Multi-dimensional accounting data integration and analysis system

Abstract

The invention provides a multi-dimensional accounting data integration and analysis system, and belongs to the technical field of accounting information processing, the multi-dimensional accounting data integration and analysis system comprises a data acquisition system, a data source interface unit of the data acquisition system can be connected with a plurality of data sources, and an acquisition control unit manages acquisition; the data integration system comprises a preprocessing module (format conversion, data cleaning and fusion modules); the data analysis system is provided with an analysis dimension setting module, an algorithm execution module and a result visualization module; the data storage system comprises a storage architecture management and index module, solves the problems that a traditional method is difficult in data source integration and single in analysis dimension, can effectively integrate multi-dimensional accounting data, improves the comprehensive analysis capability, improves the data security and query performance, and visually presents an analysis result to facilitate decision making.

Description

Technical Field

[0001] The present invention belongs to the technical field of accounting information processing, and specifically relates to a multi-dimensional accounting data integration and analysis system. Background Art

[0002] With the increasing complexity and diversification of business activities, accounting data presents the characteristics of multi-source and heterogeneity. These data are scattered in various data sources such as various financial software databases, Excel format financial statements, XML format transaction data, etc. The data formats, structures and semantics of different data sources are very different, which brings great difficulties to data integration. Traditional data processing methods can only perform simple processing on a single data source or a limited number of data sources, and cannot effectively integrate multi-dimensional accounting data. At the same time, most existing analysis methods are limited to a single dimension and lack the ability to conduct comprehensive analysis of accounting data from multiple angles (such as time, organization, project, product, etc.), making it difficult to fully and accurately reflect the financial status and operating performance of the enterprise and unable to meet the increasingly complex decision-making needs of the enterprise;

[0003] In order to solve the above problems, the present invention provides a multi-dimensional accounting data integration and analysis system. The system can connect various types of data sources by designing a special data acquisition system, and use corresponding drivers and protocol parsing modules to ensure the safe collection of data. At the same time, the data preprocessing module and data fusion module in the data integration system can unify the format of the collected data, process outliers, and fuse the data based on semantic mapping and association rules to form a complete multi-dimensional accounting data set. In addition, the data analysis system has a flexible analysis dimension setting module and a rich analysis algorithm execution module, which can conduct in-depth analysis of data from multiple dimensions and intuitively present the analysis results through a result visualization module. Finally, the data storage system adopts a distributed storage architecture and an efficient data indexing mechanism to ensure data security and query performance, thereby comprehensively improving the company's ability to integrate and analyze accounting data and providing strong support for corporate decision-making. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-dimensional accounting data integration and analysis system, which aims to solve the problem that traditional data processing methods in the prior art can only process a single or a few data sources and cannot integrate multi-dimensional accounting data, and the existing analysis methods are limited to a single dimension, making it difficult to fully and accurately reflect the financial status and operating performance of the enterprise to meet decision-making needs.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A multi-dimensional accounting data integration and analysis system, characterized by comprising:

[0007] The data collection system includes: a data source interface unit for connecting various types of data sources, including but not limited to financial software databases, financial statements in Excel format, and transaction data in XML format, each interface being equipped with a corresponding driver and protocol parsing module to achieve secure data collection;

[0008] The data acquisition control unit is used to manage and coordinate the work of the data source interface unit according to the preset acquisition plan and rules, set the acquisition frequency, and monitor the acquisition process. When an abnormality occurs, it can promptly alarm and re-acquire;

[0009] A data integration system comprising:

[0010] Data preprocessing module, including:

[0011] The format conversion unit is used to unify the collected data in different formats, so that the date is formatted into the form of "YYYY-MM-DD", the currency amount is unified into the specified currency symbol and decimal places, and the text type data is encoded and converted;

[0012] Data cleaning unit, used to identify and process outliers, duplicate values ​​and missing values ​​in data through data validation algorithms and rules, where outliers are marked or corrected according to predefined accounting business logic, duplicate values ​​are deleted using data deduplication algorithms, and missing values ​​are filled in using appropriate methods based on data correlation analysis;

[0013] Data fusion module, which is used to fuse data from different data sources based on their internal connections using semantic mapping and association rule algorithms to form a complete multi-dimensional accounting data set;

[0014] A data analysis system comprising:

[0015] An analysis dimension setting module is used to provide a visual interface for users to flexibly set analysis dimensions, including but not limited to time dimension (year, quarter, month, day), organizational dimension (company, department, branch), project dimension, and product dimension;

[0016] Analysis algorithm execution module, including:

[0017] A set of financial ratio analysis algorithms, used to calculate solvency ratio, profitability ratio, and operating capacity ratio. The current ratio formula in solvency ratio calculation is: current ratio = current assets ÷ current liabilities, and the quick ratio formula is: quick ratio = (current assets - inventory) ÷ current liabilities; the gross profit margin formula in profitability ratio calculation is: gross profit margin = (sales revenue - sales cost) ÷ sales revenue × 100%, and the net profit margin formula is: net profit margin = net profit ÷ sales revenue × 100%; the accounts receivable turnover formula in operating capacity ratio calculation is: accounts receivable turnover = net credit sales revenue ÷ average accounts receivable balance, and the inventory turnover formula is: inventory turnover = sales cost ÷ average inventory balance;

[0018] Trend analysis algorithm, used to analyze the changing trends of financial data in different time dimensions;

[0019] Cost-effectiveness analysis algorithms, used to assess the cost-effectiveness of different projects or products;

[0020] Budget variance analysis algorithm, used to compare actual financial data with budget data to find out the differences and reasons;

[0021] The result visualization module is used to present the analysis results in an intuitive visualization form, including but not limited to bar charts, line charts, pie charts, radar charts and other chart forms and formatted financial analysis reports, and users can customize the visualization style and content;

[0022] A data storage system comprising:

[0023] The storage architecture management module is used to use a distributed storage architecture combined with data redundancy technology to store the integrated accounting data in multiple storage nodes, manage storage node allocation, data shard storage, and data synchronization between nodes, while ensuring data security;

[0024] The data index module is used to create an index structure based on the dimensional attributes and key indicators of the data, including establishing a time index for the time dimension and an organizational index for the department dimension, in order to improve the speed of data query and retrieval.

[0025] As a preferred solution of the present invention, the security authentication mechanism of the data source interface unit transmits data through an encrypted channel to ensure the security and integrity of the data collection process.

[0026] As a preferred solution of the present invention, in the data cleaning unit, the filling method for missing values ​​includes mean filling and filling based on model prediction.

[0027] As a preferred solution of the present invention, the visualization interface of the analysis dimension setting module supports multi-dimensional combination selection to meet the needs of different analysis scenarios.

[0028] As a preferred solution of the present invention, the storage architecture management module ensures data security through data encryption and access control mechanisms.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. In this solution, multiple interfaces are designed through the data source interface unit in the data collection system, which can connect various types of data sources, and the driver and protocol parsing modules are used to ensure safe collection. The format conversion unit in the data integration system unifies the data format, the data cleaning unit processes abnormal values, duplicate values ​​and missing values, and the data fusion module fuses data based on semantic mapping and association rule algorithms, and finally forms a complete multi-dimensional accounting data set, which solves the difficulty of data source integration;

[0031] The analysis dimension setting module of the data analysis system provides a visual interface, supporting users to freely select and combine multiple dimensions for analysis. The analysis algorithm execution module contains a variety of algorithms, such as a set of financial ratio analysis algorithms (debt-paying ability ratio, profitability ratio, operating ability ratio, etc.), trend analysis algorithms, cost-benefit analysis algorithms, and budget variance analysis algorithms, etc., which can conduct in-depth analysis of data from multiple dimensions, overcoming the problem of a single analysis dimension, thereby more comprehensively and accurately reflecting the company's financial status and operating performance and meeting the company's decision-making needs.

[0032] 2. In this solution, the data storage system adopts a distributed storage architecture combined with data redundancy technology to store data in multiple nodes, ensuring high data availability. At the same time, the storage architecture management module ensures data security through data encryption and access control mechanisms. The data index module creates an index structure based on dimension attributes and key indicators to improve query speed and ensure that the system can efficiently process large amounts of accounting data.

[0033] The result visualization module presents complex analysis results to users in an intuitive visualization form (bar charts, line charts, pie charts, radar charts and other charts as well as formatted financial analysis reports), and users can customize the visualization style and content, which enables managers at all levels of the enterprise to understand the analysis results more easily and improve decision-making efficiency. DETAILED DESCRIPTION

[0034] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0035] Example 1

[0036] The present invention provides the following technical solutions:

[0037] A multi-dimensional accounting data integration and analysis system, characterized by comprising:

[0038] The data collection system includes: a data source interface unit for connecting various types of data sources, including but not limited to financial software databases, financial statements in Excel format, and transaction data in XML format, each interface being equipped with a corresponding driver and protocol parsing module to achieve secure data collection;

[0039] The data acquisition control unit is used to manage and coordinate the work of the data source interface unit according to the preset acquisition plan and rules, set the acquisition frequency, and monitor the acquisition process. When an abnormality occurs, it can promptly alarm and re-acquire;

[0040] A data integration system comprising:

[0041] Data preprocessing module, including:

[0042] The format conversion unit is used to unify the collected data in different formats, so that the date is formatted into the form of "YYYY-MM-DD", the currency amount is unified into the specified currency symbol and decimal places, and the text type data is encoded and converted;

[0043] Data cleaning unit, used to identify and process outliers, duplicate values ​​and missing values ​​in data through data validation algorithms and rules, where outliers are marked or corrected according to predefined accounting business logic, duplicate values ​​are deleted using data deduplication algorithms, and missing values ​​are filled in using appropriate methods based on data correlation analysis;

[0044] Data fusion module, which is used to fuse data from different data sources based on their internal connections using semantic mapping and association rule algorithms to form a complete multi-dimensional accounting data set;

[0045] A data analysis system comprising:

[0046] An analysis dimension setting module is used to provide a visual interface for users to flexibly set analysis dimensions, including but not limited to time dimension (year, quarter, month, day), organizational dimension (company, department, branch), project dimension, and product dimension;

[0047] Analysis algorithm execution module, including:

[0048] A set of financial ratio analysis algorithms, used to calculate solvency ratio, profitability ratio, and operating capacity ratio. The current ratio formula in solvency ratio calculation is: current ratio = current assets ÷ current liabilities, and the quick ratio formula is: quick ratio = (current assets - inventory) ÷ current liabilities; the gross profit margin formula in profitability ratio calculation is: gross profit margin = (sales revenue - sales cost) ÷ sales revenue × 100%, and the net profit margin formula is: net profit margin = net profit ÷ sales revenue × 100%; the accounts receivable turnover formula in operating capacity ratio calculation is: accounts receivable turnover = net credit sales revenue ÷ average accounts receivable balance, and the inventory turnover formula is: inventory turnover = sales cost ÷ average inventory balance;

[0049] Trend analysis algorithm, used to analyze the changing trends of financial data in different time dimensions;

[0050] Cost-effectiveness analysis algorithms, used to assess the cost-effectiveness of different projects or products;

[0051] Budget variance analysis algorithm, used to compare actual financial data with budget data to find out the differences and reasons;

[0052] The result visualization module is used to present the analysis results in an intuitive visualization form, including but not limited to bar charts, line charts, pie charts, radar charts and other chart forms and formatted financial analysis reports, and users can customize the visualization style and content;

[0053] A data storage system comprising:

[0054] The storage architecture management module is used to use a distributed storage architecture combined with data redundancy technology to store the integrated accounting data in multiple storage nodes, manage storage node allocation, data shard storage, and data synchronization between nodes, while ensuring data security;

[0055] The data index module is used to create an index structure based on the dimensional attributes and key indicators of the data, including establishing a time index for the time dimension and an organizational index for the department dimension, in order to improve the speed of data query and retrieval.

[0056] In a specific embodiment of the present invention, the data acquisition system:

[0057] Data source interface unit: The accounting data of modern enterprises comes from a wide range of sources and of various types. In response to this situation, this unit has designed a variety of interfaces to adapt to different data sources. For example, financial software databases may use different database management systems such as SQL Server and Oracle. Each system has its own specific interface protocol. The driver and protocol parsing module of this unit can accurately handle these differences and ensure accurate data collection. For financial statements in Excel format, the interface unit can parse the table structure and identify the accounting data content therein. The XML format transaction data interface can process complex data structures based on the XML standard and extract accounting-related information from them. This multi-type interface design greatly improves the system's adaptability to the complex data source environment of the enterprise.

[0058] The security authentication mechanism for transmitting data through encrypted channels is the key to ensuring the security of corporate financial data. In the process of data collection, especially when obtaining data from data sources involving the core financial information of the enterprise, there may be risks of data leakage and malicious attacks. The encrypted channel uses advanced encryption algorithms (such as AES, SSL / TLS, etc.) to encrypt the transmitted data. The data can only be restored by using the corresponding decryption key at the receiving end, thereby ensuring the confidentiality and integrity of the data during the collection process and preventing the data from being stolen or tampered with during the transmission process.

[0059] Data collection control unit: The preset collection plan and rules are the core of realizing data collection automation and order. The accounting data of an enterprise has different update frequencies and collection requirements in different business scenarios. For example, for transaction data with high real-time requirements, it may be necessary to set it to real-time collection; while for some relatively stable financial statement data, it can be collected at fixed time intervals (such as daily or weekly). The collection control unit accurately controls the startup and data collection behavior of the data source interface unit based on these rules.

[0060] The monitoring function of the collection process is crucial to the stability of the system and the accuracy of the data. During the data collection process, various abnormal situations may occur, such as the interruption of the data source connection due to network failure, data transmission errors caused by temporary changes in the data format at the data source end, etc. When these abnormalities occur, the control unit can promptly issue an alarm to notify the system administrator or relevant personnel. At the same time, it can also automatically try to re-collect to ensure the integrity and timeliness of the data and reduce the impact of missing or incorrect data on subsequent analysis.

[0061] Data Integration System:

[0062] Data preprocessing module - format conversion unit: The accounting data formats generated by different data sources in an enterprise are often inconsistent. For example, business systems in different regions or departments may use different date representation methods, some are "MM / DD / YYYY" and some are "DD-MM-YYYY". The unified date format "YYYY-MM-DD" helps to make accurate time dimension comparisons and calculations in subsequent data processing and analysis. For monetary amounts, different business scenarios may involve different currency symbols (such as RMB ￥, US dollar $) and decimal places. The unified format can avoid calculation errors and confusion in data understanding caused by format differences. The encoding conversion of text-type data is similar, ensuring the consistency and standardization of data in the entire system, laying the foundation for subsequent data fusion and analysis.

[0063] Data preprocessing module - data cleaning unit: outliers, duplicate values ​​and missing values ​​in the data will seriously affect the quality of accounting data and the accuracy of analysis results. In the accounting field, outliers may be caused by data entry errors, special business situations or system failures. For example, the value of an asset suddenly appears far beyond the normal range, which may be due to a few extra zeros when the data is entered. It is necessary to mark or correct these outliers according to the predefined accounting business logic. Duplicate values ​​may be generated during data collection or merging. For example, when integrating multiple business reports of the same type, there may be duplicate records. The data deduplication algorithm can identify and delete these redundant records. Missing values ​​are more common in actual data, such as missing fields in some transaction records. Selecting appropriate filling methods through data correlation analysis, such as mean filling (for numerical data, if a certain expense data in a certain accounting period is missing, it can be filled with the mean of the expense in other similar periods) or model-based prediction filling (using historical data to establish a prediction model to estimate missing values), can ensure data integrity and improve data quality.

[0064] Data fusion module: In the business process of an enterprise, there are complex internal connections between data from different data sources. For example, the order information in the business transaction data is closely related to the revenue items in the financial statements, and the completion of the order directly affects the confirmation of revenue. Semantic mapping and association rule algorithms can deeply explore these relationships. Semantic mapping associates data representing the same or related concepts in different data sources by establishing semantic correspondences between data elements. Association rule algorithms mine frequent patterns and associations between different data items based on a large amount of data. For example, through analysis, it is found that there is a certain correlation between the sales of a certain type of product and the cost expenditure of a specific project. Using these technologies, the data fusion module can accurately integrate the data scattered in various data sources to form a comprehensive, multi-dimensional accounting data set, providing a complete data foundation for subsequent analysis.

[0065] Data analysis system:

[0066] Analysis Dimension Setting Module: The financial analysis of an enterprise needs to be conducted from multiple perspectives in order to fully understand the financial status and operating performance of the enterprise. The visual interface provided by the Analysis Dimension Setting Module provides users with a convenient operation method. Users can freely select different dimensions to combine according to specific analysis needs. For example, when analyzing the financial performance of a department of an enterprise in a specific project, you can select the organizational dimension (department) and the project dimension at the same time, and combine it with the time dimension (such as a specific quarter). This multi-dimensional combination can meet various complex analysis scenarios, making the analysis results more targeted and practical, and helping enterprise managers gain insight into financial information from different levels.

[0067] Analysis algorithm execution module: The set of financial ratio analysis algorithms is an important tool for evaluating the financial status of an enterprise. The solvency ratio reflects the ability of an enterprise to repay short-term and long-term debts. In the current ratio formula, current assets include assets that can be converted into cash in the short term, such as cash, accounts receivable, and inventory, while current liabilities are debts that need to be repaid in the short term. The higher the current ratio, the stronger the short-term solvency of the enterprise. The quick ratio deducts inventory, an asset that is relatively difficult to convert into cash quickly, from the current ratio, and more strictly measures the short-term solvency of the enterprise. The gross profit margin in the profitability ratio reflects the basic profitability of the enterprise's products or services. The ratio of sales revenue after deducting sales costs can reflect the profit margin of the enterprise without considering other expenses. The net profit margin further considers the ratio of net profit to sales revenue after all costs and expenses, and more comprehensively reflects the profitability of the enterprise. The accounts receivable turnover rate in the operating capacity ratio reflects the turnover speed of the enterprise's accounts receivable and reflects the efficiency of the enterprise's collection. The inventory turnover rate reflects the management level and sales ability of the enterprise's inventory. The faster the turnover speed, the higher the utilization efficiency of the inventory. These ratios conduct quantitative analysis of a company's financial status from different angles, providing important decision-making basis for corporate managers.

[0068] Trend analysis algorithms can help business managers observe the changing trends of financial data in different time dimensions. For example, by analyzing the changing trends of a company's sales in the past few years, future sales can be predicted, providing a reference for market strategy adjustments and resource allocation. Cost-benefit analysis algorithms can deeply evaluate the cost-effectiveness of different projects or products. For multiple projects carried out by the company at the same time, the cost input and benefits generated by each project can be compared to determine which projects are profitable and which projects may need to adjust their strategies or be stopped. The budget variance analysis algorithm is crucial to the company's budget management. By comparing actual financial data with budget data, it is possible to find out which aspects of expenditure or income are inconsistent with expectations, analyze the reasons for the differences, such as market changes, internal management issues, etc., and adjust the budget and business strategy in a timely manner.

[0069] Result visualization module: Presenting complex analysis results in an intuitive visual form is the key to improving decision-making efficiency. Bar charts are suitable for comparing financial indicators in different dimensions, such as comparing the expenses of different departments in the same period. Line charts can clearly show the trend of financial data, such as the trend of changes in corporate net profit in consecutive quarters. Pie charts can be used to analyze the proportion of each part, such as analyzing the proportion of various expenses in the total cost of an enterprise. Radar charts can comprehensively evaluate multi-dimensional indicators, such as visually displaying the comprehensive performance of an enterprise in multiple dimensions such as debt repayment ability, profitability, and operating ability. Formatted financial analysis reports elaborate on the analysis results and conclusions in text form to provide users with more comprehensive information. Users can customize visualization styles and content according to their own needs, such as adjusting the color, font, coordinate axis range, etc. of the chart to better meet personalized analysis and display needs.

[0070] Data Storage System:

[0071] Storage architecture management module: With the development of enterprise business and the continuous increase in accounting data volume, traditional centralized storage methods are facing storage capacity and performance bottlenecks. Distributed storage architecture stores data in multiple storage nodes, effectively solving this problem. Through data redundancy technology, even if a storage node fails, data can still be recovered from other redundant nodes, ensuring high data availability. In terms of storage node allocation, the system reasonably allocates data according to the characteristics and access frequency of the data. For example, frequently accessed recent accounting data is stored on nodes with higher performance to improve data access efficiency. Data sharding storage technology divides large accounting data sets into multiple smaller fragments and stores them on different nodes, further improving storage and query efficiency. At the same time, the storage architecture management module ensures data security through data encryption and access control mechanisms. Data encryption uses advanced encryption algorithms to encrypt data stored on nodes, and only authorized users can access the data through decryption keys. The access control mechanism strictly limits users' access rights to data, determines the scope of data they can access based on their roles and responsibilities, and prevents data leakage and illegal access.

[0072] Data index module: Creating an index structure based on the data's dimension attributes and key indicators is the key to improving data query and retrieval speed. In the time dimension, establishing a time index can quickly locate accounting data within a specific time period. For example, when a user needs to query the financial statement data of a certain year, the system can quickly retrieve the relevant data through the time index. Establishing an organizational index for the department dimension can quickly locate all data records related to a department when analyzing the financial data of a department. This index structure greatly improves the system's response speed when processing complex query requests, allowing users to quickly obtain the required accounting data for analysis and decision-making.

[0073] Specifically in this solution, the security authentication mechanism of the data source interface unit transmits data through an encrypted channel to ensure the security and integrity of the data collection process.

[0074] In this embodiment: when the data source interface unit establishes a connection with each data source, the SSL / TLS (Secure Sockets Layer / Transport Layer Security) protocol is used to build an encrypted channel. For example, when establishing a connection with a financial software database, the system first performs an SSL / TLS handshake with the database server to negotiate an encryption algorithm (such as the AES-256 encryption algorithm) and a key exchange method. During the data transmission process, each accounting data record obtained from the data source, whether it is a data row in a financial statement or a transaction record in a database, is encrypted into ciphertext at the sending end and then transmitted over the network. The receiving end (i.e., the data acquisition system) uses the corresponding decryption key to restore the ciphertext to the original data. At the same time, identity authentication will be performed between the interface unit and the data source, and the legitimacy of both parties will be ensured by means of digital certificates. For example, the data source server holds a server certificate issued by an authoritative certificate authority, and the data acquisition system verifies the certificate when connecting to prevent man-in-the-middle attacks, thereby ensuring the security and integrity of the data acquisition process.

[0075] Specifically in this solution, in the data cleaning unit, the methods for filling missing values ​​include mean filling and filling based on model prediction.

[0076] In this embodiment: Mean filling: Taking the expense data of an enterprise as an example, if there are missing values ​​in the office supplies expense data of a department during a certain accounting period. The system will first calculate the average value of the office supplies expenses of the department in the past several identical accounting periods (such as the past 5 quarters) based on historical data. Then, this average value is filled into the position of the currently missing expense data. For other similar numerical accounting data, such as water and electricity charges, travel expenses, etc., if missing data occurs, the mean filling method of the same type of data in the same department is also used.

[0077] Model-based prediction and filling: Assume that a company has product sales data, and the sales data of some products in a certain period of time is missing. The system can use historical sales data to establish a time series prediction model, such as ARIMA (autoregressive moving average model). By analyzing past sales trends, seasonal factors, etc., the model is used to predict and fill in the missing sales data. For some accounting data related to business volume, such as commission data accrued based on business volume, if it is missing, a regression model can be established based on the relationship between business volume and commission for prediction and filling, so as to improve the integrity and availability of the data.

[0078] Specifically in this solution, the visualization interface of the analysis dimension setting module supports multi-dimensional combination selection to meet the needs of different analysis scenarios.

[0079] In this embodiment: the visualization interface of the analysis dimension setting module is a web-based interactive interface. After the user logs in to the system and enters the data analysis page, he will see a dimension selection area. This area presents different dimension options in the form of a checkable list, including time dimension (year, quarter, month, day as independent options), organizational dimension (presented in the form of an enterprise organizational structure tree, users can expand and check the company, various departments, branches, etc.), project dimension (listing the names of ongoing and completed projects of the enterprise), product dimension (displaying the names of various products produced or sold by the enterprise). Users can freely check multiple dimensions for combination according to analysis needs. For example, if you want to analyze the sales performance of a new product in a specific project in various departments in the first half of this year, users can check "Product: [New Product Name]", "Project: [Specific Project Name]", "Time: First Half of 20XX (composed of 20XX selected by the 'Year' dimension and the first and second quarters selected by the 'Season' dimension)", "Organization: [Related Department Name]". The system extracts the corresponding data from the data storage system according to the dimension combination selected by the user, and performs targeted analysis in the subsequent analysis algorithm execution module to meet the needs of such complex analysis scenarios.

[0080] Specifically in this solution, the storage architecture management module ensures data security through data encryption and access control mechanisms.

[0081] In this embodiment: Data encryption: In the storage architecture management module, a symmetric encryption algorithm (such as AES-128) is used to encrypt the accounting data stored on the distributed storage nodes. Before the data is written to the storage node, the system uses a pre-configured encryption key to encrypt the data. For example, for financial statement data, transaction records, etc., their content is encrypted into ciphertext form for storage. Each storage node has a corresponding encryption module, and the data can only be decrypted using the same encryption key when the data is read and authorized. At the same time, the encryption key is updated regularly, and a secure key exchange mechanism is used during the update process to ensure the secure transmission and application of the new key.

[0082] Access control mechanism: The system has established a role-based access control (RBAC) model. First, define different user roles, such as financial manager, financial analyst, department head, etc. Each role is assigned different permissions. For example, the financial manager can access all the company's financial data, including sensitive cost data and budget data; the financial analyst can only access data related to the analysis, such as data that has been integrated and partially anonymized; and the department head can only access the financial data related to the department. When a user logs in to the system, the system dynamically assigns corresponding access rights based on the role to which his account belongs. When a user tries to access data in the storage system, the access control module verifies his permissions, and only users with matching permissions can obtain the data, thereby ensuring the security of the data.

[0083] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A multi-dimensional accounting data integration and analysis system, characterized in that: include: The data collection system includes: a data source interface unit for connecting various types of data sources, including but not limited to financial software databases, financial statements in Excel format, and transaction data in XML format, each interface being equipped with a corresponding driver and protocol parsing module to achieve secure data collection; The data acquisition control unit is used to manage and coordinate the work of the data source interface unit according to the preset acquisition plan and rules, set the acquisition frequency, and monitor the acquisition process. When an abnormality occurs, it can promptly alarm and re-acquire; A data integration system comprising: Data preprocessing module, including: The format conversion unit is used to unify the collected data in different formats, so that the date is formatted into the form of "YYYY-MM-DD", the currency amount is unified into the specified currency symbol and decimal places, and the text type data is encoded and converted; Data cleaning unit, used to identify and process outliers, duplicate values ​​and missing values ​​in data through data validation algorithms and rules, where outliers are marked or corrected according to predefined accounting business logic, duplicate values ​​are deleted using data deduplication algorithms, and missing values ​​are filled in using appropriate methods based on data correlation analysis; Data fusion module, which is used to fuse data from different data sources based on their internal connections using semantic mapping and association rule algorithms to form a complete multi-dimensional accounting data set; A data analysis system comprising: An analysis dimension setting module is used to provide a visual interface for users to flexibly set analysis dimensions, including but not limited to time dimension (year, quarter, month, day), organizational dimension (company, department, branch), project dimension, and product dimension; Analysis algorithm execution module, including: A set of financial ratio analysis algorithms, used to calculate solvency ratio, profitability ratio, and operating capacity ratio. The current ratio formula in solvency ratio calculation is: current ratio = current assets ÷ current liabilities, and the quick ratio formula is: quick ratio = (current assets - inventory) ÷ current liabilities; the gross profit margin formula in profitability ratio calculation is: gross profit margin = (sales revenue - sales cost) ÷ sales revenue × 100%, and the net profit margin formula is: net profit margin = net profit ÷ sales revenue × 100%; the accounts receivable turnover formula in operating capacity ratio calculation is: accounts receivable turnover = net credit sales revenue ÷ average accounts receivable balance, and the inventory turnover formula is: inventory turnover = sales cost ÷ average inventory balance; Trend analysis algorithm, used to analyze the changing trends of financial data in different time dimensions; Cost-effectiveness analysis algorithms, used to assess the cost-effectiveness of different projects or products; Budget variance analysis algorithm, used to compare actual financial data with budget data to find out the differences and reasons; The result visualization module is used to present the analysis results in an intuitive visualization form, including but not limited to bar charts, line charts, pie charts, radar charts and other chart forms and formatted financial analysis reports, and users can customize the visualization style and content; A data storage system comprising: The storage architecture management module is used to use a distributed storage architecture combined with data redundancy technology to store the integrated accounting data in multiple storage nodes, manage storage node allocation, data shard storage, and data synchronization between nodes, while ensuring data security; The data index module is used to create an index structure based on the dimensional attributes and key indicators of the data, including establishing a time index for the time dimension and an organizational index for the department dimension, in order to improve the speed of data query and retrieval.

2. A multi-dimensional accounting data integration and analysis system according to claim 1, characterized in that: The security authentication mechanism of the data source interface unit transmits data through an encrypted channel to ensure the security and integrity of the data collection process.

3. A multi-dimensional accounting data integration and analysis system according to claim 2, characterized in that: In the data cleaning unit, the methods for filling missing values ​​include mean filling and filling based on model prediction.

4. A multi-dimensional accounting data integration and analysis system according to claim 3, characterized in that: The visualization interface of the analysis dimension setting module supports multi-dimensional combination selection to meet the needs of different analysis scenarios.

5. A multi-dimensional accounting data integration and analysis system according to claim 4, characterized in that: The storage architecture management module ensures data security through data encryption and access control mechanisms.