Financial financing planning scheme providing system based on large language model

Abstract

The invention relates to the technical field of financial financing, in particular to a financial financing planning scheme providing system based on a large language model, which comprises a market dynamic analysis module, an asset and liability management module, a financing cost evaluation module and a financing planning optimization module. According to the method, deep learning analysis and real-time processing of financial data are realized by combining a natural language generation technology and a BERT model, the analysis capability of market data is greatly improved, and the system can adjust a financing strategy in real time to adapt to market changes by dynamically monitoring market fluctuation and trading volume, so that the decision process is optimized, and the decision efficiency is improved. Compared with the prior art, the accuracy and timeliness of decision making are improved, particularly in a high-fluctuation market environment, accurate analysis of asset and liability management helps a user to understand and manage financial conditions of the user in a refined mode, effective control over the financing cost is ensured through deep risk assessment, and the overall financial safety and sustainability are improved.

Description

Technical Field

[0001] The present invention relates to the field of financial financing technology, and in particular to a financial financing planning solution providing system based on a large language model. Background Art

[0002] Financial financing technology covers a variety of strategies used to provide individuals, businesses, or governments with the necessary funds to support their economic activities, including debt financing (through loans or bond issuance) and equity financing (obtaining funds through the sale of company shares). With the advancement of technology, especially the development of artificial intelligence and machine learning, the financial financing field is undergoing a transformation to use technology to optimize loan approval processes, risk assessment, portfolio management, and market forecasting.

[0003] Among them, the financial financing planning solution provision system based on the large language model is a system that uses natural language processing technology to optimize financial services. The system mainly uses the large language model to provide financial planning and financing solutions by understanding and generating natural language. Its uses include automated financial consulting, customer service, investment advice generation, and market trend analysis. The system can provide targeted, data-driven financial advice based on a large amount of historical data and real-time market information to help users make more informed financial decisions.

[0004] Existing technologies lack the ability to quickly respond to real-time market data and lack dynamic adaptation mechanisms. Under rapidly changing market conditions, this processing method that relies on historical data cannot effectively predict and respond to market emergencies, resulting in delayed decision-making and an inability to adjust financing strategies in a timely manner. In addition, existing technologies are inflexible when dealing with complex or non-standard financial situations. For emerging markets or innovative companies, they are unable to accurately assess their financial risks and capital needs, affecting the effectiveness of financing decisions. This limitation can easily lead to lost opportunities and increased potential risks in the modern financial environment. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and propose a financial financing planning solution providing system based on a large language model.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a financial financing planning solution providing system based on a large language model, the system comprising: The market dynamics analysis module uses natural language generation (NLG) technology to collect transaction data and market volatility indicators, monitor price fluctuations and trading volume trends in real time, evaluate the correlation between price change rates and trading volumes, and determine the intensity of fluctuations based on financing rates to obtain trading volatility intervals. The asset-liability management module analyzes asset categories and their distribution based on asset-liability and capital flow data, compares asset-liability ratios with available capital, calculates liability pressure, and analyzes capital surplus and cash flow trends to derive a cash flow stability index. The financing cost assessment module assesses the impact of market fluctuations on funding costs based on the transaction volatility range and the capital flow stability index, calculates the adjusted financing cost, and compares and analyzes it with the default risk data to obtain a financing risk rating; Based on the financing risk rating, the financing planning optimization module uses the BERT model to analyze the user's financing needs and the current market financing costs, evaluates the difference between the differential financing amount and the expected rate of return, constructs a financing schedule, and optimizes the financing portfolio according to user needs to obtain a financing plan configuration table.

[0007] The present invention is improved in that the step of evaluating the correlation between the price change rate and the trading volume is specifically as follows: Based on natural language generation (NLG) technology, we collect transaction data and market volatility indicators, monitor the collected data in real time, and use the formula: ; Calculate the ratio of price fluctuation range to volume change , we get the price to volume ratio, where represents the length of the time window, Indicates time The rate of price change within Indicates time The rate of change of trading volume within Based on the price-to-volume ratio, the correlation between price changes and volume changes is evaluated to obtain a correlation analysis result.

[0008] The present invention is improved in that the step of obtaining the trading volatility interval value is specifically as follows: Based on the correlation between the price change rate and trading volume, analyze the current financing interest rate data, obtain interest rate information from the market financing data source, calculate its average value or trend line, and obtain the current market financing cost; Based on the current market financing costs, the volatility intensity is determined using the formula: ; Calculate trading volatility range values ,in, is the scaling factor, used to adjust the volatility level, is an adjustment factor used to enhance or weaken the impact of changes in financing rates on volatility. is the current financing rate, It is the benchmark financing rate.

[0009] The present invention is improved in that the steps for calculating the debt pressure value are specifically as follows: Based on asset-liability and capital flow data, analyze different asset categories and their distribution ratio in total assets, and extract the current capital availability to obtain asset category distribution data; Based on the asset category distribution data, compare the asset category distribution ratio with the total capital available to obtain the asset-liability ratio; Based on the asset-liability ratio, the formula is used: ; Calculate debt pressure value ,in, is the total debt, It is the amount of capital available.

[0010] The present invention is improved in that the steps for obtaining the capital flow stability index are specifically as follows: Based on the debt pressure value, capital surplus data and cash flow data are extracted from the financial statements, and the average capital surplus and average cash flow over multiple periods are analyzed to obtain average surplus flow data; Based on the average data of the surplus flow, the trend of change of capital surplus and cash flow in multiple periods is compared to conduct stability analysis and obtain the trend stability analysis results; Based on the trend stability analysis results and the impact of the economic cycle, the formula is adopted: ; Calculating the Money Flow Stability Index ,in, represent The average capital surplus during the period, represent The average cash flow for the period.

[0011] The present invention is improved in that the calculation steps of the adjusted financing cost are specifically as follows: Based on the trading volatility range and the capital flow stability index, analyze the market volatility range to determine the impact of market volatility on capital costs, and compare the market volatility range with historical data to assess the degree of deviation and obtain market volatility impact data; Based on the market volatility impact data, the formula is adopted: ; Calculating Adjusted Financing Costs ,in, represents the economic impact coefficient, represents the adjustment factor, represents the market discount rate, is the base interest rate.

[0012] The present invention is improved in that the steps for obtaining the financing risk rating are specifically as follows: Based on the adjusted financing costs, the default rates and credit scores in the current market are collected, and the data is cleaned and preliminarily analyzed to obtain a preliminary data set of default risk; Comparing the preliminary default risk data set with the adjusted financing cost, exploring the correlation or trend between the two, and obtaining relationship analysis results; Based on the relationship analysis results, the impact of default risk on financing costs is evaluated to obtain a financing risk rating.

[0013] The present invention is improved in that the steps of obtaining the financing scheme configuration table are specifically as follows: Based on the financing risk rating, the BERT model is used to analyze the user's financing needs and the current market financing costs, predict financing options that match the user's needs, and obtain preliminary financing matching data; Based on the preliminary financing matching data, the relationship between the differential financing amount and the expected rate of return is evaluated using the formula: ; Calculating expected returns , and obtain the differential financing evaluation results, where Representative The expected growth rate of each financing option, Indicates the The probability weight of each financing option, Representative The cost growth rate of each financing option, Indicates the The weight of each financing option, is the total number of financing options; Based on the differential financing assessment results, target ranges for returns and risks are set, the financing schedule is adjusted, and the financing portfolio is optimized in combination with user needs to obtain a financing plan configuration table.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In this invention, by combining natural language generation technology and the BERT model, deep learning analysis and real-time processing of financial data are achieved, which greatly improves the ability to analyze market data. By dynamically monitoring market fluctuations and trading volume, the system can adjust financing strategies in real time to adapt to market changes, thereby optimizing the decision-making process and improving the accuracy and timeliness of decisions. Especially in a highly volatile market environment, the precise analysis of asset-liability management helps users to understand and manage their financial status in a refined manner, while in-depth risk assessment ensures the effective control of financing costs and improves overall financial security and sustainability. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart for evaluating the correlation between price change rate and trading volume in the present invention; Figure 3 This is a flow chart for obtaining the trading volatility interval value in the present invention; Figure 4 This is a flow chart for calculating the debt pressure value in the present invention; Figure 5 This is a flow chart for obtaining the capital flow stability index in the present invention; Figure 6 is a flow chart for calculating the adjusted financing cost in the present invention; Figure 7 A flowchart for obtaining a financing risk rating in the present invention; Figure 8 This is a flowchart for obtaining the financing plan configuration table in the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description. They do not indicate or imply that the devices or elements referred to must have a specific direction, be constructed and operate in a specific direction, and therefore should not be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined. Example

[0018] See also Figure 1The present invention provides a technical solution: a financial financing planning solution providing system based on a large language model, comprising: The market dynamics analysis module uses natural language generation (NLG) technology to collect transaction data and market volatility indicators, monitor price fluctuations and trading volume trends in real time, evaluate the correlation between price change rates and trading volumes, and determine the intensity of fluctuations based on financing rates to obtain trading volatility intervals. The asset-liability management module analyzes asset categories and their distribution based on asset-liability and capital flow data, compares asset-liability ratios with available capital, calculates liability pressure, and analyzes capital surplus and cash flow trends to derive a cash flow stability index. The financing cost assessment module evaluates the impact of market fluctuations on funding costs based on the transaction volatility range and the capital flow stability index, calculates the adjusted financing cost, and compares and analyzes it with the default risk data to obtain a financing risk rating. The financing planning optimization module uses the BERT model to analyze user financing needs and current market financing costs based on financing risk ratings, evaluates the difference between differential financing amounts and expected returns, constructs a financing schedule, and optimizes the financing portfolio according to user needs to obtain a financing plan configuration table.

[0019] The trading volatility range includes the upper limit, lower limit, and median value of volatility. The cash flow stability index includes the current ratio, quick ratio, and cash ratio. The financing risk rating includes the credit score, default probability, and sensitivity analysis results. The financing plan configuration table includes the financing period, financing cost, and profit forecast results.

[0020] See also Figure 2 , the evaluation steps of the correlation between price change rate and trading volume are as follows: Based on natural language generation (NLG) technology, we collect transaction data and market volatility indicators, monitor the collected data in real time, and use the formula: ; Calculate the ratio of price fluctuation range to volume change , we get the price to volume ratio, where represents the length of the time window, Indicates time The rate of price change within a time window, calculated by subtracting the price at the beginning of the window from the price at the end of the time window. Indicates time The rate of change of volume within a time window is calculated as the volume at the end of the time window minus the volume at the beginning of the window. The logarithmic part is used to adjust the time window the impact caused; Analyze the transaction data sources and market volatility indicators, obtain real-time data related to price fluctuation range and trading volume change trend by calling the data interface of the trading platform, use data analysis technology to standardize the data format, and filter invalid data according to transaction time and data anomaly detection rules. After cleaning, generate real-time monitoring data stream, which is used for subsequent ratio calculation and correlation analysis. Data monitoring is obtained with a time window of 1 hour ( minutes), the price during this period is determined by Metamorphosis Yuan( ), trading volume from Hands increased to hand( ), substitute into the formula to calculate: ; ; ; Within the time window of 1 hour, the ratio of price change to trading volume change is 0.008925, indicating that price fluctuations and trading volume fluctuations show a low dynamic correlation within this time window. This ratio can be further used to evaluate market change characteristics.

[0021] Based on the price-volume ratio, the correlation between price changes and volume changes is evaluated to obtain correlation analysis results. The ratio data is partitioned and reclassified into days, hours, and minutes according to the length of the time window. Historical data is used to perform statistical analysis on the upper and lower bounds of the ratio within each time window. The mean and standard deviation of the ratio are calculated, and dynamic thresholds based on the upper and lower bounds are set. The distribution characteristics of the ratio within the dynamic range are further extracted, and the characteristic data are fitted using a linear regression model. The overall correlation between price changes and trading volume changes is evaluated through the correlation coefficient, generating correlation analysis results between the price change rate and the trading volume change rate.

[0022] See also Figure 3 , the specific steps for obtaining the trading volatility interval value are: Based on the correlation between price change rate and trading volume, analyze the current financing interest rate data, obtain interest rate information from market financing data sources, calculate its average value or trend line, and obtain the current market financing cost; Financing interest rate data for multiple time periods, including daily, monthly, and annual interest rates, are collected from market financing data sources. After deduplication and cleaning of the data and elimination of outliers, time series analysis is used to extract key interest rate change trends, and its linear trend line is calculated to obtain benchmark data representing the market average interest rate. Subsequently, the fluctuation range contained in the time series is further statistically analyzed to obtain the main characteristics of the financing cost, including its upper and lower bounds, fluctuation range, and mean. Based on the characteristic results, the main parameters of the current market financing cost are confirmed and used for subsequent analysis.

[0023] Based on the current market financing cost, the volatility intensity is determined using the formula: ; Calculate trading volatility range values ,in, is the scaling factor, used to adjust the volatility level, is an adjustment factor used to enhance or weaken the impact of changes in financing rates on volatility. is the current financing rate, obtained directly from market data. The interest rate reflects the cost of borrowing funds. is the benchmark financing rate; The following data was collected: the current market financing rate , benchmark interest rate , proportionality coefficient , adjustment coefficient , substitute the value into the formula to calculate the volatility interval value: ; ; ; The trading volatility range value is 1.2193, which reflects the degree of market volatility under the current financing rate conditions. The value can be used for further risk assessment and market strategy adjustment.

[0024] See also Figure 4 , the specific steps for calculating the debt pressure value are as follows: Based on asset-liability and capital flow data, analyze different asset categories and their distribution ratio in total assets, and extract the current capital availability to obtain asset category distribution data; Parse the different asset categories in the balance sheet and their corresponding numerical ranges, extract the numerical values of current assets, fixed assets, and intangible assets, calculate based on the proportion of each category of assets in total assets, call the numerical fields of the corresponding categories in the financial statements, calculate the total value of the asset category through summation, and distribute the total assets as the denominator to each category of assets to obtain the distribution ratio of each asset category. At the same time, extract the current available capital quota field in the capital flow report and classify it into a dynamic numerical library for subsequent analysis to form asset category distribution data.

[0025] Based on the asset class distribution data, compare the distribution ratio of asset classes with the total capital available to obtain the asset-liability ratio; Compare the distribution ratios and available capital of each asset category one by one, extract the short-term asset and long-term asset fields in the balance sheet, determine the asset distribution ratio by calculating the capital utilization ratio corresponding to each asset category, divide the sum of short-term assets and long-term assets by the available capital, compare and determine the asset-liability ratio, store the ratio value in the distribution data set for subsequent operations, and generate the analysis results of the asset-liability ratio.

[0026] Based on the asset-liability ratio, the formula is: ; Calculate debt pressure value , used to measure the degree of pressure a company faces in the face of its total liabilities, among which, is the total liabilities, including but not limited to short-term and long-term liabilities, It is the amount of capital available, reflecting the company's working capital and other available funds; The following data is collected: the company's total liabilities are 10,000,000, and the available capital is 15,000,000. Substitute the values into the formula for calculation: ; ; ; The debt pressure value is 66.67%, indicating that the company's debt accounts for a high proportion of its available capital, which means that the company needs to pay more attention to the stability of cash flow in financial management to avoid excessive financial pressure.

[0027] See also Figure 5 , the specific steps for obtaining the capital flow stability index are as follows: Based on the debt pressure value, capital surplus data and cash flow data are extracted from the financial statements, and the average capital surplus and average cash flow over multiple periods are analyzed to obtain the average data of surplus flow; Capital surplus data for multiple time periods are sorted out from the financial statements and recorded in chronological order in one column. Cash flow data is recorded in another column. After the data is divided into multiple quarterly or annual periods, the capital surplus and cash flow of each time period are summarized one by one, and the average capital surplus data of all time periods is calculated, including adding all capital surplus data and dividing it by the total number of time periods. The average cash flow is calculated in the same way. The average capital surplus and average cash flow obtained are used to analyze the average data of surplus flow. Finally, the data records and calculation results of each time period are summarized to form the average value of surplus flow as the basis for subsequent analysis.

[0028] Based on the average data of surplus flow, the trend of capital surplus and cash flow in multiple periods is compared to conduct stability analysis and obtain the trend stability analysis results; The obtained average capital surplus and average cash flow are arranged in chronological order, and the changes in capital surplus and cash flow in each time period are calculated respectively. The change in each period comes from the difference between the data of the current time period and the data of the previous time period. The differences are recorded one by one and sorted out into a multi-period change data table. Then, line graphs are drawn for the change trends of capital surplus and cash flow respectively. By observing the fluctuation direction and amplitude of the trend, the correlation between the two is analyzed, the difference between the two change curves is sorted out and the stability of the difference is analyzed. Finally, the trend stability analysis results are obtained based on the fluctuation amplitude and consistency of the two sets of data.

[0029] Based on the trend stability analysis results and the impact of the economic cycle, the formula is adopted: ; Calculating the Money Flow Stability Index ,in, represent The average capital surplus in a period is obtained by dividing the total capital surplus in each period by the number of periods. represent The average cash flow for a period is calculated by dividing the total cash flow for each period by the number of periods; The data of a certain enterprise in four periods are as follows (in ten thousand yuan): Average capital surplus ( ) are: 1000, 1200, 1100, 1050; Average cash flow ( ) are: 800, 850, 900, 950; Substitute into the formula to calculate: ; ; ; ; The cash flow stability index is 36.74%, indicating that the stability of the company's cash flow is affected by changes in capital surplus and cash flow. This value can be used to further assess the health of cash flow and its long-term development trend.

[0030] See also Figure 6 , the calculation steps of the adjusted financing cost are as follows: Based on the trading volatility range and the capital flow stability index, the market volatility range is analyzed to determine the impact of market fluctuations on capital costs. The market volatility range is then compared with historical data to assess the degree of deviation and obtain market volatility impact data. The upper and lower limits of trading volatility are extracted from historical trading data and organized into interval records in chronological order. The capital flow stability index is extracted and classified period by period. The upper and lower limits of the trading volatility interval are used to calculate the average volatility of each period. By matching the classification results of the capital flow stability index, its correlation with the market volatility is observed. At the same time, the historical data of the market volatility interval is organized to establish a comparative data set containing the current interval values and the historical interval values. The degree of deviation between the two sets of data is calculated. The deviation value is obtained by comparing period by period. After integrating the above-organized data and analysis results, an analytical data set containing the market volatility interval and its volatility impact is formed.

[0031] Based on the market volatility impact data, the formula is used: ; Calculating Adjusted Financing Cost ,in, Represents the economic impact coefficient, reflecting the impact of macroeconomic conditions on financing costs, and is used to adjust the base interest rate to match the current economic environment. It stands for adjustment factor, which is calculated based on the fund flow stability index and market volatility data. It is used to adjust the financing cost to reflect the stability of fund flow and market volatility. Represents the market discount rate, which is used to adjust the calculated financing cost according to the current market situation and reflect the market's immediate adjustment demand for funding costs. is the base interest rate; The following data were collected: It is the base interest rate, which is obtained through the current interest rate policy data of financial institutions. The base interest rate is 5%. is the economic impact coefficient, which is calculated by economic indicators such as GDP growth rate and inflation rate, and is 0.03. is the adjustment factor, which is calculated by combining the capital flow stability index and market volatility, and is 0.02. is the market discount rate, which is calculated by the bond price fluctuation and trading volume in the capital market and is 0.01. Substitute it into the formula: ; ; ; ; The result shows that the adjusted financing cost is 5.05%, a slight increase from the base rate, reflecting the combined impact of market fluctuations and economic conditions on funding costs.

[0032] See also Figure 7 The specific steps for obtaining financing risk rating are as follows: Based on the adjusted financing costs, we collected the current market default rates and credit scores, cleaned and preliminarily analyzed the data, and obtained a preliminary data set on default risk. Collect the current default rates and credit scores in the market, extract default rate data and credit score data including different time periods from public market data, classify and organize them into multiple interval values, and eliminate obvious outliers, such as credit scores outside the established range or negative default rates. After cleaning the data, ensure that the remaining data meets the basic distribution requirements, distribute the cleaned default rate data by interval statistics, and sort them from high to low according to credit scores to form a preliminary data set. Through comparative analysis of default rates and credit scores, record the correlation between high default rates and low credit scores, and organize the analysis results into a preliminary data set of default risk.

[0033] Compare the initial default risk data set with the adjusted financing cost, explore the correlation or trend between the two, and obtain relationship analysis results; The preliminary data set of default risk is compared with the adjusted financing cost. The default rate, credit score and financing cost data of each period are extracted from the data set. The financing cost is arranged in time series and matched item by item with the default rate and credit score of the corresponding time period. By calculating the difference in the impact of the default rate on the financing cost, the trend of the financing cost changing with the default rate and credit score in different intervals is statistically analyzed. At the same time, the comparative data of different time periods are organized into a time series graph, and the correlation between its trend changes and market conditions is analyzed. The correlation or trend between the two is explored from the data fluctuations, and finally the relationship analysis results are formed.

[0034] Based on the relationship analysis results, the impact of default risk on financing costs is assessed to obtain a financing risk rating; First, we extract key data from the analysis results, including the changing trends of default rates, credit scores, and financing costs. We classify each period of data by impact magnitude, calculate the average impact of each category, and sort out the impact magnitude range. At the same time, we establish a default risk impact rating, and divide the financing cost impact into low, medium, and high levels. We determine the risk level based on the match between the default rate and the credit score. Through multiple verification and evaluation analyses of the specific impact of default risk on financing costs, we obtain a complete financing risk rating result.

[0035] See also Figure 8 , the specific steps for obtaining the financing plan configuration table are: Based on financing risk ratings, the BERT model is used to analyze user financing needs and current market financing costs, predict financing options that match user needs, and obtain preliminary financing matching data; The BERT model is used to analyze user financing needs and the current market financing costs. First, relevant information is extracted from the financing demand form submitted by the user, including the financing amount, target interest rate and financing term, and the information is classified and organized according to the demand category. At the same time, current financing cost-related indicators are extracted from market data, including interest rate level, cost range and term characteristics. After aligning user needs with market data, the BERT model is input to explore potential matching relationships in text descriptions, analyze the correspondence between user needs and market financing costs, sort the matching results by priority, record the financing options closest to the user needs, and form preliminary financing matching data.

[0036] Based on the preliminary financing matching data, the relationship between the differential financing amount and the expected rate of return is evaluated using the formula: ; Calculating expected returns , and obtain the differential financing evaluation results, where Representative The expected growth rate of each financing option, Indicates the The probability weight of each financing option, Representative The cost growth rate of each financing option, Indicates the The weight of each financing option, is the total number of financing options; There are the following data, It is The expected growth rate of each financing option is calculated by retrospective analysis of market data and matching with user demand data. 、 、 , It is The probability weight of each financing option is calculated based on the priority of user needs, which is 、 、 , It is The cost growth rate of each financing option is calculated by the rate of change of market cost data. 、 、 , It is The weight of each financing option is calculated by analyzing the importance of market and user demand to each option. 、 、 , is 3, substitute it into the formula for calculation: ; Molecular computing: ; Denominator calculation: ; Substituting into the formula: ; This result shows that the expected rate of return calculated , indicating that under the current configuration, the benefit brought by each unit of financing cost is 1.568 times its cost, providing a quantitative basis for optimizing the financing portfolio.

[0037] Based on the differential financing assessment results, target ranges for returns and risks are set, financing schedules are adjusted, and financing combinations are optimized based on user needs to obtain a financing solution configuration table. First, extract the return and risk data of each financing option in the evaluation results, compare and classify the return value and risk value of each option by time period, set a specific return target range and risk tolerance range, eliminate options that exceed the target range, optimize and adjust the financing time of the remaining options, combine the time preferences clearly stated in user needs, adjust the options according to priority, construct financing combinations for each time period, integrate the matching results of user needs and financing options to form an optimized financing combination table, and finally generate a financing plan configuration table.

[0038] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A financial financing planning scheme providing system based on a large language model, characterized by: The system comprises: The market dynamics analysis module uses natural language generation (NLG) technology to collect transaction data and market volatility indicators, monitor price fluctuations and trading volume trends in real time, evaluate the correlation between price change rates and trading volumes, and determine the intensity of fluctuations based on financing rates to obtain trading volatility intervals. The asset-liability management module analyzes asset categories and their distribution based on asset-liability and capital flow data, compares asset-liability ratios with available capital, calculates liability pressure, and analyzes capital surplus and cash flow trends to derive a cash flow stability index. The financing cost assessment module assesses the impact of market fluctuations on funding costs based on the transaction volatility range and the capital flow stability index, calculates the adjusted financing cost, and compares and analyzes it with the default risk data to obtain a financing risk rating; Based on the financing risk rating, the financing planning optimization module uses the BERT model to analyze the user's financing needs and the current market financing costs, evaluates the difference between the differential financing amount and the expected rate of return, constructs a financing schedule, and optimizes the financing portfolio according to user needs to obtain a financing plan configuration table.

2. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The specific steps for evaluating the correlation between the price change rate and trading volume are as follows: Based on natural language generation (NLG) technology, we collect transaction data and market volatility indicators, monitor the collected data in real time, and use the formula: ； Calculate the ratio of price fluctuation range to volume change , we get the price to volume ratio, where represents the length of the time window, Indicates time The rate of price change within Indicates time The rate of change of trading volume within Based on the price-to-volume ratio, the correlation between price changes and volume changes is evaluated to obtain a correlation analysis result.

3. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The specific steps for obtaining the trading volatility interval value are: Based on the correlation between the price change rate and trading volume, analyze the current financing interest rate data, obtain interest rate information from the market financing data source, calculate its average value or trend line, and obtain the current market financing cost; Based on the current market financing costs, the volatility intensity is determined using the formula: ； Calculate trading volatility range values ,in, is the scaling factor, used to adjust the volatility level, is an adjustment factor used to enhance or weaken the impact of changes in financing rates on volatility. is the current financing rate, It is the benchmark financing rate.

4. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The specific steps for calculating the debt pressure value are: Based on asset-liability and capital flow data, analyze different asset categories and their distribution ratio in total assets, and extract the current capital availability to obtain asset category distribution data; Based on the asset category distribution data, compare the asset category distribution ratio with the total capital available to obtain the asset-liability ratio; Based on the asset-liability ratio, the formula is used: ； Calculate debt pressure value ,in, is the total debt, It is the amount of capital available.

5. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The steps for obtaining the capital flow stability index are as follows: Based on the debt pressure value, capital surplus data and cash flow data are extracted from the financial statements, and the average capital surplus and average cash flow of multiple periods are analyzed to obtain average surplus flow data; Based on the average data of the surplus flow, the trend of change of capital surplus and cash flow in multiple periods is compared to conduct stability analysis and obtain the trend stability analysis results; Based on the trend stability analysis results and the impact of the economic cycle, the formula is adopted: ； Calculating the Money Flow Stability Index ,in, represent The average capital surplus during the period, represent The average cash flow for the period.

6. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The specific steps for calculating the adjusted financing cost are as follows: Based on the trading volatility range and the capital flow stability index, analyze the market volatility range to determine the impact of market volatility on capital costs, and compare the market volatility range with historical data to assess the degree of deviation and obtain market volatility impact data; Based on the market volatility impact data, the formula is adopted: ； Calculating Adjusted Financing Cost ,in, represents the economic impact coefficient, represents the adjustment factor, represents the market discount rate, is the base interest rate.

7. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The specific steps for obtaining the financing risk rating are as follows: Based on the adjusted financing costs, the default rates and credit scores in the current market are collected, and the data is cleaned and preliminarily analyzed to obtain a preliminary data set of default risk; Comparing the preliminary default risk data set with the adjusted financing cost, exploring the correlation or trend between the two, and obtaining relationship analysis results; Based on the relationship analysis results, the impact of default risk on financing costs is evaluated to obtain a financing risk rating.

8. The financial financing planning scheme providing system based on a large language model according to claim 1 is characterized in that: The steps for obtaining the financing plan configuration table are specifically as follows: Based on the financing risk rating, the BERT model is used to analyze the user's financing needs and the current market financing costs, predict financing options that match the user's needs, and obtain preliminary financing matching data; Based on the preliminary financing matching data, the relationship between the differential financing amount and the expected rate of return is evaluated using the formula: ； Calculating expected returns , and obtain the differential financing evaluation results, where Representative The expected growth rate of each financing option, Indicates the The probability weight of each financing option, Representative The cost growth rate of each financing option, Indicates the The weight of each financing option, is the total number of financing options; Based on the differential financing assessment results, target ranges for returns and risks are set, the financing schedule is adjusted, and the financing portfolio is optimized in combination with user needs to obtain a financing plan configuration table.

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