Financial intelligent evaluation system based on digital twinborn fusion

Abstract

The invention provides a financial intelligent evaluation system based on digital twinning fusion, and relates to the technical field of financial management and digital twinning, and the system comprises a model construction module which constructs a digital twinning virtual three-dimensional model of enterprise financial activities based on a BIM technology; the acquisition and transmission module is used for acquiring financial data, business data and Internet of Things data of an enterprise, encrypting and transmitting the financial data, the business data and the Internet of Things data to the digital twin virtual three-dimensional model; the digital mapping module is used for mapping the acquired data to a digital twin virtual three-dimensional model and providing three visual modes of a two-dimensional chart, a three-dimensional stereo and virtual reality; and the data analysis module is used for analyzing and processing data in the digital twin virtual three-dimensional model. According to the method, the corresponding three-dimensional model is constructed for the financial activities of the enterprise based on digital twinning, multi-source data real-time mapping and multi-mode visualization are achieved, dynamic rendering, early warning and trend prediction are combined, the problem of data lag is solved, and real-time accurate financial analysis is provided for the enterprise.

Description

Technical Field

[0001] The present invention relates to the field of financial management and digital twin technology, and in particular to a financial intelligence evaluation system based on digital twin fusion. Background Art

[0002] In today's digital age, businesses face increasingly complex and volatile markets and fierce competition. Traditional financial assessment methods, which primarily rely on static financial statement data and manual analysis, suffer from numerous limitations. For example, data acquisition often lags behind, failing to reflect a company's financial status and business operations in real time. Analysis is relatively limited, typically focusing solely on financial indicators, making it difficult to gain comprehensive and in-depth insight into the inherent connections between a company's financial status and business activities. Furthermore, the lack of effective forecasting and dynamic simulation capabilities for future trends means that corporate management lacks accurate and timely support when making strategic decisions and responding to risks.

[0003] The rise of digital twin technology, which can construct virtual models of physical entities and map their status, behavior, and performance in real time, offers new insights and approaches to addressing the pain points of traditional financial assessments. Combining digital twin technology with financial assessments promises to enable comprehensive, real-time, and accurate monitoring and intelligent evaluation of corporate financial activities, providing more valuable information support for corporate decision-making. Therefore, it is necessary to provide a financial intelligence assessment system based on the integration of digital twins to address these technical issues. Summary of the Invention

[0004] The purpose of this invention is to provide a financial intelligent evaluation system based on digital twin fusion to solve the problems of data lag, single analysis dimension and lack of dynamic simulation in traditional financial evaluation, to achieve real-time monitoring, intelligent analysis, accurate prediction and efficient decision-making support of the company's financial status, and to improve the company's financial management level and competitiveness.

[0005] To solve the above technical problems, the present invention provides a financial intelligence evaluation system based on digital twin fusion, including: Model building module, which builds a digital twin virtual 3D model of the company's financial activities based on BIM technology; The collection and transmission module is used to collect the company's financial data, business data, and IoT data, and encrypt and transmit them to the digital twin virtual 3D model; The digital mapping module is used to map the collected data onto the digital twin virtual 3D model, providing three visualization modes: 2D chart, 3D stereo and virtual reality; The data analysis module is used to analyze and process the data in the digital twin virtual 3D model, including calculating financial indicators, correlation analysis and trend prediction; The result feedback unit is used to display visual warnings based on data analysis results, generate warning reports and recommend response strategies.

[0006] Preferably, the model building module specifically includes submodules of data collection and standardization, three-dimensional modeling of financial elements, business process association modeling, market environment data fusion, dynamic mapping, and model verification and optimization; The data collection and standardization submodule is used to obtain the company's financial data, business data and Internet of Things data; and standardize the collected data; among them, financial data includes assets, liabilities, owner's equity in the balance sheet, income, cost, and cash flow data in the income statement; business data includes order volume, purchase amount, inventory turnover rate; Internet of Things data includes equipment operating time, energy consumption, logistics trajectory coordinates; the standardization processing includes normalization processing and missing value filling: normalizing financial indicators of different magnitudes to interval; The financial element three-dimensional modeling submodule is used to perform three-dimensional modeling of financial elements, specifically including asset modeling, liability modeling, and equity modeling, and constructs a tree structure of asset modeling, liability modeling, and equity modeling according to the accounting subject hierarchy; The business process association modeling submodule is used to perform business process association modeling, including capital flow path modeling and cost driver association; The market environment data fusion submodule is used to perform market environment data fusion, specifically including industry competition situation fusion and policy impact quantification; The dynamic mapping submodule is used to perform dynamic mapping algorithm integration, including data synchronization frequency control and trend prediction model; The model validation and optimization submodule is used to perform model validation and optimization, including root mean square error validation and sensitivity analysis.

[0007] Preferably, the acquisition and transmission module uses the AES encryption algorithm to encrypt the transmitted data, realizes real-time transmission of data through the MQTT protocol, and supports edge computing nodes to pre-process the data.

[0008] Preferably, in the financial element three-dimensional modeling sub-module of the model building module, fixed assets of the asset class are modeled as three-dimensional geometric bodies such as cuboids or cylinders, and their volume is proportional to the original value of the assets; current assets are represented by cubes or spheres, and their size is related to the amount of assets; liabilities are represented by transparent cones, the height of the cone is positively correlated with the scale of liabilities, and the transparency reflects the debt repayment risk level; owner's equity is represented by a sphere, and the radius of the sphere is proportional to the cube root of the owner's equity amount.

[0009] As a preference, in the business process association modeling submodule, the method for modeling the capital flow path is: by using a preset formula Calculating Cash Flow ,in is the i-th income item, For inflow mark, is the i-th cost item, is the outflow mark, and p is the sum of income and cost items. In the three-dimensional model, the flow direction of funds is represented by the fluid animation with arrows, and the flow rate is proportional to the is proportional to the value of; The cost driver association method is: based on the activity-based costing method, using the formula Indirect costs Apportioned to the business activity with specific number j, where is the business activity volume of the j-th business activity, are the fixed cost part and unit variable cost coefficient of the jth business activity, and m is the number of business activities.

[0010] Preferably, in the dynamic mapping submodule, the data synchronization frequency is controlled by Calculate indicator fluctuations and control data synchronization frequency; where r is the time constant and k is the number of financial indicators. is the change in the i-th financial indicator; when the indicator fluctuates When the preset fluctuation threshold is exceeded, the synchronization frequency will be automatically increased; The trend prediction model adopts exponential smoothing method, using the formula Make a prediction to get the predicted value at the next time point ,in is the historical value at time t, is the predicted value at time t, is the random error term, d represents the smoothing coefficient, and its value range is 0-1, which is used to control the weight of historical data and current data in the prediction.

[0011] Preferably, in the model verification and optimization submodule, the root mean square error verification uses the formula Calculate the root mean square error between the model prediction value and the actual value ; where n is the number of samples, 、 are the model predicted value and actual value of the i-th sample respectively; when RMSE is less than When , the model passes the verification; otherwise, parameter tuning is triggered; The sensitivity analysis is specifically performed by formula , calculate each financial factor Sensitivity of impact on core indicator Y ,in Core indicator Y for the i-th financial factor The partial derivative of The degree of influence of a small change in Y; set a contribution threshold. If the impact sensitivity is less than its contribution threshold, identify the financial factors corresponding to the impact sensitivity, and delete the corresponding financial factors to simplify the model.

[0012] As a preference, in the market environment data fusion submodule, the industry competition situation fusion uses dynamic particles of the surrounding model to represent competitors, and the particle density and market share Inversely proportional, satisfying the formula ,in is the environmental impact coefficient; The policy impact is quantified using the formula Quantify the impact of policies on corporate equity, including is the benchmark equity value, is the policy influencing factor, The overall color of the model changes with the policy implementation strength. Green indicates growth and red indicates decay.

[0013] Preferably, the digital mapping module includes a data dimension mapping unit, a visualization mode switching unit and a data synchronization rendering unit; A data dimension mapping unit is used to map financial data, business data, and IoT data to the geometric attributes, color attributes, and dynamic attributes of a virtual three-dimensional model, respectively. The asset amount in the financial data is mapped to the volume or radius of a three-dimensional geometric body, the debt scale is mapped to the height of a transparent cone, and the owner's equity amount is mapped to the cube root of the radius of a sphere. The order volume and purchase amount in the business data are mapped to the color saturation of the corresponding business process nodes, and the inventory turnover rate is mapped to the node flashing frequency. The device operating time and energy consumption in the IoT data are mapped to the luminous intensity of the device's three-dimensional model, and the logistics trajectory coordinates are mapped to the dynamic lines of the transportation path. A visualization mode switching unit is used to switch between 2D chart mode, 3D stereo mode, and virtual reality mode according to user operation instructions. The 2D chart mode supports the dynamic generation of line charts, bar charts, and heat maps. The 3D stereo mode supports 360-degree rotation, zooming, and layered expansion of models. The virtual reality mode supports gesture-based or device-based interactive operations, including data drilling, warning area positioning, and multi-dimensional data linkage viewing. The data synchronization rendering unit is used to dynamically render the virtual three-dimensional model based on the real-time data of the acquisition and transmission module at a refresh rate of not less than the set frame rate. When it is detected that the fluctuation of financial indicators exceeds the preset corresponding threshold, it automatically triggers the highlight display or color warning of the corresponding model area, where the highlight color is associated with the warning level, red indicates high risk, yellow indicates medium risk, and green indicates normal status.

[0014] Compared with related technologies, the financial intelligence evaluation system based on digital twin fusion provided by the present invention has the following beneficial effects: 1. This invention uses digital twin technology to construct a three-dimensional model, mapping financial, business, and IoT data in real time, and supports multi-modal visualization including two-dimensional charts, three-dimensional stereo, and virtual reality. Dynamic rendering and early warning mechanisms can instantly reflect fluctuations in financial indicators. Combined with trend prediction models, this provides enterprises with real-time and accurate financial status analysis, assisting management in making quick decisions and shortening data response cycles.

[0015] 2. The present invention integrates multi-dimensional data, not only considering financial data, but also integrating business data and Internet of Things data, and verifies the optimization model through sensitivity analysis and root mean square error. In the market environment data fusion sub-module, the industry competition situation and policy impact can be quantified, allowing enterprises to comprehensively and deeply identify various risks, such as market competition risks, policy change risks, etc. When the fluctuation of financial indicators exceeds the preset threshold, the model will automatically trigger an early warning, and the enterprise can take timely measures to deal with risks and reduce losses.

[0016] To sum up, the present invention builds a corresponding three-dimensional model for the financial activities of the enterprise based on digital twins, realizes real-time mapping and multi-mode visualization of multi-source data, combines dynamic rendering, early warning and trend prediction, solves the problem of data lag, provides enterprises with real-time and accurate financial analysis, and accelerates decision-making; integrates multi-dimensional data and optimizes models, quantifies the impact of the market environment, enhances the enterprise's risk identification and response capabilities, comprehensively responds to various risks, and ensures stable operation of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of the connection relationship between various modules in the financial intelligent evaluation system based on digital twin fusion provided by the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0019] The terms used in this disclosure are for the purpose of describing specific embodiments only and are not intended to limit the disclosure. As used in this disclosure and the appended claims, the singular forms "group," "class," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0020] It should be understood that although the terms first, second, third, etc. may be used in this disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this disclosure, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining."

[0021] Please refer to Figure 1 The financial intelligence evaluation system based on digital twin fusion includes: Model building module, which builds a digital twin virtual 3D model of the company's financial activities based on BIM technology; The collection and transmission module is used to collect the company's financial data, business data, and IoT data, and encrypt and transmit them to the digital twin virtual 3D model; The digital mapping module is used to map the collected data onto the digital twin virtual 3D model, providing three visualization modes: 2D chart, 3D stereo and virtual reality; The data analysis module is used to analyze and process the data in the digital twin virtual 3D model, including calculating financial indicators, correlation analysis and trend prediction; It should be further explained that the data analysis module includes a financial indicator calculation unit, a correlation analysis unit, and a trend prediction unit. The financial indicator calculation unit is used to calculate a series of core financial indicators, including but not limited to debt-to-asset ratio, current ratio, return on equity, gross profit margin, etc., based on the financial data in the digital twin virtual 3D model. It performs precise calculations according to common financial calculation formulas, such as debt-to-asset ratio = total liabilities / total assets, and current ratio = current assets / current liabilities. It also calculates and updates various financial indicators in real time at a set update frequency. The association analysis unit is used to perform multi-dimensional association analysis on financial data, business data, and IoT data. For example, it analyzes the relationship between financial indicators and business order volume and customer satisfaction, as well as the relationship between equipment operating status and production costs and product quality. It also uses data mining algorithms, such as the Apriori algorithm, to discover association rules between data. Based on the association rules, it generates an association analysis report that details the strength of the association and the degree of influence between each data dimension. The trend forecasting unit is used to forecast the trend of data in the digital twin virtual 3D model using time series analysis methods, such as the ARIMA model and exponential smoothing method. It also predicts the changing trends of financial indicators and business indicators over a period of time, such as sales and profits for the next three months. While making trend forecasts, the uncertainty of the forecast results is assessed; by calculating indicators such as confidence intervals and forecast errors, users are provided with a reference for the reliability of the forecast results; based on the forecast results and uncertainty assessments, support is provided for enterprises to make corresponding decisions; The result feedback unit is used to display visual warnings based on data analysis results, generate warning reports and recommend response strategies; It should be further explained that the result feedback unit uses different visual indicators for early warning display according to different types of data analysis results; for abnormal financial indicators, such as excessively high debt-to-asset ratios and low profit margins, the relevant data areas are highlighted in specific colors in two-dimensional charts or three-dimensional models, such as red for high-risk abnormalities and yellow for medium-risk abnormalities; for abnormal business processes, such as delayed order processing and inventory backlogs, flashing icons or dynamic arrows point to abnormal links; the latest early warning information is displayed in real time at a frequency not less than the set update frequency; if in virtual reality mode, the early warning information is presented in the form of a floating window or holographic projection to ensure that users can obtain the latest risk status in a timely manner; Automatically generate warning reports based on the severity and type of warning information; the report content includes the specific value of abnormal data, change trend, comparative analysis with historical data, possible impact, etc. Establish a response strategy library and match corresponding response strategies from the strategy library according to different warning types and severity levels; for example, for warnings of insufficient liquidity, recommended strategies may include accelerating the collection of accounts receivable, adjusting inventory management strategies, applying for short-term loans, etc.

[0022] It should be noted that this solution combines digital twin technology with financial assessment, and uses BIM technology to build a three-dimensional virtual model of financial activities to achieve real-time mapping and dynamic visualization of financial data, business data, and IoT data. Based on the model construction module, collection and transmission module, digital mapping module, data analysis module, and result feedback unit, it realizes a complete closed loop of "model construction → data collection → digital mapping → data analysis → result feedback", breaking through the single analysis dimension of the traditional financial system and forming a unique technical path.

[0023] In the present invention, the model building module specifically includes data collection and standardization, three-dimensional modeling of financial elements, business process association modeling, market environment data fusion, dynamic mapping and model verification and optimization submodules; The data collection and standardization submodule is used to obtain the company's financial data, business data and IoT data; and standardize the collected data; among them, financial data includes assets, liabilities, owner's equity in the balance sheet, income, cost, and cash flow data in the income statement; business data includes order volume, purchase amount, and inventory turnover rate; IoT data includes equipment operating time, energy consumption, and logistics trajectory coordinates; standardization processing includes normalization and missing value filling: normalizing financial indicators of different magnitudes to Interval; identify missing values in data, using the formula Fill in missing values, where is the missing value after filling, is the mean of the data, representing the average level of the data. is the standard deviation of the data column, which measures the degree of dispersion of the data, and Z is a random normal variable; The financial element 3D modeling submodule is used to perform 3D modeling of financial elements, including asset modeling, liability modeling, and equity modeling, and constructs a tree structure for asset modeling, liability modeling, and equity modeling according to the accounting subject level; The business process association modeling submodule is used to perform business process association modeling, including capital flow path modeling and cost driver association; The market environment data fusion submodule is used to integrate market environment data, including industry competition situation integration and policy impact quantification; The dynamic mapping submodule is used to integrate dynamic mapping algorithms, including data synchronization frequency control and trend prediction models; The model validation and optimization submodule is used to perform model validation and optimization, including root mean square error validation and sensitivity analysis.

[0024] It should be noted that the model building module of the present invention includes multiple submodules to build a digital twin model. The data acquisition and standardization submodule obtains enterprise financial, business and Internet of Things data and performs normalization and missing value filling. The financial element three-dimensional modeling submodule models assets, liabilities, and equity and constructs a tree structure. The business process association modeling submodule conducts fund flow path and cost driver association modeling. The market environment data fusion submodule integrates industry competition situation and quantitative policy impact. The dynamic mapping submodule integrates data synchronization frequency control and trend prediction model. The model verification and optimization submodule verifies and optimizes the model through root mean square error verification and sensitivity analysis.

[0025] In the present invention, the collection and transmission module uses the AES encryption algorithm to encrypt the transmitted data, realizes real-time transmission of data through the MQTT protocol, and supports edge computing nodes to pre-process the data.

[0026] It should be noted that the collection and transmission module of the present invention ensures the security of data transmission through the AES encryption algorithm, uses the MQTT protocol to realize real-time transmission of financial, business and Internet of Things data, and supports edge computing nodes to pre-process the original data, thereby improving data quality and transmission efficiency, and providing basic guarantees for the real-time and accuracy of the digital twin model.

[0027] In the present invention, in the financial element three-dimensional modeling sub-module of the model building module, fixed assets of the asset class are modeled as three-dimensional geometric bodies such as cuboids or cylinders, and their volumes are proportional to the original values of the assets; current assets are represented by cubes or spheres, and their sizes are related to the amount of assets; liabilities are represented by transparent cones, the height of the cones is positively correlated with the size of the liabilities, and the transparency reflects the debt repayment risk level; owner's equity is represented by a sphere, and the radius of the sphere is proportional to the cube root of the amount of owner's equity.

[0028] It should be noted that the three-dimensional modeling sub-module of financial elements of the present invention models assets, liabilities and owner's equity as different three-dimensional geometric bodies respectively, and intuitively maps the numerical scale and risk level of the corresponding financial elements through geometric features (volume, size, height, transparency, radius, etc.), forming a visual financial digital twin model, which facilitates users to intuitively understand the company's financial structure and risk status.

[0029] In the present invention, in the business process association modeling submodule, the method of capital flow path modeling is: through the preset formula Calculating Cash Flow ,in is the i-th income item, For inflow mark, is the i-th cost item, is the outflow mark, and p is the sum of income and cost items. In the three-dimensional model, the flow direction of funds is represented by the fluid animation with arrows, and the flow rate is proportional to the is proportional to the value of; The cost driver association method is: based on activity-based costing, using the formula Indirect costs Apportioned to the business activity with specific number j, where is the business activity volume of the j-th business activity, are the fixed cost part and unit variable cost coefficient of the jth business activity, and m is the number of business activities.

[0030] It should be noted that in the business process association modeling sub-module of the present invention, the capital flow path modeling calculates the capital flow value through a formula, and uses a fluid animation with an arrow to intuitively display the capital flow direction in the three-dimensional model. The flow rate is proportional to the capital flow value, realizing dynamic visualization of the capital flow; the cost driver association is based on the activity-based costing method, and the indirect costs are allocated to each business activity through a formula, and the cost density is displayed with a color heat map, which clearly presents the cost distribution and driver association of the business links, helping enterprises to optimize business processes and cost management.

[0031] In the present invention, in the dynamic mapping submodule, the data synchronization frequency is controlled by Calculate indicator fluctuations and control data synchronization frequency; where r is the time constant and k is the number of financial indicators. is the change in the i-th financial indicator; when the indicator fluctuates When the preset fluctuation threshold is exceeded, the synchronization frequency will be automatically increased; The trend forecast model uses exponential smoothing method, using the formula Make a prediction to get the predicted value at the next time point ,in is the historical value at time t, is the predicted value at time t, is the random error term, d represents the smoothing coefficient, and its value range is 0-1, which is used to control the weight of historical data and current data in the prediction.

[0032] It should be noted that the dynamic mapping submodule of the present invention uses a data synchronization frequency control algorithm to calculate the fluctuation value based on the change in financial indicators, automatically adjusts the data synchronization frequency, and ensures real-time data updates when the indicator fluctuations are abnormal; at the same time, it uses the exponential smoothing method to construct a trend prediction model, and dynamically adjusts the weights of historical data and current data through the smoothing coefficient to achieve dynamic prediction of financial indicators, providing enterprises with real-time data support and trend insights.

[0033] In the present invention, in the model verification and optimization submodule, the root mean square error is verified using the formula Calculate the root mean square error between the model prediction value and the actual value ; where n is the number of samples, 、 are the model predicted value and actual value of the i-th sample respectively; when RMSE is less than When , the model passes the verification; otherwise, parameter tuning is triggered; Sensitivity analysis is specifically done through the formula , calculate each financial factor Sensitivity of impact on core indicator Y ,in Core indicator Y for the i-th financial factor The partial derivative of The degree of influence of a small change in Y; set a contribution threshold. If the impact sensitivity is less than its contribution threshold, identify the financial factors corresponding to the impact sensitivity, and delete the corresponding financial factors to simplify the model.

[0034] In this application, It should be noted that the model verification and optimization submodule of the present invention verifies the model prediction accuracy through the root mean square error, compares the predicted value with the actual value calculation error, and confirms that the model is valid when the RMSE is less than the preset threshold θ, otherwise it triggers parameter tuning; at the same time, the sensitivity analysis formula is used to quantify the degree of influence of each financial factor on the core indicator Y, and deletes low-contribution factors to simplify the model, thereby ensuring the accuracy and efficiency of the digital twin model.

[0035] In the present invention, in the market environment data fusion submodule, the industry competition situation fusion adopts the dynamic particles of the surrounding model to represent competitors, and the particle density and market share Inversely proportional, satisfying the formula ,in is the environmental impact coefficient, which can be adjusted according to the industry concentration; Policy impact quantification using formula Quantify the impact of policies on corporate equity, including is the benchmark equity value, is the policy impact factor (tax incentives are positive, tariff increases are negative), is the policy implementation strength (value range is 0-1); the overall color of the model changes with Green indicates growth and red indicates decay.

[0036] It should be noted that the market environment data fusion sub-module of the present invention realizes the digital mapping of the external environment through the integration of industry competition situation and the quantification of policy impact: the industry competition pattern is characterized by a model in which the dynamic particle density is inversely proportional to the market share, and the particle density is adapted to the industry concentration through the environmental impact coefficient; the policy impact formula is used to quantify the impact of policies on corporate equity, and the overall color change of the model is used to intuitively display the increase or decrease of equity (green growth, red decay), helping companies to evaluate the dynamic impact of the external environment on their financial status.

[0037] In the present invention, the digital mapping module includes a data dimension mapping unit, a visualization mode switching unit and a data synchronization rendering unit; A data dimension mapping unit is used to map financial data, business data, and IoT data to the geometric attributes, color attributes, and dynamic attributes of a virtual three-dimensional model, respectively. The asset amount in the financial data is mapped to the volume or radius of a three-dimensional geometric body, the debt scale is mapped to the height of a transparent cone, and the owner's equity amount is mapped to the cube root of the radius of a sphere. The order volume and purchase amount in the business data are mapped to the color saturation of the corresponding business process nodes, and the inventory turnover rate is mapped to the node flashing frequency. The device operating time and energy consumption in the IoT data are mapped to the luminous intensity of the device's three-dimensional model, and the logistics trajectory coordinates are mapped to the dynamic lines of the transportation path. A visualization mode switching unit is used to switch between 2D chart mode, 3D stereo mode, and virtual reality mode according to user operation instructions. The 2D chart mode supports the dynamic generation of line charts, bar charts, and heat maps. The 3D stereo mode supports 360-degree rotation, zooming, and layered expansion of models. The virtual reality mode supports gesture-based or device-based interactive operations, including data drilling, warning area positioning, and multi-dimensional data linkage viewing. The data synchronization rendering unit is used to dynamically render the virtual three-dimensional model based on the real-time data of the acquisition and transmission module at a refresh rate of not less than the set frame rate. When it is detected that the fluctuation of financial indicators exceeds the preset corresponding threshold, it automatically triggers the highlight display or color warning of the corresponding model area, where the highlight color is associated with the warning level, red indicates high risk, yellow indicates medium risk, and green indicates normal status.

[0038] It should be noted that the digital mapping module of the present invention consists of three units: data dimension mapping, visualization mode switching, and data synchronization rendering. The data dimension mapping unit maps financial, business, and IoT data to the geometry, color, and dynamic attributes of a virtual three-dimensional model, respectively, allowing for intuitive data presentation. The visualization mode switching unit can switch between two-dimensional charts, three-dimensional stereo, and virtual reality modes based on user instructions, each with rich interactive functions. The data synchronization rendering unit dynamically renders the model based on real-time data at a refresh rate not lower than the set frame rate. When financial indicators fluctuate beyond a threshold, the corresponding area will be highlighted or color-changed according to the warning level, allowing users to promptly grasp the risk situation.

[0039] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.

[0040] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. The financial intelligent evaluation system based on digital twin fusion is characterized by: include: Model building module, which builds a digital twin virtual 3D model of the company's financial activities based on BIM technology; The collection and transmission module is used to collect the company's financial data, business data, and IoT data, and encrypt and transmit them to the digital twin virtual 3D model; The digital mapping module is used to map the collected data onto the digital twin virtual 3D model, providing three visualization modes: 2D chart, 3D stereo and virtual reality; The data analysis module is used to analyze and process the data in the digital twin virtual 3D model, including calculating financial indicators, correlation analysis and trend prediction; The result feedback unit is used to display visual warnings based on data analysis results, generate warning reports and recommend response strategies.

2. The financial intelligence evaluation system based on digital twin fusion according to claim 1 is characterized in that: The model building module specifically includes data collection and standardization, financial element three-dimensional modeling, business process association modeling, market environment data fusion, dynamic mapping and model verification and optimization submodules; The data collection and standardization submodule is used to obtain the company's financial data, business data and Internet of Things data; and standardize the collected data; among them, financial data includes assets, liabilities, owner's equity in the balance sheet, income, cost, and cash flow data in the income statement; business data includes order volume, purchase amount, inventory turnover rate; Internet of Things data includes equipment operating time, energy consumption, logistics trajectory coordinates; the standardization processing includes normalization processing and missing value filling: normalizing financial indicators of different magnitudes to interval; The financial element three-dimensional modeling submodule is used to perform three-dimensional modeling of financial elements, specifically including asset modeling, liability modeling, and equity modeling, and constructs a tree structure of asset modeling, liability modeling, and equity modeling according to the accounting subject hierarchy; The business process association modeling submodule is used to perform business process association modeling, including capital flow path modeling and cost driver association; The market environment data fusion submodule is used to perform market environment data fusion, specifically including industry competition situation fusion and policy impact quantification; The dynamic mapping submodule is used to perform dynamic mapping algorithm integration, including data synchronization frequency control and trend prediction model; The model validation and optimization submodule is used to perform model validation and optimization, including root mean square error validation and sensitivity analysis.

3. The financial intelligence evaluation system based on digital twin fusion according to claim 1 is characterized in that: The acquisition and transmission module uses the AES encryption algorithm to encrypt the transmitted data, realizes real-time transmission of data through the MQTT protocol, and supports edge computing nodes to pre-process the data.

4. The financial intelligence evaluation system based on digital twin fusion according to claim 2 is characterized in that: In the financial element three-dimensional modeling sub-module of the model building module, fixed assets of the asset class are modeled as three-dimensional geometric bodies such as cuboids or cylinders, and their volume is proportional to the original value of the assets; current assets are represented by cubes or spheres, and their size is related to the amount of assets; liabilities are represented by transparent cones, the height of the cone is positively correlated with the scale of liabilities, and the transparency reflects the debt repayment risk level; owner's equity is represented by a sphere, and the radius of the sphere is proportional to the cube root of the owner's equity amount.

5. The financial intelligence evaluation system based on digital twin fusion according to claim 2 is characterized in that: In the business process association modeling submodule, the method of capital flow path modeling is: through the preset formula Calculating Cash Flow ,in is the i-th income item, For inflow mark, is the i-th cost item, is the outflow mark, and p is the sum of income and cost items. In the three-dimensional model, the flow direction of funds is represented by the fluid animation with arrows, and the flow rate is proportional to the is proportional to the value of; The cost driver association method is: based on the activity-based costing method, using the formula Indirect costs Apportioned to the business activity with specific number j, where is the business activity volume of the j-th business activity, are the fixed cost part and unit variable cost coefficient of the jth business activity, and m is the number of business activities.

6. The financial intelligence evaluation system based on digital twin fusion according to claim 2 is characterized in that: In the dynamic mapping submodule, the data synchronization frequency is controlled by Calculate indicator fluctuations and control data synchronization frequency; where r is the time constant and k is the number of financial indicators. is the change in the i-th financial indicator; when the indicator fluctuates When the preset fluctuation threshold is exceeded, the synchronization frequency will be automatically increased; The trend prediction model adopts exponential smoothing method, using the formula Make a prediction to get the predicted value at the next time point ,in is the historical value at time t, is the predicted value at time t, is the random error term, d represents the smoothing coefficient, and its value range is 0-1, which is used to control the weight of historical data and current data in the prediction.

7. The financial intelligence evaluation system based on digital twin fusion according to claim 2 is characterized in that: In the model validation and optimization submodule, the root mean square error is verified using the formula Calculate the root mean square error between the model prediction value and the actual value ; where n is the number of samples, 、 are the model predicted value and actual value of the i-th sample respectively; when RMSE is less than When , the model passes the verification; otherwise, parameter tuning is triggered; The sensitivity analysis is specifically performed by formula , calculate each financial factor Sensitivity of impact on core indicator Y ,in Core indicator Y for the i-th financial factor The partial derivative of The degree of influence of a small change in Y; set a contribution threshold. If the impact sensitivity is less than its contribution threshold, identify the financial factors corresponding to the impact sensitivity, and delete the corresponding financial factors to simplify the model.

8. The financial intelligence evaluation system based on digital twin fusion according to claim 2 is characterized in that: In the market environment data fusion submodule, the industry competition situation fusion uses dynamic particles of the surrounding model to represent competitors, and the particle density and market share Inversely proportional, satisfying the formula ,in is the environmental impact coefficient; The policy impact is quantified using the formula Quantify the impact of policies on corporate equity, including is the benchmark equity value, is the policy influencing factor, The overall color of the model changes with the policy implementation strength. Green indicates growth and red indicates decay.

9. The financial intelligence evaluation system based on digital twin fusion according to claim 1 is characterized in that: The digital mapping module includes a data dimension mapping unit, a visualization mode switching unit and a data synchronization rendering unit; A data dimension mapping unit is used to map financial data, business data, and IoT data to the geometric attributes, color attributes, and dynamic attributes of a virtual three-dimensional model, respectively. The asset amount in the financial data is mapped to the volume or radius of a three-dimensional geometric body, the debt scale is mapped to the height of a transparent cone, and the owner's equity amount is mapped to the cube root of the radius of a sphere. The order volume and purchase amount in the business data are mapped to the color saturation of the corresponding business process nodes, and the inventory turnover rate is mapped to the node flashing frequency. The device operating time and energy consumption in the IoT data are mapped to the luminous intensity of the device's three-dimensional model, and the logistics trajectory coordinates are mapped to the dynamic lines of the transportation path. A visualization mode switching unit is used to switch between 2D chart mode, 3D stereo mode, and virtual reality mode according to user operation instructions. The 2D chart mode supports the dynamic generation of line charts, bar charts, and heat maps. The 3D stereo mode supports 360-degree rotation, zooming, and layered expansion of models. The virtual reality mode supports gesture-based or device-based interactive operations, including data drilling, warning area positioning, and multi-dimensional data linkage viewing. The data synchronization rendering unit is used to dynamically render the virtual three-dimensional model based on the real-time data of the acquisition and transmission module at a refresh rate of not less than the set frame rate. When it is detected that the fluctuation of financial indicators exceeds the preset corresponding threshold, it automatically triggers the highlight display or color warning of the corresponding model area, where the highlight color is associated with the warning level, red indicates high risk, yellow indicates medium risk, and green indicates normal status.

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