A document data processing method for financial reimbursement

Abstract

The present invention relates to the field of document processing technology, and in particular to a document data processing method for financial reimbursement, comprising the steps of acquiring image information of each financial document, preprocessing each image information, and extracting data features of the preprocessed image information; and counting amounts to be reimbursed based on the extracted data features. Through automated data collection, recognition, and statistical processes, the time and workload of manual operations are greatly reduced, and the processing speed of the financial reimbursement process is significantly improved. Whether the data verification is qualified is determined based on a comparison result between the statistical amount and the expected amount. When it is determined that the data verification is abnormal, parameters for reprocessing the financial document are determined based on a data difference ratio, the number of items in each financial document, and the difference in item data, thereby improving the accuracy of financial data acquisition and further improving the processing efficiency of the document data.

Description

Technical Field

[0001] The present invention relates to the technical field of document processing, and in particular to a document data processing method for financial reimbursement. Background Art

[0002] In today's digital office environment, financial reimbursement processes involve extensive document processing. Traditionally, data processing relies primarily on manual entry and verification, which is inefficient. Faced with a large number of reimbursement documents, financial personnel spend a significant amount of time entering and reviewing data, leading to lengthy reimbursement cycles and the potential for errors.

[0003] As businesses expand and their businesses diversify, the variety and complexity of expense reimbursement data continues to grow. Traditional methods struggle to comprehensively and accurately analyze and manage this data, making it difficult to promptly identify potential financial issues and anomalies. Therefore, there is an urgent need for an efficient, accurate, and intelligent method for processing financial expense document data to improve the quality and efficiency of financial work and reduce financial risks.

[0004] Chinese Patent Publication No.: CN119107659A, discloses a document-type financial report recognition method and device in a natural scene, including: receiving a financial report file to be recognized, converting it into an image format, and obtaining a financial report image; performing direction correction on the financial report image, and erasing the covering on the financial report image; using a first preset deep learning algorithm to detect the position of the table on the financial image, and cropping the table area; in the table area, using a second preset deep learning algorithm to detect the unit lines of the table structure, and dividing the cells according to the unit lines; performing centralization processing on each cell to extract the information of each cell; standardizing the extracted information according to the subject category, and obtaining the recognition result in a unified description form; outputting the recognition result in a structured manner according to the business scenario; It can be seen that the above technical solution has the following problems: It does not take into account the analysis of the extracted data, and timely adjusts the parameters for processing financial documents when abnormal situations occur, which affects the processing efficiency of the document data. Summary of the Invention

[0005] To this end, the present invention provides a document data processing method for financial reimbursement, which is used to overcome the problem in the prior art that the extracted data is not analyzed and the parameters for processing financial documents are not adjusted in time when abnormal situations occur, thereby affecting the processing efficiency of the document data.

[0006] To achieve the above object, the present invention provides a document data processing method for financial reimbursement, comprising:

[0007] S1, obtaining image information of each financial document;

[0008] S2, preprocessing each image information;

[0009] S3, extracting data features of the preprocessed image information;

[0010] S4, based on the extracted data features, calculate the amount to be reimbursed;

[0011] S5: Determine whether the data verification is qualified based on the comparison result of the statistical amount and the expected amount, including:

[0012] Determining data verification anomalies and determining parameters for reprocessing the financial documents based on the data difference ratio, the number of items in each financial document, and the amount of item data difference. The parameters may include adjusting image sharpening parameters for image information during image information preprocessing to corresponding values, or adjusting parameters for extracting data features.

[0013] Alternatively, the data is determined to be qualified and the statistical amount is stored.

[0014] Furthermore, the data feature extraction process in S3 includes:

[0015] Based on matching the pre-processed image information with each preset feature template to identify the corresponding project area of the data feature;

[0016] Matching the extracted text contour information within the project area with several standard preset contours;

[0017] Acquire text contour information with a degree of overlap higher than a preset overlap value, and record the corresponding standard preset contour as a data feature;

[0018] In S4, determining whether the data verification is qualified based on the comparison result of the statistical amount and the expected amount includes:

[0019] Compare the statistical amount with the expected amount, calculate the absolute value of the difference between the statistical amount and the expected amount, and obtain the data deviation parameter;

[0020] If the data deviation parameter is less than or equal to the first preset deviation, the data verification is determined to be qualified, and the statistical amount is stored;

[0021] If the data deviation parameter is less than or equal to the second preset deviation and greater than the first preset deviation, determining whether the data verification is qualified based on the quota ratio;

[0022] If the data deviation parameter is greater than a second preset deviation, it is determined that the data verification is abnormal, and parameters for reprocessing the financial document are determined based on the data difference ratio.

[0023] Furthermore, the process of determining whether the data verification is qualified based on the quota ratio includes:

[0024] Calculate the ratio of the data deviation parameter to the expected amount to obtain the amount ratio;

[0025] If the credit ratio is less than or equal to the preset credit ratio, the first preset deviation amount and the second preset deviation amount are adjusted based on the credit ratio;

[0026] If the amount ratio is less than or equal to the preset amount ratio, the data verification is determined to be abnormal, and the parameters for reprocessing the financial document are determined based on the data difference ratio.

[0027] Furthermore, the first preset deviation amount and the second preset deviation amount are adjusted based on the quota ratio, wherein:

[0028] The increase in the first preset deviation amount and the second preset deviation amount is inversely proportional to the quota ratio.

[0029] Furthermore, the process of determining parameters for reprocessing the financial document based on the data difference ratio includes:

[0030] Calculating a ratio of the data deviation parameter to a second preset deviation to obtain a data difference ratio;

[0031] If the data difference ratio is less than or equal to the first preset data difference ratio, it is determined that abnormal data exists and a document verification notification is issued;

[0032] If the data difference ratio is less than or equal to the second preset data difference ratio and greater than the first preset data difference ratio, determining parameters for reprocessing the financial documents based on the number of items in each acquired financial document;

[0033] If the data difference ratio is greater than the second preset data difference ratio, the image sharpening parameter for the image information in the image information preprocessing process is adjusted to a corresponding value based on the data difference ratio.

[0034] Furthermore, determining the parameters for reprocessing the financial documents based on the number of items in each acquired financial document includes:

[0035] If the number of items is less than or equal to the preset number of items, adjusting the image sharpening parameter for the image information in the image information preprocessing process to a corresponding value based on the data difference ratio;

[0036] If the number of items is greater than the preset number of items, the parameters for reprocessing the financial document are determined based on the amount of item data discrepancies.

[0037] Furthermore, the parameters for reprocessing the financial document based on the project data difference amount include:

[0038] Calculate the variance of the amount data for each project area to obtain the project data difference;

[0039] If the project data difference amount is less than or equal to the preset project data difference amount, the preset overlap value is increased to a corresponding value based on the project data difference amount;

[0040] If the project data difference amount is greater than the preset project data difference amount, the image sharpening parameter for the image information in the image information preprocessing process is adjusted to a corresponding value based on the data difference ratio.

[0041] Furthermore, the preset overlap value is adjusted up to a corresponding value based on the amount of difference in the project data, where:

[0042] The preset overlap value increases in direct proportion to the amount of difference in the project data.

[0043] Furthermore, the image sharpening parameters for the image information in the image information preprocessing process are adjusted to corresponding values based on the data difference ratio, wherein,

[0044] The increase in the image sharpening parameter is proportional to the data difference ratio.

[0045] Compared with the prior art, the beneficial effects of the present invention are that the image information of each financial document is obtained, each image information is preprocessed, and data features of the preprocessed image information are extracted; based on the extracted data features, the amount to be reimbursed is counted; through automated data collection, recognition, and statistical processes, the time and workload of manual operations are greatly reduced, and the processing speed of the financial reimbursement process is significantly improved. Based on the comparison results of the statistical amount and the expected amount, it is determined whether the data verification is qualified. When it is determined that the data verification is abnormal, the parameters for reprocessing the financial document are determined based on the data difference ratio, the number of items in each financial document, and the difference in item data, thereby improving the accuracy of financial data acquisition and thus improving the processing efficiency of document data.

[0046] Furthermore, the data verification is determined to be qualified based on a data deviation parameter. The data deviation parameter represents the actual amount of abnormalities. When the data deviation parameter is less than or equal to a first preset deviation, it is determined to be qualified, that is, the actual reimbursement amount is relatively close to the expected amount, and the extraction of data features is within a normal range. The currently extracted data is stored. When the data deviation parameter is less than or equal to a second preset deviation and greater than the first preset deviation, a secondary judgment process is entered. The amount ratio represents the specific situation of the difference and fluctuation of the current amount with the expected amount as the reference scale. When the amount ratio is less than or equal to the preset amount ratio, the judgment standard for the current data is lowered to determine whether there are any abnormalities in the processing of the document data. The data verification is determined to be qualified based on the actual situation of the data. When there are any abnormalities in the processing of the financial document data, the parameters for processing the financial document are promptly re-determined, realizing the digital management of financial data. By determining detailed extraction standards and a strict data verification mechanism, abnormal situations are intelligently analyzed and processed, effectively reducing the problems of data entry errors and calculation errors, improving the accuracy and reliability of financial data, and thereby improving the efficiency of document data processing.

[0047] Furthermore, the parameters for reprocessing the financial document are determined based on the data difference ratio. The data difference ratio represents the abnormality of the amount compared with the preset difference amount. When the data difference ratio is less than or equal to the first preset data difference ratio, the abnormality of the amount is within a reasonable range. It is determined that there is no abnormality in the extraction of the document, but there is an abnormality in the reimbursement amount. A document verification notification is issued to prompt relevant personnel to check the accuracy of the document data. When the data difference ratio is less than or equal to the second preset data difference ratio and greater than the first preset data difference ratio, the specific situation is determined in combination with the number of items in the financial document, that is, the number of types of reimbursement amounts. When the number of items is less than or equal to the preset number of items, the number is small and there is a large amount error. In this case, it is determined that the data extraction error is due to an error in the preprocessing of the image information and the image is not clear. In this case, the image sharpening parameters of the image information are adjusted to the corresponding values to improve the accuracy of data extraction; when the number of items is greater than the preset number of items, the specific abnormal situation is further determined in combination with the project data difference amount, and the cause of the abnormality is gradually determined; the project data difference amount characterizes the difference in data of each item. When the project data difference amount is less than or equal to the preset project data difference amount, the difference in the amount data extracted for each item is small. It is determined that due to the preset overlap value being too low, some real data was not accurately extracted, thereby affecting the amount statistics. In this case, the preset overlap value is adjusted to the corresponding value based on the project data difference amount to improve data acquisition accuracy. When the difference in project data is greater than the preset difference in project data, the difference in the amount data of each item extracted is large, which is determined to be an abnormal image processing parameter. Due to image blur, the text outline extraction is inaccurate. The image sharpening parameters are increased to achieve image processing and data extraction effects suitable for the current financial documents. Problems in data processing are discovered in a timely manner. The causes of the problems are determined through analysis of different situations, and targeted solutions are provided to improve the processing efficiency of document data. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flowchart of the steps of a document data processing method for financial reimbursement according to an embodiment of the present invention;

[0049] Figure 2 This is a logic decision diagram for the process of determining whether data verification is qualified based on the quota ratio in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying 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. Therefore, it cannot be understood as a limitation on the present invention.

[0053] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal connections between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0054] See also Figure 1 as well as Figure 2 As shown in the figure, they are respectively a flowchart of the steps of the document data processing method for financial reimbursement according to an embodiment of the present invention and a logic decision diagram of the process of determining whether the data verification is qualified based on the quota ratio; a document data processing method for financial reimbursement according to an embodiment of the present invention includes:

[0055] S1, obtaining image information of each financial document;

[0056] S2, preprocessing each image information;

[0057] S3, extracting data features of the preprocessed image information;

[0058] S4, based on the extracted data features, calculate the amount to be reimbursed;

[0059] S5: Determine whether the data verification is qualified based on the comparison result of the statistical amount and the expected amount, including:

[0060] Determining data verification anomalies and determining parameters for reprocessing the financial documents based on the data difference ratio, the number of items in each financial document, and the amount of item data difference. The parameters may include adjusting image sharpening parameters for image information during image information preprocessing to corresponding values, or adjusting parameters for extracting data features.

[0061] Alternatively, the data is determined to be qualified and the statistical amount is stored.

[0062] Specifically, the image information of each financial document is obtained, each image information is preprocessed, and the data features of the preprocessed image information are extracted; based on the extracted data features, the amount to be reimbursed is counted; through automated data collection, recognition, and statistical processes, the time and workload of manual operations are greatly reduced, and the processing speed of the financial reimbursement process is significantly improved. Based on the comparison results of the statistical amount and the expected amount, it is determined whether the data verification is qualified. When it is determined that the data verification is abnormal, the parameters for reprocessing the financial document are determined based on the data difference ratio, the number of items in each financial document, and the amount of project data difference, thereby improving the accuracy of financial data acquisition and thus improving the processing efficiency of document data.

[0063] Specifically, there is no limitation on the specific method of S1 obtaining the image information of each financial document. The image information of the financial document can be obtained by scanning equipment or taking photos. It can be understood that the image information that can be obtained is clear and complete, and covers all the key contents of the document. This will not be repeated.

[0064] Specifically, the process of preprocessing each image information in S2 includes sharpening the image information, grayscale processing the sharpened image information, removing noise interference in the grayscale image information through Gaussian filtering, converting the grayscale image information into a binary image, highlighting text and key information, setting pixel values greater than a preset binary threshold to white, and setting pixel values less than or equal to the preset binary threshold to black, so as to obtain preprocessed image information.

[0065] Specifically, the preset binarization threshold can be automatically calculated using the Otsu algorithm.

[0066] Specifically, the process of extracting data features from the preprocessed image information includes:

[0067] Withdrawal of amount for each project;

[0068] Matching the image information with the corresponding preset feature template to identify the area of the amount of the item;

[0069] The contour detection algorithm is used for this area. The Canny edge detection algorithm is combined with the contour discovery function to obtain the outline of the text. The database contains several standard preset contours obtained through learning from a large number of samples.

[0070] When the degree of overlap between the text outline information and the corresponding standard preset outline is higher than a preset overlap value, it is determined that the area records the reimbursement amount corresponding to the item, and the amount is extracted to obtain data features;

[0071] Specifically, the degree of overlap can be calculated using a contour matching algorithm, or the text contour information can be compared with various standard preset contours for overlap, and the ratio of the area of the overlapped area to the total area of the text contour information can be recorded as the degree of overlap;

[0072] Specifically, other key information extraction, including the extraction of reimbursement date, reimbursement person, and reimbursement item, is performed by matching and extracting the corresponding feature template and the corresponding preset profile.

[0073] Specifically, in S4, the process of counting the amount to be reimbursed based on the extracted data features includes determining the amount data of each project area based on the data features, traversing all the extracted amount data, adding up their values, and obtaining the final counted amount to be reimbursed.

[0074] Specifically, the process of extracting data features in S3 includes:

[0075] Based on matching the pre-processed image information with each preset feature template to identify the corresponding project area of the data feature;

[0076] Matching the extracted text contour information within the project area with several standard preset contours;

[0077] Acquire text contour information with a degree of overlap higher than a preset overlap value, and record the corresponding standard preset contour as a data feature;

[0078] In S4, determining whether the data verification is qualified based on the comparison result of the statistical amount and the expected amount includes:

[0079] Compare the statistical amount with the expected amount, calculate the absolute value of the difference between the statistical amount and the expected amount, and obtain the data deviation parameter;

[0080] If the data deviation parameter is less than or equal to the first preset deviation, the data verification is determined to be qualified, and the statistical amount is stored;

[0081] If the data deviation parameter is less than or equal to the second preset deviation and greater than the first preset deviation, determining whether the data verification is qualified based on the quota ratio;

[0082] If the data deviation parameter is greater than a second preset deviation, it is determined that the data verification is abnormal, and parameters for reprocessing the financial document are determined based on the data difference ratio.

[0083] Specifically, the first preset deviation is selected within the interval [300, 700], and the second preset deviation is selected within the interval [1000, 3000], and the unit is yuan.

[0084] Specifically, the expected amount can be the expected reimbursement amount for the business determined based on a pre-established budget plan. It can also be the expected amount for the business determined based on historical data, where the average reimbursement amount for the business in the statistical historical data is used as the expected amount.

[0085] Specifically, the process of determining whether data verification is qualified based on the quota ratio includes:

[0086] Calculate the ratio of the data deviation parameter to the expected amount to obtain the amount ratio;

[0087] If the credit ratio is less than or equal to the preset credit ratio, the first preset deviation amount and the second preset deviation amount are adjusted based on the credit ratio;

[0088] If the amount ratio is less than or equal to the preset amount ratio, the data verification is determined to be abnormal, and the parameters for reprocessing the financial document are determined based on the data difference ratio.

[0089] Specifically, the preset quota ratio B0 is selected within the interval [0.06, 0.1].

[0090] Specifically, the data verification is determined to be qualified based on the data deviation parameter. The data deviation parameter represents the actual amount of abnormalities. When the data deviation parameter is less than or equal to the first preset deviation, it is determined to be qualified, that is, the actual reimbursement amount is close to the expected amount, and the extraction of data features is within the normal range. The currently extracted data is stored. When the data deviation parameter is less than or equal to the second preset deviation and greater than the first preset deviation, the secondary judgment process is entered. The amount ratio represents the difference and fluctuation of the current amount with the expected amount as the reference scale. When the amount ratio is less than or equal to the preset amount ratio, the judgment standard for the current data is lowered to determine whether there are abnormalities in the processing of the document data. The data verification is determined to be qualified based on the actual situation of the data. When there are abnormalities in the processing of financial document data, the parameters for processing financial documents are re-determined in time to realize the digital management of financial data. By determining detailed extraction standards and a strict data verification mechanism, the abnormal situations are intelligently analyzed and processed, effectively reducing the problems of data entry errors and calculation errors, improving the accuracy and reliability of financial data, and thus improving the efficiency of document data processing.

[0091] Specifically, the first preset deviation amount and the second preset deviation amount are adjusted based on the quota ratio, wherein:

[0092] The increase in the first preset deviation amount and the second preset deviation amount is inversely proportional to the quota ratio.

[0093] In this embodiment, optionally,

[0094] Comparing the credit limit ratio with a first preset credit limit comparison threshold and a second preset credit limit comparison threshold;

[0095] If the credit limit ratio is less than or equal to the first preset credit limit comparison threshold, the first preset deviation is adjusted to 1.08 times the initial first preset deviation, and the second preset deviation is adjusted to 1.08 times the initial second preset deviation;

[0096] If the credit limit ratio is less than or equal to the second preset credit limit comparison threshold and greater than the first preset credit limit comparison threshold, the first preset deviation is adjusted to 1.05 times the initial first preset deviation, and the second preset deviation is adjusted to 1.05 times the initial second preset deviation;

[0097] If the credit limit ratio is greater than the second preset credit limit comparison threshold, the first preset deviation is adjusted to 1.03 times the initial first preset deviation, and the second preset deviation is adjusted to 1.03 times the initial second preset deviation;

[0098] The first preset amount comparison threshold is 0.5B0, and the second preset amount comparison threshold is 0.7B0.

[0099] Specifically, whether the data verification is qualified is determined again based on the data deviation parameter according to the adjusted first preset deviation. If the data deviation parameter is less than or equal to the adjusted first preset deviation, the data verification is determined to be qualified, and the statistical amount is stored; if the data deviation parameter is less than or equal to the second preset deviation and greater than the first preset deviation after adjustment of the first preset deviation, the data verification is determined to be abnormal, and the parameters for reprocessing the financial document are determined based on the data difference ratio.

[0100] Specifically, the process of determining parameters for reprocessing financial documents based on data discrepancy ratios includes:

[0101] Calculating a ratio of the data deviation parameter to a second preset deviation to obtain a data difference ratio;

[0102] If the data difference ratio is less than or equal to the first preset data difference ratio, it is determined that abnormal data exists and a document verification notification is issued;

[0103] If the data difference ratio is less than or equal to the second preset data difference ratio and greater than the first preset data difference ratio, determining parameters for reprocessing the financial documents based on the number of items in each acquired financial document;

[0104] If the data difference ratio is greater than the second preset data difference ratio, the image sharpening parameter for the image information in the image information preprocessing process is adjusted to a corresponding value based on the data difference ratio.

[0105] Specifically, the first preset data difference ratio Y1 is selected within the interval [0.2, 0.25], and the second preset data difference ratio Y2 is selected within the interval [0.3, 0.42].

[0106] Specifically, determining the parameters for reprocessing the financial documents based on the number of items in each acquired financial document includes:

[0107] If the number of items is less than or equal to the preset number of items, adjusting the image sharpening parameter for the image information in the image information preprocessing process to a corresponding value based on the data difference ratio;

[0108] If the number of items is greater than the preset number of items, the parameters for reprocessing the financial document are determined based on the amount of item data discrepancies.

[0109] Specifically, the number of preset items is selected within the interval [5, 8].

[0110] Specifically, the parameters for reprocessing financial documents based on the amount of project data discrepancies include:

[0111] Calculate the variance of the amount data for each project area to obtain the project data difference;

[0112] If the project data difference amount is less than or equal to the preset project data difference amount, the preset overlap value is increased to a corresponding value based on the project data difference amount;

[0113] If the project data difference amount is greater than the preset project data difference amount, the image sharpening parameter for the image information in the image information preprocessing process is adjusted to a corresponding value based on the data difference ratio.

[0114] Specifically, the preset project data difference amount X0 is selected within the interval [1.6Q0, 2.5Q0], and Q0 is the average value of the difference amounts of each project data of the corresponding business in the historical data.

[0115] Specifically, the preset overlap value is adjusted up to the corresponding value based on the amount of project data difference, where:

[0116] The preset overlap value increases in direct proportion to the amount of difference in the project data.

[0117] In this embodiment, optionally,

[0118] Comparing the project data difference amount with a first preset project comparison threshold and a second preset project comparison threshold;

[0119] If the project data difference is less than or equal to the first preset project comparison threshold, the preset overlap value is adjusted to 1.11 times the initial preset overlap value;

[0120] If the project data difference is less than or equal to the second preset project comparison threshold and greater than the first preset project comparison threshold, the preset overlap value is adjusted to 1.19 times the initial preset overlap value;

[0121] If the difference in the project data is greater than the second preset project comparison threshold, the preset overlap value is adjusted to 1.25 times the initial preset overlap value;

[0122] The first preset item comparison threshold is 0.8X0, and the second preset item comparison threshold is 0.65X0.

[0123] Specifically, the image sharpening parameters for the image information during the image information preprocessing process are adjusted to corresponding values based on the data difference ratio, wherein,

[0124] The increase in the image sharpening parameter is proportional to the data difference ratio.

[0125] In this embodiment, optionally,

[0126] Comparing the data difference ratio with a first preset data comparison threshold and a second preset data comparison threshold;

[0127] If the data difference ratio is less than or equal to the first preset data comparison threshold, adjusting the image sharpening parameter to 1.1 times the initial image sharpening parameter;

[0128] If the data difference ratio is less than or equal to the second preset data comparison threshold and greater than the first preset data comparison threshold, adjusting the image sharpening parameter to 1.2 times the initial image sharpening parameter;

[0129] If the data difference ratio is greater than a second preset data comparison threshold, adjusting the image sharpening parameter to 1.3 times the initial image sharpening parameter;

[0130] The first preset data comparison threshold is 1.25Y2, and the second preset data comparison threshold is 1.47Y2.

[0131] Specifically, the parameters for reprocessing financial documents are determined based on the data difference ratio. The data difference ratio represents the abnormality of the amount compared with the preset difference amount. When the data difference ratio is less than or equal to the first preset data difference ratio, the abnormality of the amount is within a reasonable range. It is determined that there is no abnormality in the extraction of the document, but there is an abnormality in the reimbursement amount. A document verification notification is issued to prompt relevant personnel to check the accuracy of the document data. When the data difference ratio is less than or equal to the second preset data difference ratio and greater than the first preset data difference ratio, the specific situation is determined in combination with the number of items in the financial document, that is, the number of types of reimbursement amounts. When the number of items is less than or equal to the preset number of items, the number is small and there is a large amount error. In this case, it is determined that the data extraction error is due to an error in the preprocessing of the image information and the image is not clear. In this case, the image sharpening parameters of the image information are adjusted to the corresponding values to improve the accuracy of data extraction; when the number of items is greater than the preset number of items, the specific abnormal situation is further determined in combination with the project data difference amount, and the cause of the abnormality is gradually determined; the project data difference amount characterizes the difference in data of each item. When the project data difference amount is less than or equal to the preset project data difference amount, the difference in the amount data extracted for each item is small. It is determined that due to the preset overlap value being too low, some real data was not accurately extracted, thereby affecting the amount statistics. In this case, the preset overlap value is adjusted to the corresponding value based on the project data difference amount to improve data acquisition accuracy. When the difference in project data is greater than the preset difference in project data, the difference in the amount data of each item extracted is large, which is determined to be an abnormal image processing parameter. Due to image blur, the text outline extraction is inaccurate. The image sharpening parameters are increased to achieve image processing and data extraction effects suitable for the current financial documents. Problems in data processing are discovered in a timely manner. The causes of the problems are determined through analysis of different situations, and targeted solutions are provided to improve the processing efficiency of document data.

[0132] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0133] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A document data processing method for financial reimbursement, characterized in that: include: S1, obtaining image information of each financial document; S2, preprocessing each image information; S3, extracting data features of the preprocessed image information; S4, based on the extracted data features, calculate the amount to be reimbursed; S5: Determine whether the data verification is qualified based on the comparison result of the statistical amount and the expected amount, including: Compare the statistical amount with the expected amount, calculate the absolute value of the difference between the statistical amount and the expected amount, and obtain the data deviation parameter; If the data deviation parameter is less than or equal to the second preset deviation amount and greater than the first preset deviation amount, determining whether the data verification is qualified based on the amount ratio; calculating the ratio of the data deviation parameter to the expected amount to obtain the amount ratio; the first preset deviation amount is less than the second preset deviation amount; Determining data verification anomalies and re-determining parameters for processing the financial documents based on the data difference ratio, the number of items in each financial document, and the amount of item data difference. The parameters may include adjusting image sharpening parameters for image information during image information preprocessing to corresponding values, or adjusting parameters for extracting data features. Or, determine whether the data verification is qualified and store the statistical amount; The process of re-determining parameters for processing financial documents based on data discrepancy ratios, including: Calculating a ratio of the data deviation parameter to a second preset deviation to obtain a data difference ratio; If the data difference ratio is less than or equal to the first preset data difference ratio, it is determined that abnormal data exists and a document verification notification is issued; If the data difference ratio is less than or equal to the second preset data difference ratio and greater than the first preset data difference ratio, re-determining the parameters for processing the financial documents based on the number of items in each acquired financial document; If the data difference ratio is greater than the second preset data difference ratio, the image sharpening parameter for the image information in the image information preprocessing process is adjusted to a corresponding value based on the data difference ratio.

2. The document data processing method for financial reimbursement according to claim 1, characterized in that: The process of extracting data features in S3 includes: Based on matching the pre-processed image information with each preset feature template to identify the corresponding project area of the data feature; Matching the extracted text contour information within the project area with several standard preset contours; Acquire text contour information with a degree of overlap higher than a preset overlap value, and record the corresponding standard preset contour as a data feature; In S5, determining whether the data verification is qualified based on the comparison result of the statistical amount and the expected amount includes: If the data deviation parameter is less than or equal to the first preset deviation, the data verification is determined to be qualified, and the statistical amount is stored; If the data deviation parameter is greater than a second preset deviation, it is determined that the data verification is abnormal, and the parameters for processing the financial document are re-determined based on the data difference ratio.

3. The document data processing method for financial reimbursement according to claim 2, characterized in that: The process of determining whether data verification is qualified based on the quota ratio includes: If the credit ratio is less than or equal to the preset credit ratio, the first preset deviation amount and the second preset deviation amount are adjusted based on the credit ratio; If the amount ratio is greater than the preset amount ratio, the data verification is determined to be abnormal, and the parameters for processing the financial document are re-determined based on the data difference ratio.

4. The document data processing method for financial reimbursement according to claim 3, characterized in that: The first preset deviation amount and the second preset deviation amount are adjusted based on the quota ratio, wherein: The increase in the first preset deviation amount and the second preset deviation amount is inversely proportional to the quota ratio.

5. The document data processing method for financial reimbursement according to claim 4, characterized in that: Re-determining parameters for processing financial documents based on the number of items in each acquired financial document includes: If the number of items is less than or equal to the preset number of items, adjusting the image sharpening parameter for the image information in the image information preprocessing process to a corresponding value based on the data difference ratio; If the number of items is greater than the preset number of items, the parameters for processing the financial document are re-determined based on the amount of item data discrepancies; Calculate the variance of the amount data for each project area to obtain the project data difference.

6. The document data processing method for financial reimbursement according to claim 5, characterized in that: The parameters for re-processing financial documents based on the amount of project data discrepancies include: If the project data difference amount is less than or equal to the preset project data difference amount, the preset overlap value is increased to a corresponding value based on the project data difference amount; If the project data difference amount is greater than the preset project data difference amount, the image sharpening parameter for the image information in the image information preprocessing process is adjusted to a corresponding value based on the data difference ratio.

7. The document data processing method for financial reimbursement according to claim 6, characterized in that: Based on the amount of project data difference, the preset overlap value is adjusted to the corresponding value, where: The preset overlap value increases in direct proportion to the amount of difference in the project data.

8. The document data processing method for financial reimbursement according to claim 7, characterized in that: An image sharpening parameter for the image information during image information preprocessing is adjusted to a corresponding value based on the data difference ratio, wherein an increase in the image sharpening parameter is proportional to the data difference ratio.

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