Enterprise financial health assessment method based on big data

Abstract

The present invention relates to the technical field of enterprise evaluation, and discloses an enterprise financial health evaluation method based on big data, comprising the following steps: step one, establishing a dynamic model, adding a data collection module, a data preprocessing module, a data evaluation and analysis module, an evaluation risk identification module and an evaluation risk response module in the model; step two, collecting various types of enterprise-related data in the data collection module, including but not limited to financial data, market data, industry data, and macroeconomic data; step three, cleaning the collected data in the data preprocessing module, removing erroneous, repeated or incomplete data, and performing data standardization and normalization operations on the cleaned data; step four, calculating the preprocessed data in the data evaluation and analysis module; step five, performing risk identification on the calculation results in the evaluation risk identification module; step six, formulating corresponding risk response strategies for risk factors affecting the financial health of the enterprise.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise evaluation, and in particular to an enterprise financial health evaluation method based on big data. Background Art

[0002] In today's business environment, the assessment of corporate financial health is crucial for administrative, commercial, financial, management, supervisory or forecasting purposes. However, traditional technologies face many challenges when applied across industries and fields. Enterprises in different industries have significant differences in financial status, business models and market environment. These differences may not be fully considered when existing technologies are applied across industries, resulting in inaccurate assessment results. In addition, financial health assessment not only involves the financial field, but also requires the combination of knowledge and data from multiple fields such as market, management, and technology. However, existing technologies still have shortcomings in field integration, which limits the comprehensiveness and depth of assessment results. Therefore, a more advanced and comprehensive enterprise financial health assessment method based on big data is needed to overcome these shortcomings and improve the accuracy and effectiveness of the assessment. Summary of the invention

[0003] In view of the shortcomings of the existing technology, the present invention provides a corporate financial health assessment method based on big data, which has the advantages of high adaptability and flexibility when applied across industries and fields, and solves the many challenges faced by traditional technologies when applied across industries and fields.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for evaluating the financial health of an enterprise based on big data, comprising the following steps:

[0005] Step 1: Establish a dynamic model and add data collection module, data preprocessing module, data evaluation and analysis module, evaluation risk identification module and evaluation risk response module to the model;

[0006] Step 2: Collect various data related to the enterprise in the data collection module, including but not limited to financial data, market data, industry data, and macroeconomic data;

[0007] Step 3: Clean the collected data in the data preprocessing module, remove erroneous, duplicated or incomplete data, and perform data standardization and normalization operations on the cleaned data;

[0008] Step 4: Calculate the preprocessed data in the data evaluation and analysis module;

[0009] Step 5: Identify the risks of the calculation results in the risk identification module;

[0010] Step 6: Develop corresponding risk response strategies for risk factors that affect the financial health of the company.

[0011] Preferably, the data collection module includes a balance sheet data unit, an income statement data unit, a cash flow statement data unit and an external data unit. The balance sheet data unit collects balance sheet data through an enterprise data collection interface, the income statement data unit collects balance sheet data through an enterprise data collection interface, the cash flow statement data unit collects cash flow statement data through an enterprise data collection interface, and the external data unit collects external data through an enterprise data collection interface. The data collection module is connected to the data preprocessing module through a network.

[0012] Preferably, the data preprocessing module performs data preprocessing on the balance sheet data, the income statement data, the cash flow statement data and the external data, and after data numbering, connects with the data evaluation and analysis module through the network.

[0013] Preferably, the balance sheet data numbers the original balance sheet data and the pre-processed balance sheet data according to data classification, and the income statement data numbers the original income statement data and the pre-processed income statement data according to data classification.

[0014] Preferably, the cash flow statement data numbers the cash flow statement original data and the cash flow statement pre-processed data according to data classification, and the external data numbers the external original data and the external pre-processed data according to data classification.

[0015] Preferably, the data evaluation and analysis module includes a model data integrity determination unit, a cross-domain fusion unit and a model dynamic evaluation unit. The model data integrity determination unit calculates the model data integrity based on the balance sheet data, the income statement data, the cash flow statement data and the external data. The cross-domain fusion unit calculates the cross-domain fusion index based on the balance sheet data, the income statement data, the cash flow statement data and the external data. The model dynamic evaluation unit calculates the dynamic model evaluation accuracy based on the balance sheet data, income statement data, cash flow statement data and external data .

[0016] Preferably, the model data integrity determination unit calculates the model data integrity based on the balance sheet data, the income statement data, the cash flow statement data and the external data. , and its calculation formula is: ;In the formula, Indicates the model data integrity, They represent the original data of the balance sheet and the pre-processed data of the balance sheet respectively. Respectively represent the original data of the income statement and the pre-processed data of the income statement, Respectively represent the original data of cash flow statement and the pre-processed data of cash flow statement, represent external raw data and external preprocessed data respectively, Respectively represent the weights of balance sheet data, income statement data, cash flow statement data and external data in the model data, and .

[0017] Preferably, the cross-domain fusion unit calculates the cross-domain fusion index based on the balance sheet data, the income statement data, the cash flow statement data and the external data. , and its calculation formula is: ;In the formula, represents the cross-domain integration index, They represent the ideal preprocessed data of the balance sheet model and the preprocessed data of the balance sheet, respectively. They represent the ideal preprocessed data of the income statement model and the preprocessed data of the income statement, respectively. They represent the ideal preprocessed data of the cash flow statement model and the preprocessed data of the cash flow statement, respectively. They represent the ideal preprocessed data of the external model and the external preprocessed data respectively.

[0018] Preferably, the model dynamic evaluation unit calculates the dynamic model evaluation accuracy based on the balance sheet data, the income statement data, the cash flow statement data and the external data. , and its calculation formula is: ;In the formula, represents the dynamic model evaluation accuracy, They represent the ideal preprocessed data of the balance sheet model and the preprocessed data of the balance sheet, respectively. They represent the ideal preprocessed data of the income statement model and the preprocessed data of the income statement, respectively. They represent the ideal preprocessed data of the cash flow statement model and the preprocessed data of the cash flow statement, respectively. represent the ideal preprocessed data of the external model and the external preprocessed data, respectively. Respectively represent the weights of balance sheet data, income statement data, cash flow statement data and external data in the dynamic model evaluation, and .

[0019] Preferably, the risk identification module evaluates the model data integrity , cross-domain integration index and dynamic model evaluation accuracy Risk identification is carried out, and the identification process is as follows:

[0020] When model data integrity When it is lower than 98%, it enters the data preprocessing risk state;

[0021] When the cross-domain integration index When it is lower than 1, it enters the risk state of failing to meet the cross-domain integration standards;

[0022] When the dynamic model evaluates accuracy When it is lower than 1, the dynamic model enters a risk state of inaccuracy in evaluation.

[0023] Compared with the prior art, the present invention provides a method for evaluating the financial health of an enterprise based on big data, which has the following beneficial effects:

[0024] 1. The present invention calculates the data integrity of the model The formula integrates the balance sheet, income statement, cash flow statement and external data to provide the model with a comprehensive perspective on the company's financial status. This multi-dimensional data integration helps capture financial trends that may be overlooked by a single data set, thereby providing companies with deeper insights. The model allows the weight of each data source to be adjusted according to different business needs and data characteristics, which enables the model to adapt to various specific analysis scenarios. At the same time, the modular design of the model allows it to easily expand its functions to adapt to new data sources, making the model flexible and effective in calculating model data integrity. Previously, the data preprocessing module in the model preprocessed the raw data, which helped to eliminate noise and outliers in the data, thereby improving the accuracy of the analysis. At the same time, the data evaluation and analysis module used precise algorithms to calculate the model data integrity. This algorithm can handle complex data relationships and take into account the mutual influence between different data sources. Therefore, when applied across industries and fields, it has the advantages of high adaptability and flexibility, which means that the model can be flexibly applied in different industries and fields, thereby solving the problems faced by traditional technologies when applied across industries and fields.

[0025] 2. By calculating the cross-domain integration index Since the data in the calculation formula involves data from multiple fields, the index can be adjusted and optimized according to different industries and enterprises of different sizes to adapt to different analysis needs, making the model flexible. At the same time, by comparing the differences between actual data and ideal data, the index can serve as an early warning system for potential financial risks of enterprises, helping managers to identify and respond to risks in a timely manner, and by integrating data from different fields, it can encourage enterprises to achieve cross-departmental and cross-field collaboration in finance, operations and strategic planning, so as to improve the overall efficiency and effectiveness of promoting cross-field collaboration. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0027] 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] See also Figure 1 ,The enterprise financial health assessment method based on big data includes the following steps:

[0029] Step 1: Establish a dynamic model and add data collection module, data preprocessing module, data evaluation and analysis module, evaluation risk identification module and evaluation risk response module to the model;

[0030] Step 2: Collect various data related to the enterprise in the data collection module, including but not limited to financial data (such as balance sheet, income statement, cash flow statement), market data, industry data, and macroeconomic data;

[0031] Step 3: Clean the collected data in the data preprocessing module to remove erroneous, duplicate or incomplete data (to ensure the accuracy and completeness of the data), and perform data standardization and normalization operations on the cleaned data (for subsequent analysis);

[0032] Step 4: Calculate the preprocessed data in the data evaluation and analysis module;

[0033] Step 5: Identify the risks of the calculation results in the risk identification module;

[0034] Step 6: Develop corresponding risk response strategies for risk factors that affect the financial health of the company.

[0035] The data collection module includes a balance sheet data unit, an income statement data unit, a cash flow statement data unit and an external data unit. The balance sheet data unit collects balance sheet data through the enterprise data collection interface, the income statement data unit collects balance sheet data through the enterprise data collection interface, the cash flow statement data unit collects cash flow statement data through the enterprise data collection interface, and the external data unit collects external data through the enterprise data collection interface. The data collection module is connected to the data preprocessing module through the network.

[0036] The data preprocessing module preprocesses the balance sheet data, income statement data, cash flow statement data and external data, and after data numbering, connects with the data evaluation and analysis module through the network.

[0037] The balance sheet data is numbered according to the data classification of the original balance sheet data and the pre-processed balance sheet data, and the income statement data is numbered according to the data classification of the original income statement data and the pre-processed income statement data.

[0038] The cash flow statement data is numbered according to the data classification, including the original data of the cash flow statement and the pre-processed data of the cash flow statement; the external data is numbered according to the data classification, including the external original data and the external pre-processed data.

[0039] The data evaluation and analysis module includes a model data integrity measurement unit, a cross-domain fusion unit, and a model dynamic evaluation unit. The model data integrity measurement unit calculates the model data integrity based on the balance sheet data, income statement data, cash flow statement data, and external data. The cross-domain integration unit calculates the cross-domain integration index based on the balance sheet data, income statement data, cash flow statement data and external data. The model dynamic evaluation unit calculates the dynamic model evaluation accuracy based on the balance sheet data, income statement data, cash flow statement data and external data. .

[0040] The model data integrity determination unit calculates the model data integrity based on the balance sheet data, income statement data, cash flow statement data and external data , and its calculation formula is: ;In the formula, Indicates the model data integrity, They represent the original data of the balance sheet and the pre-processed data of the balance sheet respectively. Respectively represent the original data of the income statement and the pre-processed data of the income statement, Respectively represent the original data of cash flow statement and the pre-processed data of cash flow statement, represent external raw data and external preprocessed data respectively, Respectively represent the weights of balance sheet data, income statement data, cash flow statement data and external data in the model data, and .

[0041] Advantages: Data integrity through calculation model The formula integrates the balance sheet, income statement, cash flow statement and external data to provide the model with a comprehensive perspective on the company's financial status. This multi-dimensional data integration helps capture financial trends that may be overlooked by a single data set, thereby providing companies with deeper insights. The model allows the weight of each data source to be adjusted according to different business needs and data characteristics, which enables the model to adapt to various specific analysis scenarios. At the same time, the modular design of the model allows it to easily expand its functions to adapt to new data sources, making the model flexible and effective in calculating model data integrity. Previously, the data preprocessing module in the model preprocessed the raw data, which helped to eliminate noise and outliers in the data, thereby improving the accuracy of the analysis. At the same time, the data evaluation and analysis module used precise algorithms to calculate the model data integrity. This algorithm can handle complex data relationships and take into account the mutual influence between different data sources. Therefore, when applied across industries and fields, it has the advantages of high adaptability and flexibility, which means that the model can be flexibly applied in different industries and fields, thereby solving the problems faced by traditional technologies when applied across industries and fields.

[0042] The cross-domain integration unit calculates the cross-domain integration index based on the balance sheet data, income statement data, cash flow statement data and external data , and its calculation formula is: ;In the formula, represents the cross-domain integration index, They represent the ideal preprocessed data of the balance sheet model and the preprocessed data of the balance sheet, respectively. They represent the ideal preprocessed data of the income statement model and the preprocessed data of the income statement, respectively. They represent the ideal preprocessed data of the cash flow statement model and the preprocessed data of the cash flow statement, respectively. They represent the ideal preprocessed data of the external model and the external preprocessed data respectively.

[0043] The advantage is: by calculating the cross-domain integration index Since the data in the calculation formula involves data from multiple fields, the index can be adjusted and optimized according to different industries and enterprises of different sizes to adapt to different analysis needs, making the model flexible. At the same time, by comparing the differences between actual data and ideal data, the index can serve as an early warning system for potential financial risks of enterprises, helping managers to identify and respond to risks in a timely manner, and by integrating data from different fields, it can encourage enterprises to achieve cross-departmental and cross-field collaboration in finance, operations and strategic planning, so as to improve the overall efficiency and effectiveness of promoting cross-field collaboration.

[0044] The model dynamic evaluation unit calculates the dynamic model evaluation accuracy based on the balance sheet data, income statement data, cash flow statement data and external data , and its calculation formula is: ;In the formula, represents the dynamic model evaluation accuracy, They represent the ideal preprocessed data of the balance sheet model and the preprocessed data of the balance sheet, respectively. They represent the ideal preprocessed data of the income statement model and the preprocessed data of the income statement, respectively. They represent the ideal preprocessed data of the cash flow statement model and the preprocessed data of the cash flow statement, respectively. represent the ideal preprocessed data of the external model and the external preprocessed data, respectively. Respectively represent the weights of balance sheet data, income statement data, cash flow statement data and external data in the dynamic model evaluation, and ; By converting the model deviation of the data (the difference between the actual data and the ideal data) into a value between 0 and 1, it represents the accuracy of dynamic model evaluation , when the actual data is completely consistent with the ideal data, the dynamic model evaluation accuracy Highest (close to 1); when the difference is larger, the dynamic model evaluation accuracy The lower (closer to 0), the above formula setting can make the evaluation results more intuitive and easy to understand. The calculation formula of the model dynamic evaluation unit provides a structured method to evaluate the accuracy of the dynamic model, which helps to improve the predictive ability of the model.

[0045] Evaluate the risk identification module for model data integrity , cross-domain integration index and dynamic model evaluation accuracy Risk identification is carried out, and the identification process is as follows:

[0046] When model data integrity When it is lower than 98%, it enters the data preprocessing risk state;

[0047] When the cross-domain integration index When it is lower than 1, it enters the risk state of failing to meet the cross-domain integration standards;

[0048] When the dynamic model evaluates accuracy When it is lower than 1, the dynamic model enters a risk state of inaccuracy in evaluation.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The enterprise financial health assessment method based on big data is characterized by: The following steps are involved: Step 1: Establish a dynamic model and add data collection module, data preprocessing module, data evaluation and analysis module, evaluation risk identification module and evaluation risk response module to the model; Step 2: Collect various data related to the enterprise in the data collection module, including but not limited to financial data, market data, industry data, and macroeconomic data; Step 3: Clean the collected data in the data preprocessing module, remove erroneous, duplicated or incomplete data, and perform data standardization and normalization operations on the cleaned data; Step 4: Calculate the preprocessed data in the data evaluation and analysis module; Step 5: Identify the risks of the calculation results in the risk identification module; Step 6: Develop corresponding risk response strategies for risk factors that affect the financial health of the enterprise; The data collection module includes a balance sheet data unit, an income statement data unit, a cash flow statement data unit and an external data unit. The balance sheet data unit collects balance sheet data through an enterprise data collection interface, the income statement data unit collects balance sheet data through an enterprise data collection interface, the cash flow statement data unit collects cash flow statement data through an enterprise data collection interface, and the external data unit collects external data through an enterprise data collection interface. The data collection module is connected to the data preprocessing module through a network; The data preprocessing module performs data preprocessing on the balance sheet data, the income statement data, the cash flow statement data and the external data, and after data numbering, connects with the data evaluation and analysis module through the network; The balance sheet data is numbered according to the data classification of the original balance sheet data and the pre-processed balance sheet data, and the profit statement data is numbered according to the data classification of the original profit statement data and the pre-processed profit statement data; The cash flow statement data is numbered according to data classification for the original data of the cash flow statement and the pre-processed data of the cash flow statement, and the external data is numbered according to data classification for the external original data and the external pre-processed data; The data evaluation and analysis module includes a model data integrity determination unit, a cross-domain fusion unit and a model dynamic evaluation unit. The model data integrity determination unit calculates the model data integrity based on the balance sheet data, the income statement data, the cash flow statement data and the external data. The cross-domain fusion unit calculates the cross-domain fusion index based on the balance sheet data, the income statement data, the cash flow statement data and the external data. The model dynamic evaluation unit calculates the dynamic model evaluation accuracy based on the balance sheet data, income statement data, cash flow statement data and external data ; The model data integrity determination unit calculates the model data integrity based on the balance sheet data, the income statement data, the cash flow statement data and the external data , and its calculation formula is: ;In the formula, Indicates the model data integrity, They represent the original data of the balance sheet and the pre-processed data of the balance sheet respectively. Respectively represent the original data of the income statement and the pre-processed data of the income statement, Respectively represent the original data of cash flow statement and the pre-processed data of cash flow statement, represent external raw data and external preprocessed data respectively, Respectively represent the weights of balance sheet data, income statement data, cash flow statement data and external data in the model data, and ; The cross-domain fusion unit calculates the cross-domain fusion index based on the balance sheet data, the income statement data, the cash flow statement data and the external data , and its calculation formula is: ;In the formula, represents the cross-domain integration index, They represent the ideal preprocessed data of the balance sheet model and the preprocessed data of the balance sheet, respectively. They represent the ideal preprocessed data of the income statement model and the preprocessed data of the income statement, respectively. They represent the ideal preprocessed data of the cash flow statement model and the preprocessed data of the cash flow statement, respectively. They represent the ideal preprocessed data of the external model and the external preprocessed data respectively; The model dynamic evaluation unit calculates the dynamic model evaluation accuracy based on the balance sheet data, the income statement data, the cash flow statement data and the external data , and its calculation formula is: ;In the formula, represents the dynamic model evaluation accuracy, They represent the ideal preprocessed data of the balance sheet model and the preprocessed data of the balance sheet, respectively. They represent the ideal preprocessed data of the income statement model and the preprocessed data of the income statement, respectively. They represent the ideal preprocessed data of the cash flow statement model and the preprocessed data of the cash flow statement, respectively. represent the ideal preprocessed data of the external model and the external preprocessed data, respectively. Respectively represent the weights of balance sheet data, income statement data, cash flow statement data and external data in the dynamic model evaluation, and .

2. The enterprise financial health assessment method based on big data according to claim 1 is characterized by: The risk identification module evaluates the model data integrity , cross-domain integration index and dynamic model evaluation accuracy Risk identification is carried out, and the identification process is as follows: When model data integrity When it is lower than 98%, it enters the data preprocessing risk state; When the cross-domain integration index When it is lower than 1, it enters the risk state of failing to meet the cross-domain integration standards; When dynamic model evaluation accuracy When it is lower than 1, the dynamic model enters a risk state of inaccuracy in evaluation.

Images