Intelligent financial management system based on data feature analysis

Abstract

The present invention discloses an intelligent financial management system based on data feature analysis, which relates to the field of financial management; it comprises an intelligent data acquisition module, a data supervision module, a feature analysis module and a risk analysis module; the intelligent data acquisition module collects company data and network data; the data supervision module verifies the security and accuracy of company data; the feature analysis module performs security data feature analysis and data quality analysis on the data; the risk analysis module performs risk prediction and visual display on the data; the data is sent to the intelligent terminal of relevant financial management personnel to accelerate the discovery and processing of anomalies, and the review and re-proofreading can effectively prevent potential financial risks; the data is strictly security inspected, and through multi-dimensional quality analysis, the credibility and quality of network data are effectively improved, efficiency is improved, manual operations are reduced, and the security and quality of the data are also guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of financial management, and in particular to an intelligent financial management system based on data feature analysis. Background Art

[0002] Intelligent financial management system technology uses advanced artificial intelligence, big data analysis and automation technology to achieve efficient management in the financial field. The system can automatically collect, integrate and analyze a large amount of financial data, generate accurate financial indicators and risk assessments through intelligent algorithms, and help companies make accurate financial decisions. The intelligent financial management system also has functions such as automated accounting, invoice recognition, and financial report generation, which improves financial work efficiency and reduces error rates. At the same time, the system monitors assets, cash flow, risk indicators, etc. in real time, promptly detects potential risks, and provides personalized warnings and suggestions, providing comprehensive financial management support for enterprises and helping them achieve financial digital transformation.

[0003] However, financial statements may contain a large number of tables and charts. The system may encounter difficulties in parsing and processing these elements, requiring manual review and verification, which is time-consuming and labor-intensive. At the same time, financial management cannot perform security and quality analysis on data files, resulting in low security and credibility of the files. Summary of the invention

[0004] The purpose of the present invention is to solve the problems that the financial management system may encounter difficulties in parsing and processing these elements, which require manual review and verification, which is time-consuming and labor-intensive. At the same time, the financial management cannot perform security and quality analysis on the data files, resulting in low security and credibility of the files. An intelligent financial management system based on data feature analysis is proposed.

[0005] The purpose of the present invention can be achieved through the following technical solutions: an intelligent financial management system based on data feature analysis, including an intelligent data acquisition module, a data supervision module, a feature analysis module and a risk analysis module;

[0006] Includes data supervision module and feature analysis module;

[0007] The data supervision module is used to import financial employee information into the financial system and obtain financial statement information, and to perform supervision and analysis on the financial statement information. The specific analysis process is as follows:

[0008] The company seal is identified on the financial statements. If the company seal is not identified, a report seal exception instruction is directly generated. If the company seal is identified, the authenticity of the company seal is identified, the number and company name in the company seal are obtained, and they are compared one by one with the preset company name and seal number. If the two are inconsistent, a report seal exception instruction is generated; if the two are consistent, the seal outline of the company seal is extracted, compared with the preset outline, and the direction of the seal outline is adjusted to make it the same as the starting position of the preset outline. The area where the seal outline does not overlap with the preset outline is calculated, and the numerical value of the area is marked as the authenticity value. When the authenticity value is greater than the preset authenticity threshold, a report seal exception instruction is generated. Otherwise, the data value in the financial statement is checked. The verification is performed. If the verification result is inconsistent, a report data abnormal instruction is generated. If not, the handwriting and name of the signer of the financial report are recognized, and the recognized name is matched with the preset name. If the corresponding preset name is not matched, a report signature abnormal instruction is generated; if the corresponding preset name is matched, the recognized handwriting is compared with the preset handwriting, and the handwriting matching value is calculated. When the handwriting matching value is greater than the set handwriting matching threshold, a report handwriting abnormal instruction is generated; when a report seal abnormal instruction or a report data abnormal instruction or a report signature abnormal instruction or a report handwriting abnormal instruction is generated, the preset abnormal prompt information corresponding to the instruction is directly matched and sent to the intelligent terminal of the corresponding financial management personnel together with the financial report for review and re-proofreading;

[0009] The feature analysis module is used to perform quality feature analysis on the data file to obtain the data file quality value and data source degree; when the data source degree is within the preset source threshold, the data file quality value is compared, otherwise the data file is deleted; when the data file quality value is within the preset quality threshold, the data file is retained, otherwise the data file is deleted.

[0010] As a preferred embodiment of the present invention, it also includes an intelligent data collection module, which is connected to various data sources, including company data sources and network data sources; and collects company data, network data industry reports, interval pixels, web page feedback and data source IP addresses according to page views and audience size; company data includes financial employee information and financial information.

[0011] As a preferred embodiment of the present invention, the specific process of calculating the handwriting matching value is as follows:

[0012] The handwriting and the preset handwriting are divided into several stroke outlines according to the corresponding areas, and the stroke outlines of the two are subjected to stroke length recognition to obtain the stroke length value; the stroke outlines of the two are placed in parallel, and several points are drawn on the stroke outlines of the two, and the corresponding points on the stroke outlines of the two are connected to obtain the contour line, and the length of the contour line is calculated, and the variance of the lengths of all contour connections is calculated to obtain the line fluctuation value; the difference of the stroke length values ​​of the two is calculated to obtain the length difference, the length difference and the line fluctuation value are normalized and the processed values ​​are multiplied by the corresponding weight factors respectively, and then the sum is obtained to obtain the handwriting matching value.

[0013] As a preferred embodiment of the present invention, the specific process of performing quality feature analysis on the data file is as follows:

[0014] Collect web page IPs and divide them according to page views and audience size, content type, and function and service; divide the web page IPs into websites according to page views and audience size and assign them values ​​A01, A02, and A03; divide the web page IPs into websites according to content type and assign them values ​​B01, B02, B03, and B04; divide the web page IPs into websites according to function and service and assign them values ​​C01, C02, and C03; substitute into the formula Output data source degree JZ, e is a natural number, a4, a5, a6 are preset weight factors, z = 1, 2, 3, v = 1, 2, 3, 4, m = 1, 2, 3;

[0015] By monitoring the clarity of the data file, identifying the uniformity of the text size in the data file, the anti-counterfeiting mark in the data file and the feedback of the data file, and then comparing the data files of the same type released in the same time interval, specifically: the file clarity is calculated by the ratio of the white pixels in a certain pixel interval of the data file to the pixels used for the data text under the preset standard white page. The file clarity qη is calculated by averaging the clarity of all intervals; the uniformity of the text size is obtained by presetting a uniform size pixel interval, collecting the number of texts in the pixel interval and the number of texts in another interval, and then performing standard deviation calculation to obtain the uniformity Tκ; if there is an anti-counterfeiting mark in the data file, λ is assigned a value of 1, and if there is no anti-counterfeiting mark, λ is assigned a value of 0. The feedback is obtained by evaluating and scoring the web page by netizens, identifying the evaluation content and judging the score. By identifying the number of occurrences of a preset phrase, the score value is divided into a value range. According to the number of occurrences of the phrase and the value range, when it belongs to the excellent evaluation and score, it is assigned a value of H1, when it belongs to the poor evaluation and score, it is assigned a value of H3, and when it belongs to the medium evaluation and score, it is assigned a value of H2; Substitute into the formula Output data file quality value ZL, e is a natural number, g1, g2, g3, g4 and g5 are all preset weight factors, λ=1 or 0, μ=1, 2, 3.

[0016] As a preferred implementation of the present invention, the feature analysis module is also used to perform security feature analysis on the data file. The specific analysis process is: the preset error range is divided according to the size of the data file to obtain interval one, interval two, interval three and interval four; the corresponding network traffic is compared with the data corresponding to the collected data. When the network traffic is greater than the size of the corresponding data file, the difference between the network traffic and the corresponding data file size is calculated, and the difference is compared with interval one, interval two and interval three. If the difference belongs to interval four, the corresponding data file is deleted immediately; when the difference belongs to interval If the data file is within interval three, the data file will be screened, and then the interval comparison will be performed again after the screening. When the corresponding difference is within interval one, the data file passes the security check. If not, the corresponding data file will be directly deleted. If the difference is within interval two, the data file passes the security check. After screening, the interval comparison will be performed again. When the difference is within interval one, the data file passes the security check. If not, the screening will be performed again. After the screening, the interval comparison will be performed again. When the difference is within interval one, the data file passes the security check. If not, the corresponding data file will be directly deleted. When the difference is within interval one, the corresponding data file will be directly retained.

[0017] As a preferred embodiment of the present invention, it also includes a risk analysis module, which is used to collect data company data and network data for analysis. The company data analysis includes data correlation analysis, income and expenditure analysis, cash flow analysis and trend and financial ratio analysis. The network data analysis includes industry report analysis, financial risk analysis and visualization operation. The financial risk value MQ is calculated comprehensively, and the preset interval and the obtained financial risk value MQ are divided into risk levels. The financial risk value MQ is then tested to ensure the accuracy of the financial risk value MQ; corresponding operation commands are performed according to the risk level; and the analyzed results are visualized, the collected data are classified, and the data are distinguished according to categories and data characteristics, and applicable charts are screened out.

[0018] The beneficial effects of the present invention are:

[0019] 1. The present invention can connect with various data sources through intelligent data acquisition module collection, and realizes comprehensive financial anomaly detection by automatically identifying and verifying company seals, data values, signer's handwriting and names in financial statements. If inconsistencies are found, corresponding abnormal instructions are generated. The abnormal instructions generated by the system are directly sent to the intelligent terminals of relevant financial management personnel to accelerate the abnormal discovery and processing process. Through review and re-proofreading, potential financial risks can be effectively prevented and the accuracy and reliability of financial data can be improved.

[0020] 2. The present invention uses a risk analysis module. This feature analysis module performs security feature analysis and quality feature analysis on network data to achieve comprehensive data processing. First, the error range is preset to divide the data into intervals, the network traffic is compared with the collected data, and the different difference intervals are tested. Secondly, the quality of the collected data is analyzed by dividing the page views, audience size, content type and function through the web page IP address, calculating the data source degree, performing quality analysis, and deciding whether to retain the data file.

[0021] In the data file quality feature analysis stage, the module monitors the clarity, uniformity of text size and anti-counterfeiting marks, as well as the comparison of the same type of data files released in the same time period, to obtain multiple quality indicators such as file clarity, uniformity of text size and the presence or absence of anti-counterfeiting marks. Then, based on the preset weight factors and calculations, the quality value is obtained to decide whether to retain it. The data is strictly inspected for security, and through multi-dimensional quality feature analysis, the credibility and quality of network data are effectively improved, efficiency is improved, and manual operations are reduced, while also ensuring the security and quality of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0023] Figure 1 It is a principle block diagram of the present invention;

[0024] Figure 2 is a trend curve diagram of the present invention;

[0025] Figure 3 It is a characteristic analysis flow chart of the present invention. DETAILED DESCRIPTION

[0026] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Example 1

[0028] See also Figure 1-Figure 3 As shown, an intelligent financial management system based on data feature analysis includes an intelligent data acquisition module, a data supervision module, a feature analysis module and a risk analysis module.

[0029] The intelligent data collection module can connect to various data sources, including company data sources and network data sources; company data sources include income, expenditure, cost, budget, gross profit, net profit, fixed assets, current assets, liabilities, current liabilities, cash flow and shareholders' equity, etc., for timely collection; as well as financial employee information; network data sources include market data and industry report data, and its industry report data includes industry reports from all walks of life. When connecting to the data source, the intelligent data collection module collects the IP address of the data source and divides it into large websites, medium-sized websites and small websites according to the number of views and audience size; it is divided into news portals, social media, e-commerce platforms, blogs and personal websites according to content type; it is divided into professional service websites, education platforms and entertainment platforms according to functions and services; the intelligent data collection module collects interval pixels and web page feedback, including netizens' evaluation and scoring of web pages.

[0030] The data supervision module is used to import financial employee information into the financial system and obtain financial statement information, and to perform supervision and analysis on the financial statement information. The specific analysis process is as follows:

[0031] The company seal is identified on the financial statements. If the company seal is not identified, a report seal exception instruction is directly generated. If the company seal is identified, the authenticity of the company seal is identified, the number and company name in the company seal are obtained, and they are compared one by one with the preset company name and seal number. If the two are inconsistent, a report seal exception instruction is generated; if the two are consistent, the seal contour of the company seal is extracted, compared with the preset contour, and the direction of the seal contour is adjusted to make it the same as the starting position of the preset contour, and the area where the seal contour does not overlap with the preset contour is calculated, and the numerical value of the area is marked as the authenticity value. When the authenticity value is greater than the preset authenticity threshold, a report seal exception instruction is generated. Otherwise, the data value in the financial statement is verified. If the verification result is inconsistent, a report data exception instruction is generated. Otherwise, the handwriting and name of the signer of the financial statement are recognized, and the recognized name is compared with the preset name. A match is performed. If the corresponding preset name is not matched, a report signature exception instruction is generated; if the corresponding preset name is matched, the recognized handwriting is compared with the preset handwriting, and the handwriting matching value is calculated, specifically: the handwriting and the preset handwriting are divided into a number of stroke contours according to the corresponding areas, and the stroke contours of the two are subjected to stroke length recognition to obtain the stroke length value; the stroke contours of the two are placed in parallel, and a number of points are drawn on the stroke contours of the two, and the corresponding points on the stroke contours of the two are connected to obtain a contour line, and the length of the contour line is calculated, and the variance of the lengths of all contour connections is calculated to obtain a line fluctuation value; the difference between the stroke length values ​​of the two is calculated to obtain a length difference, the length difference and the line fluctuation value are normalized and the processed values ​​are multiplied by the corresponding weight factors, and then the sum is obtained to obtain a handwriting matching value. When the handwriting matching value is greater than the set handwriting matching threshold, a report handwriting exception instruction is generated;

[0032] When a report stamp exception instruction, report data exception instruction, report signature exception instruction, or report handwriting exception instruction is generated, the preset exception prompt information corresponding to the instruction is directly matched and sent together with the financial report to the intelligent terminal of the corresponding financial management personnel for review and re-proofreading.

[0033] The feature analysis module performs security feature analysis and quality feature analysis on network data, specifically:

[0034] The preset error range is divided according to the size of the data file, and interval one, interval two, interval three and interval four are obtained, which are (0, 1%], (1%, 5%], (5%, 10%], (10%, 100%] respectively.

[0035] Compare the corresponding network traffic with the data corresponding to the collected data. When the network traffic is larger than the size of the corresponding data file, calculate the difference between the network traffic and the size of the corresponding data file, and compare the difference with interval one, interval two and interval three. If the difference belongs to interval four, delete the corresponding data file immediately; when the difference belongs to interval three, screen the data file, and perform interval comparison again after screening. When the corresponding difference is in interval one, the data file passes the security check. Otherwise, delete the corresponding data file directly; when the difference belongs to interval two, the data file passes the security check. After screening, perform interval comparison again. When the difference is in interval one, the data file passes the security check. Otherwise, screen again. After screening, perform interval comparison. When the difference is in interval one, the data file passes the security check. Otherwise, delete the corresponding data file directly; when the difference belongs to interval one, retain the corresponding data file directly;

[0036] Analyze the quality characteristics of the collected data files; divide the collected web page IP addresses into large websites, medium-sized websites and small websites according to the number of views and audience size; divide them into news portals, social media, e-commerce platforms, blogs and personal websites according to content type; divide them into professional service websites, education platforms and entertainment platforms according to functions and services; if the web page IP address of the data file belongs to a large website, assign A01 to the scale value of the data file, A02 to medium-sized websites, and A03 to small websites; if the web page of the data file belongs to a news portal, assign B01 to the scale value of the data file, B02 to social media, B03 to e-commerce platforms, and B04 to blogs and personal websites; if the web page of the data file belongs to a professional service website, assign C01 to the scale value of the data file, C02 to education platforms, and C03 to entertainment platforms; substitute into the formula Output data source degree JZ, e is a natural number, a4, a5, a6 are preset weight factors, z = 1, 2, 3, v = 1, 2, 3, 4, m = 1, 2, 3; when JZ is within the preset threshold, perform data file quality feature analysis, otherwise delete the data file;

[0037] The data file quality feature analysis is performed by monitoring the clarity within the data file, identifying the uniformity of the text size within the data file and the anti-counterfeiting mark within the data file, and then comparing the data files of the same type released in the same time interval to obtain the feedback of the data file; the file clarity is calculated by the ratio of the white pixels in a certain pixel interval of the data file to the pixels used for the data text under the preset standard white page. The file clarity qη is obtained by averaging the clarity of all intervals; the uniformity of the text size is obtained by presetting a uniform size pixel interval, collecting the number of texts in the pixel interval and the number of texts in another interval, and then performing standard deviation calculation to obtain the uniformity Tκ; if there is an anti-counterfeiting mark, it is assigned a value of 1, and if there is no anti-counterfeiting mark, it is assigned a value of 0. The feedback is obtained by evaluating and scoring the web page by netizens, identifying the evaluation content and judging the score. By identifying the number of occurrences of a preset phrase, the score value is divided into a value range. According to the number of occurrences of the phrase and the value range, when it belongs to the excellent evaluation and score, it is assigned a value of H1, when it belongs to the poor evaluation and score, it is assigned a value of H3, and when it belongs to the medium evaluation and score, it is assigned a value of H2; Substitute into the formula Output data file quality value ZL, e is a natural number, g1, g2, g3, g4 and g5 are preset weight factors, λ=1 or 0, μ=1, 2, 3, when ZL is within the preset threshold, the data is retained, otherwise the data file is deleted.

[0038] The risk analysis module analyzes the collected data. The specific analysis process is as follows:

[0039] Perform trend analysis, and substitute the operating income q, net profit e, and assets w in different time periods into the formula. QS is the trend value, a1, a2, a3 are the preset weight factors, and is the operating income, net profit and asset value corresponding to the time period t, It is the operating income, net profit, and asset value corresponding to the time period y. When QS is less than 1, the time period is in an upward trend. When QS≦1 / n (n is greater than 1), the size of the upward trend is determined according to the size of n. Conversely, when QS is greater than 1, the time period is in a downward trend. When QS≧n1 (n is greater than 1), the size of the downward trend is determined according to the size of n.

[0040] Perform data correlation analysis, select two variables from the variables income S, cost C, profit L, asset Z, liability F, etc. for correlation and monotonic analysis, such as the correlation and monotonic analysis between income and cost, and find the linear relationship between income S and cost C in a certain interval. Substitute the variables into the formula r is the correlation coefficient, i is a certain interval, when |r| is in [0.8, 1], it is extremely strongly correlated, when |r| is in [0.6, 0.8], it is strongly correlated, when |r| is in [0.4, 0.6], it is moderately correlated, when |r| is in [0.2, 0.4], it is weakly correlated, when |r| is in [0.0, 0.2], it is extremely weakly correlated or irrelevant; then perform a monotonic relationship between income and cost in a certain interval, substitute the variables into the formula ρ is the monotonicity coefficient. The value range of the Spearman correlation coefficient is between -1 and 1. ρ=1 indicates a complete positive correlation, ρ=-1 indicates a complete negative correlation, and ρ=0 indicates no monotonicity. A positive correlation means that the two variables increase or decrease together, and a negative correlation means that one variable increases while the other decreases.

[0041] Conduct cash flow analysis by analyzing the cash flow statement within a fixed time standard, and classify the cash flow statement into three categories: operating activities, investment activities, and financing activities cash flow; calculate the cash inflows and cash outflows of the three categories separately, sum them up, and calculate the net cash flow; compare the trend and change of cash flow with those of the same industry and competitors, and obtain the absolute difference between the trend and change within a certain period of time and the same industry and competitors, marked as γ. When the trend and change of cash flow are higher than those of the same industry and competitors, it is γ1, otherwise it is γ2.

[0042] Perform income and expenditure analysis, and classify income into income sources, income amount Q, and income time t. Among them, income sources include customer types, sales channels, and geographical regions; customer types are divided into D01, D02, and D03; sales channels are divided into E01, E02, and E03; geographical regions are divided into F01, F02, and F03; different customer types, different sales channels, different regions, amounts, and times are calculated, and the maximum and minimum values ​​of different income sources are screened out, marked as S max and S min , S max Analyze and screen out the corresponding sales channels, regions and customer types; classify the expenses into human resource costs, rental and real estate costs, procurement and supply chain costs, maintenance and repair costs and other costs, and then calculate the corresponding expenditure return rate. When the return rate is less than 10%, it is unnecessary expenditure.

[0043] Conduct financial ratio analysis, and calculate the gross profit rate c1, net profit rate c2, current ratio c3, return on equity c4, debt service ratio c5 and cost-to-income ratio c6 from the collected financial data of income, expenditure, cost, gross profit, net profit, current liabilities and shareholders' equity; according to the monotonicity of the correlation between the data, substitute the obtained data into the formula Output the comprehensive impact value CWBL of financial ratios, b1, b2, b3, b4, b5 and b6 are preset weight factors, the preset impact value is CWBL1, when CWBL is less than CWBL1, it is an irrelevant impact value, when CWBL1 is less than CWBL, it is a relevant impact value.

[0044] Conduct industry report analysis, input the industry type, filter out upstream and downstream industry reports, analyze upstream and downstream market sizes and growth trends, as well as production and supply chains, consumer behavior and demand, compare the market size and growth trends with historical data to calculate the industry's growth potential and competitive environment, and analyze the industry's demand stability through consumer demand as well as production and supply chain analysis. Calculate the growth potential, competitive environment and demand stability values ​​and output ZJX. ZJX is the industry coefficient. ZJX takes values ​​in the interval (0, 2). When ZJX takes the value of 1, the industry coefficient is most stable.

[0045] The risk analysis module calculates the obtained data and substitutes it into the formula Output the financial risk value MQ, c1, c2, c3 and c4 are preset weight factors, preset intervals 1, 2, 3, 4 and 5, marked as M1, M2, M3, M4 and M5 respectively; when MQ is at M1, it belongs to the low risk value MQ1, when MQ is at M2, it belongs to the medium-low risk value MQ2, when MQ is at M3, it belongs to the medium risk value MQ3, when MQ is at M4, it belongs to the medium-high risk value MQ4 with a small amplitude, and when MQ is at M5, it belongs to the high risk value MQ5; then test the risk value MQ, by analyzing the QS, γ, CWBL and ZJX data, to detect whether QS is in an upward trend or a stable trend, whether γ is γ1, whether CWBL is greater than CWBL1, and whether ZJX is greater than or equal to 1; ensure the accuracy of the financial risk value MQ; when the risk value is MQ1, the intelligent financial system will send the risk level to the financial statistician, count the assets and cash flow, monitor the cash flow, short-term debt maturity, credit limit, etc., and max Analyze and select the corresponding sales channels, regions and customer types, and formulate countermeasures; when the risk value is MQ2, the intelligent financial system will send the risk level to the financial management personnel, count the assets and cash flow, monitor the cash flow, short-term debt maturity, credit limit, etc., and maxAnalyze and select the corresponding sales channels, regions and customer types, formulate response measures, supervise expenditures, classify expenditures and calculate the rate of return to reduce unnecessary expenditures; when the risk value is MQ3, MQ4 and MQ5, the intelligent financial system will send the risk level to the company's senior management; provide the company with multi-level financial supervision and risk management methods; help to make comprehensive strategic decisions, enhance the company's overall response capabilities and achieve refined management, improve decision-making efficiency, and reduce losses caused by potential risks.

[0046] Visualize the analyzed results and classify the collected data according to the corresponding collection time and data unit, as well as the category at the time of collection; display the data feature change, trend, correlation, proportion and ratio, and substructure; distinguish the data according to category and data feature, and filter out applicable charts, such as trend display, data features: time series data, changes in variables over time; recommended charts: line chart or area chart.

[0047] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent financial management system based on data feature analysis, characterized in that: Includes data supervision module and feature analysis module; The data supervision module is used to import financial employee information into the financial system and obtain financial statement information, and to perform supervision and analysis on the financial statement information. The specific analysis process is as follows: The company seal is identified on the financial statements. If the company seal is not identified, a report seal exception instruction is directly generated. If the company seal is identified, the authenticity of the company seal is identified, the number and company name in the company seal are obtained, and they are compared one by one with the preset company name and seal number. If the two are inconsistent, a report seal exception instruction is generated; if the two are consistent, the seal outline of the company seal is extracted, compared with the preset outline, and the direction of the seal outline is adjusted to make it the same as the starting position of the preset outline, and the area where the seal outline does not overlap with the preset outline is calculated, and the numerical value of the area is marked as the authenticity value. When the authenticity value is greater than the preset authenticity threshold, a report seal exception instruction is generated. If normal, the data value in the financial statement is checked. Verification: if the verification result is inconsistent, a report data abnormality instruction is generated; if it is normal, the handwriting and name of the signer of the financial report are recognized, and the recognized name is matched with the preset name. If the corresponding preset name is not matched, a report signature abnormality instruction is generated; if the corresponding preset name is matched, the recognized handwriting is compared with the preset handwriting, and the handwriting matching value is calculated. When the handwriting matching value is greater than the set handwriting matching threshold, a report handwriting abnormality instruction is generated; when a report seal abnormality instruction or a report data abnormality instruction or a report signature abnormality instruction or a report handwriting abnormality instruction is generated, the preset abnormal prompt information corresponding to the instruction is directly matched and sent to the corresponding financial management personnel's intelligent terminal for review and re-proofreading; The feature analysis module is used to perform quality feature analysis on the data file to obtain the data file quality value and data source degree; when the data source degree is within the preset source threshold, the data file quality value is compared, otherwise the data file is deleted; When the quality value of the data file is within the preset quality threshold, the data file is retained, otherwise, the data file is deleted; The specific process of quality feature analysis of data files is as follows: Collect web page IPs and divide them according to page views and audience size, content type, and function and service; divide the web page IPs into websites according to page views and audience size and assign them values ​​A01, A02, and A03; divide the web page IPs into websites according to content type and assign them values ​​B01, B02, B03, and B04; divide the web page IPs into websites according to function and service and assign them values ​​C01, C02, and C03; substitute into the formula Output data source degree JZ, e is a natural number, a4, a5, a6 are preset weight factors, z=1, 2, 3, v=1, 2, 3, 4, m=1, 2, 3; By monitoring the clarity of the data file, identifying the uniformity of the text size in the data file, the anti-counterfeiting mark in the data file and the feedback of the data file, and then comparing the data files of the same type released in the same time interval, specifically: the file clarity is calculated by the ratio of the white pixels in a certain pixel interval of the data file to the pixels used for the data text under the preset standard white page. The file clarity qη is calculated by averaging the clarity of all intervals; the uniformity of the text size is obtained by presetting a uniform size pixel interval, collecting the number of texts in the pixel interval and the number of texts in another interval, and then performing standard deviation calculation to obtain the uniformity Tκ; if there is an anti-counterfeiting mark in the data file, λ is assigned a value of 1, and if there is no anti-counterfeiting mark, λ is assigned a value of 0. The feedback is obtained by evaluating and scoring the web page by netizens, identifying the evaluation content and judging the score. By identifying the number of occurrences of a preset phrase, the score value is divided into a value range. According to the number of occurrences of the phrase and the value range, when it belongs to the excellent evaluation and score, it is assigned a value of H1, when it belongs to the poor evaluation and score, it is assigned a value of H3, and when it belongs to the medium evaluation and score, it is assigned a value of H2; Substitute into the formula Output data file quality value ZL, e is a natural number, g1, g2, g3, g4 and g5 are preset weight factors, λ=1 or 0, μ=1, 2, 3.

2. The intelligent financial management system based on data feature analysis according to claim 1 is characterized in that: It also includes an intelligent data collection module, which is connected to various data sources, including company data sources and network data sources; and collects company data, network data industry reports, interval pixels, web page feedback, and data source IP addresses according to page views and audience size; company data includes financial employee information and financial information.

3. The intelligent financial management system based on data feature analysis according to claim 1 is characterized in that: The specific process of calculating the handwriting matching value is: The handwriting and the preset handwriting are divided into several stroke outlines according to the corresponding areas, and the stroke outlines of the two are subjected to stroke length recognition to obtain the stroke length value; the stroke outlines of the two are placed in parallel, and several points are drawn on the stroke outlines of the two, and the corresponding points on the stroke outlines of the two are connected to obtain the contour line, and the length of the contour line is calculated, and the variance of the lengths of all contour connections is calculated to obtain the line fluctuation value; the difference of the stroke length values ​​of the two is calculated to obtain the length difference, the length difference and the line fluctuation value are normalized and the processed values ​​are multiplied by the corresponding weight factors respectively, and then the sum is obtained to obtain the handwriting matching value.

4. The intelligent financial management system based on data feature analysis according to claim 1 is characterized in that: The feature analysis module is also used to perform security feature analysis on the data file. The specific analysis process is as follows: the preset error range is divided according to the size of the data file to obtain interval one, interval two, interval three and interval four; the corresponding network traffic is compared with the data corresponding to the collected data. When the network traffic is greater than the size of the corresponding data file, the difference between the network traffic and the size of the corresponding data file is calculated, and the difference is compared with interval one, interval two and interval three. If the difference belongs to interval four, the corresponding data file is deleted immediately; when the difference belongs to interval three, the data file is screened, and the interval comparison is performed again after the screening. When the corresponding difference is in interval one, the data file passes the security check. Otherwise, the corresponding data file is directly deleted; when the difference belongs to interval two, the data file passes the security check and the interval comparison is performed again after the screening. When the difference is in interval one, the data file passes the security check. Otherwise, the screening is performed again, and the interval comparison is performed after the screening. When the difference is in interval one, the data file passes the security check. Otherwise, the corresponding data file is directly deleted; when the difference belongs to interval one, the corresponding data file is directly retained.

5. The intelligent financial management system based on data feature analysis according to claim 1 is characterized in that: It also includes a risk analysis module, which is used to collect data company data and network data for analysis. The company data analysis includes data correlation analysis, income and expenditure analysis, cash flow analysis, trend and financial ratio analysis, and the network data analysis includes industry report analysis, financial risk analysis and visualization operation, and the financial risk value MQ is obtained by comprehensive calculation, and the preset interval and the obtained financial risk value MQ are divided into risk levels, and then the financial risk value MQ is tested to ensure the accuracy of the financial risk value MQ; corresponding operation commands are performed according to the risk level; Then visualize the analyzed results, classify the collected data, differentiate the data according to categories and data characteristics, and filter out applicable charts.

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