Enterprise financial management and decision analysis method based on decision tree algorithm
Through the enterprise financial management and decision-making analysis method based on the decision tree algorithm, the problem that enterprises find it difficult to extract the internal laws in massive data and market information is solved, and in-depth analysis of financial data and future trend prediction are achieved, providing strong corporate decision-making Strong support.
Patent Information
- Application Number
- CN202411543973.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-31
- Publication Date
- 2025-02-11
AI Technical Summary
It is difficult for existing companies to efficiently and accurately extract the inherent laws and characteristics of massive data and variable market information, which affects the effectiveness of financial management and decision-making analysis.
Using enterprise financial management and decision analysis methods based on decision tree algorithm, through data preprocessing, data warehouse and decision tree model are constructed, key factors affecting financial status and predict future trends.
It realizes in-depth analysis of corporate financial data and market information, automatically identify key factors, predict future development trends, provides strong data support for corporate strategic planning and investment decisions, and improves the automation and intelligence level of financial management.
Smart Images
Figure CN119417628A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of enterprise data information management, and more specifically, to an enterprise financial management and decision analysis method based on a decision tree algorithm. Background Art
[0002] With the rapid development of the Internet, the digitalization and intelligence of corporate operations have become an inevitable trend. Traditional corporate financial management and decision-making analysis methods have gradually shown their limitations and shortcomings when dealing with massive data and complex and changing market environments. The financial data of existing tobacco companies is closely related to the market. It is difficult to make accurate scientific decisions on the future market based on the collected market information alone. Therefore, how to provide a more efficient, accurate and intelligent financial management and decision-making analysis method has become an urgent need for companies to enhance their competitiveness and achieve sustainable development. Summary of the invention
[0003] The present invention provides a corporate financial management and decision analysis method based on a decision tree algorithm, which solves the problem that existing enterprises find it difficult to efficiently and accurately extract the inherent laws and characteristics of data from massive data and changeable market information. It can conduct in-depth analysis of corporate financial data and market information, automatically identify key factors affecting the financial status of the enterprise, and predict future development trends, providing strong data support for the strategic planning and investment decisions of the enterprise.
[0004] To achieve the above objectives, the present invention provides the following technical solutions:
[0005] A method for enterprise financial management and decision analysis based on a decision tree algorithm, comprising:
[0006] Acquire the data source of the enterprise decision-making system and pre-process the data of the data source;
[0007] Extracting, converting and loading the preprocessed data, and then building a data warehouse, so that the data warehouse becomes an analysis model library;
[0008] Based on the data in the data warehouse, a decision tree algorithm is used to perform data mining to construct a corresponding decision tree;
[0009] According to the results of the decision tree analysis, a financial statement is generated, and the generated financial statement is saved in the enterprise decision-making system.
[0010] Preferably, the preprocessing of the data from the data source includes:
[0011] Performing a format check on the raw data received from the data source to determine whether the format of all data meets the system requirements, and if so, performing data cleaning and conversion;
[0012] Integrate all cleaned and converted data, eliminate data of no use value, and ensure the value, efficiency and reliability of the data.
[0013] Preferably, the data cleaning and conversion includes:
[0014] Clean the structured data. The data cleaning process includes: removing duplicate data, filling missing values, correcting erroneous data, and detecting and processing outliers, and using statistical methods or based on business rules to identify and correct abnormal data.
[0015] Preferably, the data cleaning and conversion further includes:
[0016] Convert unstructured data. The data conversion process includes: converting text data into structured data and extracting key information using natural language processing technology.
[0017] Preferably, the extracting, converting and loading the preprocessed data to build a data warehouse includes:
[0018] Build a subject-oriented and analytical data warehouse through two data extraction, transformation and loading processes;
[0019] The first data extraction, conversion and loading process is to carry out data integration oriented to the subject, extract data from multiple data sources, carry out preliminary data integration, and build a data model library oriented to the subject;
[0020] The second data extraction, transformation and loading process converts the initially integrated data into the form required by the analysis model library to ensure that the data is suitable for in-depth decision analysis.
[0021] Preferably, the data in the data warehouse is mined using a decision tree algorithm, including:
[0022] By building a decision tree, the inherent laws and characteristics of the data are gradually extracted;
[0023] During the construction process, the decision tree construction algorithm is executed according to the attributes and categories of the total sample training set E. If E is an empty set, a failure value node is returned; check whether the data in the total sample training set E are of the same category. If all the data in E belong to the same category C, a single node with the C category label is returned; for a set A containing continuous attributes, the information gain of the attributes of set A is first calculated, and the attribute with the largest information gain is selected as the split point. The split point selection process is recursively performed on each subset until the stop condition is met; divide the total sample training set E into multiple subsets, and construct nodes according to each split point to recursively construct subtrees until the stop condition is met;
[0024] Then pruning and feature selection are performed to obtain the output decision tree model.
[0025] Preferably, the data mining based on the data in the data warehouse using a decision tree algorithm also includes:
[0026] In the process of building the decision tree, the redundancy of the attributes is calculated, and the attributes with little influence on the classification results are eliminated by using feature selection methods. The nodes of the decision tree are gradually established according to the data characteristics and target variables.
[0027] Preferably, the data mining based on the data in the data warehouse using a decision tree algorithm also includes:
[0028] In the process of decision tree construction, the data set is divided into different subsets by splitting the samples until the stopping condition is met to form leaf nodes. Finally, the test conditions are judged on the leaf nodes, and the samples are classified or regressed to complete the data analysis process.
[0029] If the test condition is not met, select the test attribute and mark the node, divide the training set according to the test attribute, recursively call each divided training set, and add the generated nodes to the decision tree.
[0030] Preferably, the data mining based on the data in the data warehouse using a decision tree algorithm also includes:
[0031] During the construction of the decision tree, input the training set subset corresponding to the current node, the remaining unused feature set, select the test attribute and mark the node:
[0032] Select the best feature from the remaining feature set as the test attribute, and use the test attribute to update the label information of the current node;
[0033] According to different values of the test attribute, the current training set subset is divided into multiple smaller subsets;
[0034] For each divided training set subset, recursively execute the test attribute step to generate a new subtree and add the generated nodes to the current decision tree;
[0035] Output the updated decision tree model. As the recursive call proceeds, the decision tree model contains more nodes and branches.
[0036] Preferably, it also includes:
[0037] Set up user interaction interfaces to display, view, download, and export reports.
[0038] The present invention provides a method for enterprise financial management and decision analysis based on a decision tree algorithm, which pre-processes the data source of the access system and builds a data warehouse. Based on the data in the data warehouse, a decision tree algorithm is used to perform data mining to build a corresponding decision tree, so as to extract the inherent laws and characteristics of the data and generate financial statements. The method solves the problem that existing enterprises are difficult to efficiently and accurately extract the inherent laws and characteristics of data from massive data and changeable market information, can conduct in-depth analysis of enterprise financial data and market information, automatically identify key factors affecting the financial status of enterprises, and predict future development trends, providing strong data support for the strategic planning and investment decisions of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS
[0039] In order to more clearly illustrate the specific embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below.
[0040] Figure 1 It is a schematic diagram of an enterprise financial management and decision analysis method based on a decision tree algorithm provided by the present invention.
[0041] Figure 2 The present invention provides a flowchart of enterprise financial management and decision analysis based on a decision tree algorithm.
[0042] Figure 3 It is a schematic diagram of a decision tree construction process provided by an embodiment of the present invention. DETAILED DESCRIPTION
[0043] In order to enable persons skilled in the art to better understand the solutions of the embodiments of the present invention, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings and implementation modes.
[0044] In view of the problem that some enterprises currently find it difficult to efficiently and accurately extract the inherent laws and characteristics of data from massive data and changeable market information, the present invention provides an enterprise financial management and decision analysis method based on a decision tree algorithm to solve the problem that existing enterprises find it difficult to efficiently and accurately extract the inherent laws and characteristics of data from massive data and changeable market information. It can conduct in-depth analysis of enterprise financial data and market information, automatically identify key factors affecting the financial status of the enterprise, and predict future development trends.
[0045] like Figures 1 to 3 As shown, a method for enterprise financial management and decision analysis based on a decision tree algorithm includes:
[0046] S1: Acquire the data source of the enterprise decision-making system and pre-process the data of the data source.
[0047] S2: Extract, transform and load the preprocessed data, and then build a data warehouse so that the data warehouse becomes an analysis model library.
[0048] S3: Based on the data in the data warehouse, a decision tree algorithm is used to perform data mining to construct a corresponding decision tree.
[0049] S4: Generate financial statements based on the results of the decision tree analysis, and save the generated financial statements to the enterprise decision-making system.
[0050] Specifically, in the data preprocessing stage, raw data from multiple data sources are first received. These data sources may include structured and unstructured data from financial systems, sales systems, supply chain systems, etc. The input of data preprocessing includes the raw data of the data source and data cleaning and conversion rules. The input raw data sources include sales, costs, inventory and other data; data cleaning and conversion rules include format checking, data type conversion, missing value processing, etc. Data preprocessing is performed on the data sources connected to the system, including format checking of the data sources connected to the system to ensure that the format of all data meets the system requirements, such as date format, number format, etc. Structured data is cleaned, including removing duplicate data, filling missing values, correcting erroneous data, etc., and the cleaned data is output to ensure data consistency and accuracy. Unstructured data is converted, such as converting text data into structured data, and using natural language processing technology to extract key information. All cleaned and converted data are integrated, and data with no use value is eliminated to ensure the value, efficiency and reliability of the data. The data set after eliminating invalid data can improve the efficiency and accuracy of subsequent analysis. The data cleaning process also includes the detection and processing of outliers, using statistical methods or based on business rules to identify and correct abnormal data.
[0051] Extract, transform and load the preprocessed data to build a data warehouse; in the data warehouse construction phase, load the preprocessed data into the data warehouse and perform further extraction, transformation and loading operations. This process is divided into two times, each targeting different goals. In the first data extraction, transformation and loading process, extract data from multiple data sources, perform preliminary data integration, and build a data model library for the subject. In the second data extraction, transformation and loading process, further transform the preliminary integrated data into the form required by the analysis model library to ensure that the data is suitable for in-depth decision analysis. The data warehouse is completed by two data extraction, transformation and loading process tools. In the first data extraction, transformation and loading process, input the cleaned data set, data model library and analysis model library, and output the integrated data from the data model library to the analysis model library. The data integration completed by the first data extraction, transformation and loading is subject-oriented, which is the process of integrating from the data model library to the analysis model library, realizing efficient data extraction and preliminary transformation. The second data extraction, conversion and loading process inputs the output of the first data extraction, conversion and loading, the basic model library, and outputs the integrated data from the basic model library to the analysis model library. The data integration completed by the second data extraction, conversion and loading is an information conversion process for the analysis subject, from the basic model library to the analysis model library. This process focuses on the deep processing and conversion of data to meet specific analysis needs, which may involve complex operations such as data aggregation, summary, and calculation.
[0052] Based on the data in the data warehouse, the decision tree algorithm is used for data mining; the decision tree model is a tree structure containing multiple nodes and branches, and each leaf node corresponds to a category. In the process of building a decision tree, the total sample training set is set to E. According to the attributes and categories of E, the decision tree construction algorithm is executed. If E is an empty set, the failure value node is returned; if all data in E belongs to the same category C, a single node with a C-class label is returned; for the set A containing continuous attributes, its information gain is calculated, and the attribute with the largest information gain is selected as the split point; the above process is recursively performed on each subset until the stopping condition is met (such as the number of samples contained in the node reaches the threshold, all attributes have been used, etc.). In the process of building a decision tree, pruning is performed to prevent overfitting. Finally, the obtained financial statements are saved to the system and displayed. Assume that the total sample training set is E. If the E training set is an empty set, a single node of F will be returned, which is a failure value. If the data of the same attribute class C constitute the training set E, a single node with the C class label will be returned. If there is an unclassified set A containing continuous attributes, and the value of A is empty, the attribute value returned is the attribute value with the largest number of samples in the sample training set E. Traverse all the elements in the set A. If the element in the set A is a continuous attribute, let The maximum value is , the minimum value is , execute the For loop, set the initial value of j to 2, and add 1 to j after one execution until j = n -2, , let B be equal to The maximum information gain attribute value of the element, the maximum information gain attribute value in set A is X, and the mapping relationship set of set X is established ,according to The value in the node is used to construct the node, and the node label is set , , , … , , repeat the above process to establish the remaining subtrees. First, data preprocessing is performed, then the redundancy of the attributes is calculated, and the attributes that have little impact on the classification results are eliminated through methods such as feature selection. According to the data characteristics and target variables, the nodes of the decision tree are gradually established. By splitting the samples, the data set is divided into different subsets until the stop condition is met to form leaf nodes. Finally, the test conditions are judged on the leaf nodes, and the samples are classified or regressed to complete the data analysis process. If the test conditions are not met, the test attributes are selected and the nodes are marked; the training set is divided according to the test attributes; each divided training set is recursively called, and the generated nodes are added to the decision tree.
[0053] The construction of decision tree takes input data set D, which contains multiple samples, each of which has multiple attributes (features) and a target variable (category label). The stopping conditions are reaching the maximum depth, the minimum number of samples, the information gain is less than the threshold, etc. First, data preprocessing is performed. For missing values in the data set, filling methods (such as mean filling, median filling or mode filling) or deletion methods are used for processing. According to the data characteristics, the data is standardized or normalized to eliminate the dimensional differences between different features. The correlation between each attribute and the target variable is calculated (such as using indicators such as information gain, gain rate, Gini index, etc.). According to the correlation sorting, attributes with strong correlation with the target variable are selected as candidate features. Feature selection methods (such as Wrapper method, Embedded method, etc.) are used to further eliminate attributes with little influence on the classification results. Decision tree nodes are established, starting from the root node, and the optimal feature is selected as the test attribute. According to different values of the test attribute, the data set D is divided into multiple subsets. For each subset, recursively perform the following steps to select the best feature in the current subset as the test attribute; further divide the current subset into smaller subsets according to the different values of the test attribute; repeat this process until the stop condition is met and a leaf node is formed. The test condition judgment and classification are as follows: at each leaf node, the classification rule (such as majority voting method) is determined according to the sample set corresponding to the node; when a new sample arrives, it is judged according to the test condition in the decision tree, and finally the new sample is classified into the category corresponding to a leaf node.
[0054] Recursive construction of decision tree, input the training set subset corresponding to the current node and the remaining unused feature set, select test attributes and mark the node: select the best feature from the remaining feature set as the test attribute, and use the test attribute to update the label information of the current node. According to the different values of the test attribute, the current training set subset is divided into multiple smaller subsets. For each divided training set subset, recursively execute the test attribute step to generate a new subtree, and add the generated node to the current decision tree. Output the updated decision tree model. As the recursive call proceeds, the decision tree model is gradually improved and contains more nodes and branches.
[0055] Generate financial statements based on the results of decision tree analysis, including income statement, balance sheet, cash flow statement, etc. Save the generated financial statements to the system and provide a user interface for display, supporting users to view, download and export reports.
[0056] This method introduces a decision tree algorithm to analyze massive and complex financial data efficiently and accurately, improve the automation and intelligence level of corporate financial management, and thus significantly improve management efficiency. The decision tree model is used to divide the data layer by layer, and the inherent laws and characteristics of the data are gradually extracted to help companies automatically identify the key factors that affect the financial situation and provide strong support for accurate decision-making. It can be seen that this method not only improves the efficiency and accuracy of financial management, but also helps companies achieve accurate management and risk control, reduce operating costs, and enhance market competitiveness. At the same time, data mining technology based on the decision tree algorithm provides companies with the ability to deeply understand the inherent laws and characteristics of financial data, and provides strong data support for the company's strategic planning and investment decisions.
[0057] Correspondingly, the present invention also provides an enterprise financial management and decision analysis system based on the decision tree algorithm, comprising a data preprocessing module, a data warehouse module and an output module.
[0058] Data preprocessing module: Input the original data source, which can be financial data from multiple channels, and may include Excel files, CSV files, database records and other formats. Perform data validation rules and define validation rules such as data format, field integrity, and outliers. The system first checks whether the connected original data source meets the predefined format requirements. For example, check the version of the Excel file, the separator of the CSV file, etc. For data that does not meet the format requirements, the system attempts to automatically repair or mark it as abnormal data. Convert data in different formats into a unified format within the system for subsequent processing. For example, convert Excel or CSV data into structured database records. Output the cleaned financial data and convert it to a unified format.
[0059] Data warehouse module: Input the cleaned and converted financial data set. Define the rules and processes for data extraction, conversion, and loading. The first data extraction, conversion, and loading is to extract the required fields from the financial data set, perform conversion operations such as aggregation and calculation on the data according to business needs, and load the converted data into the temporary area of the data warehouse. The second data extraction, conversion, and loading is to verify the data in the temporary area to ensure the accuracy and completeness of the data, perform optimization operations such as compression and indexing on the data to improve query performance, and load the optimized data into the final area of the data warehouse. Output the constructed data warehouse, which contains structured financial data and can be used for report generation and analysis.
[0060] Decision tree analysis module: Use decision tree algorithms to conduct in-depth analysis of data in the data warehouse and dig out valuable information hidden behind the data. In the field of financial statement analysis, the decision tree analysis module can automatically identify key factors that affect financial statements, such as revenue, cost, profit, etc., and demonstrate the logical relationship between these factors by building a decision tree model. In this way, users can clearly see the mutual influence between different financial indicators and their role in the overall performance of financial statements. In addition, the decision tree analysis module can also predict future financial trends based on historical data, providing strong support for the company's strategic planning and investment decisions.
[0061] Output module: Input the constructed data warehouse. Define the layout, style and data source of the report. Query the required data from the data warehouse according to the data source definition in the report template. Fill the queried data into the report template to generate financial reports. Save the generated financial reports to the storage area inside the system. Provide a user interface that allows users to view, download or print financial reports. Output the generated financial reports and save them in PDF, Excel or other formats for users to view and use. Users can intuitively understand the financial status of the enterprise through the system interface, providing strong support for the enterprise's strategic planning and investment decisions.
[0062] It can be seen that the present invention provides a method for enterprise financial management and decision analysis based on a decision tree algorithm, which pre-processes the data source accessing the system and builds a data warehouse, and uses a decision tree algorithm to perform data mining based on the data in the data warehouse to build a corresponding decision tree, so as to extract the inherent laws and characteristics of the data and generate financial statements. It solves the problem that existing enterprises are difficult to efficiently and accurately extract the inherent laws and characteristics of data from massive data and changeable market information, can conduct in-depth analysis of enterprise financial data and market information, automatically identify key factors affecting the financial status of enterprises, and predict future development trends, providing strong data support for the strategic planning and investment decisions of enterprises.
[0063] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made in accordance with the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.
Claims
1. A method for enterprise financial management and decision analysis based on decision tree algorithm, characterized in that: include: Acquire the data source of the enterprise decision-making system and pre-process the data of the data source; Extracting, converting and loading the preprocessed data, and then building a data warehouse, so that the data warehouse becomes an analysis model library; Based on the data in the data warehouse, a decision tree algorithm is used to perform data mining to construct a corresponding decision tree; According to the results of the decision tree analysis, a financial statement is generated, and the generated financial statement is saved in the enterprise decision-making system.
2. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 1 is characterized in that: The preprocessing of the data from the data source includes: Performing a format check on the raw data received from the data source to determine whether the format of all data meets the system requirements, and if so, performing data cleaning and conversion; Integrate all cleaned and converted data, eliminate data of no use value, and ensure the value, efficiency and reliability of the data.
3. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 2 is characterized in that: The data cleaning and conversion includes: Clean the structured data. The data cleaning process includes: removing duplicate data, filling missing values, correcting erroneous data, and detecting and processing outliers, and using statistical methods or based on business rules to identify and correct abnormal data.
4. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 3 is characterized in that: The data cleaning and conversion also includes: Convert unstructured data. The data conversion process includes: converting text data into structured data and extracting key information using natural language processing technology.
5. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 4 is characterized in that: The extraction, conversion and loading of the preprocessed data to build a data warehouse includes: Build a subject-oriented and analytical data warehouse through two data extraction, transformation and loading processes; The first data extraction, conversion and loading process is to carry out data integration oriented to the subject, extract data from multiple data sources, carry out preliminary data integration, and build a data model library oriented to the subject; The second data extraction, transformation and loading process converts the initially integrated data into the form required by the analysis model library to ensure that the data is suitable for in-depth decision analysis.
6. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 5 is characterized in that: The data based on the data warehouse is mined using a decision tree algorithm, including: By building a decision tree, the inherent laws and characteristics of the data are gradually extracted; During the construction process, the decision tree construction algorithm is executed according to the attributes and categories of the total sample training set E. If E is an empty set, a failure value node is returned; check whether the data in the total sample training set E are of the same category. If all the data in E belong to the same category C, a single node with the C category label is returned; for a set A containing continuous attributes, the information gain of the attributes of set A is first calculated, and the attribute with the largest information gain is selected as the split point. The split point selection process is recursively performed on each subset until the stop condition is met; the total sample training set E is divided into multiple subsets, and nodes are constructed according to each split point to recursively construct subtrees until the stop condition is met; Then pruning and feature selection are performed to obtain the output decision tree model.
7. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 6 is characterized in that: The data in the data warehouse is used to perform data mining using a decision tree algorithm, and further includes: In the process of building the decision tree, the redundancy of the attributes is calculated, and the attributes with little influence on the classification results are eliminated by using feature selection methods. The nodes of the decision tree are gradually established according to the data characteristics and target variables.
8. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 7 is characterized in that: The data in the data warehouse is used to perform data mining using a decision tree algorithm, and further includes: In the process of decision tree construction, the data set is divided into different subsets by splitting the samples until the stopping condition is met to form leaf nodes. Finally, the test conditions are judged on the leaf nodes, and the samples are classified or regressed to complete the data analysis process. If the test condition is not met, select the test attribute and mark the node, divide the training set according to the test attribute, recursively call each divided training set, and add the generated nodes to the decision tree.
9. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 8 is characterized in that: The data in the data warehouse is used to perform data mining using a decision tree algorithm, and further includes: During the construction of the decision tree, input the training set subset corresponding to the current node, the remaining unused feature set, select the test attribute and mark the node: Select the best feature from the remaining feature set as the test attribute, and use the test attribute to update the label information of the current node; According to different values of the test attribute, the current training set subset is divided into multiple smaller subsets; For each divided training set subset, recursively execute the test attribute step to generate a new subtree and add the generated nodes to the current decision tree; Output the updated decision tree model. As the recursive call proceeds, the decision tree model contains more nodes and branches.
10. The enterprise financial management and decision analysis method based on decision tree algorithm according to claim 9 is characterized in that: Also includes: Set up user interaction interfaces to display, view, download, and export reports.