Financial statement data comprehensive management method and system based on big data analysis

Abstract

The invention relates to the technical field of computers, and provides a financial statement data comprehensive management method and system based on big data analysis, and the method comprises the steps: carrying out the data division of collected financial statement data, and constructing a data storage structure tree according to the data type and hierarchical relation of a data division result; mapping each node in the data storage structure tree to one dimension of a multi-dimensional cube to construct a multi-dimensional cube structure; mapping each data element in the financial statement data to a data storage structure tree and a multi-dimensional cube structure to obtain an initial data network; and associating different nodes and corresponding data elements on different dimensions in the initial data network based on the association relationship between the financial statement data to obtain a final data network. According to the embodiment of the invention, the financial statement data is managed through the data network constructed by the data storage structure tree and the multi-dimensional cube structure, so that the management efficiency and the management accuracy of the financial statement data are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for comprehensive management of financial statement data based on big data analysis. Background Art

[0002] In today's digital age, companies are faced with massive amounts of data such as financial statements and contract texts, and it is crucial to efficiently manage and accurately retrieve these data. Currently, common financial statement data management methods mainly rely on traditional rule engines and manual annotation.

[0003] Traditional rule engines classify and mark financial statement data based on predefined rules. For example, by setting specific keyword matching rules, when certain preset keywords appear in financial statements, they are classified into corresponding categories. However, with the development of business and the diversification of financial statement content, new situations continue to emerge, and the rules need to be constantly updated and adjusted, which requires a lot of manpower and time. Manual labeling is the manual classification and labeling of financial statement data by professionals. However, when faced with a large amount of financial statement data, the speed of manual labeling is far behind the speed of data growth, and human errors are prone to occur, resulting in inconsistencies in classification and labeling.

[0004] Therefore, in the process of managing financial statement data, the existing methods have low management efficiency and low management accuracy. Summary of the invention

[0005] The present invention provides a method and system for comprehensive management of financial statement data based on big data analysis, so as to improve the management efficiency and accuracy of financial statement data.

[0006] In a first aspect, the present invention provides a method for comprehensive management of financial statement data based on big data analysis, comprising: Divide the collected financial statement data and build a data storage structure tree based on the data categories and hierarchical relationships of the data division results; Mapping each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; each node in the data storage structure tree represents a data category; Mapping each data element in the financial statement data to the data storage structure tree and the multi-dimensional cube structure to obtain an initial data network; Based on the association relationship between the financial statement data, the data elements corresponding to different nodes and different dimensions in the initial data network are associated to obtain a final data network.

[0007] In a second aspect, the present invention further provides a comprehensive management system for financial statement data based on big data analysis, which is applied to the comprehensive management method for financial statement data based on big data analysis as described in the first aspect; the comprehensive management system for financial statement data based on big data analysis includes: A structure tree construction module is used to divide the collected financial statement data and construct a data storage structure tree based on the data categories and hierarchical relationships of the data division results; A node mapping module, used for mapping each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; each node in the data storage structure tree represents a data category; A data mapping module, used for mapping each data element in the financial statement data to the data storage structure tree and the multi-dimensional cube structure to obtain an initial data network; The data association module is used to associate the data elements corresponding to different nodes and different dimensions in the initial data network based on the association relationship between the financial statement data to obtain a final data network.

[0008] In a third aspect, the present invention further provides an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing any of the above-mentioned methods for comprehensive management of financial statement data based on big data analysis.

[0009] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, wherein a computer software program is stored in the storage medium, and when the computer software program is executed by a processor, the method for comprehensive management of financial statement data based on big data analysis as described in any one of the above is implemented.

[0010] In a fifth aspect, the present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for comprehensive management of financial statement data based on big data analysis.

[0011] The comprehensive management method of financial statement data based on big data analysis provided by the embodiment of the present invention manages financial statement data in the form of a data network through a data storage structure tree and a multidimensional cube structure. Therefore, in the process of managing financial statement data, each data category in the financial statement data can be efficiently organized and stored through the data storage structure tree. When the data is updated later, it is only necessary to adjust the relationship between the nodes of the data in the data storage structure tree, and it is not necessary to continuously update and adjust the rules, thereby improving the management efficiency of the financial statement data. At the same time, when the new financial statement data is subsequently entered into the data network, it can quickly locate the insertion position in the data storage structure tree according to the data category to which it belongs, and enter the data according to the association relationship between the parent and child nodes in the data storage structure tree, without the need for manual participation, avoiding chaotic data storage and erroneous data storage, and improving the management accuracy of the financial statement data. On the other hand, the multidimensional cube structure can manage the financial statement data from multiple dimensions, eliminating complex manual calculations, reducing human errors, and improving the management accuracy of the financial statement data.

[0012] Therefore, the embodiment of the present invention manages the financial statement data through a data network constructed by a data storage structure tree and a multi-dimensional cube structure, thereby improving the management efficiency and accuracy of the financial statement data. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a flow chart of a method for comprehensive management of financial statement data based on big data analysis provided by an embodiment of the present invention; Figure 2 It is a structural diagram of a comprehensive management system for financial statement data based on big data analysis provided by an embodiment of the present invention; Figure 3 An embodiment diagram of an electronic device provided by an embodiment of the present invention; Figure 4 An embodiment diagram of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0015] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0016] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0017] Optional, see Figure 1 , Figure 1 It is a flow chart of the comprehensive management method of financial statement data based on big data analysis provided by the present invention. The execution subject of the comprehensive management method of financial statement data based on big data analysis in the embodiment of the present invention is the data management system. Therefore, the comprehensive management method of financial statement data based on big data analysis includes: Step 10: divide the collected financial statement data, and construct a data storage structure tree based on the data categories and hierarchical relationships of the data division results.

[0018] Optionally, the data management system collects the financial statement data of the enterprise and divides the financial statement data, wherein the classification may be based on various factors such as the nature, source, and purpose of the financial data. For example, according to the classification of accounting subjects, the data may be divided into major categories such as assets, liabilities, owner's equity, income, and expenses. Under each major category, it may be further subdivided, such as assets may be divided into current assets, non-current assets, etc., and current assets may be further subdivided into monetary funds, accounts receivable, etc.

[0019] Furthermore, the data management system constructs a data storage structure tree based on the data categories and the hierarchical relationships between them, as described in steps 101 to 104, wherein the root node of the tree can be a major category of data categories, and starting from the root node, various child nodes extend downward according to the hierarchical relationship, each child node represents a specific data category, and the connection between the nodes represents the hierarchical relationship.

[0020] In one embodiment, a company's annual financial statement data is collected, including the balance sheet, income statement and cash flow statement data. According to the accounting subjects, they are classified as follows: 1. Assets: 1.1 Current assets: monetary funds (including cash, bank deposits, etc.); accounts receivable; inventory; 1.2 Non-current assets: fixed assets; intangible assets; 2. Liabilities: 2.1 Current liabilities: short-term loans; accounts payable; 2.2 Non-current liabilities: long-term loans; 3. Owners' equity: paid-in capital; capital reserve; 4. Income: income from main business; income from other businesses; 5. Expenses: cost of main business; administrative expenses; sales expenses.

[0021] According to the above division, a data storage structure tree is constructed. The root nodes can be "Assets", "Liabilities", "Owner's Equity", "Income" and "Expenses". Starting from each root node, each root node is further subdivided into the next level of sub-nodes. For example, under "Assets", there are "Current Assets" and "Non-Current Assets" sub-nodes, and so on.

[0022] Step 20, mapping each node in the data storage structure tree to a dimension of the multidimensional cube to construct a multidimensional cube structure.

[0023] Optionally, each node in the data storage structure tree represents a specific data category. The multidimensional cube is a structure used for data storage and analysis, and each dimension represents a feature or attribute of the data. Therefore, the data management system maps the nodes of the data storage structure tree to the dimensions, so that the data can be organized and stored in the multidimensional space according to the corresponding categories and hierarchical relationships, as described in steps 201 to 204. In the mapping process, it is necessary to ensure that the mapping of each node is unique and reasonable, and can accurately reflect the characteristics and relationships of the data.

[0024] Continuing with the above embodiment, the nodes in the data storage structure tree are mapped: "Asset Class" is mapped to one dimension of the multidimensional cube, for example, named "Asset Dimension". "Liability Class" is mapped to another dimension, named "Liability Dimension". "Owner's Equity Class" is mapped to "Owner's Equity Dimension". "Income Class" is mapped to "Income Dimension". "Expense Class" is mapped to "Expense Dimension".

[0025] For the sub-nodes under "Asset Class", such as "Current Assets" and "Non-Current Assets", the levels can be further subdivided under "Asset Dimension", and "Current Assets" and "Non-Current Assets" are used as different level elements under "Asset Dimension". Similarly, the sub-nodes under other categories are hierarchically mapped under the corresponding dimensions in this way. Finally, a multi-dimensional cube structure with multiple dimensions and hierarchical relationships is constructed to store and organize financial statement data.

[0026] Step 30, mapping each data element in the financial statement data to the data storage structure tree and the multi-dimensional cube structure to obtain an initial data network.

[0027] Optionally, each specific data element in the financial statement data, such as a specific monetary fund amount, a value of a certain account receivable, etc., is mapped to the corresponding node of the constructed data storage structure tree according to the data category to which it belongs by the data management system. At the same time, the data management system maps the data element to the corresponding position of the multidimensional cube according to the dimension of the multidimensional cube corresponding to the data category. Through the mapping operation, the data element has a clear position in the data storage structure tree and the multidimensional cube structure, and an initial data network is constructed, as described in steps 301 to 304, wherein the initial data network shows the relationship between the data element and the data category and dimension.

[0028] In one embodiment, in the balance sheet of an enterprise, the amount of monetary funds is 100,000 yuan. In the data storage structure tree, "monetary funds" belongs to the child node of "current assets" under "asset class", so the data element of 100,000 yuan is mapped to the node of "monetary funds". In the multidimensional cube structure, "asset class" corresponds to "asset dimension", "current assets" is a level under "asset dimension", and "monetary funds" is a specific element under "current assets", so 100,000 yuan is mapped to the "monetary funds" position of the "current assets" level under "asset dimension". Similarly, for other data elements in the financial statements, such as the amount of accounts receivable of 50,000 yuan and the amount of short-term loans of 80,000 yuan, they are mapped to the data storage structure tree and the multidimensional cube structure in a similar manner, and finally form an initial data network including all financial statement data elements.

[0029] Step 40, based on the association relationship between the financial statement data, the corresponding data elements at different nodes and different dimensions in the initial data network are associated to obtain the final data network.

[0030] Optionally, there are various inherent associations between financial statement data, such as the balance relationship between assets and liabilities (assets = liabilities + owner's equity), the calculation relationship between income and expenses and profits (profit = income - expenses), etc. Therefore, according to the association relationship between financial statement data, the data management system reasonably associates data elements located at different nodes (different data categories) and different dimensions in the initial data network to obtain the final data network, as described in steps 401 to 404. Therefore, through the association operation, the final data network is not just a simple collection of data elements, but an organic whole that can reflect the logical relationship between data.

[0031] The embodiment of the present invention manages financial statement data in the form of a data network through a data storage structure tree and a multi-dimensional cube structure. Therefore, in the process of managing financial statement data, each data category in the financial statement data can be efficiently organized and stored through the data storage structure tree. When the data is updated later, it is only necessary to adjust the relationship between the nodes of the data in the data storage structure tree, and it is not necessary to continuously update and adjust the rules, thereby improving the management efficiency of the financial statement data. At the same time, when the new financial statement data is subsequently entered into the data network, it can quickly locate the insertion position in the data storage structure tree according to the data category to which it belongs, and enter the data according to the association relationship between the parent and child nodes in the data storage structure tree, without the need for manual participation, avoiding chaotic data storage and erroneous data storage, and improving the management accuracy of the financial statement data. On the other hand, the multi-dimensional cube structure can manage the financial statement data from multiple dimensions, eliminating complex manual calculations, reducing human errors, and improving the management accuracy of the financial statement data.

[0032] In one embodiment, the description of steps 101 to 104 is as follows: Step 101, in a multidimensional financial data space, with each data point in the financial statement data as the center, determine the regional density of each data point according to the number of data points within a preset range of each data point, and cluster the data points with the same regional density to obtain initial clusters.

[0033] Optionally, in the multidimensional financial data space, the data management system processes each data point in the financial report, and sets a specific preset range with a certain data point as the center, wherein the preset range can be a multidimensional space region determined based on the characteristic scale of the data, business requirements, and other factors.

[0034] Furthermore, the data management system counts the number of data points that fall within this preset range, where the number represents the regional density of the data point. For example, if in a two-dimensional financial data space (dimensions are revenue and cost), a circular range with a radius of r is set with a certain data point (representing the revenue and cost values ​​of a business unit) as the center, and there are n other data points within this circular range, then the regional density of the data point is n.

[0035] Furthermore, the data management system groups data points with the same regional density into one group to obtain initial clusters. Therefore, data points with similar density characteristics are preliminarily clustered together through the above steps.

[0036] In one embodiment, there is a financial report that includes the revenue and profit data of multiple business departments of an enterprise in different quarters. The data dimensions are quarter (4 values), business department (5), revenue value, and profit value, forming a four-dimensional financial data space. For one of the data points A, it represents that the revenue of business department X in the second quarter is 1 million yuan and the profit is 200,000 yuan. Set a multidimensional cube range centered on data point A. Within this range, after statistics, there are 10 other data points (these data points may come from the revenue and profit data of different business departments in similar quarters), then the area density of data point A is 10. After calculating all the data points, it is found that the area density of 30 data points is 10, and the system clusters these 30 data points into an initial cluster. At the same time, there may be other data points with area densities of 5, 15, etc., forming different initial clusters respectively.

[0037] Step 102, if the density change of the target data points at the boundary between the initial cluster and its adjacent clusters is less than or equal to a preset degree threshold, and the distance of the target data points in the feature space is less than or equal to a preset distance threshold, each initial cluster is merged with its adjacent clusters to obtain an optimized cluster.

[0038] Furthermore, for each initial cluster, the data management system checks the boundary between it and the adjacent clusters, where the adjacent clusters refer to other clusters that are close in position to the initial cluster in the multidimensional financial data space. For the target data points at the boundary (i.e., the data points near the boundary of the initial cluster), the data management system calculates their density changes, where the density change refers to the degree of difference between the density of the target data point in the cluster and the density in the adjacent cluster. At the same time, the data management system calculates the distance of the target data point in the feature space (i.e., the space formed by the various dimensions of the financial data), where the distance can be measured using a suitable distance measurement method such as the Euclidean distance.

[0039] Furthermore, if the density change is less than or equal to a preset degree threshold (pre-set based on business experience and data characteristics, used to determine whether the density change is small enough, indicating the similarity of the two clusters in density characteristics), and the distance between the target data points in the feature space is less than or equal to a preset distance threshold (used to determine the degree of proximity of the two clusters in spatial position), then it is determined that the two clusters have a strong correlation. Therefore, the data management system merges the initial cluster with its adjacent clusters into a new cluster, namely the optimized cluster, so that the clustering result is more in line with the intrinsic distribution characteristics of the data.

[0040] Continuing with the above embodiment, there is an initial cluster C1 and an adjacent initial cluster C2. There is a data point B at the boundary of C1. The regional density of B in C1 is 12, and the regional density of the data point B' close to B in C2 is 10. Their density changes are |12-10|=2, and the preset degree threshold is 3, which meets the density change condition. In the feature space, the Euclidean distance calculation result of the data point B (whose coordinates are [second quarter, business department X, 1 million yuan in revenue, 200,000 yuan in profit]) and the data point B' (whose coordinates are [second quarter, business department Y, 950,000 yuan in revenue, 180,000 yuan in profit]) is 5 (the preset distance threshold is 8), which meets the distance condition, and the initial clusters C1 and C2 are merged into an optimized cluster.

[0041] Step 103: for each optimized cluster, determine the sub-topic financial data category corresponding to each optimized cluster according to the distribution shape, density characteristics and cluster characteristics of the data points in the optimized cluster.

[0042] Furthermore, for each optimized cluster, the data management system deeply analyzes various features of its internal data points, where various features include the distribution shape, density features and clustering features of the data points. The distribution shape refers to the distribution pattern of the data points in multidimensional space, such as linear distribution, cluster distribution or other specific shapes. In addition to regional density, density features may also involve density change trends, etc. Clustering features include the size of the cluster (the number of data points), the compactness of the cluster, etc.

[0043] Furthermore, the data management system determines a corresponding sub-theme financial data category based on the distribution shape, density characteristics and cluster characteristics of the data points in the optimized cluster. For example, if the data points in an optimized cluster mainly revolve around the enterprise's R&D expense-related data, and the distribution shape shows a trend of gradually increasing over time, and the density characteristics also conform to the characteristics of R&D expense data, it will be determined as the "R&D expense sub-theme financial data category".

[0044] In one embodiment, there is an optimized cluster C, in which the data points are mainly concentrated on the sales cost data of different product lines of the enterprise. From the distribution shape, the product dimension and time dimension show the characteristics of the fluctuation of sales costs of different product lines over time, and the data points are relatively concentrated. From the density characteristics, the regional density is relatively stable in different time periods and product lines. In terms of clustering characteristics, the cluster size is moderate and the compactness is high. Combining these characteristics, the data management system determines that the sub-theme financial data category corresponding to the optimized cluster is "product line sales cost sub-theme financial data category".

[0045] Step 104: construct a data storage structure tree based on each sub-theme financial data category and the hierarchical relationship of each sub-theme financial data category in its subject financial data category.

[0046] Further, for each sub-topic financial data category, the data management system determines the topic financial data category corresponding to each sub-topic financial data category.

[0047] Furthermore, with the subject financial data category as the root node, the data management system connects the sub-subject financial data category corresponding to the subject financial data category as a sub-node to the root node, and obtains a hierarchical data storage structure tree, as described in steps 1041 to 1044. For example, "research and development expense sub-subject financial data category" and "marketing expense sub-subject financial data category" may belong to the next level of "expense subject financial data category".

[0048] In one embodiment, there are the following topic financial data category and sub-topic financial data category relationships: 1. For the “Assets Topic”: it includes the “Current Assets Sub-Topic” and the “Non-Current Assets Sub-Topic”.

[0049] “Current assets sub-topic”: includes “cash and cash equivalents sub-topic” and “accounts receivable sub-topic”.

[0050] “Non-current assets sub-topic”: includes “fixed assets sub-topic” and “intangible assets sub-topic”.

[0051] 2. For the “Expense Topic”: it includes the “Operating Expenses Sub-Topic” and the “R&D Expenses Sub-Topic”.

[0052] “Operating expense sub-topic”: includes “sales expense sub-topic” and “administrative expense sub-topic”.

[0053] With "Asset Topic" and "Expense Topic" as root nodes, "Asset Topic" has "Current Asset Sub-Topic" and "Non-Current Asset Sub-Topic" sub-nodes, "Current Asset Sub-Topic" has "Monetary Fund Sub-Topic" and "Accounts Receivable Sub-Topic" sub-nodes, and so on, to build a data storage structure tree.

[0054] The embodiment of the present invention constructs a data storage structure tree, so that financial statement data can be stored and managed in a more reasonable manner that is more in line with the internal relationships of financial data and business needs. Therefore, the management efficiency and accuracy of financial statement data are improved through the data storage structure tree.

[0055] In one embodiment, the description of steps 1041 to 1044 is as follows: Step 1041 , for each sub-theme financial data category, determine the data level of each sub-theme financial data category in its sub-theme financial data category according to the data volume of the sub-theme financial data category and the relevance to the core business in its theme financial data category.

[0056] Optionally, the data management system conducts analysis for each identified sub-topic financial data category. On the one hand, the data volume of the sub-topic financial data category is considered. The sub-topic with a large amount of data may be in a relatively more important or more basic position in the data hierarchy. On the other hand, the degree of relevance of the sub-topic to the core business in the financial data category to which it belongs is evaluated. The higher the degree of relevance, the higher its position in the data hierarchy. Therefore, the data management system combines these two factors to determine the relative data hierarchy of each sub-topic financial data category among all sub-topic financial data categories.

[0057] In one embodiment, under the financial data category of "expense topic", there are "research and development expense sub-topic", "sales expense sub-topic", "management expense sub-topic", etc. The data volume of "sales expense sub-topic" is the largest among all expense sub-topics, and the sales business is the core business of the enterprise, and sales expenses are closely related to the core business. However, the data volume of "management expense sub-topic" is relatively small, and its direct correlation with the core business is not as good as that of sales expenses. Therefore, the "sales expense sub-topic" can be determined as a higher data level, such as level one; and the "management expense sub-topic" can be determined as a lower data level, such as level two.

[0058] Step 1042, for each sub-theme financial data category, divide the data points in the sub-theme financial data category into sub-categories according to the data features of the data points in the sub-theme financial data category, and sort the sub-categories according to the differences in data features between the sub-categories to obtain optimized data categories for each sub-theme financial data category.

[0059] Furthermore, for each sub-topic financial data category, the data management system deeply analyzes the data features of the data points therein, where the data features include the numerical range of the data, the time attributes of the data, the business links involved in the data, etc., and divides the data points in the sub-topic financial data category into different sub-categories based on the data features. For example, for the "accounts receivable sub-topic", the data points can be divided into short-term accounts receivable (account age within 1 year), medium-term accounts receivable (account age 1-3 years), long-term accounts receivable (account age more than 3 years) and other sub-categories according to the age of the accounts.

[0060] Furthermore, the data management system sorts the subcategories in each sub-theme financial data category according to the degree of difference in data features between the subcategories in each sub-theme financial data category, wherein the sorting can be based on the importance of the features, chronological order, or business logic order, etc., to obtain optimized data categories for each sub-theme financial data category.

[0061] In one embodiment, taking the "fixed asset sub-theme" as an example, the data features of the data points include asset categories (such as buildings, machinery and equipment, transportation tools, etc.), purchase time, usage status, etc. The data management system divides the data points into "building sub-category", "machinery and equipment sub-category", and "transportation tool sub-category" according to the asset category. Then, these sub-categories are sorted according to the importance that the company attaches to different asset categories and their importance in production and operation. Buildings are crucial to the production and operation of enterprises, followed by machinery and equipment, and transportation tools are relatively unimportant. Then the sorting result is that the "building sub-category" ranks first, the "machinery and equipment sub-category" ranks second, and the "transportation tool sub-category" ranks last, forming the optimized data category of the "fixed asset sub-theme".

[0062] Step 1043, construct an initial storage structure tree with each subject financial data category as the root node, the optimized data category under each subject financial data category as the leaf node, and the data level of each sub-subject financial data category in its sub-subject financial data category as the node level of the leaf node.

[0063] Furthermore, the data management system uses each subject financial data category (such as "asset subject", "liability subject", "income subject", etc.) as the root node of the initial storage structure tree. For each sub-subject financial data category under each root node, the optimized data category is connected to the corresponding root node as a leaf node. At the same time, according to the data level of each sub-subject financial data category in its sub-subject financial data category determined in step 1041, the corresponding node level is assigned to these leaf nodes to construct the initial storage structure tree, so that the constructed initial storage structure tree not only reflects the hierarchical relationship between the subject and the sub-subject, but also reflects the relative importance of the sub-subject and the hierarchical structure of the data organization through the node level.

[0064] In one embodiment, for the "asset theme", there are "current asset sub-theme" and "non-current asset sub-theme". After the processing of step 1042, the optimized data categories of the "current asset sub-theme" include "monetary fund sub-category", "accounts receivable sub-category", etc.; the optimized data categories of the "non-current asset sub-theme" include "fixed asset sub-category", "intangible asset sub-category", etc. In step 1041, it is determined that the data level of the "current asset sub-theme" is the first level, and the data level of the "non-current asset sub-theme" is the second level. Then when constructing the initial storage structure tree, the "asset theme" is the root node, the "monetary fund sub-category", "accounts receivable sub-category", etc. are the leaf nodes corresponding to the "current asset sub-theme", and the node level is the first level; the "fixed asset sub-category", "intangible asset sub-category", etc. are the leaf nodes corresponding to the "non-current asset sub-theme", and the node level is the second level.

[0065] Step 1044: Based on the actual access frequency and business logic association between the financial data categories, the corresponding nodes in the initial storage structure tree are associated to obtain a data storage structure tree.

[0066] Furthermore, the data management system analyzes the access frequency between financial data categories in actual business operations and their inherent business logic associations. For example, in financial analysis, the "Main Business Income Sub-Topic" under the "Income Topic" and the "Main Business Cost Sub-Topic" under the "Expense Topic" are often viewed at the same time because they are directly related to the calculation of the company's main business profit.

[0067] Furthermore, the data management system connects closely related nodes in the initial storage structure tree in a corresponding manner (such as connecting lines, establishing pointers and other conceptual associations) according to actual access frequency and business logic relationships to obtain a data storage structure tree.

[0068] In one embodiment, in actual business, enterprises often associate the "inventory sub-topic" (belonging to the "current asset sub-topic", under the "asset topic") with the "sales cost sub-topic" (belonging to the "operating expense sub-topic", under the "expense topic") for analysis, because the consumption of inventory is directly related to the sales cost. Therefore, in the initial storage structure tree, the node corresponding to the "inventory sub-topic" and the node corresponding to the "sales cost sub-topic" are associated. Similarly, for the "short-term loan sub-topic" (under the "liability topic") and the "interest expense sub-topic" (under the "expense topic") that are often queried and analyzed together, the nodes are also associated to obtain the final data storage structure tree.

[0069] The embodiment of the present invention optimizes the initial storage structure tree by considering the actual access frequency and business logic association, and connects the related data nodes, so that the data storage structure tree not only reflects the hierarchical relationship of the data, but also reflects the actual business needs, so that the constructed financial data storage structure tree is more scientific, reasonable and in line with the actual business of the enterprise. In terms of data storage, it can more efficiently use the storage space and reasonably organize data. When querying and analyzing data, users can quickly find relevant data in the storage structure tree based on business logic and common analysis paths, thereby improving the management efficiency of financial data.

[0070] In one embodiment, the description of steps 201 to 204 is as follows: Step 201: Identify in the data storage structure tree a first target node related to time, a second target node related to a financial subject, and a third target node related to an amount.

[0071] Optionally, the data management system traverses the data storage structure tree, and for the first target node related to time, searches for data nodes representing time attributes, such as "year", "quarter", "month" and other nodes.

[0072] For the second target node related to financial subjects, the data management system identifies nodes such as "Assets - Current Assets - Cash and Cash Equivalents" and "Liabilities - Current Liabilities - Short-term Loans". These nodes correspond to specific financial subject classifications and are the core classification identifiers of financial data.

[0073] For the third target node related to the amount, the data management system directly stores the data node of the specific amount value, such as a specific monetary fund amount, the amount of an accounts receivable, etc.

[0074] In one embodiment, there is a root node "Enterprise Annual Financial Statement Data" in the data storage structure tree, and there are multiple branches under it. One of the branches is the "Time Dimension Branch", which contains nodes such as "2024" and "2025", which are the first target nodes related to time. Under the "Financial Account Dimension Branch", there are nodes such as "Assets-Current Assets-Accounts Receivable" and "Liabilities-Non-Current Liabilities-Long-Term Loans", which are the second target nodes related to financial accounts. In the leaf nodes of each financial account node, such as "Accounts Receivable-The Accounts Receivable Amount of Customer A is 50,000 yuan", the node where "50,000 yuan" is located is the third target node related to the amount.

[0075] Step 202, according to the node characteristics of each node in the data storage structure tree, the first target node, the second target node and the third target node are respectively mapped to the first initial positions corresponding to the time dimension, the subject dimension and the amount dimension of the multidimensional cube.

[0076] Furthermore, the data management system determines the initial position of each target node on the corresponding dimension of the multidimensional cube based on the characteristics of each target node in the data storage structure tree. For the first target node (time-related), if it is a "year" node, it may be mapped to the time dimension in the order of the years, from early to late. For example, the "2020" node will be placed in a relatively early position on the time dimension, and the "2025" node will be placed in a relatively late position.

[0077] For the second target node (related to financial accounts), mapping is performed based on the hierarchical relationship and classification system of financial accounts. For example, in the account dimension, "Assets" as a large category will be at a higher level, and its "Current Assets" subcategory will be at a level below "Assets", while "Monetary Funds" as a subnode of "Current Assets" will be at a lower level.

[0078] For the third target node (amount related), its position on the amount dimension will be preliminarily determined based on the financial account and time node to which it belongs. Generally speaking, the amount nodes at different time points under the same financial account will be arranged in chronological order on the amount dimension.

[0079] Continuing with the above embodiment, the "2024" node will be mapped to the time scale position corresponding to 2024 in the time dimension, and the "2025" node will be mapped to the time scale position corresponding to 2025. In the subject dimension, the "asset class" node will be placed at a higher level, the "current assets" node will be at the next level, and the "accounts receivable" node will be at the next level of "current assets". For the third target node "accounts receivable-customer A's accounts receivable amount is 50,000 yuan", since it belongs to the data of the "accounts receivable" subject at a certain point in time (assuming it is 2024), it will be preliminarily mapped to the position corresponding to the "2024" time node and the "accounts receivable" subject node in the amount dimension.

[0080] Step 203, based on the parent-child relationship and sibling relationship of each node in the data storage structure tree, optimize the first initial position of the dimension where each node is located, and obtain the first target position of the dimension where each node is located.

[0081] Furthermore, the data management system determines the parent-child relationship and sibling relationship of nodes in the data storage structure tree. For the parent-child relationship, for example, in the financial account dimension, if "asset class" is the parent node of "current assets", then the position of the "current assets" node in the account dimension should be below the "asset class" node and have a certain logical hierarchical interval to reflect this parent-child hierarchical relationship. In the time dimension, if the "quarter" node is the parent node of the "month" node, the "month" node should be arranged in the order of months within the time interval determined by the "quarter" node to further optimize its position.

[0082] For sibling relationships, for example, in the time dimension, different quarterly nodes in the same year are siblings and should be closely arranged in quarterly order in the time dimension, with reasonable intervals. In the financial account dimension, "cash and cash equivalents" and "accounts receivable" under "current assets" are sibling nodes. They should be at the same level in the account dimension, and their relative positions should be reasonably adjusted based on factors such as business logic and data usage frequency. By considering the parent-child relationship and the sibling relationship in this way, the first initial position of each node in its respective dimension is optimized to obtain a more reasonable first target position.

[0083] In one embodiment, in the subject dimension, "Asset Class" serves as the parent node, and "Current Assets" and "Non-Current Assets" under it are brother nodes. "Money" and "Accounts Receivable" under "Current Assets" are brother nodes. According to the common analysis logic of financial data, the "Money" node with higher frequency of use is placed in a relatively forward and easily accessible position in the "Current Assets" sub-node, followed by the "Accounts Receivable" node. In the time dimension, assuming that there are four quarterly nodes under "2024", namely "First Quarter", "Second Quarter", "Third Quarter" and "Fourth Quarter", they are brother nodes. In order of quarters, they are closely arranged in the time interval corresponding to the "2024" time node to obtain the optimized first target position.

[0084] Step 204 , in the multidimensional cube, the financial data category of each node is used as the attribute value of each node, combined with the first target position of the dimension where each node is located, to construct a multidimensional cube structure.

[0085] Furthermore, the data management system assigns an attribute value to each node of the multidimensional cube, and this attribute value is the category of financial data represented by the node. For example, the attribute value of the node "Asset Class-Current Assets-Money Funds" is "Money Funds Financial Data Category". Furthermore, the data management system combines the first target position of each node in each dimension (time dimension, account dimension, amount dimension, etc.) determined in the above steps, places these nodes in the corresponding position of the multidimensional cube, and constructs a complete multidimensional cube structure, so that in the multidimensional cube structure, the position and attributes of each node are clear, and the distribution and correlation of financial data in different dimensions can be intuitively displayed.

[0086] In one embodiment, when constructing a multidimensional cube structure, for the node "2024-Asset Class-Current Assets-Monetary Funds-Amount is 100,000 yuan", its attribute value is "Monetary Funds Financial Data Category". In the multidimensional cube, this node is located at the position corresponding to 2024 in the time dimension, at the hierarchical position corresponding to "Asset Class-Current Assets-Monetary Funds" in the subject dimension, and at the numerical position corresponding to 100,000 yuan in the amount dimension. By placing all similar nodes in the multidimensional cube in this way, a multidimensional cube structure is finally constructed. For example, the changes in the amount of monetary funds in different years and quarters can be viewed from the time dimension, and the amount distribution under different financial accounts can be viewed from the subject dimension.

[0087] The embodiment of the present invention constructs a multi-dimensional cube structure that is efficient, intuitive and in line with financial business logic. Therefore, through the multi-dimensional cube structure, financial statement data can be managed from multiple dimensions, eliminating complex manual calculations, reducing human errors, and improving the accuracy of financial statement data management.

[0088] In one embodiment, the description of steps 301 to 304 is as follows: Step 301 , extracting features from each data element in the financial statement data to obtain element features of each data element.

[0089] Optionally, the data management system performs feature extraction on each data element in the financial statement data to obtain element features of each data element, wherein the element features include at least the element name, element type, financial statement category, time attribute, financial account information involved, and amount involved.

[0090] For the element name, directly obtain the specific name represented by the data element, such as "amount of monetary funds" and "main business income". The element type determines whether the data element is numeric, textual or other types. For example, amount data is usually numeric, while financial account names are mostly textual. The financial statement category to which it belongs clarifies whether the data element comes from the balance sheet, income statement or cash flow statement. The time attribute determines the time point or time period corresponding to the data element, such as "the first quarter of 2024". The financial account information involved indicates the financial account associated with the data element, such as "Asset Class-Current Assets-Accounts Receivable". If the amount value is involved, the actual amount number in the data element is extracted if the data element itself is an amount representation.

[0091] In one embodiment, there is a data element in the financial statement data, which is described as "In December 2024, the amount of monetary funds in the balance sheet is 80,000 yuan". The data management system extracts its element features as follows: element name: monetary funds amount; element type: numeric type; financial statement category: balance sheet; time attribute: December 2024; financial account information involved: asset class - current assets - monetary funds; amount involved: 80,000 yuan.

[0092] Step 302, for each data element, path matching is performed in the data storage structure tree according to the element characteristics to obtain a fourth target node in the data storage structure tree, and each data element is mapped to the second initial position of the corresponding dimension of the multi-dimensional cube structure based on the element characteristics.

[0093] Furthermore, for each data element, the data management system performs path matching in the data storage structure tree. Taking the financial account information involved in the element as an example, starting from the root node of the data storage structure tree, the search is gradually downward along the hierarchy of the financial account classification until a fully matched node is found, which is the fourth target node. For example, for the financial account information of "Assets - Current Assets - Monetary Funds", the corresponding "Monetary Funds" node is found in the data storage structure tree.

[0094] At the same time, for each data element, the data management system maps the data element to the second initial position of the corresponding dimension of the multidimensional cube structure according to the element characteristics. For example, according to the time attribute, the data element is mapped to the corresponding time point position on the time dimension; according to the financial account information involved, it is mapped to the corresponding account level position on the account dimension; according to the amount involved, the corresponding value position is found on the amount dimension. Through such mapping, the position of the data element in the multidimensional cube is preliminarily determined.

[0095] In one embodiment, for the above data element "In December 2024, the amount of monetary funds in the balance sheet is 80,000 yuan". In the data storage structure tree, the fourth target node "monetary funds" is found through the path of "asset class-current assets-monetary funds". In the multidimensional cube structure, in the time dimension, the data element is mapped to the time scale position corresponding to December 2024; in the subject dimension, it is mapped to the hierarchical position corresponding to "asset class-current assets-monetary funds"; in the amount dimension, it is mapped to the numerical position corresponding to 80,000 yuan, that is, its second initial position is obtained.

[0096] Step 303: For each data element, the second initial position is optimized according to the parent-child relationship and the sibling relationship of the fourth target node in the data storage structure tree to obtain the second target position of each data element in the corresponding dimension of the multi-dimensional cube structure.

[0097] Further, the data management system determines the parent-child relationship and sibling relationship of the data element in the fourth target node in the data storage structure tree. In the account dimension, if the parent node of the fourth target node "monetary funds" is "current assets", the sibling nodes are "accounts receivable" and so on. Considering the usage frequency and association relationship of monetary funds and accounts receivable in actual financial analysis, the position of the data element in the account dimension may be appropriately adjusted. If the analysis frequency of monetary funds data is high, its second initial position in the account dimension may be optimized in a direction that is more accessible. In the time dimension, if the time corresponding to the fourth target node is a month in a quarter, according to the order of months in the quarter and the logical relationship between data, the position of the data element in the time dimension is optimized to keep a reasonable order and interval with the data elements of other months in the same quarter. By considering the parent-child relationship and the sibling relationship in this way, the second initial position of each data element in each dimension of the multidimensional cube is adjusted to obtain a second target position that is more in line with the internal logic of financial data and business usage habits.

[0098] Continuing with the above embodiment, let's take the monetary funds data element as an example. In the data storage structure tree, the "monetary funds" node and the "accounts receivable" node are siblings, and companies often focus on monetary funds first in financial analysis. Then in the subject dimension, the system will optimize the monetary funds data element to a more forward and easier to access position based on the second initial position to obtain the second target position. In the time dimension, if the data element is in December of the fourth quarter of 2024, the system will optimize its position in the time dimension to a position that is reasonably arranged with the October and November data elements based on the time sequence within the quarter, so as to reflect the temporal consistency and logic.

[0099] Step 304 , based on the fourth target node and the second target position, each data element is mapped to a data storage structure tree and a multi-dimensional cube structure respectively to obtain an initial data network.

[0100] Further, the data management system maps each data element to the data storage structure tree and the multidimensional cube structure according to the fourth target node and the second target position, respectively, to obtain an initial data network, as specifically described in steps 3041 to 3044.

[0101] The embodiment of the present invention maps each data element in the financial statement data to the data storage structure tree and the multidimensional cube structure to obtain an initial data network. On the one hand, the data storage structure tree and the multidimensional cube structure can improve the management efficiency and accuracy of the financial statement data. On the other hand, the initial data network can quickly find the location of a specific data element in different structures, facilitate cross-structure data association analysis, and improve the analysis efficiency of the financial statement data.

[0102] In one embodiment, the description of steps 3041 to 3044 is as follows: Step 3041: Associating the financial data category of each data element as an attribute value with the second target position of each dimension in the multi-dimensional cube structure.

[0103] Optionally, the data management system further refines the operation of each data element in the multidimensional cube structure. After determining the second target position of each data element in the corresponding dimension of the multidimensional cube structure in step 303, the financial data category of the data element is used as a key attribute value to establish a close connection with these second target positions. For example, for the financial data category of "monetary funds", there are second target positions of corresponding data elements in the time dimension, subject dimension and amount dimension of the multidimensional cube, and the attribute value of "monetary funds" is matched one by one with these positions. Therefore, in the multidimensional cube structure, the category of the data element represented by each position can be clearly identified, so that the distribution of a certain type of financial data in each dimension can be quickly located in the multidimensional space.

[0104] In one embodiment, there is a data element whose financial data category is "main business income". In the multidimensional cube structure, the second target position on the time dimension corresponds to "the third quarter of 2024", the hierarchical position on the subject dimension corresponds to "income category-main business income", and the numerical position on the amount dimension corresponds to "250,000 yuan". The "main business income" attribute value is associated with the second target position on the three dimensions, that is, when the position combination of "third quarter of 2024-income category-main business income-250,000 yuan" is queried in the multidimensional cube, the corresponding financial data category is "main business income".

[0105] Step 3042, for each data element, if the position of the fourth target node in the data storage structure tree is consistent with the second target position of the corresponding dimension in the multidimensional cube structure, and the attribute of the fourth target node in the data storage structure tree is associated with the attribute of the corresponding dimension in the multidimensional cube structure, then a bidirectional index relationship is established between the position of the fourth target node and the second target position.

[0106] Further, the data management system compares the relevant information of each data element in the data storage structure tree and the multidimensional cube structure. For a certain data element, first check whether its fourth target node position in the data storage structure tree is logically consistent with the second target position of the corresponding dimension in the multidimensional cube structure. For example, whether the hierarchical structure of the "monetary funds" node in the data storage structure tree matches the hierarchical position corresponding to "monetary funds" in the subject dimension of the multidimensional cube. Further, the data management system determines whether the attributes of the fourth target node (such as financial subject category and other attributes) are related to the attributes of the corresponding dimension of the multidimensional cube structure (also related attributes such as financial subject category). When both conditions are met, the data management system establishes a bidirectional index relationship between the fourth target node position and the second target position, wherein the bidirectional index indicates that from a certain node position in the data storage structure tree, its corresponding position in the multidimensional cube structure can be quickly found, and vice versa.

[0107] In a temporary embodiment, the data element "fixed assets-buildings" is taken as an example. In the data storage structure tree, the "buildings" node is in the hierarchical position of "asset class-non-current assets-fixed assets" (the fourth target node position). In the multidimensional cube structure, the hierarchical position (the second target position) corresponding to "buildings" in the subject dimension is logically consistent with the hierarchical position in the data storage structure tree. In addition, the attributes of the "buildings" node in the data storage structure tree (belonging to the fixed asset category, etc.) are associated with the attributes of the "buildings" position in the multidimensional cube subject dimension (also indicating that it belongs to the fixed asset category). At this time, the data management system establishes a bidirectional index relationship between the two positions. When the user queries the "buildings" node in the data storage structure tree, through this bidirectional index, the corresponding position of "buildings" in the subject dimension in the multidimensional cube structure, as well as the data in the related time dimension and amount dimension can be quickly located; conversely, when the data of the relevant position of "buildings" is queried in the multidimensional cube structure, the corresponding node in the data storage structure tree can also be quickly found.

[0108] Step 3043, mapping each data element to the fourth target node of the data storage structure tree, and mapping each data element to the second target position of the corresponding dimension of the multi-dimensional cube structure.

[0109] Furthermore, the data management system accurately places each data element on the corresponding fourth target node in the data storage structure tree, and obtains the mapped data storage structure tree, so that the data element has a clear ownership and position identification in the data storage structure tree. At the same time, the data management system places the data element in the specific position of the corresponding dimension of the multidimensional cube structure according to the determined second target position, and obtains the mapped multidimensional cube structure.

[0110] Step 3044 constructs an initial data network by fusing the bidirectional index relationship, the mapped data storage structure tree and the mapped multidimensional cube structure.

[0111] Furthermore, the data management system connects the mapped data storage structure tree and the multidimensional cube structure through a bidirectional index relationship to construct an initial data network, wherein the bidirectional index relationship enables the data elements in the two structures to be associated and accessed with each other. Therefore, in the data storage structure tree, each node can find the corresponding data element position in the multidimensional cube structure through the bidirectional index, and vice versa.

[0112] In one embodiment, there are multiple financial account nodes in the data storage structure tree, such as "monetary funds", "accounts receivable", "short-term loans", etc., and each node is connected to the multidimensional cube structure through a bidirectional index. In the multidimensional cube structure, the positions of data elements in each dimension also correspond to the nodes in the data storage structure tree through a bidirectional index. When the user wants to query the "changes in monetary funds throughout 2024", he can first find the "monetary funds" node in the data storage structure tree, and quickly jump to the position corresponding to "2024" in the time dimension in the multidimensional cube structure through the bidirectional index, and obtain all relevant data on the monetary funds in the amount dimension during this time period. Or starting from the position of the time dimension "2024" in the multidimensional cube structure, use the bidirectional index to find all financial account nodes related to this time period in the data storage structure tree, and then fully understand the situation of all financial data in 2024.

[0113] The embodiment of the present invention constructs a highly integrated, closely related, and easy-to-query and -analyze financial data network. On the one hand, the management efficiency and accuracy of financial statement data can be improved through the data storage structure tree and the multi-dimensional cube structure. On the other hand, when an enterprise processes financial data, the initial data network can greatly improve the data processing efficiency.

[0114] In one embodiment, the description of steps 401 to 404 is as follows: Step 401 , analyzing the financial statement data to obtain first data elements that are associated with each other in the same dimension and second data elements that are associated with each other in different dimensions.

[0115] Optionally, the association relationship in the embodiment of the present invention includes causal association, time series association and subject association. Therefore, the data management system analyzes the financial statement data to obtain first data elements with association relationships in the same dimension and second data elements with association relationships in different dimensions.

[0116] For data elements that are associated in the same dimension, that is, the first data element, in terms of causal association, for example, in the amount dimension, the cost data and profit data of an enterprise are causally associated, and an increase in cost often leads to a decrease in profit. In terms of temporal association, in the time dimension, there is a temporal association between sales data for several consecutive quarters, and the sales in the next quarter may be affected by factors such as the sales situation and market trends in the previous quarter. Account association is reflected in the account dimension, such as "main business income" and "main business cost" are both financial accounts related to the main business, and there is a close account association.

[0117] For the second data elements with correlations in different dimensions, the causal relationship is, for example, between the time of promotion activities in the time dimension and the sales in the amount dimension. The implementation of promotion activities (time dimension) may lead to an increase in sales (amount dimension). In terms of temporal correlation, the year-end closing time point in the time dimension will affect the data update and adjustment of some accounting accounts in the account dimension, and there is a temporal sequence correlation. Account correlation can be manifested as the correlation between the "fixed assets" account in the account dimension and the purchase amount, depreciation amount and other data elements of fixed assets in the amount dimension, because different fixed asset accounts correspond to specific amount values. Through in-depth analysis of financial statement data, the data management system identifies these data elements with different types and dimensions.

[0118] In one embodiment, in the financial statement data, the two data elements "sales cost" and "sales profit" have a causal relationship in the amount dimension and belong to the first data element. Because the increase in sales cost will directly lead to a decrease in sales profit. At the same time, "sales in the first quarter of 2024" and "sales in the second quarter of 2024" have a time series relationship in the time dimension and are also the first data element. In the account dimension, the "raw material purchase" account and the "inventory goods" account have an account relationship and belong to the category of the first data element.

[0119] For the second data element, "Promotional activities carried out in June 2024" (time dimension) is causally related to "Sales increased significantly in June 2024" (amount dimension). "Year-end closing time" (time dimension) is temporally related to "Adjustment of some account data in financial statements" (account dimension). "Fixed assets - building construction" account (account dimension) is account-related to "Purchase amount of building construction, annual depreciation amount" (amount dimension).

[0120] Step 402: for the first data element, a node association is established between nodes corresponding to the first data element in the data storage structure tree, and a position association is established between positions corresponding to the first data element in the multi-dimensional cube structure.

[0121] Furthermore, for the first data element, in the data storage structure tree, the data management system finds its corresponding node and establishes an association. If the data storage structure tree nodes corresponding to the "sales cost" and "sales profit" data elements are "sales cost node" and "sales profit node" respectively, a connection is established between the two nodes to indicate the causal relationship between them. For the "sales node in the first quarter of 2024" and "sales node in the second quarter of 2024" that have a time series association, a node association is established in chronological order to reflect the time series relationship. In terms of subject association, if there is a subject association between the "raw material procurement node" and the "inventory commodity node", associate them.

[0122] In the multidimensional cube structure, the corresponding position of the first data element is also associated. In the amount dimension, the corresponding position of "sales cost" and the corresponding position of "sales profit" are associated to show the cause-effect relationship. In the time dimension, the corresponding position of "sales in the first quarter of 2024" and the corresponding position of "sales in the second quarter of 2024" are associated in chronological order. In the subject dimension, the corresponding position of "raw material purchase" and the corresponding position of "inventory goods" are associated to reflect the subject association.

[0123] Step 403: For the second data element, a cross-dimensional association between corresponding positions of the second data element is established in the multi-dimensional cube structure.

[0124] Furthermore, for the second data elements with association relationships in different dimensions, when there is a causal relationship, such as "promotional activities carried out in June 2024" (time dimension) and "sales increased significantly in June 2024" (amount dimension), a cross-dimensional association is established between the position of "June 2024" in the time dimension and the corresponding position of "sales increased significantly in June 2024" in the amount dimension. For time series associations, such as "year-end closing time" (time dimension) and "adjustment of some account data in financial statements" (account dimension), an association is established between the year-end closing time position in the time dimension and the corresponding position of the relevant account data adjustment in the account dimension, reflecting the time sequence. In terms of account association, for the "fixed assets-buildings" account (account dimension) and the "purchase amount and annual depreciation amount of buildings" (amount dimension), a cross-dimensional association is established between the position of "fixed assets-buildings" in the account dimension and the corresponding purchase amount and depreciation amount position in the amount dimension.

[0125] In one embodiment, in a multidimensional cube structure, the time scale position of "June 2024" is found in the time dimension, and the amount value position corresponding to "sales increased significantly in June 2024" is found in the amount dimension, and an association line across the time dimension and the amount dimension is created to connect the two positions to reflect the causal relationship. For the position of "year-end closing time" in the time dimension and the position corresponding to the adjustment of relevant account data in the account dimension, an association is established to indicate a time series association. The hierarchical position of "fixed assets-buildings" in the account dimension and the numerical positions corresponding to "purchase amount of buildings" and "annual depreciation amount" in the amount dimension are used to establish a cross-dimensional association to present the account association.

[0126] Step 404 , the first data element and the second data element in the initial data network are associated based on node association, position association and cross-dimensional association to obtain a final data network.

[0127] Furthermore, in the initial data network, the data management system closely links the first data element together through node association and position association, and displays the second data element in a multi-dimensional cube structure through cross-dimensional association, and finally integrates all these associations, so that the first data element and the second data element form an organic whole in the entire data network, and constructs the final data network. In this final data network, various associations between different data elements are clearly presented, whether it is the association within the same dimension or the association between different dimensions, it can be easily queried and analyzed, providing enterprises with more comprehensive and in-depth financial data insights.

[0128] In one embodiment, there are many first data elements and second data elements in the initial data network. The first data elements such as "sales cost", "sales profit", "sales in each quarter of 2024", "raw material purchase", "inventory goods", etc. have established connections in the data storage structure tree and the multidimensional cube structure through node association and position association. The second data elements such as "promotion activity time and sales change", "year-end settlement time and account data adjustment", "fixed asset account and amount data", etc. are mutually associated in the multidimensional cube structure through cross-dimensional association. These different types of association relationships are unified and integrated to connect all related data elements. When financial personnel want to analyze the impact of corporate sales strategies on profits, in the final data network, they can start from the promotion activity time node in the time dimension, find the sales change data in the amount dimension through cross-dimensional association, and then find the sales cost data through position association, and then find the sales profit data through node association and position association, so as to fully understand the data association relationship in the entire sales business chain, provide strong support for decision-making, and complete the construction and application of the final data network.

[0129] The embodiment of the present invention constructs a comprehensive, detailed and highly correlated financial data network, which presents various correlation relationships between different data elements. Therefore, when an enterprise conducts financial analysis, whether it is the correlation within the same dimension or the correlation between different dimensions, it can use the final data network to deeply explore the internal connections between financial data from multiple angles, facilitate query and analysis, provide enterprises with more comprehensive and in-depth financial data insights, and improve analysis efficiency and query efficiency.

[0130] In one embodiment, the description of step 4041 to step 4042 is as follows: Step 4041, based on the association strength between the first data elements, respectively optimize the initial association path of the node association and the initial association mode of the position association to obtain an optimized path and an optimized mode.

[0131] Optionally, the data management system analyzes the strength of association between the first data elements, wherein the strength of association can be determined based on a variety of factors, such as the consistency of changes between financial data, the closeness of business logic, etc. For the initial association path of node association, the data management system examines the connection method between the nodes in the data storage structure tree. If the nodes corresponding to the first data elements "sales cost" and "sales profit" are initially associated through a more complex and indirect path, but analysis finds that the association strength between the two elements is extremely high, an attempt will be made to find a more direct and efficient association path. For example, there may be a closer common parent node in the hierarchical structure of the data storage structure tree. By adjusting the association path, the "sales cost" and "sales profit" nodes are directly connected from this common parent node to form an optimized path, so that data query and analysis can jump between the two nodes more quickly.

[0132] Furthermore, for the initial association method of position association, in the multidimensional cube structure, if the initial association method of "sales in the first quarter of 2024" and "sales in the second quarter of 2024" in the time dimension is just a simple sequential connection, but considering that the two data elements have a high correlation strength, and companies often focus on comparing the sales changes in these two quarters when analyzing, the data management system optimizes the association method to connect with more eye-catching lines that are different from other ordinary associations, or marks special prompt information on the association line, such as "focus on comparing quarterly sales", to obtain the optimized method.

[0133] In one embodiment, in the data storage structure tree, the initial association path of the "raw material procurement" node and the "production cost" node is connected through multiple intermediate level nodes because they have undergone multiple links of transformation in the business process. However, from the analysis of the association strength of financial data, it is found that the change in the purchase volume of raw materials is closely related to the fluctuation of production costs, and the association strength is high. After searching, it is found that "raw materials and cost management" is the common parent node of these two nodes, so the association path is optimized to start directly from the "raw materials and cost management" node, and connect the "raw material procurement" and "production cost" nodes respectively, forming a more concise and efficient optimized path. In the multidimensional cube structure, the initial association method of "main business income" and "main business cost" in the amount dimension is an ordinary straight line connection. Considering the core position of these two data elements in financial analysis, the association strength is extremely large, and the association method is optimized to be connected with bold, red lines, and "main business core association" is marked next to the line to obtain the optimized association method.

[0134] Step 4042, associating the first data element in the initial data network based on the optimized path and the optimized method, and associating the second data element in the initial data network based on the cross-dimensional association, to obtain a final data network.

[0135] Furthermore, the data management system uses the optimized path and optimized method obtained in step 4041 to reassociate the first data element in the initial data network. Taking the data storage structure tree as an example, the connection relationship between the nodes corresponding to the first data element is reconstructed according to the optimized node association path to ensure that the association between the data elements in the tree structure is more consistent with its actual strong association characteristics. In the multidimensional cube structure, according to the optimized position association method, the association display between the corresponding positions of the first data element is reset to highlight the position relationship of the strongly associated data elements.

[0136] At the same time, for the second data element, the data management system maintains and improves the association of the corresponding positions of the second data element between different dimensions in the initial data network based on the cross-dimensional association established in step 403. By combining these two parts of work, the association relationship between the first data element and the second data element in the initial data network is integrated and optimized to obtain the final data network.

[0137] In one embodiment, in the initial data network, for the first data element, such as "inventory" and "cost of goods sold", according to the optimized path, in the data storage structure tree, the two nodes are directly connected from their common parent node "goods operating cost", which strengthens their association in the tree structure. In the multidimensional cube structure, according to the optimized method, the corresponding positions of "inventory" and "cost of goods sold" in the amount dimension and the account dimension are connected with specially designed association marks (such as colored lines with arrows) to highlight their strong association. For the second data element, such as "promotion time" (time dimension) and "sales growth amount" (amount dimension), the cross-dimensional association established previously is maintained. Finally, the data management system integrates these associations to construct the final data network.

[0138] The final data network generated by the embodiment of the present invention is more scientific, efficient and targeted. Therefore, when performing financial analysis, corporate financial personnel can obtain strongly associated data elements and their relationships more quickly and accurately, thereby improving analysis efficiency and query efficiency.

[0139] Furthermore, the comprehensive management system for financial statement data based on big data analysis provided by the present invention is described below. The comprehensive management system for financial statement data based on big data analysis described below and the comprehensive management method for financial statement data based on big data analysis described above can refer to each other.

[0140] Reference Figure 2 , Figure 2 It is a structural diagram of the comprehensive management system of financial statement data based on big data analysis provided by the present invention. The comprehensive management system of financial statement data based on big data analysis includes.

[0141] A structure tree construction module 210 is used to divide the collected financial statement data and construct a data storage structure tree according to the data categories and hierarchical relationships of the data division results; The node mapping module 220 is used to map each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; each node in the data storage structure tree represents a data category; A data mapping module 230 is used to map each data element in the financial statement data to a data storage structure tree and a multi-dimensional cube structure to obtain an initial data network; The data association module 240 is used to associate the corresponding data elements at different nodes and different dimensions in the initial data network based on the association relationship between the financial statement data to obtain the final data network.

[0142] The embodiment of the present invention manages financial statement data through a data network constructed by a data storage structure tree and a multi-dimensional cube structure, thereby improving the management efficiency and accuracy of the financial statement data.

[0143] See also Figure 3 , Figure 3 FIG. 1 is an embodiment diagram of an electronic device provided by an embodiment of the present invention. Figure 3 As shown, an embodiment of the present invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, the following steps are implemented: Divide the collected financial statement data and build a data storage structure tree based on the data categories and hierarchical relationships of the data division results; Map each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; each node in the data storage structure tree represents a data category; Map each data element in the financial statement data to the data storage structure tree and multidimensional cube structure to obtain the initial data network; Based on the association relationship between the financial statement data, the corresponding data elements at different nodes and dimensions in the initial data network are associated to obtain the final data network.

[0144] See also Figure 4 , Figure 4 Detailed description of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400, on which a computer program 311 is stored. When the computer program 311 is executed by a processor, the following steps are implemented: Divide the collected financial statement data and build a data storage structure tree based on the data categories and hierarchical relationships of the data division results; Map each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; each node in the data storage structure tree represents a data category; Map each data element in the financial statement data to the data storage structure tree and multidimensional cube structure to obtain the initial data network; Based on the association relationship between the financial statement data, the corresponding data elements at different nodes and dimensions in the initial data network are associated to obtain the final data network.

[0145] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the comprehensive management method of financial statement data based on big data analysis provided by the above methods.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A comprehensive management method for financial statement data based on big data analysis, characterized in that: include: Divide the collected financial statement data and build a data storage structure tree based on the data categories and hierarchical relationships of the data division results; Mapping each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; Each node in the data storage structure tree represents a data category; Mapping each data element in the financial statement data to the data storage structure tree and the multi-dimensional cube structure to obtain an initial data network; Based on the association relationship between the financial statement data, the data elements corresponding to different nodes and different dimensions in the initial data network are associated to obtain a final data network.

2. The method for comprehensive management of financial statement data based on big data analysis according to claim 1 is characterized in that: The data storage structure tree is constructed based on the data categories and hierarchical relationships of the data classification results, including: In the multidimensional financial data space, taking each data point in the financial statement data as the center, determining the regional density of each data point according to the number of data points within a preset range of each data point, and clustering the data points with the same regional density to obtain initial clusters; For each initial cluster, if the density change of the target data points at the boundary between the initial cluster and its adjacent clusters is less than or equal to the preset degree threshold, and the distance of the target data points in the feature space is less than or equal to the preset distance threshold, each initial cluster is merged with its adjacent clusters to obtain an optimized cluster; For each optimized cluster, according to the distribution shape, density characteristics and cluster characteristics of the data points in the optimized cluster, determine the sub-topic financial data category corresponding to each optimized cluster; The data storage structure tree is constructed based on each sub-theme financial data category and the hierarchical relationship of each sub-theme financial data category in its subject financial data category.

3. The method for comprehensive management of financial statement data based on big data analysis according to claim 2 is characterized in that: The data storage structure tree is constructed based on each sub-theme financial data category and the hierarchical relationship of each sub-theme financial data category in its subject financial data category, including: For each sub-theme financial data category, determine the data level of each sub-theme financial data category in its sub-theme financial data category based on the data volume of the sub-theme financial data category and its relevance to the core business in its subject financial data category; For each sub-theme financial data category, the data points in the sub-theme financial data category are divided into sub-categories according to the data features of the data points in the sub-theme financial data category, and the sub-categories are sorted according to the differences in the data features between the sub-categories to obtain the optimized data category of each sub-theme financial data category; An initial storage structure tree is constructed with each subject financial data category as a root node, the optimized data category under each subject financial data category as a leaf node, and the data level of each sub-subject financial data category in its sub-subject financial data category as the node level of the leaf node; Based on the actual access frequency and business logic association between the financial data categories, the corresponding nodes in the initial storage structure tree are associated to obtain the data storage structure tree.

4. The method for comprehensive management of financial statement data based on big data analysis according to claim 1, characterized in that: Mapping each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure includes: Identifying in the data storage structure tree a first target node related to time, a second target node related to a financial subject, and a third target node related to an amount; According to the node characteristics of each node in the data storage structure tree, the first target node, the second target node and the third target node are respectively mapped to the first initial positions corresponding to the time dimension, the subject dimension and the amount dimension of the multidimensional cube; Based on the parent-child relationship and sibling relationship of each node in the data storage structure tree, the first initial position of the dimension where each node is located is optimized to obtain the first target position of the dimension where each node is located; In the multi-dimensional cube, the multi-dimensional cube structure is constructed by taking the financial data category of each node as the attribute value of each node and combining the first target position of the dimension where each node is located.

5. The method for comprehensive management of financial statement data based on big data analysis according to claim 1, characterized in that: Mapping each data element in the financial statement data to the data storage structure tree and the multi-dimensional cube structure to obtain an initial data network includes: Extracting features from each data element in the financial statement data to obtain element features of each data element; the element features at least include element name, element type, financial statement category, time attribute, financial account information involved, and amount value involved; For each data element, path matching is performed in the data storage structure tree according to the element characteristics to obtain a fourth target node in the data storage structure tree, and each data element is mapped to a second initial position of a corresponding dimension of the multi-dimensional cube structure based on the element characteristics; For each data element, the second initial position is optimized according to the parent-child relationship and the sibling relationship of the fourth target node in the data storage structure tree to obtain the second target position of each data element in the corresponding dimension of the multi-dimensional cube structure; Based on the fourth target node and the second target position, each data element is mapped to the data storage structure tree and the multi-dimensional cube structure respectively to obtain the initial data network.

6. The method for comprehensive management of financial statement data based on big data analysis according to claim 5 is characterized in that: The mapping of each data element to the data storage structure tree and the multi-dimensional cube structure based on the fourth target node and the second target position to obtain the initial data network comprises: Associating the financial data category of each data element as an attribute value with a second target position of each dimension in the multi-dimensional cube structure; For each data element, if the position of the fourth target node in the data storage structure tree is consistent with the second target position of the corresponding dimension in the multi-dimensional cube structure, and the attribute of the fourth target node in the data storage structure tree is associated with the attribute of the corresponding dimension in the multi-dimensional cube structure, then a bidirectional index relationship between the position of the fourth target node and the second target position is established; Mapping each data element to a fourth target node of the data storage structure tree, and mapping each data element to a second target position of a corresponding dimension of the multi-dimensional cube structure; Based on the bidirectional index relationship, the mapped data storage structure tree and the mapped multi-dimensional cube structure, an initial data network is constructed by fusion.

7. The method for comprehensive management of financial statement data based on big data analysis according to claim 1, characterized in that: The step of associating data elements corresponding to different nodes and dimensions in the initial data network based on the association relationship between the financial statement data to obtain a final data network includes: Analyze the financial statement data to obtain first data elements that are associated with each other in the same dimension and second data elements that are associated with each other in different dimensions; For the first data element, a node association is established between nodes corresponding to the first data element in the data storage structure tree, and a position association is established between positions corresponding to the first data element in the multi-dimensional cube structure; For the second data element, establishing a cross-dimensional association between corresponding positions of the second data element in the multi-dimensional cube structure; The first data element and the second data element in the initial data network are associated based on node association, position association and cross-dimensional association to obtain the final data network.

8. The method for comprehensive management of financial statement data based on big data analysis according to claim 7 is characterized in that: The step of associating the first data element and the second data element in the initial data network based on node association, position association and cross-dimensional association to obtain the final data network includes: Based on the association strength between the first data elements, respectively, an initial association path of the node association and an initial association mode of the position association are optimized to obtain an optimized path and an optimized mode; The first data elements in the initial data network are associated based on the optimized path and the optimized manner, and the second data elements in the initial data network are associated based on the cross-dimensional association to obtain the final data network.

9. A comprehensive management system for financial statement data based on big data analysis, characterized in that: Applicable to the comprehensive management method of financial statement data based on big data analysis as claimed in any one of claims 1 to 8; The financial statement data comprehensive management system based on big data analysis includes: A structure tree construction module is used to divide the collected financial statement data and construct a data storage structure tree based on the data categories and hierarchical relationships of the data division results; A node mapping module, used for mapping each node in the data storage structure tree to a dimension of a multidimensional cube to construct a multidimensional cube structure; each node in the data storage structure tree represents a data category; A data mapping module, used for mapping each data element in the financial statement data to the data storage structure tree and the multi-dimensional cube structure to obtain an initial data network; The data association module is used to associate the data elements corresponding to different nodes and different dimensions in the initial data network based on the association relationship between the financial statement data to obtain a final data network.

10. A non-transitory computer-readable storage medium having a computer software program stored therein, characterized in that: When the computer software program is executed by a processor, it implements the comprehensive management method of financial statement data based on big data analysis as described in any one of claims 1 to 8.

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