difference ratio; Weighted sum the financial recognition inaccuracy rate, the financial recognition missing rate, and the chromaticity value difference ratio to obtain the invoice storage tendency characterization value.

[0010] Furthermore, the invoice analysis module determines whether to store the invoice, If the invoice storage tendency characterization value of the invoice is less than the preset invoice storage tendency characterization value comparison threshold, then it is determined to store the invoice; If the invoice storage tendency characterization value of the invoice is greater than or equal to the preset invoice storage tendency characterization value comparison threshold, then it is determined not to store the invoice.

[0011] Furthermore, the financial data encryption module is used to set an encryption password for the financial data according to a preset rule. Specifically, obtain the full name of the invoicing enterprise in the financial data, and set the first letter of the full name of the invoicing enterprise as the encryption password.

[0012] Furthermore, the financial data classification unit is also used to obtain the password input error rate of the financial data. Specifically, obtain the total number of password inputs for the financial data and the number of password input errors for the financial data, and denote the ratio of the number of password input errors to the total number of password inputs as the password input error rate.

[0013] Furthermore, the financial data classification unit is used to classify the financial data according to the viewing times of the financial data and the password input error rate of the financial data, If the financial data meets the preset conditions, then classify the financial data category as a high-risk financial data category; If the financial data does not meet the preset conditions, then classify the financial data category as a low-risk financial data category; The preset conditions are that the viewing times of the financial data are greater than the preset viewing times comparison threshold, and the password input error rate of the financial data is greater than the preset financial data password input error rate comparison threshold.

[0014] Further, the financial data encryption and adjustment unit is used to select high-risk financial data categories and perform secondary encryption on the high-risk financial data categories.

[0015] Further, the financial data includes invoice number, invoice date, invoicing enterprise name, receiving enterprise name, tax-exclusive amount, tax rate, tax amount, tax-inclusive amount, commodity description, quantity, and unit price.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows. The present invention receives the uploaded invoice through the financial data receiving module. The invoice analysis module compares the identified financial data with the manually input financial data, divides the invoice into color regions based on the clustering color segmentation algorithm, determines whether to store the invoice according to the comparison result and chromaticity value. The financial data encryption module sets an encryption password for the invoice and its financial data. The financial data classification unit records the viewing times of the financial data and the password input error rate of the financial data, classifies the financial data, and the financial data encryption and adjustment unit selects specific financial data categories for secondary encryption. The present invention improves the efficiency of invoice management, ensures the accuracy and integrity of the information in the financial data management system, and improves the security of financial data on the premise of ensuring the saving of computing resources.

[0017] In particular, the present invention compares the identified financial data with the manually input financial data through the invoice analysis module. In actual situations, due to problems such as low pixel caused by image compression during invoice upload and incomplete invoice upload, there may be financial data in the invoice that cannot be recognized or financial data that is prone to recognition errors. By obtaining the financial recognition inaccuracy rate and the financial recognition missing rate, the upload quality of the invoice can be effectively and reliably evaluated, thereby improving the efficiency of invoice management and ensuring the accuracy and integrity of the information in the financial data management system.

[0018] In particular, the present invention determines whether to store the invoice according to the comparison result and chromaticity value through the invoice analysis module. The chromaticity value in each color region of the invoice can evaluate the quality of the image. The accuracy of the chromaticity value can reflect key information such as the clarity and color restoration degree of the invoice image. If there is a large deviation between the chromaticity value of the invoice image and the standard value, it may mean that there are problems such as blurring, stains, or color distortion in the image. By combining the chromaticity value of the invoice, the financial recognition inaccuracy rate, and the financial recognition missing rate, the accuracy of the invoice quality assessment can be improved, thereby improving the efficiency of invoice management and ensuring the accuracy and integrity of the information in the financial data management system.

[0019] In particular, the present invention records the number of views of financial data and the password input error rate of financial data through the financial data classification unit. In actual situations, the number of views of financial data can to a certain extent reflect the important value of this data for their current work, decision-making, or interests, while the password input error rate of financial data can to a certain extent reflect the security level of financial data. By obtaining the number of views of financial data and the password input error rate of financial data, the security and importance of financial data can be characterized, thereby enhancing the security of financial data.

[0020] In particular, the present invention performs secondary encryption on specific financial data categories through the financial data encryption adjustment unit. In actual situations, performing secondary encryption on all financial data requires excessive computing resources, increases the computing burden of the system, slows down the processing speed, and thus requires more time and resources to configure and maintain the encryption system, resulting in an increase in maintenance costs. By selecting high-risk financial data categories for secondary encryption, the security of financial data can be enhanced while ensuring the saving of computing resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a structural diagram of the intelligent financial data management system based on big data according to an embodiment of the present invention; Figure 2 is a structural diagram of the financial data monitoring module according to an embodiment of the present invention; Figure 3 is a logical decision diagram for determining whether to store invoices in the invoice analysis module according to an embodiment of the present invention; Figure 4 is a logical decision diagram for classifying financial data by the financial data classification unit according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0023] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0024] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0025] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0026] As Figures 1-4 shown, this embodiment provides an intelligent financial data management system based on big data. The intelligent financial data management system based on big data includes: A financial data receiving module, which is used to receive the uploaded invoices, identify the financial data in the invoices, and receive the financial data of the invoices input manually; An invoice analysis module, which is connected to the financial data receiving module, and is used to compare the identified financial data with the manually input financial data, divide the color regions of the invoice based on the color segmentation algorithm of clustering, obtain the chromaticity values of several points in each color region, and determine whether to store the invoice according to the comparison result and the chromaticity values; A financial data storage module, which is connected to the invoice analysis module, and is used to store the invoices and the financial data of the invoices; A financial data encryption module, which is connected to the financial data storage module, and is used to set an encryption password for the invoices and the financial data of the invoices according to a preset rule; A financial data monitoring module, which is connected to the financial data storage module, includes a financial data classification unit and a financial data encryption adjustment unit. The financial data classification unit is used to record the viewing times of the financial data and the password input error rate of the financial data, and classify the financial data according to the viewing times of the financial data and the password input error rate of the financial data. The financial data encryption adjustment unit is used to select a specific financial data category and perform secondary encryption on the specific financial data category.

[0027] It can be understood that the uploaded invoices can be one or more pictures, which contain the financial data to be stored. In this embodiment, the financial data input manually is the financial data that has been repeatedly verified and has no errors.

[0028] In this embodiment, Python is used to divide the color regions of the invoice based on the color segmentation algorithm of clustering, and obtain the chromaticity values of several points in each color region.

[0029] It is understandable that an encryption password is set for the invoice and the financial data of the invoice according to preset rules, where each transaction's invoice and the financial data included in the invoice use one encryption password. By inputting the encryption password, the invoice and financial data of the transaction can be viewed.

[0030] It is understandable that after storing the financial data and the corresponding invoice in the system, the financial data can be viewed, and the system will automatically record the number of times the financial data is viewed, the number of times the password is correctly input, and the number of times the password is incorrectly input. Among them, the number of times the financial data is viewed is the cumulative number of times the financial data corresponding to a single transaction is viewed.

[0031] Specifically, the invoice analysis module is used to compare the identified financial data and the manually input financial data, including: Obtain the identified financial data and the manually input financial data of the invoice; Compare each manually input financial data with the corresponding identified financial data in turn; If the manually input financial data is the same as the corresponding identified financial data, it is determined that the financial data is accurately identified; If the manually input financial data is different from the corresponding identified financial data, it is determined that the financial data is not accurately identified; If there is no corresponding identified financial data for the manually input financial data, it is determined that the identified financial data is missing; Record the ratio of the number of inaccurately identified financial data to the number of manually input financial data as the financial identification inaccuracy rate; Record the ratio of the missing identified financial data to the number of manually input financial data as the financial identification missing rate.

[0032] Specifically, the present invention compares the identified financial data and the manually input financial data through the invoice analysis module. In actual situations, due to problems such as low pixel caused by compressed uploaded images of invoices and incomplete invoice uploads, there may be financial data in the invoice that cannot be identified or financial data that is easily misidentified. By obtaining the financial identification inaccuracy rate and the financial identification missing rate, the upload quality of the invoice can be effectively and reliably evaluated, thereby improving the efficiency of invoice management and ensuring the accuracy and integrity of information in the financial data management system.

[0033] Specifically, the invoice analysis module is used to divide the color regions of the invoice based on the clustering color segmentation algorithm, and obtain the chromaticity values of several points in each color region, including: Use the clustering color segmentation algorithm to divide the color regions of the invoice; Select any number of points within each color region; Obtain the chromaticity values of any number of points within each color region.

[0034] Specifically, the invoice analysis module is also used to calculate an invoice storage tendency characterization value based on the comparison result and the chromaticity value, including, Compare the chromaticity values of any number of points within each color region with the corresponding preset chromaticity value threshold; Obtain the difference between the chromaticity values of any number of points within each color region and the corresponding preset chromaticity value threshold; Obtain the ratio of the absolute value of the difference to the corresponding preset chromaticity value threshold, denoted as the chromaticity value difference ratio; Weighted sum the financial recognition inaccuracy rate, the financial recognition missing rate, and the chromaticity value difference ratio to obtain the invoice storage tendency characterization value.

[0035] In this embodiment, the average chromaticity value of any number of points obtained within each color region is compared with the preset chromaticity value threshold.

[0036] In this embodiment, the preset chromaticity value threshold is pre-measured in the experiment. Use Python to pre-divide the color regions of several invoices that meet the storage conditions, obtain the chromaticity values of each color region, and obtain the average chromaticity value of several invoices in each color region, which is denoted as the preset chromaticity value threshold.

[0037] It can be understood that when comparing the chromaticity values, the chromaticity values of the corresponding color regions will be compared with the preset chromaticity value threshold.

[0038] In this embodiment, the weighting coefficient of the financial recognition inaccuracy rate is 0.3, the weighting coefficient of the financial recognition missing rate is 0.3, and the weighting coefficient of the chromaticity value difference ratio is 0.4.

[0039] Specifically, the present invention determines whether to store the invoice through the invoice analysis module according to the comparison result and the chromaticity value. The chromaticity values within each color region of the invoice can evaluate the quality of the image. The accuracy of the chromaticity value can reflect key information such as the clarity and color restoration degree of the invoice image. If there is a large deviation between the chromaticity value of the invoice image and the standard value, it may mean that there are problems such as blurring, stains, or color distortion in the image. By combining the chromaticity value of the invoice, the financial recognition inaccuracy rate, and the financial recognition missing rate, the accuracy of the invoice quality assessment can be improved, thereby improving the efficiency of invoice management and ensuring the accuracy and integrity of the information in the financial data management system.

[0040] Specifically, the invoice analysis module determines whether to store the invoice, If the invoice storage propensity characterization value of an invoice is less than the preset invoice storage propensity characterization value comparison threshold, it is determined to store the invoice; If the invoice storage propensity characterization value of an invoice is greater than or equal to the preset invoice storage propensity characterization value comparison threshold, it is determined not to store the invoice.

[0041] In this embodiment, the preset invoice storage propensity characterization value comparison threshold is selected within the range of [0, 0.05].

[0042] Specifically, the financial data encryption module is used to set an encryption password for the financial data according to a preset rule. Among them, the full name of the invoicing enterprise in the financial data is obtained, and the first letter of the full name of the invoicing enterprise is set as the encryption password.

[0043] It can be understood that the full name of the invoicing enterprise is included in the financial data. When it is necessary to query the financial data related to the invoicing enterprise, all associated financial data can be searched by inputting the full name of the invoicing enterprise. After inputting the encryption password, the viewing permission for all the associated financial data is obtained, where the encryption password is composed of the first letters of each character of the full name of the invoicing enterprise.

[0044] Specifically, the financial data classification unit is further used to obtain the password input error rate of the financial data. Among them, the total number of password inputs for the financial data and the number of password input errors for the financial data are obtained, and the ratio of the number of password input errors to the total number of password inputs is recorded as the password input error rate.

[0045] Specifically, the present invention records the viewing times of the financial data and the password input error rate of the financial data through the financial data classification unit. In actual situations, the viewing times of the financial data can reflect to a certain extent that the data is of important value to their current work, decision-making, or interests, while the password input error rate of the financial data can reflect to a certain extent the security level of the financial data. By obtaining the viewing times of the financial data and the password input error rate of the financial data, the security and importance of the financial data can be characterized, thereby improving the security of the financial data.

[0046] Specifically, the financial data classification unit is used to classify the financial data according to the viewing times of the financial data and the password input error rate of the financial data. If the financial data meets the preset conditions, the financial data category is classified as a high-risk financial data category; If the financial data does not meet the preset conditions, the financial data category is classified as a low-risk financial data category; The preset conditions are that the viewing times of the financial data are greater than the preset viewing times comparison threshold, and the password input error rate of the financial data is greater than the preset password input error rate comparison threshold for the financial data.

[0047] In this embodiment, the preset view count comparison threshold is selected within the range of [5 times, 10 times], and the preset password input error rate comparison threshold for financial data is selected within the range of [5%, 10%].

[0048] Specifically, the financial data encryption and adjustment unit is used to select high-risk financial data categories and perform secondary encryption on the high-risk financial data categories.

[0049] Specifically, the present invention performs secondary encryption on specific financial data categories through the financial data encryption and adjustment unit. In actual situations, performing secondary encryption on all financial data consumes too much computing resources, increases the computing burden of the system, slows down the processing speed, and thus requires more time and resources to configure and maintain the encryption system, resulting in an increase in maintenance costs. By selecting high-risk financial data categories for secondary encryption, the security of financial data can be improved while saving computing resources.

[0050] In this embodiment, symmetric encryption is selected for the financial data of high-risk financial data categories, and the method of symmetric encryption can be AES.

[0051] Specifically, the financial data includes invoice number, invoice date, invoicing enterprise name, receiving enterprise name, tax-exclusive amount, tax rate, tax amount, tax-inclusive amount, commodity description, quantity, and unit price.

[0052] The modules involved in the embodiments described in this application can be implemented in software or in hardware. The described modules can also be provided in a processor.

[0053] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of apparatuses, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based device for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0054] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation manners here. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. An intelligent financial data management system based on big data, characterized in that, Including: A financial data receiving module, configured to receive uploaded invoices, identify the financial data within the invoices, and receive the financial data of the invoices input manually. An invoice analysis module, connected to the financial data receiving module, configured to compare the identified financial data and the manually input financial data, divide the invoice into color regions based on a clustering-based color segmentation algorithm, obtain the chromaticity values of several points within each color region, and determine whether to store the invoice according to the comparison result and the chromaticity values. A financial data storage module, connected to the invoice analysis module, configured to store invoices and the financial data of the invoices. A financial data encryption module, connected to the financial data storage module, configured to set an encryption password for the invoice and the financial data of the invoice according to a preset rule; the preset rule is to obtain the full name of the invoicing enterprise in the financial data and set the first letter of the full name of the invoicing enterprise as the encryption password. A financial data monitoring module, connected to the financial data storage module, including a financial data classification unit and a financial data encryption adjustment unit. The financial data classification unit is configured to record the viewing times of financial data and the password input error rate of financial data, and divide the financial data into high-risk financial data categories or low-risk financial data categories according to the viewing times of financial data and the password input error rate of financial data. The financial data encryption adjustment unit is configured to select high-risk financial data categories and perform secondary encryption on the high-risk financial data categories. The financial data includes invoice number, invoicing date, invoicing enterprise name, receiving enterprise name, tax-exclusive amount, tax rate, tax amount, tax-inclusive amount, commodity description, quantity, unit price.

2. The intelligent financial data management system based on big data according to claim 1, wherein The invoice analysis module is configured to compare the identified financial data and the manually input financial data, including: Obtaining the identified financial data and the manually input financial data of the invoice. Sequentially comparing each manually input financial data with the corresponding identified financial data. If the manually input financial data is the same as the corresponding identified financial data, it is determined that the financial data is accurately identified. If the manually input financial data is different from the corresponding identified financial data, it is determined that the financial data is not accurately identified. If there is no corresponding identified financial data for the manually input financial data, it is determined that the identified financial data is missing. Recording the ratio of the number of inaccurately identified financial data to the number of manually input financial data as the financial identification inaccuracy rate. Recording the ratio of the missing identified financial data to the number of manually input financial data as the financial identification missing rate.

3. The intelligent financial data management system based on big data according to claim 2, characterized in that The invoice analysis module is configured to divide the invoice into color regions based on a clustering-based color segmentation algorithm and obtain the chromaticity values of several points within each color region, including: Using a clustering-based color segmentation algorithm to divide the invoice into color regions. Selecting any several points within each color region. Obtaining the chromaticity values of any several points within each color region.

4. The intelligent financial data management system based on big data according to claim 3, wherein, The invoice analysis module is further configured to calculate an invoice storage tendency characterization value according to the comparison result and the chromaticity values, including: Comparing the chromaticity values of any several points within each color region with the corresponding preset chromaticity value threshold. Obtain the difference between the chromaticity values of any number of points within each color region and the corresponding preset chromaticity value threshold; Obtain the ratio of the absolute value of the difference to the corresponding preset chromaticity value threshold, denoted as the chromaticity value difference ratio; Weightedly sum the financial recognition inaccuracy rate, the financial recognition missing rate, and the chromaticity value difference ratio to obtain an invoice storage tendency characterization value.

5. The intelligent financial data management system based on big data according to claim 4, wherein The invoice analysis module determines whether to store the invoice, If the invoice storage tendency characterization value of the invoice is less than the preset invoice storage tendency characterization value comparison threshold, it is determined to store the invoice; If the invoice storage tendency characterization value of the invoice is greater than or equal to the preset invoice storage tendency characterization value comparison threshold, it is determined not to store the invoice.

6. The intelligent financial data management system based on big data according to claim 1, characterized in that The financial data classification unit is further configured to obtain the password input error rate of the financial data, where the total number of password inputs for the financial data and the number of password input errors for the financial data are obtained, and the ratio of the number of password input errors to the total number of password inputs is denoted as the password input error rate.

7. The intelligent financial data management system based on big data according to claim 6, wherein, The financial data classification unit is used to classify the financial data according to the number of views of the financial data and the password input error rate of the financial data, If the financial data meets the preset conditions, the financial data category is classified as a high-risk financial data category; If the financial data does not meet the preset conditions, the financial data category is classified as a low-risk financial data category; The preset conditions are that the number of views of the financial data is greater than the preset number of views comparison threshold, and the password input error rate of the financial data is greater than the preset financial data password input error rate comparison threshold.

Images