Data processing system suitable for accounting financial management

Abstract

The invention discloses a data processing system suitable for accounting financial management, which relates to the technical field of financial data processing and is characterized in that accounting financial management demand tasks are acquired through communication connection with a user side, and real-time data streams are screened according to association matching information between the real-time data streams and the accounting financial management demand tasks, so that the real-time data streams are acquired; obtaining task first-level data; searching historical calling data of the accounting financial management demand task, and screening the historical calling data of the accounting financial management demand task according to timeliness and usability standards of the data to obtain task secondary data; calling task third-level data based on the degree of correlation with the task second-level data; and integrating the task first-level data, the task second-level data and the task third-level data to obtain a task demand data set, and then feeding back the task demand data set to the user side. The optimization of the data screening process is realized, the real-time data processing capability and the data analysis precision are improved, and the historical calling data are efficiently utilized.

Description

Technical Field

[0001] The present invention relates to the technical field of financial data processing, and in particular to a data processing system suitable for accounting and financial management. Background Art

[0002] With the rapid development of information technology and the popularization of the Internet, the amount of global data has shown explosive growth. The essence of big data is not just the simple integration of massive data, but also the in-depth analysis and mining of these data to reveal the laws and values ​​behind the data. In the field of accounting and finance, this trend is particularly evident, because the amount of data generated by financial activities is huge and complex, requiring an efficient data processing system to support it.

[0003] Traditional data processing systems are often unable to accurately obtain the required data according to the needs of specific accounting and financial management tasks, resulting in accounting professionals spending a lot of time and energy on data screening and sorting when processing data, affecting work efficiency and accuracy; when existing technologies process large amounts of data, the data screening process is usually cumbersome, which not only consumes human resources, but may also affect the accuracy and comprehensiveness of the analysis due to imperfect data filtering; for accounting and financial management tasks that require real-time data support, the real-time data processing capabilities of existing technologies sometimes cannot meet the requirements, resulting in the management being unable to obtain the latest, real-time data support when they need to make decisions quickly; in terms of data integration and feedback, existing technologies have problems with feedback delays or incomplete information integration, which makes it impossible for decision makers to obtain complete data feedback in a timely manner when they need comprehensive information support, affecting the timeliness and accuracy of decisions.

[0004] Therefore, in response to the above problems, there is an urgent need for a data processing system suitable for accounting and financial management. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a data processing system suitable for accounting and financial management, which solves the efficiency problems that traditional data processing systems in the field of accounting and financial management may encounter when processing large amounts of accounting and financial data, inaccurate analysis results due to incomplete or inaccurate data, and incomplete information and untimely feedback.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a data processing system suitable for accounting and financial management, including a task acquisition module, a real-time data screening module, a historical data screening module, a related data calling module, a data integration feedback module and a data storage and management module, wherein: the task acquisition module is used to communicate with the user end to obtain the accounting and financial management demand tasks of the user end; the real-time data screening module is used to call the real-time data stream stored in the data storage and management module, and according to the correlation matching information between the real-time data stream and the accounting and financial management demand tasks, the real-time data stream is screened to obtain the first-level task data; the historical data screening module is used to retrieve the accounting and financial management demand tasks; The historical call data of accounting and financial management demand tasks are screened according to the timeliness and availability of the historical call data to obtain the task level 2 data; the related data call module is used to call the task level 3 data based on the degree of relevance to the task level 2 data; the data integration feedback module is used to integrate the task level 1 data, the task level 2 data and the task level 3 data to obtain the task demand data set, and then feed back the task demand data set to the user end; the data storage and management module is used to store the task level 1 data, the task level 2 data and the task level 3 data called by each accounting and financial management demand task, as well as the real-time data stream and historical call data, and provide data retrieval and recovery backup functions.

[0007] Furthermore, the accounting and financial management demand tasks specifically include data analysis and reporting tasks, risk assessment and forecasting tasks, budget cost control tasks, tax planning tasks, business decision support tasks, and user credit assessment tasks.

[0008] Furthermore, the associated matching information specifically includes: data field matching, data type format matching, and business logic matching.

[0009] Furthermore, the specific analysis of the first-level data of the task obtained by screening the real-time data stream is as follows: obtaining the data fields, data type formats, and business logic rules required for the accounting and financial management demand tasks; cleaning the real-time data stream to remove invalid, duplicate, and abnormal data, and then converting and normalizing the data format; matching the data field of each data in the real-time data stream with the data field required for the accounting and financial management demand tasks to obtain the data field matching degree of each data in the real-time data stream, screening each data based on the comparison between the data field matching threshold of the accounting and financial management demand tasks and the data field matching degree of each data, and screening out data whose data field matching degree is less than the data field matching threshold; matching the type format of each data in the real-time data stream after screening out the data whose data field matching degree is less than the data field matching threshold with the data type format required for the accounting and financial management demand tasks Matching is performed to obtain the data type format matching degree of each data, and each data is screened based on the comparison between the data type format matching threshold of the accounting and financial management demand task and the data type format matching degree of each data, and the data with the data type format matching degree less than the data type format matching threshold is screened out; the business logic rules of each data in the real-time data stream after screening out the data with the data type format matching degree less than the data type format matching threshold are matched with the business logic rules required by the accounting and financial management demand task to obtain the business logic matching degree of each data, and each data is screened based on the comparison between the business logic matching threshold of the accounting and financial management demand task and the business logic matching degree of each data, and the data with the business logic matching degree less than the business logic matching threshold is screened out; the real-time data stream after finally screening out the data with the business logic matching degree less than the business logic matching threshold is marked as task-level data.

[0010] Furthermore, the historical call data of the accounting and financial management demand tasks are retrieved, and the historical call data of the accounting and financial management demand tasks are screened according to the timeliness and usability of the historical call data, and the specific analysis of the secondary data of the task is as follows: based on the data storage and management module, the historical call data of the accounting and financial management demand tasks are retrieved; the historical call data are sorted according to time, and then the historical call data falling within the time window is screened out according to the set time window; the usability information is further identified for the screened historical call data falling within the time window, and the usability information specifically includes data anomaly marking status, data security attack status, and data source credibility status; historical call data that does not have data anomaly marking status or data security attack status or data source credibility status is screened out and marked as task secondary data.

[0011] Furthermore, the setting of the time window also includes separate settings for different types of historical call data. The specific analysis of the separate settings for different types of historical call data is: obtaining characteristic information of the historical call data, the characteristic information specifically includes data update cycle, business cycle and data stability; taking values ​​for the data update cycle, business cycle and data stability respectively, and then summing and averaging the data update cycle, business cycle and data stability to obtain a characteristic evaluation value; dividing the time window into different sets according to the time length, and setting time window labels for the time windows of different sets, and the time window labels specifically include a first-level time window, a second-level time window and a third-level time window; dividing the characteristic evaluation values ​​into different sets according to the size, and setting characteristic evaluation value labels for the characteristic evaluation values ​​of different sets, and the characteristic evaluation value labels specifically include a first-level characteristic evaluation value, a second-level characteristic evaluation value and a third-level characteristic evaluation value; matching the historical call data corresponding to the first-level characteristic evaluation value with the first-level time window, matching the historical call data corresponding to the second-level characteristic evaluation value with the second-level time window, and matching the historical call data corresponding to the third-level characteristic evaluation value with the third-level time window.

[0012] Furthermore, the specific analysis of calling the task tertiary data based on the degree of correlation with the task secondary data is as follows: obtaining the correlation information between the remaining data excluding the task secondary data in the data storage and management module and each task secondary data, the correlation information specifically including: the absolute value of covariance and the number of co-occurrences; averaging the absolute values ​​of covariance between the remaining data and each task secondary data to obtain the mean absolute value of covariance between the remaining data and the task secondary data set; averaging the number of co-occurrences between the remaining data and each task secondary data to obtain the mean number of co-occurrences between the remaining data and the task secondary data set; obtaining the absolute value threshold of covariance and the threshold of co-occurrence for calling accounting and financial management data; respectively comparing the mean absolute value of covariance and the mean number of co-occurrences of each remaining data and the task secondary data set with the corresponding absolute value threshold of covariance and the threshold of co-occurrence, screening out the remaining data whose absolute value of covariance mean is greater than the absolute value threshold of covariance and whose mean number of co-occurrences is greater than the threshold of co-occurrence, and marking them as task tertiary data.

[0013] The present invention has the following beneficial effects:

[0014] The data processing system suitable for accounting and financial management can quickly match and filter out data that meets task requirements through real-time data screening and historical data screening modules, reduce the time of manual screening and processing, and improve the efficiency and response speed of data processing; the real-time data screening module can capture and process data in a timely manner according to the real-time data stream to ensure the real-time and accuracy of the first-level task data, and the historical data screening module can filter the historical data according to the timeliness and availability standards to ensure the quality and applicability of the second-level task data; the related data calling module calls the third-level task data according to the relevance of the second-level task data, further enriches the content and diversity of the task requirement data set, and improves the relevance and comprehensiveness of the data; the data integration feedback module integrates the first-level task data, the second-level task data and the third-level task data into the task requirement data set, and feeds back to the user end, which helps the user to make decision support and business analysis based on comprehensive and multi-level data, and improves the accuracy and effect of the decision; through efficient data processing and integrated feedback, it can provide faster, more accurate and more comprehensive data services, improve user experience and satisfaction, and enhance the user stickiness and application value of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a structural diagram of a data processing system suitable for accounting and financial management according to the present invention.

[0016] Figure 2 The present invention is a method flow chart of a data processing system suitable for accounting and financial management. DETAILED DESCRIPTION

[0017] The embodiment of the present application optimizes the data screening process, improves the real-time data processing capability and data analysis accuracy, and efficiently utilizes historical call data through a data processing system suitable for accounting and financial management.

[0018] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0019] Communicate directly with the user side to obtain specific accounting and financial management tasks, filter and process the data according to the correlation between the real-time data flow and the task requirements to obtain the primary data of the task, ensure that the system can respond immediately when the data is generated, and provide the latest information support; use the existing historical call data, and filter and process the data according to the timeliness and availability standards to generate the secondary data of the task; based on the secondary data of the task, make relevant data calls to obtain more in-depth tertiary data of the task, help the system understand and analyze the data from multiple angles, and provide users with a more accurate and comprehensive data perspective; integrate the obtained primary, secondary and tertiary data of the task into a complete task requirement data set, and feed it back to the user side to help users make accurate and well-founded decisions, improve management efficiency and decision-making quality.

[0020] See also Figure 1 , Figure 2 The embodiment of the present invention provides a technical solution: a data processing system suitable for accounting and financial management, including a task acquisition module, a real-time data screening module, a historical data screening module, a related data calling module, a data integration feedback module and a data storage and management module, wherein: the task acquisition module is used to communicate with the user end to obtain the accounting and financial management demand tasks of the user end; the real-time data screening module is used to call the real-time data stream stored in the data storage and management module, and screen the real-time data stream according to the correlation matching information between the real-time data stream and the accounting and financial management demand tasks to obtain the first-level task data; the historical data screening module is used to retrieve the accounting and financial management demand tasks The historical call data of the accounting and financial management demand tasks are screened according to the timeliness and availability of the historical call data to obtain the second-level task data; the related data call module is used to call the third-level task data based on the degree of relevance to the second-level task data; the data integration feedback module is used to integrate the first-level task data, the second-level task data and the third-level task data to obtain the task demand data set, and then feed back the task demand data set to the user end; the data storage and management module is used to store the first-level task data, the second-level task data and the third-level task data called by each accounting and financial management demand task, as well as the real-time data stream and historical call data, and provide data retrieval and recovery backup functions.

[0021] Specifically, the accounting and financial management demand tasks include data analysis and reporting tasks, risk assessment and forecasting tasks, budget cost control tasks, tax planning tasks, business decision support tasks, and user credit assessment tasks.

[0022] In this implementation plan, the field of accounting and financial management includes a variety of complex and diverse tasks. There are significant differences in the type of data, data volume and data structure processed by different tasks. For example, the data analysis report task focuses on the summary and visualization of historical financial data; while the risk assessment and prediction task requires the use of complex methods such as time series analysis and Monte Carlo simulation to predict future trends and assess potential risks. Therefore, depending on the task, it is necessary to select appropriate targeted data for analysis and processing.

[0023] Specifically, the associated matching information includes: data field matching, data type format matching, and business logic matching.

[0024] The specific analysis of the first-level data of the task is obtained by screening the real-time data stream: obtaining the data fields, data type formats, and business logic rules required for the accounting and financial management demand tasks; cleaning the real-time data stream to remove invalid, duplicate, and abnormal data, and then converting and normalizing the data format; matching the data field of each data in the real-time data stream with the data field required for the accounting and financial management demand tasks to obtain the data field matching degree of each data in the real-time data stream, and screening each data based on the comparison between the data field matching threshold of the accounting and financial management demand tasks and the data field matching degree of each data, and screening out the data with a data field matching degree less than the data field matching threshold; matching the type format of each data in the real-time data stream after screening out the data with a data field matching degree less than the data field matching threshold with the data type format required for the accounting and financial management demand tasks. , obtain the data type format matching degree of each data, screen each data based on the comparison between the data type format matching threshold of the accounting and financial management demand task and the data type format matching degree of each data, and screen out the data with a data type format matching degree less than the data type format matching threshold; match the business logic rules of each data in the real-time data stream after screening out the data with a data type format matching degree less than the data type format matching threshold with the business logic rules required by the accounting and financial management demand task, and obtain the business logic matching degree of each data, screen each data based on the comparison between the business logic matching threshold of the accounting and financial management demand task and the business logic matching degree of each data, and screen out the data with a business logic matching degree less than the business logic matching threshold; mark the real-time data stream after finally screening out the data with a business logic matching degree less than the business logic matching threshold as task-level data.

[0025] In this implementation plan, the data fields required by the accounting and financial management demand tasks represent specific data items or specific indicators, such as amount, date, and account number; the specific method for obtaining the data field matching degree is analyzed as follows: considering the partial matching between data fields, use word frequency statistics or feature vector-based similarity calculation methods, such as cosine similarity and Jaccard similarity, to evaluate the similarity between data fields, or set different weights for different data fields, and then calculate the weighted matching degree. The specific example of obtaining the data field matching degree is as follows: Now there is a real-time data stream containing the following fields: transaction number, account number, transaction date, transaction amount, transaction type, and transaction status. Assuming that the data fields that need to be paid attention to for a specific accounting demand task are: account number, transaction date, and transaction amount, for each data record in the real-time data stream, use the feature vector similarity calculation method, such as cosine similarity: account number of the data record = "12345", transaction date = "2024-07-31", transaction amount = 100.00, similarity between account number and required account number = 1 (complete match), similarity between transaction date and required transaction date is calculated based on date similarity, similarity between transaction amount and required transaction amount is calculated based on amount value, and finally overall matching is calculated based on weight; if account number is very important for required task, while transaction date and transaction amount are secondary, higher weight can be given to account number, account number of data record = "12345", transaction date = "2024-07-31", transaction amount = 100.00, then account number matching = 1 (complete match) * 0.6 (weight), transaction date matching = 1 (complete match) * 0.2 (weight), transaction amount matching = 1 (complete match) * 0.2 (weight), total matching = (0.6 + 0.2 + 0.2) = 1 (complete match).

[0026] The data type formats required by accounting and financial management tasks include string types and data fields with specific format requirements. For example, the account number is represented as a string type with a fixed length of numeric characters, the transaction date is represented as a date type with a format of YYYY-MM-DD, and the transaction amount is represented as a numeric type with a precision requirement of two decimal places. The specific method for obtaining the data type format matching degree is as follows: for each data record in the real-time data stream, the actual data type format is obtained through the information of the data source. For each data field, the actual data type is compared with the required data type to see if it matches. If the actual data type is exactly the same as the required data type, the data type matching degree is 1. If the actual data type is partially similar to the required data type, fuzzy matching methods such as text similarity algorithms or pattern matching can be considered to evaluate the matching degree. For data fields involving specific format requirements (such as date, amount, etc.), it is necessary to ensure that the format of the actual data is consistent with the required format, and check whether the actual date format meets the format requirements of YYYY-MM-DD. For the amount type, it is necessary to check whether the actual value is in a format with a fixed number of decimal places. According to the specific requirements, the weight is set to comprehensively consider the data type matching degree and the data format matching degree to obtain a comprehensive matching degree. An example of obtaining the data type format matching degree is as follows: Assume that the accounting and financial management demand task requires the following data type format: Account number: string type, length is 6 digits, transaction date: format is YYYY-MM-DD, transaction amount: numeric type, accurate to two decimal places, for data records in the real-time data stream, data record 1: account number = "123456", transaction date = "2024-07-31", transaction amount = 100.00, data record 2: account number = "987654", transaction date = "2024 / 08 / 01", transaction amount = 150.005, for the above data, The account number type matching degree of data record 1 is 1 (complete match, length meets the requirements), the transaction date format matching degree is 1 (complete match, format meets YYYY-MM-DD), the transaction amount format matching degree is 1 (complete match, accurate to two decimal places), and the overall matching degree is (1+1+1) / 3=1 (complete match). The account number type matching degree of data record 2 is 0 (length does not meet the requirements), the transaction date format matching degree is 0 (format does not meet the requirements), and the transaction amount format matching degree is 0.5 (format is not accurate, extra decimal places), so the overall matching degree is (0+0+0.5) / 3=0.17.

[0027] The business logic rules required for accounting and financial management tasks refer to the logical conditions or algorithms that need to be followed when processing, calculating, verifying or analyzing data. For example, for transaction verification rules, check the validity of each transaction, including whether the amount is within a reasonable range, whether the account exists, etc.; for account balance calculation rules, update the account balance according to the transaction record to ensure the correctness and consistency of the balance; for report generation rules, generate financial statements based on specific account data to ensure the accuracy and completeness of the statements; the specific way to obtain the business logic matching degree is analyzed as follows: for each data record in the real-time data stream, obtain the business logic rules for its actual application through the business logic definition of the system, and specifically convert the business logic rules defined in the accounting and financial management tasks into computable logical conditions or algorithms. For each data record in the real-time data stream, determine the business logic rules for its actual application, involving the data processing process, the business logic definition of the system or the real-time analysis of the data stream, and compare the actual application business logic of each data record. The rules are evaluated for their matching degree with the business logic rules defined in the requirement task; a specific example of obtaining the business logic matching degree is as follows: assuming that the accounting and financial management requirement task requires the following business logic rules: transaction verification rules, used to check that the transaction amount must not exceed the account balance, account balance calculation rules, used to update the account balance according to the transaction record; report generation rules, used to generate financial reports based on specific accounting data, the business logic rules applied to the data records in the existing real-time data stream are: data record 1 applies the transaction verification rules and account balance calculation rules, data record 2 applies the transaction verification rules and report generation rules, then the matching degree is calculated as: data record 1 completely matches the transaction verification rules in the requirement task, and partially matches the account balance calculation rules in the requirement task, then the business logic matching degree is: (1+partial matching degree) / total number of rules, data record 2 completely matches the transaction verification rules in the requirement task, and partially matches the report generation rules in the requirement task, then the business logic matching degree is: (1+partial matching degree) / total number of rules.

[0028] Data field matching threshold, data type format matching threshold and business logic matching threshold are used to control the precision and accuracy of data processing. The specific data field matching threshold indicates the minimum matching degree that must be achieved between two data fields when matching data fields. It determines whether a data field is considered to meet the standards of a specific demand task when filtering and processing data in a real-time data stream. By specifying a specific value, such as 0.8, it means that only when the data field matching degree reaches more than 80% is it considered a match. Or, according to the complexity of specific demand tasks and the diversity of data, the matching threshold is dynamically adjusted to ensure the adaptability and flexibility of the system in different situations. The data type format matching threshold indicates the allowable difference between the actual data and the data required for the demand task when checking the data type format. It determines whether the data field is considered to meet the standards of a specific demand task during data processing. Whether it complies with the specified format and type requirements, by specifying the precise data type format requirements, such as the date must be in the YYYY-MM-DD format, the amount must be accurate to two decimal places, or a certain degree of difference is allowed, such as the date format can be allowed to change within a certain date range, and the decimal places of the amount can be set with a slight deviation; the business logic matching threshold indicates the matching degree requirement between the business logic actually applied and the business logic defined by the required task when executing the business logic rule, which affects the strictness of the system on the data processing logic in real-time data processing, ensuring that each data record fully complies with all business logic rules to ensure the accuracy and consistency of data processing, and also allows the application of some business logic rules, such as partial mismatch of some rules can be tolerated in certain circumstances to improve the flexibility and response speed of the system. The specific data field matching threshold, data type format matching threshold, and business logic matching threshold are set and adjusted by the system administrator or data analyst according to actual needs and business scenarios. The setting method depends on the system configuration interface or configuration file, and can also be hard-coded in the code of the data processing system by programming. When setting, the characteristics of the data, the complexity of the processing requirements, and the balance of system performance are comprehensively considered to achieve the best data processing effect and user experience.

[0029] Specifically, the historical call data of the accounting and financial management demand tasks are retrieved, and the historical call data of the accounting and financial management demand tasks are screened according to the timeliness and usability of the historical call data, and the specific analysis of the task secondary data is as follows: based on the data storage and management module, the historical call data of the accounting and financial management demand tasks are retrieved; the historical call data are sorted according to time, and then the historical call data falling within the time window is screened out according to the set time window; the usability information is further identified for the screened historical call data falling within the time window, and the usability information specifically includes data anomaly marking status, data security attack status, and data source credibility status; historical call data that does not have data anomaly marking status or data security attack status or data source credibility status is screened out and marked as task secondary data.

[0030] In this implementation scheme, the data anomaly marking status refers to the anomalies or errors that may exist in the data itself, such as unexpected values, format errors, and missing values, which are identified through data verification and anomaly detection algorithms, such as statistical analysis, data rule verification, and anomaly detection models; the data security attack status refers to the unauthorized access, tampering, or other security threats that the data may be subject to, and possible security attack conditions are detected and identified through security log analysis, intrusion detection system (IDS), access control log, encryption, and digital signature technologies; the data source trust status refers to the legitimacy and credibility of the data source, ensuring that the data is generated or provided by authorized and trusted sources, and confirming the credibility of the data source through digital certificate verification, data source identity authentication, data source history review, and other means.

[0031] A specific example of obtaining task secondary data is as follows: suppose there is an accounting and financial management system that needs to process bank transaction data. The historically called bank transaction data is retrieved from the data storage and management module, and the historical call data is sorted in chronological order. Then, the data falling within the time window is filtered out according to the set time window. Data quality checks and anomaly detection are performed on the filtered historical call data to identify whether there are data anomaly marking conditions, such as numerical anomalies, missing values, etc., and data security logs and security monitoring information are analyzed to detect whether any historical call data has been accessed or tampered with without authorization. Based on the identity authentication of the data source, historical credible records, etc., it is confirmed whether the data source of the historical call data is credible. The historical call data that passes the data quality, security and credibility checks are marked as data that meets the requirements of the task secondary data and can be safely used for accounting and financial management tasks.

[0032] By screening and analyzing historical call data, we can ensure that the data used in processing accounting and financial management tasks is of high quality and good security, thereby improving the reliability and stability of the system; by screening out historical call data that does not have data anomalies and security attacks, we can ensure the accuracy and completeness of the data during the processing process and avoid errors and anomalies caused by data quality problems; by identifying data security attacks, we can ensure that the historical call data used is not subject to unauthorized access or tampering, thereby improving the system's ability to protect data security; confirm the credibility of the data source, ensure the legitimacy and authenticity of the data used, and avoid using data from unreliable sources, thereby ensuring the legitimacy and transparency of data processing.

[0033] Specifically, the setting of the time window also includes separate settings for different types of historical call data. The specific analysis of the separate settings for different types of historical call data is: obtaining characteristic information of historical call data, the characteristic information specifically includes data update cycle, business cycle and data stability; taking values ​​for the data update cycle, business cycle and data stability respectively, and then summing and averaging the data update cycle, business cycle and data stability to obtain a characteristic evaluation value; dividing the time window into different sets according to the time length, and setting time window labels for the time windows of different sets, the time window labels specifically include first-level time windows, second-level time windows and third-level time windows; dividing the characteristic evaluation values ​​into different sets according to the size, and setting characteristic evaluation value labels for the characteristic evaluation values ​​of different sets, the characteristic evaluation value labels specifically include first-level characteristic evaluation values, second-level characteristic evaluation values ​​and third-level characteristic evaluation values; matching the historical call data corresponding to the first-level characteristic evaluation value with the first-level time window, matching the historical call data corresponding to the second-level characteristic evaluation value with the second-level time window, and matching the historical call data corresponding to the third-level characteristic evaluation value with the third-level time window.

[0034] In this implementation scheme, the data update cycle is obtained by calculating the time interval between each data point or the mean, standard deviation and other characteristics of the statistical time interval; the business cycle is obtained by using Fourier transform or other frequency analysis methods to identify the main business periodic characteristics; data stability is obtained by calculating the volatility of the data, such as standard deviation, root mean square and other indicators; the feature evaluation value is obtained by standardizing or normalizing each feature to ensure that different features have the same weight in the evaluation value calculation, and according to the logical relationship that the data update cycle is proportional to the feature evaluation value, the business cycle is proportional to the feature evaluation value, and the data stability is proportional to the feature evaluation value, the data update cycle, the business cycle and the data stability are summed and averaged to obtain the feature evaluation value. The specific feature evaluation value calculation formula is: Where T represents the feature evaluation value, ε 1Indicates the data update period, ε 2 represents the business cycle, ε 3 Indicates data stability. The longer the data update cycle, the longer the business cycle, and the more stable the data, the higher the feature evaluation value. According to the feature evaluation value, the time window is divided into different sets, such as short-term, medium-term, and long-term, to reflect different time spans and data update frequencies.

[0035] An example of a specific time window setting is: assuming that the historical call data of an accounting and financial management demand task includes transaction data, financial statement data, and market data, the characteristic information of these data is obtained, including data update cycle, business cycle, and data stability. The update cycle of specific transaction data, such as stock transaction data, is usually updated daily or even minute-level; the update cycle of financial statement data, such as company financial statements, is usually updated quarterly; the update cycle of market data, such as market indexes, is usually updated daily; the business cycle of transaction data is highly related to daily trading and market opening times; the business cycle of financial statement data is specifically related to quarterly reporting time and annual audit time; the business cycle of market data is specifically related to market fluctuation cycle and economic report release cycle; the data stability of transaction data is relatively stable, but may fluctuate violently during major economic events; the data stability of financial statement data is stable, but fluctuates greatly before and after the financial report release period; the data stability of market data is generally stable, but fluctuates significantly during market fluctuations or policy changes. The data update cycle, business cycle and data stability are respectively taken values, and each feature is quantified: the update cycle of transaction data is 1 day, the business cycle is 1 day, and the data stability is 0.7 (high stability); the update cycle of financial statement data is 90 days, the business cycle is 90 days, and the data stability is 0.9 (very high stability); the update cycle of market data is 1 day, the business cycle is 30 days, and the data stability is 0.8 (high stability). The feature evaluation values ​​are: 0.9, 60.3, and 10.6 respectively. Assume that the time windows are divided into the following types: the first-level time window is short-term (1 -7 days), the secondary time window is medium-term (7-30 days), and the tertiary time window is long-term (more than 30 days). The feature evaluation values ​​are divided according to size: the primary feature evaluation value is low (0-10), the secondary feature evaluation value is medium (10-50), and the tertiary feature evaluation value is high (more than 50), and then the feature evaluation value and time window are matched: the transaction data feature evaluation value is 0.9, which corresponds to the primary time window (short-term), the financial statement data feature evaluation value is 60.3, which corresponds to the tertiary time window (long-term); the market data feature evaluation value is 10.6, which corresponds to the secondary time window (medium-term).

[0036] Based on the characteristic information of historical data, different types of data can be accurately allocated to appropriate time windows to improve the effectiveness of promotional strategies. By automatically processing historical data and characteristic evaluation values, human intervention can be reduced and decision-making efficiency can be improved. Based on the different characteristics of commodities, personalized promotional strategies can be customized to enhance market competitiveness. Effective use of the characteristic information of historical call data can provide a scientific basis for the setting of time windows, thereby optimizing business operations and marketing strategies.

[0037] Specifically, the specific analysis of calling the task third-level data based on the degree of correlation with the task second-level data is as follows: obtain the correlation information between the remaining data excluding the task second-level data in the data storage and management module and each task second-level data, and the correlation information specifically includes: covariance absolute value and co-occurrence number; take the average of the covariance absolute value between the remaining data and each task second-level data, and obtain the covariance absolute value mean of the remaining data and the task second-level data set; take the average of the co-occurrence number between the remaining data and each task second-level data, and obtain the co-occurrence number mean of the remaining data and the task second-level data set; obtain the covariance absolute value threshold and co-occurrence number threshold for accounting and financial management data calls; respectively compare the covariance absolute value mean and co-occurrence number mean of each remaining data and task second-level data set with the corresponding covariance absolute value threshold and co-occurrence number threshold, and screen out the remaining data whose covariance absolute value mean is greater than the covariance absolute value threshold and whose co-occurrence number mean is greater than the co-occurrence number threshold and are marked as task third-level data.

[0038] In this implementation, the covariance measures the overall error of two random variables. The absolute value of the covariance indicates the strength and direction of the linear relationship between the two variables. Specifically, the covariance between each data is obtained by mathematically calculating the historical data, and then its absolute value is taken. The number of co-occurrences indicates the number of times two variables appear at the same time under the same conditions. For each data in the data set, the number of times they appear at the same time in the same time window or event can be counted to obtain the number of co-occurrences.

[0039] The absolute value threshold of covariance for accounting and financial management data calls indicates that when the mean absolute value of covariance is higher than this threshold, the two data are considered to have a strong linear relationship. The co-occurrence threshold indicates that when the mean co-occurrence is higher than this threshold, the two data are considered to have a high correlation. The setting of the absolute value threshold of covariance and the co-occurrence threshold for accounting and financial management data calls is adjusted and optimized based on the specific business and data characteristics, and the optimal threshold setting can be determined through experiments and statistical analysis.

[0040] A specific example of obtaining task-level 3 data is as follows: assuming that task-level 2 data is the daily transaction amount data of a bank account, perform covariance calculation and co-occurrence statistics on the daily transaction amount data of task-level 2 data with all other data except task-level 2 data. Assuming that the set covariance absolute value threshold is 0.7 and the co-occurrence number threshold is 50, find out among all the data the data whose covariance absolute value mean with the daily transaction amount data is greater than 0.7 and whose co-occurrence number mean is greater than 50 as task-level 3 data. For example, through the above steps, it is found that the customer's monthly deposit data has a higher covariance absolute value and co-occurrence number mean with the daily transaction amount data. Therefore, the customer's monthly deposit data is marked as task-level 3 data, ensuring that the task-level 3 data has a high correlation with the task-level 2 data, thereby improving the accuracy and efficiency of data processing.

[0041] By analyzing the degree of correlation with the task's second-level data, the highly correlated task's third-level data is screened out, thereby improving the accuracy and efficiency of data processing: ensuring that the task's third-level data has a high correlation with the task's second-level data, making subsequent data analysis and processing more accurate and effective; by excluding data with low correlation with the task's second-level data, system resources and processing time are reduced, and the data processing process is optimized.

[0042] In summary, this application has at least the following effects:

[0043] Through the task acquisition module, it is possible to communicate with the user end and obtain specific accounting and financial management tasks, ensuring that data can be processed according to actual needs rather than processing all data in a general way; the real-time data screening module can instantly screen and process data according to the correlation and matching information between the real-time data stream and the task requirements to obtain the primary data of the task, which can ensure that the system can respond immediately when needed and provide timely updated data; the historical data screening module can screen and process historical data according to the timeliness and availability standards of historical call data to obtain the secondary data of the task, which can utilize existing data resources and improve data reuse and efficiency; the related data call module obtains the tertiary data of the task by analyzing and calling the secondary data of the task according to the degree of relevance, which can help the system analyze the data more deeply and provide more accurate and comprehensive data support; the data integration feedback module integrates the primary, secondary and tertiary data of the task into a complete task requirement data set and feeds it back to the user end, which can help users make more accurate and well-founded decisions and improve management efficiency and decision-making quality.

[0044] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0045] The present invention is described with reference to the block diagram of the system according to the embodiment of the present invention. It should be understood that the combination of each structure in the block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate a device for implementing the functions specified in each structure in the block diagram.

[0046] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in each structure of the structural diagram.

[0047] These computer program instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in each structure in the structural diagram.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A data processing system suitable for accounting and financial management, characterized in that: It includes task acquisition module, real-time data screening module, historical data screening module, related data call module, data integration feedback module and data storage and management module, among which: The task acquisition module is used to communicate with the user end and obtain the accounting and financial management tasks required by the user end; The real-time data screening module is used to call the real-time data stream stored in the data storage and management module, and screen the real-time data stream according to the correlation matching information between the real-time data stream and the accounting and financial management demand tasks to obtain the first-level task data; The historical data screening module is used to retrieve the historical call data of the accounting and financial management demand tasks, and screen the historical call data of the accounting and financial management demand tasks according to the timeliness and availability of the historical call data to obtain the secondary data of the task; A related data calling module is used to call the task level 3 data based on the degree of relevance to the task level 2 data; The data integration and feedback module is used to integrate the first-level data, the second-level data and the third-level data of the task to obtain the task requirement data set, and then feed back the task requirement data set to the user end; The data storage and management module is used to store the first-level task data, second-level task data and third-level task data called by various accounting and financial management demand tasks, as well as real-time data streams and historical call data, and provide data retrieval and recovery backup functions.

2. A data processing system suitable for accounting and financial management according to claim 1, characterized in that: The accounting and financial management demand tasks specifically include data analysis and reporting tasks, risk assessment and forecasting tasks, budget cost control tasks, tax planning tasks, business decision support tasks, and user credit assessment tasks.

3. A data processing system suitable for accounting and financial management according to claim 1, characterized in that: The associated matching information specifically includes: data field matching, data type format matching, and business logic matching.

4. A data processing system suitable for accounting and financial management according to claim 3, characterized in that: The specific analysis of the task-level data obtained by screening the real-time data stream is as follows: Obtain the data fields, data type formats, and business logic rules required for accounting and financial management tasks; Clean the real-time data stream, remove invalid, duplicate and abnormal data, and then convert and normalize the data format; Match the data field of each data in the real-time data stream with the data field required by the accounting and financial management demand task to obtain the data field matching degree of each data in the real-time data stream, and screen each data based on the comparison between the data field matching threshold of the accounting and financial management demand task and the data field matching degree of each data, and screen out the data whose data field matching degree is less than the data field matching threshold; Match the type format of each data in the real-time data stream after filtering out the data whose data field matching degree is less than the data field matching threshold with the data type format required by the accounting and financial management demand task to obtain the data type format matching degree of each data; filter each data based on the comparison between the data type format matching threshold of the accounting and financial management demand task and the data type format matching degree of each data, and filter out the data whose data type format matching degree is less than the data type format matching threshold; After filtering out data with a data type format matching degree less than a data type format matching threshold, the business logic rules of each data in the real-time data stream are matched with the business logic rules required by the accounting and financial management demand task to obtain the business logic matching degree of each data; based on the comparison between the business logic matching threshold of the accounting and financial management demand task and the business logic matching degree of each data, each data is filtered to filter out data with a business logic matching degree less than the business logic matching threshold; The real-time data stream after finally filtering out the data whose business logic matching degree is less than the business logic matching threshold is marked as task-level data.

5. A data processing system suitable for accounting and financial management according to claim 1, characterized in that: Retrieve the historical call data of accounting and financial management demand tasks, filter the historical call data of accounting and financial management demand tasks according to the timeliness and availability of the historical call data, and obtain the specific analysis of the secondary data of the task as follows: Retrieve historical call data of accounting and financial management demand tasks based on the data storage and management module; Sort the historical call data by time, and then filter out the historical call data that falls within the time window according to the set time window; Further identify availability information for the filtered historical call data that falls within the time window. The availability information specifically includes data anomaly marking status, data security attack status, and data source credibility status; Filter out historical call data that does not have data anomaly marking conditions, data security attack conditions, or data source trustworthy conditions, and mark them as task secondary data.

6. A data processing system suitable for accounting and financial management according to claim 5, characterized in that: The time window setting also includes separate settings for different types of historical call data. The specific analysis of separate settings for different types of historical call data is as follows: Obtain characteristic information of historical call data, including data update cycle, business cycle and data stability; The data update cycle, business cycle and data stability are respectively taken values, and then the data update cycle, business cycle and data stability are summed and averaged to calculate the characteristic evaluation value; Divide the time windows into different sets according to their time lengths, and set time window labels for the time windows in different sets. The time window labels specifically include primary time windows, secondary time windows, and tertiary time windows. The feature evaluation values ​​are divided into different sets according to their sizes, and feature evaluation value labels are set for the feature evaluation values ​​of different sets, wherein the feature evaluation value labels specifically include a primary feature evaluation value, a secondary feature evaluation value, and a tertiary feature evaluation value; Match the historical call data corresponding to the first-level feature evaluation value with the first-level time window, match the historical call data corresponding to the second-level feature evaluation value with the second-level time window, and match the historical call data corresponding to the third-level feature evaluation value with the third-level time window.

7. A data processing system suitable for accounting and financial management according to claim 1, characterized in that: The specific analysis of calling the task level 3 data based on the degree of relevance to the task level 2 data is as follows: Obtaining correlation information between the remaining data excluding the task secondary data in the data storage and management module and the secondary data of each task, wherein the correlation information specifically includes: the absolute value of the covariance and the number of co-occurrences; The absolute value of the covariance between the remaining data and the secondary data of each task is averaged to obtain the absolute value mean of the covariance between the remaining data and the secondary data set of the task; The average number of co-occurrences between the remaining data and the secondary data of each task is taken to obtain the mean number of co-occurrences between the remaining data and the secondary data set of the task; Get the absolute value threshold of covariance and the number of co-occurrences for accounting and financial management data calls; The covariance absolute value mean and co-occurrence number mean of each remaining data and the task secondary data set are compared with the corresponding covariance absolute value threshold and co-occurrence number threshold, and the remaining data with the covariance absolute value mean greater than the covariance absolute value threshold and the co-occurrence number mean greater than the co-occurrence number threshold are screened out and marked as task level 3 data.

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