Financial calculation system and method based on financing project

Abstract

The invention relates to the technical field of financial management and financial analysis, in particular to a financial calculation system and method based on a financing project, and the system comprises a multi-dimensional data source integration module, a big data analysis processing module, a machine learning prediction model construction module, a dynamic adjustment and real-time prediction module and a risk assessment and strategy suggestion module. By introducing the dynamic adjustment and real-time prediction module, real-time monitoring and automatic updating of an external data source are realized, and the intelligent dynamic adjustment mechanism not only can quickly capture key factors such as market changes, policy and regulation updating and the like, but also can automatically optimize prediction model parameters according to latest data. Compared with a traditional static prediction method, the dynamic adjustment strategy can significantly improve the precision and real-time performance of financial prediction, helps enterprises to quickly make accurate financial decisions in a complex and changeable market environment, and effectively avoids potential risks.

Description

Technical Field

[0001] The present invention relates to the technical field of financial management and financial analysis, and specifically to a financial calculation system and method based on financing projects. Background Art

[0002] In the current complex and ever-changing business environment, the success of financing projects depends not only on the quality of the project itself and market demand, but also highly depends on the accurate prediction of the project's financial status and risk management. However, traditional financial measurement methods have many shortcomings, which seriously restrict the scientificity and effectiveness of project decision-making.

[0003] First, traditional methods are unable to cope with multi-dimensional data sources. The market environment, policies and regulations, industry trends, and dynamic changes in competitors are indispensable factors in the financial analysis of financing projects, but traditional methods often rely on manual collection and processing of these data, which is not only inefficient, but also difficult to ensure the comprehensiveness and real-time nature of the data. The lag and inaccuracy of this data processing directly affect the reliability of financial forecast results.

[0004] Secondly, traditional financial forecasting models lack flexibility in dealing with complex and changing external environments. External factors such as changes in the market environment, adjustments to policies and regulations, and adjustments to competitors' strategies may have a significant impact on the financial status of financing projects. However, traditional forecasting models are often based on static historical data and cannot reflect these external changes in real time, resulting in a disconnect between forecast results and actual conditions and an inability to provide strong support for decision makers.

[0005] In addition, traditional methods also have obvious deficiencies in risk assessment and strategic recommendations. The various risks faced by financing projects, such as market risk, credit risk, liquidity risk and operational risk, require comprehensive quantitative assessments, and the formulation of targeted financing strategies, cost control measures and market response recommendations based on these assessments. However, traditional methods often lack scientific assessment models and effective strategic recommendations, making it difficult for projects to respond effectively and in a timely manner when faced with risks.

[0006] In summary, the traditional financial calculation method of financing projects has many deficiencies in data processing, model prediction and risk assessment, and cannot meet the requirements of modern business environment for the refinement, real-time and intelligent financial management of financing projects. Therefore, it is necessary to develop a financial calculation system based on multi-dimensional data source integration, big data analysis and processing, machine learning prediction model construction, dynamic adjustment and real-time prediction, so as to overcome the defects of traditional methods and improve the scientificity and effectiveness of financial management of financing projects. Summary of the invention

[0007] The purpose of the present invention is to make up for the shortcomings of the prior art and provide a financial calculation system and method based on financing projects. By comprehensively using multi-dimensional data source integration, big data analysis and processing, machine learning prediction model construction, dynamic adjustment and real-time prediction, as well as risk assessment and strategy recommendation modules, efficient, accurate and real-time prediction and evaluation of the financial performance of financing projects are achieved.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a financial calculation system and method based on financing projects, including a multi-dimensional data source integration module, a big data analysis and processing module, a machine learning prediction model construction module, a dynamic adjustment and real-time prediction module, and a risk assessment and strategy recommendation module;

[0009] Multi-dimensional data source integration module: collects and integrates data from different channels, including but not limited to market environment, policies and regulations, industry trends and competitors, using API interfaces, data crawlers, and database synchronization;

[0010] Big data analysis and processing module: cleans, removes duplicates, and standardizes the collected multi-dimensional data, eliminates noise data, improves data quality, and applies data analysis technology to explore potential relationships between data;

[0011] Machine learning forecasting model building module: Combine the knowledge in the field of financial forecasting to design and train machine learning models. During the model training process, cross-validation is performed using historical financial data and integrated multi-dimensional data to continuously optimize model parameters.

[0012] Dynamic adjustment and real-time forecasting module: monitors and updates external data sources in real time, automatically adjusts forecasting model parameters based on the latest data, and provides a visual interface to display the financial forecast results of financing projects at different time points, including key indicators of revenue, cost, profit, and cash flow;

[0013] Risk assessment and strategy recommendation module: Based on the forecast results and combined with the risk assessment model, the potential risks of the financing project are quantitatively assessed. According to the assessment results, targeted financing strategies, cost control measures and market response suggestions are provided.

[0014] Furthermore, the multi-dimensional data source integration module integrates authoritative data sources from reports from major market research institutions, policy release websites of government departments, databases of industry associations, and official websites of competitors. For data sources that provide API interfaces, the system achieves efficient docking by applying for interface permissions. For internal databases, a regular synchronization mechanism is established to ensure data consistency and timeliness. The system unifies data formats and encodings, sets standard sets, and classifies and stores data according to data types, including but not limited to market environment and policies and regulations. Among them, policy and regulatory documents are specially stored in special regulations folders for easy management and retrieval. A real-time monitoring mechanism is established to dynamically adjust the crawler execution time according to the data update frequency of each website to ensure the timeliness and accuracy of the data. Data fusion technology is used to integrate and associate relevant data from different channels, establish indexes and relationships between data, and enhance the application value of data. At the same time, data quality is regularly evaluated, and data collection and processing processes are continuously optimized based on the evaluation results to ensure data accuracy and reliability. Data fusion technology is used to integrate and associate relevant data from different channels. Specifically, indexes and relationships between data are established. Let the data sets from different channels be D′ i , the fused data set is D fusion , then through association and integration operations: D fusion =Fusion(D′1,D′2,…,D′ n ), regularly evaluate the quality of data, and optimize the data collection and processing processes based on the evaluation results every month.

[0015] Furthermore, the big data analysis and processing module selects filling and direct deletion for data containing missing values, uses statistical methods or model-based methods to detect abnormal values, and processes them in parallel. All data types meet the analysis requirements and are processed by data standardization. Let the data set after standardization be D standardized , for numerical data, Z-score standardization: Eliminate noise data, apply data analysis technology to explore the potential relationship between market environment, policies and regulations and financial data, use clustering algorithm, K-Means algorithm, and group by calculating the distance from data points to cluster centers. Distance measurement:

[0016] Furthermore, the machine learning prediction model building module collects historical financial data and integrated multi-dimensional data, set as data set D, pre-processes the data, including data cleaning, deduplication, and standardization operations, extracts meaningful features from the original data, and sets the feature set as F. For time series data, the moving average and growth rate features can be calculated. According to the task and data characteristics of financial forecasting, a linear regression machine learning model is selected. The linear regression model is expressed as: y = β0 + β1x1 + β2x2 + ... + β n x n , where y is the predicted value and x is i is a feature, β i are model parameters.

[0017] Furthermore, the machine learning prediction model building module divides the data set D into a training set D train , validation set D val and the test set D test , it can be divided into 70% training set, 15% validation set, and 15% test set. t Rain trains the model and continuously adjusts the model parameters through the optimization algorithm, taking the mean square error (MSE) as the loss function, which is expressed as where y i is the true value, is the predicted value, m is the number of samples, and the validation set D is used val Perform cross-validation, calculate the evaluation indicators on the validation set, and adjust the hyperparameters of the model based on the results of cross-validation.

[0018] Furthermore, the dynamic adjustment and real-time prediction module establishes a connection with an external data source, obtains the latest data in real time, cleans, denoises and standardizes the newly acquired data, adjusts the model parameters, and recalculates the statistics of the model using the new data, the mean: variance: The parameters of the forecasting model are automatically adjusted according to the new statistics, and the adjusted model is used to forecast the financial indicators of the financing project. The key indicators of income, cost, profit and cash flow at different time points are calculated respectively, and the forecast results are displayed in the form of charts on the visual interface. The line chart shows the change of income over time, and the bar chart compares the cost at different time points.

[0019] Furthermore, the risk assessment and strategy recommendation module conducts in-depth analysis on various aspects of the financing project, identifies possible risk factors, including market risk, credit risk, liquidity risk and operational risk, and sorts and classifies these risk factors for subsequent quantification and assessment, determines the risk factors, and sets them as variables X1, X2, ..., X nSet weights w1, w2, …, w for each risk factor n ,satisfy Establish risk assessment function, linear model: R = w1X1+w2X2+…+w n X n , based on the identified risk factors, select assessment methods and models for quantification, collect data related to risk factors and input them into the risk assessment model, operate the risk assessment model, calculate the potential impact and probability of occurrence of each risk factor, and finally output the overall risk assessment report, analyze the risk assessment report, determine which risk factors have the greatest impact on the project, as well as the possible sources and triggering conditions of these risks, evaluate the project's current risk tolerance and risk control capabilities, and determine whether additional risk management measures are needed.

[0021] Furthermore, the risk assessment and strategy recommendation module proposes targeted financing strategies based on the risk assessment results, selects financing methods, determines financing scale and financing timing, proposes specific measures for cost control and saving in view of cost risks, analyzes the cost structure, and assumes that the total cost is C and the various costs are C1, C2, ..., C m , calculate the proportion of each cost to the total cost: Propose control measures for cost items with a relatively high proportion to reduce the cost of raw material procurement: C′1=C1-ΔC1, including optimizing supply chain management, reducing operating costs and improving production efficiency. Propose market development and expansion strategies for market risks, organize risk assessment reports and strategic recommendations into easy-to-understand reports or presentations, report to decision makers, adjust and improve strategic recommendations based on decision makers’ feedback and needs, and after implementing financing projects and corresponding risk management measures, continuously monitor the progress and risk status of the projects, and adjust financing strategies and risk management measures in a timely manner based on actual conditions and market changes.

[0022] On the other hand, a financial estimation method based on a financing project, the specific steps of the method are:

[0023] S100, multi-dimensional data source integration: collect and integrate data from different channels, using API interfaces, data crawlers, and database synchronization to ensure the comprehensiveness and real-time nature of the data;

[0024] S200, big data analysis and processing: clean, remove duplicates, and standardize the collected multi-dimensional data, eliminate noise data, and apply data analysis technology to explore the potential relationships between data;

[0025] S300, machine learning forecasting model construction: combining knowledge in the field of financial forecasting, designing and training machine learning models, using historical financial data and integrated multi-dimensional data for cross-validation, and continuously optimizing model parameters;

[0026] S400, dynamic adjustment and real-time forecasting: real-time monitoring and updating of external data sources, automatic adjustment of forecasting model parameters based on the latest data, and provision of a visual interface to display the financial forecast results of financing projects at different time points;

[0027] S500, Risk Assessment and Strategic Recommendations: Conduct quantitative assessments on the potential risks of financing projects, and provide targeted financing strategies, cost control measures and market response recommendations based on the assessment results to help decision makers make scientific and reasonable decision plans.

[0028] Furthermore, the S300 machine learning prediction model is constructed by collecting historical financial data from the enterprise financial system and financial statement channels, including but not limited to key financial indicators of revenue, cost, profit and cash flow, and using the market environment, policies and regulations, industry trends and competitors collected by the multi-dimensional data source integration module as data, and performing data cleaning, deduplication and standardization to ensure data consistency and availability, identify and process missing values ​​and outliers, ensure that there are no duplicate records in the data set, and perform standardization on numerical data, using the Z-score standardization method. Z-score standardization: In order to eliminate the influence of different dimensions on model training, meaningful features can be extracted from the preprocessed data. For time series data, the moving average and growth rate features can be calculated, and the feature set can be set to prepare for subsequent model training. According to the tasks and data characteristics of financial forecasting, the linear regression learning model is selected. The linear regression model is expressed as: y = β0 + β1x1 + β2x2 + ... + β n x n , where y is the predicted value and x is i is a feature, β i is the model parameter. According to the selected model, combined with the knowledge in the field of financial forecasting, the model architecture and parameters are designed. The data set is divided into training set, validation set and test set. The data set can be divided according to the ratio of 70% training set, 15% validation set and 15% test set. The model is trained with the training set, and the model parameters are continuously adjusted through the optimization algorithm to minimize the loss function. The mean square error (MSE) is used as the loss function. Its expression is where y i is the true value, is the predicted value, m is the number of samples, and the validation set D is used valPerform cross-validation to evaluate the performance of the model on unknown data, calculate evaluation indicators, adjust the model's hyperparameters based on the results of cross-validation, use the test set to evaluate the final model, verify the model's prediction accuracy and stability, and further optimize the model based on the evaluation results until a satisfactory prediction effect is achieved. Deploy the trained model to the financial measurement system, combine it with the dynamic adjustment and real-time prediction modules, and realize real-time financial forecasting of financing projects. Provide a visual interface to display the forecast results, including key financial indicators of revenue, cost, profit, and cash flow, to help decision makers intuitively understand the financial performance of the project.

[0029] Compared with the prior art, a financial calculation system and method based on financing projects has the following beneficial effects:

[0030] 1. The present invention realizes real-time monitoring and automatic updating of external data sources by introducing dynamic adjustment and real-time prediction modules. This intelligent dynamic adjustment mechanism can not only quickly capture key factors such as market changes and policy and regulatory updates, but also automatically optimize the prediction model parameters according to the latest data. Compared with traditional static prediction methods, this dynamic adjustment strategy can significantly improve the accuracy and real-time performance of financial forecasts, help enterprises make quick and accurate financial decisions in a complex and changing market environment, and effectively avoid potential risks.

[0031] 2. The system's built-in risk assessment and strategy recommendation module achieves a quantitative assessment of the potential risks of financing projects by comprehensively integrating multi-dimensional data sources such as market environment, policies and regulations, industry trends and competitors. This innovative risk assessment framework not only covers key areas such as market risk, credit risk, liquidity risk and operational risk, but also provides customized financing strategies, cost control measures and market response recommendations based on the assessment results. This personalized and precise risk response strategy greatly enhances the company's risk response capabilities in the face of uncertainty and provides a solid guarantee for the company's stable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 An operation flow chart of a financial estimation system and method based on financing projects. DETAILED DESCRIPTION

[0034] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Embodiment 1

[0036] Integrate reports from market research organizations to obtain the latest industry trends, market size, growth rate, competitor market share, and product strategy information; collect information on technological innovation, R&D investment subsidies, and tax exemptions from government policy release websites to ensure that companies can fully utilize policy dividends; use data crawler technology to capture the average financial indicators of peer companies in the industry association database, including revenue, cost, and profit margin, to provide an industry benchmark for forecasting; integrate historical financial data from corporate financial systems and financial statements, including but not limited to key financial indicators of revenue, cost, profit, and cash flow, to ensure data accuracy and completeness.

[0037] The collected multi-dimensional data is cleaned, deduplicated, and standardized to eliminate noise and redundancy in the data, improve data quality, and apply data analysis technology to explore the potential relationship between market environment, policies and regulations, and financial data. Clustering algorithms, such as the K-Means algorithm, are used to group data points by calculating the distance from the cluster center. Distance measurement: Identify the key factors that influence the financial performance of a business.

[0038] Design and train a linear regression machine learning model that can capture the linear relationship between historical financial data and multi-dimensional data. The linear regression model is expressed as: y = β0 + β1x1 + β2x2 + … + β n x n , where y is the predicted value and x is i is a feature, β i It is the model parameter. Meaningful features are extracted from the preprocessed data as the input variables of the model. Historical financial data and integrated multi-dimensional data are used for cross-validation, and the model parameters are continuously adjusted and optimized to improve the prediction accuracy.

[0039] Establish a real-time connection mechanism with external data sources to ensure timely acquisition of the latest market trends and policy changes, automatically adjust the forecast model parameters according to the latest data, ensure the timeliness and accuracy of the forecast results, and provide a visual interface to display the revenue, cost, profit and cash flow forecast results for the next three years in the form of charts to help decision makers intuitively understand the financial performance of the project.

[0040] Conduct quantitative assessments on potential risks of financing projects, including but not limited to market risk, credit risk, liquidity risk and operational risk. Based on the risk assessment results, make targeted financing strategy recommendations, including selecting financing methods, determining financing scale and timing, proposing cost control and saving measures for cost risks, and proposing market development and expansion strategies for market risks. Organize risk assessment reports and strategic recommendations into easy-to-understand reports or presentations, report to decision makers, and make adjustments and improvements based on feedback.

[0041] Embodiment 2

[0042] Focus on collecting the target company's historical financial data, including but not limited to financial statements, cash flow statements, income statements and balance sheets, in order to understand the target company's financial health and profitability. Utilize market research agency reports, industry association databases and competitors' official websites to collect information on the target company's performance in the market, customer feedback, and technology patents, assess its market competitiveness and technological strength, collect industry analysis reports, analyze industry development trends, competitive landscape and future forecasts. At the same time, pay attention to government policy release websites to understand the possible impact of changes in policies and regulations on M&A projects.

[0043] The collected multi-dimensional data is cleaned, deduplicated, and standardized. For the financial data, a detailed financial ratio analysis is performed to evaluate the financial status of the target company. The financial data of the parent company and the target company are deeply analyzed. By comparing the complementarity of the two parties in terms of business, technology, and market, the possible synergy after the merger and acquisition is evaluated. The potential relationship between the data is explored using data analysis technology to provide a basis for the subsequent construction of a predictive model.

[0044] Design and train machine learning models to capture nonlinear relationships in data, combine financial data before and after the merger, market environment, and multi-dimensional data on policies and regulations, perform cross-validation, continuously optimize model parameters, and extract meaningful features from pre-processed data. For time series data, calculate moving averages and growth rate features. For time series data, calculate moving averages (MA): Among them, x t is the value at the current time point, n is the window size of the moving average to capture the dynamic changes of the data, based on the constructed machine learning model, with mean square error (MSE) as the loss function, its expression is where y i is the true value, is the predicted value, m is the number of samples, and the validation set D is used val Cross-validation is performed to predict the financial performance after the merger and acquisition, including key financial indicators of revenue, cost, profit and cash flow after the merger and acquisition.

[0045] Establish connections with external data sources to monitor market dynamics, policy changes and the financial status of target companies during the M&A process in real time, automatically adjust forecast model parameters based on the latest data to ensure the accuracy and timeliness of forecast results, and provide a visual interface to display financial forecast results under different M&A plans, including key indicators of revenue, cost, profit and cash flow, providing intuitive data support for decision makers.

[0046] Based on the prediction results and risk assessment models, we conduct quantitative assessments on the market integration risks, legal compliance risks, and financial risks in the M&A process. We use the risk assessment model to calculate the potential impact and probability of each risk factor, and generate an overall risk assessment report. Based on the risk assessment results, we propose targeted M&A strategies, financing plans, and cost control measures. For market integration risks, we propose market development and expansion strategies. For legal compliance risks, we develop compliance review plans and response measures. For financial risks, we propose cost control and financing optimization suggestions. We organize risk assessment reports and strategic suggestions into easy-to-understand reports or presentations, report to decision makers, and adjust and improve strategic suggestions based on the feedback and needs of decision makers.

[0047] Carry out M&A projects in accordance with the formulated M&A strategies and plans to ensure the smooth progress of all work. During the implementation of the M&A projects and for a period of time after the M&A, continuously monitor the progress and risk status of the projects, and adjust financing strategies and risk management measures in a timely manner according to actual conditions and market changes.

[0048] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A financial calculation system based on financing projects, characterized in that: The system includes a multi-dimensional data source integration module, a big data analysis and processing module, a machine learning prediction model construction module, a dynamic adjustment and real-time prediction module, and a risk assessment and strategy recommendation module; Multi-dimensional data source integration module: This module is responsible for collecting and integrating data from different channels, including but not limited to: market environment data, policy and regulation data, industry trend data and competitor data; Big data analysis and processing module: cleans, removes duplicates, and standardizes the collected multi-dimensional data, eliminates noise data, improves data quality, and applies data analysis technology to explore potential relationships between data; Machine learning forecasting model building module: Combine the knowledge in the field of financial forecasting to design and train machine learning models. During the model training process, cross-validation is performed using historical financial data and integrated multi-dimensional data to continuously optimize model parameters. Dynamic adjustment and real-time forecasting module: monitors and updates external data sources in real time, automatically adjusts forecasting model parameters based on the latest data, and provides a visual interface to display the financial forecast results of financing projects at different time points, including key indicators of revenue, cost, profit, and cash flow; Risk assessment and strategy recommendation module: Based on the forecast results and combined with the risk assessment model, the potential risks of the financing project are quantitatively assessed. According to the assessment results, targeted financing strategies, cost control measures and market response suggestions are provided.

2. According to claim 1, a financial calculation system based on financing projects is characterized in that: The multi-dimensional data source integration module integrates authoritative data sources from reports from major market research institutions, policy release websites of government departments, databases of industry associations, and official websites of competitors. For data sources that provide API interfaces, the system achieves efficient docking by applying for interface permissions. For internal databases, a regular synchronization mechanism is established. The system unifies data formats and encodings, sets standard sets, and classifies and stores data according to data types, including but not limited to market environment and policies and regulations. Among them, policy and regulatory documents are specially stored in special regulations folders. A real-time monitoring mechanism is established to dynamically adjust the crawler execution time according to the data update frequency of each website. Data fusion technology is used to integrate and associate relevant data from different channels, establish indexes and relationships between data, and enhance the application value of data. At the same time, data quality is regularly evaluated, and data collection and processing processes are continuously optimized based on evaluation results to ensure data accuracy and reliability. Data fusion technology is used to integrate and associate relevant data from different channels. Specifically, indexes and relationships between data are established. Let the data sets from different channels be D′ i , the fused data set is D fusion , then through association and integration operations: D fusion =Fusion(D′1,D′2,…,D′ n ), regularly evaluate the quality of data, and optimize the data collection and processing processes based on the evaluation results every month.

3. According to claim 1, a financial calculation system based on financing projects is characterized in that: The big data analysis and processing module selects filling and direct deletion for data with missing values, uses statistical methods or model-based methods to detect outliers, and all data types meet the analysis requirements. Data standardization is performed. Let the data set after standardization be D standardized , for numerical data, Z-score standardization: Eliminate noise data, apply data analysis technology to explore the potential relationship between market environment, policies and regulations and financial data, use clustering algorithm, K-Means algorithm, and group by calculating the distance from data points to cluster centers. Distance measurement:

4. According to claim 1, a financial calculation system based on financing projects is characterized in that: The machine learning prediction model building module collects historical financial data and integrated multi-dimensional data, set as data set D, pre-processes the data, including data cleaning, deduplication, and standardization operations, extracts meaningful features from the original data, and sets the feature set as F. For time series data, the moving average and growth rate features can be calculated. According to the task and data characteristics of financial forecasting, a linear regression machine learning model is selected. The linear regression model is expressed as: y = β0 + β1x1 + β2x2 + ... + β n x n , where y is the predicted value and x is i is a feature, β i are model parameters.

5. A financial calculation system based on financing projects according to claim 4, characterized in that: The machine learning prediction model building module divides the data set D into a training set D train , validation set D val and the test set D test , it can be divided into 70% training set, 15% validation set, and 15% test set. t Rain trains the model and continuously adjusts the model parameters through the optimization algorithm, taking the mean square error (MSE) as the loss function, which is expressed as where y i is the true value, is the predicted value, m is the number of samples, and the validation set D is used val Perform cross validation, calculate the evaluation indicators on the validation set, and adjust the hyperparameters of the model based on the results of cross validation.

6. The financial calculation system based on financing projects according to claim 1, characterized in that: The dynamic adjustment and real-time prediction module establishes a connection with an external data source, obtains the latest data in real time, cleans, denoises and standardizes the newly acquired data, adjusts the model parameters, and uses the new data to recalculate the model's statistics, the mean: variance: The parameters of the forecasting model are automatically adjusted according to the new statistics, and the adjusted model is used to forecast the financial indicators of the financing project. The key indicators of income, cost, profit and cash flow at different time points are calculated respectively, and the forecast results are displayed in the form of charts on the visual interface. The line chart shows the change of income over time, and the bar chart compares the cost at different time points.

7. The financial calculation system based on financing projects according to claim 1 is characterized in that: The risk assessment and strategy recommendation module conducts in-depth analysis on various aspects of the financing project, identifies possible risk factors, including market risk, credit risk, liquidity risk and operational risk, sorts and classifies these risk factors, determines the risk factors, and sets them as variables X1, X2, ..., X n Set weights w1, w2, …, w for each risk factor n ,satisfy Establish risk assessment function, linear model: R = w1X1+w2X2+…+w n X n , based on the identified risk factors, select assessment methods and models for quantification, collect data related to risk factors and input them into the risk assessment model, operate the risk assessment model, calculate the potential impact and probability of occurrence of each risk factor, and finally output the overall risk assessment report, analyze the risk assessment report, determine the risk factors that have the greatest impact on the project, as well as the possible sources and triggering conditions of these risks, and evaluate the project's current risk tolerance and risk control capabilities.

8. A financial calculation system based on financing projects according to claim 7, characterized in that: The risk assessment and strategy recommendation module proposes targeted financing strategies based on the risk assessment results, selects financing methods, determines financing scale and financing timing, proposes specific measures for cost control and saving in view of cost risks, analyzes the cost structure, and assumes that the total cost is C and the various costs are C1, C2, ..., C m , calculate the proportion of each cost to the total cost: Propose control measures for cost items with a relatively high proportion to reduce the cost of raw material procurement: C′1=C1-ΔC1, including optimizing supply chain management, reducing operating costs and improving production efficiency. Propose market development and expansion strategies for market risks, organize risk assessment reports and strategic recommendations into easy-to-understand reports or presentations, report to decision makers, adjust and improve strategic recommendations based on decision makers’ feedback and needs, and after implementing financing projects and corresponding risk management measures, continuously monitor the progress and risk status of the projects, and adjust financing strategies and risk management measures in a timely manner based on actual conditions and market changes.

9. A financial calculation method based on a financing project, characterized in that: The specific steps of this method are: S100, multi-dimensional data source integration: collect and integrate data from different channels, using API interfaces, data crawlers, and database synchronization to ensure the comprehensiveness and real-time nature of the data; S200, big data analysis and processing: clean, remove duplicates, and standardize the collected multi-dimensional data, eliminate noise data, and apply data analysis technology to explore the potential relationships between data; S300, machine learning forecasting model construction: combining knowledge in the field of financial forecasting, designing and training machine learning models, using historical financial data and integrated multi-dimensional data for cross-validation, and continuously optimizing model parameters; S400, dynamic adjustment and real-time forecasting: real-time monitoring and updating of external data sources, automatic adjustment of forecasting model parameters based on the latest data, and provision of a visual interface to display the financial forecast results of financing projects at different time points; S500, Risk Assessment and Strategic Recommendations: Conduct quantitative assessments on the potential risks of financing projects, and provide targeted financing strategies, cost control measures and market response recommendations based on the assessment results to help decision makers make scientific and reasonable decision plans.

10. A financial calculation method based on a financing project according to claim 9, characterized in that: The S300 machine learning prediction model is constructed by collecting historical financial data from the enterprise financial system and financial statement channels, including but not limited to key financial indicators of revenue, cost, profit and cash flow, and using the market environment, policies and regulations, industry trends and competitors collected by the multi-dimensional data source integration module for data cleaning, deduplication and standardization. It identifies and processes missing values ​​and outliers, and standardizes numerical data using the Z-score standardization method. Z-score standardization: In order to eliminate the influence of different dimensions on model training, meaningful features can be extracted from the preprocessed data. For time series data, the moving average and growth rate features can be calculated. The feature set is set. According to the task and data characteristics of financial forecasting, the linear regression learning model is selected. The linear regression model is expressed as: y = β0 + β1x1 + β2x2 + ... + β n x n , where y is the predicted value and x is i is a feature, β i is the model parameter. According to the selected model, combined with the knowledge in the field of financial forecasting, the model architecture and parameters are designed. The data set is divided into training set, validation set and test set. The data set can be divided according to the ratio of 70% training set, 15% validation set and 15% test set. The model is trained with the training set, and the model parameters are continuously adjusted through the optimization algorithm to minimize the loss function. The mean square error (MSE) is used as the loss function. Its expression is where y i is the true value, is the predicted value, m is the number of samples, and the validation set D is used val Perform cross-validation to evaluate the performance of the model on unknown data, calculate evaluation indicators, adjust the model's hyperparameters based on the cross-validation results, use the test set to evaluate the final model, verify the model's prediction accuracy and stability, further tune the model based on the evaluation results, deploy the trained model to the financial measurement system, combine it with the dynamic adjustment and real-time prediction modules, and provide a visual interface.

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