Business and financial fusion analysis decision management system and method based on multi-modal AI

Abstract

The invention relates to the technical field of business and financial management, in particular to a business and financial fusion analysis decision management system and method based on multi-modal AI, and the method comprises the steps: configuring deep learning and testing according to financial use and formation information, and obtaining a multi-source contact analysis record; according to the multi-source connection analysis record, feature line and non-feature line difference is configured, and a fusion decision result is obtained; according to the fusion decision result, configuring information-driven machine learning and clustering analysis to obtain a decision value decision record; configuring real-time information analysis according to the real-time decision plan to obtain a real-time financial analysis record; configuring flow analysis according to the real-time financial analysis record to obtain a flow decision result; and configuring real-time information flow analysis and personalized management according to a process decision result to obtain a real-time management result. According to the method, the capability of effectively integrating different information sources is achieved, and the real-time decision-making and long-term financial demand evaluation effects are good.

Description

Technical Field

[0001] The present application relates to the field of business and financial management technology, and in particular to a business and financial fusion analysis and decision-making management system and method based on multimodal AI. Background Art

[0002] The multimodal AI-based business-finance fusion analysis and decision-making management system is a professional application in the field of financial information analysis technology, especially in the analysis of the formation process of key businesses. This technical field involves collecting and analyzing financial usage information to optimize business operations and reduce financial costs. In the business of processing reports, the system is particularly important because the formation process of reports is financially intensive and costly. By accurately monitoring and analyzing financial usage, these systems can help companies detect areas of financial waste, improve energy efficiency, reduce operating costs, and ensure the environmental sustainability of the formation process.

[0003] The main purpose of the multimodal AI-based business-finance fusion analysis and decision management system is to improve the overall operational value by efficiently managing financial use. In the report formation process, this means the financial use of key decision-making components. The system helps enterprise managers make more informed decisions by analyzing financial use information, such as correcting the formation process or updating the decision configuration to achieve more effective financial use. In order to achieve these effects, the multimodal AI-based business-finance fusion analysis and decision management system is based on multiple means. First, it collects financial use information in real time through advanced sensors and monitoring decisions, and then uses information storage and management technologies to analyze and organize this information. Next, based on information analysis and modeling technologies such as machine learning and statistical analysis, it detects financial use patterns and potential energy-saving opportunities. Finally, through information visualization and recording tools, this complex information is transformed into actionable insights to support management in making information-based decisions, thereby determining financial use, reducing costs, and improving the environmental and economic sustainability of the report formation process.

[0004] Although existing multimodal AI-based business-finance fusion analysis and decision-making management systems can collect information through sensors, analyze information using information storage and management technologies, detect energy-saving opportunities through machine learning and statistical analysis, and support decision-making through information visualization, they still have the following shortcomings. Existing systems are limited in the in-depth analysis and understanding of multi-source connections, and it is difficult to accurately reveal the complex interactions between financial use and formation processes. Secondly, although existing systems can analyze and interpret information, they are limited in their capabilities in information integration, pattern detection, and real-time feedback correction. For example, they lack the ability to effectively integrate different information sources, or are not advanced enough in real-time information analysis and personalized management. In addition, existing systems are also insufficient in real-time decision-making and long-term financial needs evaluation, which limits their effectiveness in financial management and differentiation. Summary of the Invention

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] According to a first aspect of the present invention, the present invention claims protection for a multimodal AI-based business and financial fusion analysis and decision management system, characterized in that: the system includes a multi-source fusion unit, a financial management fusion decision unit, a financial decision value assessment unit, a periodic record evaluation unit, a mapping real-time modeling unit, an information analysis model unit, a process analysis unit, and a real-time management unit;

[0007] The multi-source fusion unit configures deep learning and testing based on financial usage and formation information to obtain multi-source connection analysis records;

[0008] The financial management fusion decision unit configures the difference between characteristic lines and non-characteristic lines based on the multi-source connection analysis records, and obtains the fusion decision result;

[0009] The financial decision value assessment unit configures information-driven machine learning and cluster analysis based on the fusion decision result to obtain a decision value decision record;

[0010] The cycle record evaluation unit configures cycle record parsing based on the decision value decision record to obtain a financial demand evaluation record;

[0011] The mapping real-time modeling unit configures system multimodal modeling based on the financial demand evaluation record to obtain a real-time decision plan;

[0012] The information analysis model unit configures real-time information analysis according to the real-time decision plan to obtain real-time financial analysis records;

[0013] The process analysis unit configures process analysis based on real-time financial analysis records to obtain process decision results;

[0014] The real-time management unit configures real-time information flow analysis and personalized management according to the process decision result to obtain a real-time management result.

[0015] Furthermore, the multi-source connection analysis record includes a multi-source connection diagram, a possibility distribution table, and a multi-source fusion result; the fusion decision result includes the formation of a scheduling plan, a financial allocation plan, and a decision value record; the decision value decision record includes financial value analysis, decision results, and value update evaluation; the financial demand evaluation record includes change analysis, periodic patterns, and evaluation information charts; the real-time decision plan includes financial usage simulation results, value update results, and plan revision results; the real-time financial analysis record includes risk feedback records, financial usage change diagrams, and real-time analysis results; the process decision result includes the formation of a flow chart, a financial usage pattern, and a decision strategy list.

[0016] Furthermore, the multi-source fusion unit includes a semantic model building sub-unit, a structure learning sub-unit, and a multi-source connection parsing sub-unit;

[0017] The semantic model construction subunit constructs an initial semantic model structure based on the financial usage and formation information and a deep method to obtain a preliminary semantic model structure;

[0018] The structure learning subunit makes a semantic model structure decision based on the preliminary semantic model structure and the decision tree structure learning model to obtain the semantic model structure after the decision;

[0019] The multi-source connection analysis subunit analyzes the multi-source connection between variables based on the semantic model structure after the decision and the possibility fusion technology to obtain a multi-source connection analysis record;

[0020] The in-depth method is specifically a directed acyclic graph construction method, the decision tree structure learning model includes a scoring benchmark method and a search plan, and the possibility fusion technology includes maximum likelihood estimation and decision tree parameter estimation.

[0021] Furthermore, the financial management fusion decision-making unit includes a model building subunit, a parameter decision subunit, and a result formulation subunit;

[0022] The model building subunit builds a fusion decision model of financial use and formation parameters based on multi-source connection analysis records and a system real-time modeling method to obtain a preliminary fusion decision model;

[0023] The parameter decision subunit makes decisions on the model parameters based on the preliminary fusion decision model and the feature line difference and non-feature line difference technology to obtain the fusion decision model after the decision;

[0024] The result formulation subunit formulates the overall financial management and forms a decision plan based on the fusion decision model after the decision and the decision analysis method to obtain the fusion decision result;

[0025] The system real-time modeling method includes real-time simulation of financial flow and formation flow, the characteristic line difference and non-characteristic line difference technology are specifically the simplex method and the interior point method, and the decision analysis method is specifically multi-objective decision and sensitivity analysis.

[0026] Furthermore, the financial decision value assessment unit includes an effectiveness diagnosis subunit, an information-driven analysis subunit, and a decision plan implementation subunit;

[0027] The effectiveness diagnosis subunit performs a preliminary financial value assessment on the financial decision based on the fusion decision result and the financial value assessment technology to obtain a decision financial value diagnosis record;

[0028] The information-driven analysis subunit performs value analysis based on the decision-making financial value diagnosis record and the Gaussian kernel function and the SVM model to obtain an information-driven analysis record;

[0029] The decision plan implementation subunit makes a second decision on the financial value of the financial decision based on the information-driven analysis record and Gaussian process cluster analysis to obtain a decision value record;

[0030] The financial value assessment technology includes thermodynamic analysis and energy consumption analysis. The Gaussian kernel function and SVM model are specifically feature filtering and model fusion technology. The Gaussian process clustering analysis is specifically kernel function filtering and loss function decision technology.

[0031] Furthermore, the periodic record evaluation unit includes an information division subunit, a model fitting subunit, and an evaluation result obtaining subunit;

[0032] The information division subunit divides the periodic record information into periodicity and change based on the decision value decision record, based on the periodic division model and the X-11 division method, to obtain the divided periodic record information;

[0033] The model fitting subunit constructs a periodic self-clustering integrated moving average model based on the divided periodic record information and the self-clustering sliding average method and the periodic difference method to obtain a fitted periodic record model;

[0034] The evaluation result obtaining subunit evaluates future financial needs based on the fitted period record model and the cycle evaluation and change synchronization technology to obtain a financial needs evaluation record;

[0035] The periodic division model includes local weighted clustering technology, periodic element collection, and change denoising analysis. The self-clustering sliding average method is specifically the Taylor multiplier test and the regression function analysis method. The periodic difference method is specifically the periodic index denoising and the periodic period detection method. The cyclic evaluation includes real-time period window evaluation and point-by-point evaluation update. The change synchronization technology is specifically the characteristic line synchronization method and the exponential denoising synchronization.

[0036] Furthermore, the mapping real-time modeling unit includes a plan development subunit, a simulation experiment subunit, and an effect evaluation subunit;

[0037] The plan development subunit, based on the financial needs assessment records, builds a model based on feedback loop analysis and variable dynamic change, and conducts system multimodal modeling to obtain a preliminary real-time decision plan;

[0038] The simulation experiment subunit tests the effects of various plans based on the preliminary real-time decision plan and obtains simulation experiment results;

[0039] The effect evaluation subunit evaluates the impact of the plan on value formation based on the simulation experiment results, multi-source loop diagram analysis and system behavior pattern testing, and obtains a real-time decision plan;

[0040] The feedback loop analysis includes system boundary delimitation and variable detection; the variable dynamic change model construction includes flow management equations and real-time storage change analysis; the simulation includes random number acquisition and probability distribution fitting; the multi-source loop diagram analysis includes real-time system structure mapping and feedback loop detection; the system behavior pattern test includes behavior pattern matching and deviation change analysis.

[0041] Furthermore, the information parsing model unit includes an information flow analysis subunit, a risk analysis subunit, and a plan feedback subunit;

[0042] The information flow analysis subunit performs real-time analysis of information flow according to the real-time decision plan, and applies HiveAPI to construct information pipeline and aggregate real-time information to obtain the analyzed information flow;

[0043] The risk analysis subunit performs risk pattern matching and period window analysis based on the analyzed information flow and a complex risk analysis engine to obtain a risk analysis record;

[0044] The plan feedback subunit provides real-time feedback and correction of the plan based on the risk analysis record, information-driven decision support method and reminder mode, and obtains real-time financial analysis record;

[0045] The Hadoop technology includes distributed record storage, multi-copy fault tolerance mode and partition plan configuration; the Hive API includes streaming information analysis topology construction, state manager design and window aggregation; the complex risk analysis engine includes risk pattern definition and periodic window constraint configuration; the periodic window parsing specifically includes risk period record parsing and risk detection; the information-driven decision support method includes change parsing model and decision tree construction; the reminder mode includes abnormal pattern detection and real-time alarm triggering.

[0046] Furthermore, the process analysis unit includes a record parsing subunit, a pattern detection subunit, and a decision result obtaining subunit;

[0047] The record parsing subunit parses the financial usage information based on the real-time financial parsing record and record analysis technology to obtain the financial usage record parsing result;

[0048] The pattern detection subunit explores the interaction pattern between financial use and formation process based on the ERP model according to the analysis results of financial use records, and obtains pattern exploration results;

[0049] The decision result obtaining subunit updates the plan and decision strategy based on the pattern exploration result and the process decision method to obtain the process decision result;

[0050] The record analysis technology includes risk record collection and record timing analysis; the ERP model includes dependency detection and dynamic fluctuation sequence analysis; and the process decision-making method includes cost-benefit analysis, process reconstruction technology and energy efficiency benchmark configuration;

[0051] The real-time management unit includes a financial risk control sub-unit, a personalized adjustment sub-unit, and a management result implementation sub-unit;

[0052] The financial risk control subunit controls the financial usage based on the process decision results and real-time information risk control technology to obtain real-time financial risk control records;

[0053] The personalized adjustment subunit corrects financial usage based on the real-time financial risk control records and the personalized management model to obtain the financial personalized adjustment result;

[0054] The management result implementation subunit implements real-time risk control and automatic adjustment of financial use based on the ratio-compression-decompression manager according to the financial personalized adjustment results, and obtains real-time management results;

[0055] The real-time information risk control technology includes information flow capture and real-time value indicator calculation; the personalized management model includes real-time adjustment plan and feedback threshold setting; the ratio-compression-decompression manager includes a ratio manager, a compression manager, and a decompression manager.

[0056] According to a second aspect of the present invention, the present invention claims protection for a multimodal AI-based business-finance fusion analysis and decision-making management method, which is applied to the multimodal AI-based business-finance fusion analysis and decision-making management system, and is characterized by comprising:

[0057] Based on financial usage and formation information, configure deep learning and testing to obtain multi-source connection analysis records;

[0058] Based on the multi-source connection analysis records, the feature line and non-feature line differences are configured to obtain the fusion decision results;

[0059] Based on the fusion decision results, configure information-driven machine learning and cluster analysis to obtain decision value records;

[0060] Based on the decision value decision record and configuration cycle record analysis, the financial demand evaluation record is obtained;

[0061] Based on the financial needs assessment records, configure the system multimodal modeling to obtain real-time decision plans;

[0062] Based on real-time decision-making plans, configure real-time information analysis to obtain real-time financial analysis records;

[0063] Based on real-time financial analysis records, configure process analysis to obtain process decision results;

[0064] Based on the process decision results, configure real-time information flow analysis and personalized management to obtain real-time management results.

[0065] The present application relates to the field of business and financial management technology, and in particular to a business and financial fusion analysis and decision management system and method based on multimodal AI. According to the financial use and formation information, deep learning and testing are configured to obtain multi-source connection analysis records; according to the multi-source connection analysis records, characteristic line and non-characteristic line differences are configured to obtain fusion decision results; according to the fusion decision results, information-driven machine learning and clustering analysis are configured to obtain decision value decision records; according to the real-time decision plan, real-time information analysis is configured to obtain real-time financial analysis records; according to the real-time financial analysis records, process analysis is configured to obtain process decision results; according to the process decision results, real-time information flow analysis and personalized management are configured to obtain real-time management results. The present invention effectively integrates the ability of different information sources, and has good effects in real-time decision-making and long-term financial needs evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a structural module diagram of a multimodal AI-based business-finance fusion analysis and decision-making management system claimed in the embodiments of this application;

[0067] Figure 2 A workflow diagram of a multimodal AI business-finance fusion analysis and decision-making management method based on a multimodal AI business-finance fusion analysis and decision-making management system as claimed in the embodiments of this application. DETAILED DESCRIPTION

[0068] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical results of the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0069] The terms "first", "second" and "third" in this application are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first", "second" and "third" may explicitly or implicitly include at least one of such features. In the description of this application, "multiple" means at least two, for example, two, three, etc., unless otherwise clearly and specifically defined. All directional indications in the embodiments of this application (such as up, down, left, right, front, back...) are only used to explain the relative position connection, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or decision that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or decisions.

[0070] References to "embodiments" herein mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0071] Example 1: Please refer to Figure 1 , a multimodal AI-based business and financial fusion analysis and decision-making management system includes a multi-source fusion unit, a financial management fusion decision-making unit, a financial decision value assessment unit, a cycle record evaluation unit, a mapping real-time modeling unit, an information analysis model unit, a process analysis unit, and a real-time management unit;

[0072] The multi-source fusion unit conducts the possibility fusion of multi-source connections between variables based on financial usage and formation information, based on deep structure learning and conditional independence test, and obtains multi-source connection analysis records;

[0073] The financial management fusion decision-making unit creates a fusion decision model of financial use and formation parameters based on multi-source connection analysis records and characteristic line difference and non-characteristic line difference technology to obtain fusion decision results;

[0074] The financial decision value assessment unit determines the financial value of the financial decision based on the fusion decision results, using information-driven machine learning methods and Gaussian process cluster analysis, and obtains the decision value record;

[0075] The periodic record evaluation unit analyzes the periodic record of historical financial usage information based on the decision value decision record and the periodic self-clustering integrated moving average model to obtain the financial demand evaluation record;

[0076] The mapping real-time modeling unit simulates the impact of financial management plans on value formation based on the financial needs assessment records, using system multimodal modeling and cyclic multi-source analysis technology to obtain real-time decision plans;

[0077] The information analysis model unit performs real-time information analysis based on real-time decision-making plans and complex risk analysis engines to obtain real-time financial analysis records;

[0078] The process analysis unit analyzes the interaction between financial use and formation processes based on real-time financial analysis records and the ERP model to obtain process decision results;

[0079] The real-time management unit implements real-time risk control and automatic adjustment of financial use based on process decision results, real-time information flow analysis and personalized management models, and obtains real-time management results through a scale-compression-decompression manager.

[0080] Multi-source connection analysis records include multi-source connection diagrams, possibility distribution tables, and multi-source fusion results. Fusion decision results include the formation of scheduling plans, financial allocation plans, and decision value records. Decision value records include financial value analysis, decision results, and value update evaluation. Financial demand evaluation records include change analysis, periodic patterns, and evaluation information charts. Real-time decision plans include financial usage simulation results, value update results, and plan revision results. Real-time financial analysis records include risk feedback records, financial usage change diagrams, and real-time analysis results. Process decision results include the formation of flow charts, financial usage patterns, and decision strategy lists.

[0081] The application of the multi-source fusion unit enables the system to deeply analyze the complex relationship between financial usage and formation information. Through deep structural learning and conditional independence testing, the unit not only improves the understanding of financial dynamic changes in the formation process, but also clarifies the multi-source relationship of financial usage, which provides a scientific and precise basis for financial decision-making.

[0082] The introduction of the financial management fusion decision-making unit, by using characteristic line and non-characteristic line difference technology, created a fusion decision-making model of financial use and formation parameters. This method makes financial allocation more reasonable, can reduce financial costs while ensuring value formation, and improve the overall financial management value.

[0083] The addition of the financial decision value assessment unit further enhances the practicality of the system. By applying information-driven machine learning methods and Gaussian process clustering analysis, the system can accurately detect and improve the financial value issues of decisions, thereby reducing financial waste and improving the energy efficiency of the entire formation process.

[0084] The integration of the periodic record evaluation unit enables the system to accurately evaluate future financial needs. With the help of the periodic self-clustering integrated moving average model, the unit can not only analyze historical financial usage information, but also evaluate future changes in financial needs, which is crucial for long-term financial differentials and management.

[0085] The introduction of the mapping real-time modeling unit enables the system to simulate and evaluate the impact of different financial management plans on value formation. Simulation and evaluation help managers consider various possible outcomes when formulating plans, thereby making more informed decisions.

[0086] The application of information analysis model units provides strong support for real-time information analysis, which not only improves the real-time and accuracy of information analysis, but also makes immediate feedback and correction of financial use possible.

[0087] The integration of the process analysis unit provides new perspectives for discovering the interactions between financial use and formation processes by analyzing real-time financial analysis records and applying ERP models. Such insights provide the basis for formulating more effective financial use and formation plans.

[0088] The implementation of the real-time management unit enables the system to achieve real-time risk control and automatic adjustment of financial use. Real-time management not only enhances the system's feedback capability, but also improves the overall financial management value.

[0089] The multi-source fusion unit includes a semantic model building sub-unit, a structure learning sub-unit, and a multi-source connection analysis sub-unit;

[0090] The semantic model construction subunit constructs the initial semantic model structure based on the financial usage and formation information and the deep method to obtain the preliminary semantic model structure;

[0091] The structure learning subunit makes a semantic model structure decision based on the preliminary semantic model structure and the decision tree structure learning model, and obtains the semantic model structure after the decision;

[0092] The multi-source connection analysis sub-unit analyzes the multi-source connections between variables based on the semantic model structure after decision making and the possibility fusion technology to obtain the multi-source connection analysis record;

[0093] The deep method is specifically a directed acyclic graph construction method, the decision tree structure learning model includes the scoring benchmark method and search plan, and the possibility fusion technology includes maximum likelihood estimation and decision tree parameter estimation.

[0094] The semantic model building subunit operates on financial usage and formation information, and creates an initial semantic model structure through a deep-based approach, specifically by applying directed acyclic graph construction technology. At this stage, the information is converted into a series of nodes and edges, where each node represents a variable and the edges represent the potential connections between variables. Such a structure not only reveals the direct connections between information, but also helps detect deeper multi-source mappings.

[0095] The structural learning subunit makes further decisions based on this preliminary constructed semantic model structure. This step uses the decision tree structural learning model, especially the scoring benchmark method and search plan, to evaluate and update the semantic model structure. In this process, the system iteratively evaluates different semantic model configurations through the model and filters the optimal semantic model structure based on the information-driven score. This method ensures that the semantic model structure is both accurate and reliable in a statistical sense.

[0096] The multi-source connection analysis sub-unit performs in-depth multi-source connection analysis based on the semantic model structure after decision-making. This step is based on the possibility fusion technology, especially the maximum likelihood estimation and decision tree parameter estimation, to quantitatively analyze the multi-source connection between each variable in the semantic model. The result of this process is a multi-source connection analysis record, which shows the multi-source connection between each variable in detail, providing a solid information foundation for subsequent financial management and decision-making.

[0097] The financial management fusion decision-making unit includes a model building subunit, a parameter decision subunit, and a result formulation subunit;

[0098] The model building subunit builds a fusion decision model of financial use and formation parameters based on multi-source connection analysis records and a system real-time modeling method, and obtains a preliminary fusion decision model;

[0099] The parameter decision subunit makes decisions on the model parameters based on the preliminary fusion decision model and the feature line difference and non-feature line difference technology to obtain the fusion decision model after the decision;

[0100] The result-making subunit formulates the overall financial management and forms a decision plan based on the fusion decision model after the decision is made and the decision analysis method is used to obtain the fusion decision result;

[0101] The system's real-time modeling methods include real-time simulation of financial flows and formation flows, characteristic line difference and non-characteristic line difference techniques include simplex method and interior point method, and decision analysis methods include multi-objective decision making and sensitivity analysis.

[0102] The core task of the model building sub-unit is to build a fusion decision model of financial usage and formation parameters based on multi-source connection analysis records through a system real-time modeling method. In this stage, the system real-time modeling method specifically includes real-time simulation of financial flows and formation flows. Through this method, a preliminary fusion decision model reflecting the real-time characteristics of the actual formation process can be created. This model not only takes into account the current status of financial usage, but also evaluates possible future changes, providing a basis for subsequent decision-making.

[0103] In the parameter decision subunit, the system uses characteristic line difference and non-characteristic line difference technology to make decisions on the parameters in the preliminary fusion decision model. Specifically, the model parameters are accurately corrected based on advanced mathematical techniques such as the simplex method and the interior point method to ensure that the fusion decision model can achieve the best value under various operating conditions. The core of this step is to balance the relationship between financial use and value formation through precise calculation to achieve optimal financial use.

[0104] In the result formulation sub-unit, the system formulates overall financial management and forms a decision plan based on the fusion decision model after the decision and the decision analysis method. The decision analysis method here covers multi-objective decision-making and sensitivity analysis to ensure that the formulated plan not only meets the requirements of maximizing financial value, but also takes into account other factors such as cost and sustainability. The goal of this step is to obtain a fusion decision result that comprehensively considers all aspects of factors, aiming to achieve the best decision for financial use, while ensuring the efficiency and sustainability of the formation process.

[0105] The financial decision value assessment unit includes the effectiveness diagnosis sub-unit, information-driven analysis sub-unit, and decision plan implementation sub-unit;

[0106] The effectiveness diagnosis sub-unit conducts a preliminary financial value assessment of the financial decision based on the fusion decision results and financial value assessment technology, and obtains the decision financial value diagnosis record;

[0107] The information-driven analysis subunit performs value analysis based on the decision-making financial value diagnosis record, using the Gaussian kernel function and SVM model to obtain the information-driven analysis record;

[0108] The decision-making plan implementation sub-unit makes a second decision on the financial value of the financial decision based on the information-driven analysis record and Gaussian process cluster analysis, and obtains the decision value record;

[0109] Financial value assessment techniques include thermodynamic analysis and energy consumption analysis. Gaussian kernel function and SVM model are specifically feature filtering and model fusion techniques. Gaussian process clustering analysis is specifically kernel function filtering and loss function decision-making techniques.

[0110] In the performance diagnosis sub-unit, the system operates based on the fusion decision results and conducts a preliminary financial value assessment of the financial decision based on financial value assessment technology, including thermodynamic analysis and energy consumption analysis. This process involves a comprehensive analysis of the financial usage pattern, energy consumption value and its influencing factors of the decision, thereby obtaining a decision-making financial value diagnosis record, which provides basic information and preliminary insights for subsequent financial optimization.

[0111] In the information-driven analysis sub-unit, the system uses Gaussian kernel functions and SVM models to conduct in-depth analysis of the information in the decision-making financial value diagnosis records. The Gaussian kernel function model uses feature filtering technology to detect key factors affecting energy efficiency, while the SVM model uses model fusion technology to enhance the accuracy of evaluation. Information-driven value analysis helps to reveal patterns and changes hidden in the information, thereby obtaining information-driven analysis records and providing detailed guidance for the formulation of decision-making plans.

[0112] The decision-making plan implementation sub-unit further determines the financial value of financial decisions based on information-driven analysis records and Gaussian process cluster analysis. In this process, Gaussian process cluster analysis uses kernel function filtering and loss function decision-making technology to accurately correct and make operational parameters of decisions. The decision-making process based on advanced statistical methods enables the financial value of financial decisions to reach a higher level, and ultimately obtains decision value records.

[0113] The periodic record evaluation unit includes an information division subunit, a model fitting subunit, and an evaluation result obtaining subunit;

[0114] The information division subunit divides the periodic record information into periodicity and change based on the decision value record, based on the periodic division model and the X-11 division method, and obtains the divided periodic record information;

[0115] The model fitting subunit constructs a periodic self-clustering integrated moving average model based on the divided periodic record information and the self-clustering sliding average method and the periodic difference method to obtain the fitted periodic record model;

[0116] The evaluation result obtaining sub-unit evaluates future financial needs based on the fitted cycle record model, cycle evaluation and change synchronization technology, and obtains financial needs evaluation records;

[0117] The periodic division model includes local weighted clustering technology, periodic element collection, and change denoising analysis. The self-clustering sliding average method is specifically the Taylor multiplier test and the regression function analysis method. The periodic difference method is specifically the periodic exponential denoising and periodic cycle detection method. The cyclic evaluation includes real-time period window evaluation and point-by-point evaluation update. The change synchronization technology is specifically the characteristic line synchronization method and exponential denoising synchronization.

[0118] In the information division subunit, the periodic record information is divided into periodicity and change according to the decision value record, and the periodic division model and X-11 division method are used to obtain the divided periodic record information;

[0119] The mapping real-time modeling unit includes a plan development subunit, a simulation experiment subunit, and an effect evaluation subunit;

[0120] The plan development subunit, based on the financial needs assessment records, builds a model based on feedback loop analysis and variable dynamic changes, and conducts system multimodal modeling to obtain a preliminary real-time decision plan;

[0121] The simulation experiment subunit tests the effects of various plans based on the preliminary real-time decision plan and obtains the simulation experiment results;

[0122] The effect evaluation subunit evaluates the impact of the plan on value formation based on the simulation experiment results, multi-source loop diagram analysis and system behavior pattern testing, and obtains a real-time decision plan;

[0123] Feedback loop analysis includes system boundary delimitation and variable detection. Variable dynamic change model construction includes flow management equations and real-time storage change analysis. Simulation includes random number acquisition and probability distribution fitting. Multi-source loop diagram analysis includes real-time system structure mapping and feedback loop detection. System behavior pattern testing includes behavior pattern matching and deviation change analysis.

[0124] In the plan development subunit, the system conducts system multimodal modeling of the subunit based on the financial needs evaluation records, feedback loop analysis and variable dynamic change model construction methods, and thus obtains a preliminary real-time decision plan, including the delimitation of system boundaries and the detection of key variables, as well as the establishment of flow management equations and real-time change analysis of storage volume, to ensure that the plan can fully reflect the real-time characteristics of financial use and formation processes.

[0125] The simulation experiment subunit tests the effects of multiple plans based on preliminary real-time decision plans through simulation. This process covers the acquisition of random numbers and the fitting of probability distributions, allowing the system to evaluate the potential impact and effects of plans under various conditions, thereby obtaining detailed simulation experiment results.

[0126] The effect evaluation sub-unit is based on the results of simulation experiments, multi-source loop diagram analysis and system behavior pattern testing. These methods include real-time system structure mapping, feedback loop detection, behavior pattern matching and deviation change analysis. Through these complex analysis technologies, the sub-unit can evaluate the impact of different plans on the formation of value, thereby obtaining the final real-time decision plan.

[0127] The information analysis model unit includes information flow analysis subunit, risk analysis subunit, and plan feedback subunit;

[0128] The information flow analysis sub-unit performs real-time analysis of information flows based on real-time decision-making plans, and applies Hive API to construct information pipelines and aggregate real-time information to obtain the analyzed information flows.

[0129] The risk analysis sub-unit performs risk pattern matching and cycle window analysis based on the analyzed information flow and a complex risk analysis engine to obtain risk analysis records;

[0130] The plan feedback sub-unit provides real-time feedback and revisions to the plan based on risk analysis records, information-driven decision support methods, and reminder modes, obtaining real-time financial analysis records.

[0131] Hadoop technology includes distributed record storage, multi-copy fault tolerance mode and partition plan configuration; Hive API includes streaming information analysis topology construction, state manager design and window aggregation; complex risk analysis engine includes risk pattern definition and periodic window constraint configuration; periodic window parsing specifically includes risk period record parsing and risk detection; information-driven decision support methods include change parsing model and decision tree construction; reminder mode includes abnormal pattern detection and real-time alarm triggering.

[0132] In the information flow analysis subunit, the system performs real-time analysis of information flows based on real-time decision plans, including distributed record storage, multi-copy fault-tolerant mode, and partition plan configuration to ensure high availability and reliability of information. In addition, the system constructs information pipelines and aggregates real-time information, including the construction of streaming information analysis topology, the design of state managers, and windowed aggregation, making information flow analysis not only efficient but also adaptable to different information and business needs, thereby obtaining analyzed information flows.

[0133] In the risk analysis sub-unit, the system conducts in-depth information analysis based on the analyzed information flow and a complex risk analysis engine, including the definition of risk patterns and the constraint configuration of cycle windows, as well as the analysis of risk cycle records and the detection of key risks. Through these complex parsing and analysis, the system can accurately detect and parse important risks in the information flow, thereby obtaining detailed risk analysis records.

[0134] The plan feedback subunit provides real-time plan feedback and corrections based on risk analysis records, information-driven decision support methods and reminder modes, including the construction of change analysis models and decision trees, as well as the detection of abnormal patterns and the triggering of real-time alerts. These operations enable the system to not only respond quickly based on current information, but also to issue advance reminders based on changes and potential risks, ultimately obtaining real-time financial analysis records.

[0135] The process analysis unit includes a record parsing subunit, a pattern detection subunit, and a decision result obtaining subunit;

[0136] The record parsing subunit parses the financial usage information based on the real-time financial parsing records and record analysis technology to obtain the financial usage record parsing results;

[0137] The pattern detection subunit explores the interaction pattern between financial use and formation process based on the analysis results of financial use records and the ERP model, and obtains the pattern exploration results;

[0138] The decision result is obtained by the sub-unit according to the pattern exploration results, based on the process decision method, updating the plan and decision strategy to obtain the process decision result;

[0139] Record analysis technology includes risk record collection and record timing analysis; ERP models include dependency detection and dynamic fluctuation sequence analysis; process decision-making methods include cost-benefit analysis, process reconstruction technology, and energy efficiency benchmark configuration.

[0140] In the record parsing subunit, the system parses financial usage information based on real-time financial parsing records and record analysis technology, including the collection of risk records and time-series analysis of record information. Through these steps, the system can collect key information from a large amount of financial usage information and convert it into a structured format for further analysis, thereby detecting patterns and changes in financial usage and obtaining detailed financial usage record analysis results.

[0141] In the pattern detection sub-unit, the system explores the interaction pattern between financial use and the formation process based on the ERP model according to the analysis results of financial use records. The ERP model reveals the complex connection between financial use and the formation of dynamic fluctuations through dependency connection detection and dynamic fluctuation sequence analysis. It not only helps to understand the internal logic of financial use, but also reveals potential value improvement points, thereby obtaining pattern exploration results.

[0142] In the decision-making result sub-unit, the system proposes update plans and decision-making strategies based on the pattern exploration results and process decision-making methods, including cost-benefit analysis, process reconstruction technology and energy efficiency benchmark configuration, to ensure that the proposed decision-making strategies are both economical and effective. These decision-making strategies aim to improve financial use value and reduce unnecessary financial waste, and ultimately obtain comprehensive process decision results.

[0143] The real-time management unit includes the financial risk control sub-unit, the personalized adjustment sub-unit, and the management result implementation sub-unit;

[0144] The financial risk control sub-unit, based on the process decision results and real-time information risk control technology, controls the financial usage and obtains real-time financial risk control records;

[0145] The personalized adjustment sub-unit corrects financial usage based on real-time financial risk control records and the personalized management model to obtain personalized financial adjustment results;

[0146] The management result implementation sub-unit implements real-time risk control and automatic adjustment of financial use based on the ratio-compression-decompression manager according to the personalized financial adjustment results, and obtains real-time management results;

[0147] Real-time information risk control technology includes information flow capture and real-time value indicator calculation; personalized management models include real-time adjustment plans and feedback threshold settings; and the ratio-compression-decompression manager includes a ratio manager, a compression manager, and a decompression manager.

[0148] In the financial risk control sub-unit, the system uses real-time information risk control technology to control financial usage based on process decision results, including capturing information flow and calculating real-time value indicators to ensure the real-time and accuracy of financial usage information. Through these technologies, the system can continuously track real-time changes in financial usage and obtain real-time financial risk control records. This record provides an instant view of financial usage and helps detect any anomalies or deviations from predetermined values.

[0149] In the personalized adjustment sub-unit, the system corrects financial usage based on real-time financial risk control records and a personalized management model. The personalized management model in this process includes real-time adjustment plans and feedback threshold settings. These plans allow the system to automatically correct financial usage based on real-time information, thereby determining the value of financial usage. Personalized adjustment not only provides rapid feedback, but can also be flexibly modified according to actual operating conditions to obtain personalized financial adjustment results.

[0150] In the management result implementation subunit, the system implements real-time management results based on the personalized financial adjustment results using the proportional-compression-decompression manager. The PID manager is a widely used management technology that can make precise adjustments based on deviations and changes in financial use. The implementation of this step ensures real-time risk control and automatic adjustment of financial use, improves the response speed and adjustment accuracy of the overall system, thereby effectively reducing financial waste and improving energy efficiency.

[0151] According to the second embodiment of the present invention, the present invention claims protection for a multimodal AI business-finance fusion analysis and decision-making management method, which is applied to the multimodal AI business-finance fusion analysis and decision-making management system, referring to Figure 2 ,include:

[0152] Based on financial usage and formation information, configure deep learning and testing to obtain multi-source connection analysis records;

[0153] Based on the multi-source connection analysis records, the feature line and non-feature line differences are configured to obtain the fusion decision results;

[0154] Based on the fusion decision results, configure information-driven machine learning and cluster analysis to obtain decision value records;

[0155] Based on the decision value decision record and configuration cycle record analysis, the financial demand evaluation record is obtained;

[0156] Based on the financial needs assessment records, configure the system multimodal modeling to obtain real-time decision plans;

[0157] Based on real-time decision-making plans, configure real-time information analysis to obtain real-time financial analysis records;

[0158] Based on real-time financial analysis records, configure process analysis to obtain process decision results;

[0159] Based on the process decision results, configure real-time information flow analysis and personalized management to obtain real-time management results.

[0160] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not configured. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0161] In addition, each functional unit in each embodiment of the present application can be integrated into an analysis unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the content of the description and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0162] The above detailed description of the specific embodiments of the invention is intended only as an example, and the present application is not limited to the specific embodiments described above. For those skilled in the art, any equivalent modifications or substitutions of the invention are also within the scope of the present application. Therefore, equivalent changes, modifications, and updates made without departing from the spirit and scope of the present application should be included within the scope of the present application.

Claims

1. A multimodal AI-based business and financial fusion analysis and decision-making management system, characterized by: The system includes a multi-source fusion unit, a financial management fusion decision unit, a financial decision value assessment unit, a periodic record evaluation unit, a mapping real-time modeling unit, an information analysis model unit, a process analysis unit, and a real-time management unit; The multi-source fusion unit configures deep learning and testing based on financial usage and formation information to obtain multi-source connection analysis records; The financial management fusion decision unit configures the difference between characteristic lines and non-characteristic lines based on the multi-source connection analysis records, and obtains the fusion decision result; The financial decision value assessment unit configures information-driven machine learning and cluster analysis based on the fusion decision result to obtain a decision value decision record; The cycle record evaluation unit configures cycle record parsing based on the decision value decision record to obtain a financial demand evaluation record; The mapping real-time modeling unit configures system multimodal modeling based on the financial demand evaluation record to obtain a real-time decision plan; The information analysis model unit configures real-time information analysis according to the real-time decision plan to obtain real-time financial analysis records; The process analysis unit configures process analysis based on real-time financial analysis records to obtain process decision results; The real-time management unit configures real-time information flow analysis and personalized management according to the process decision result to obtain a real-time management result.

2. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 1 is characterized by: The multi-source connection analysis record includes a multi-source connection diagram, a possibility distribution table, and a multi-source fusion result. The fusion decision result includes the formation of a scheduling plan, a financial allocation plan, and a decision value record. The decision value decision record includes financial value analysis, decision results, and value update evaluation. The financial demand evaluation record includes change analysis, periodic patterns, and evaluation information charts. The real-time decision plan includes financial usage simulation results, value update results, and plan revision results. The real-time financial analysis record includes risk feedback records, financial usage change diagrams, and real-time analysis results. The process decision result includes the formation of a flow chart, a financial usage pattern, and a decision strategy list.

3. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 1 is characterized by: The multi-source fusion unit includes a semantic model building sub-unit, a structure learning sub-unit, and a multi-source connection analysis sub-unit; The semantic model construction subunit constructs an initial semantic model structure based on the financial usage and formation information and a deep method to obtain a preliminary semantic model structure; The structure learning subunit makes a semantic model structure decision based on the preliminary semantic model structure and the decision tree structure learning model to obtain the semantic model structure after the decision; The multi-source connection analysis subunit analyzes the multi-source connection between variables based on the semantic model structure after the decision and the possibility fusion technology to obtain a multi-source connection analysis record; The in-depth method is specifically a directed acyclic graph construction method, the decision tree structure learning model includes a scoring benchmark method and a search plan, and the possibility fusion technology includes maximum likelihood estimation and decision tree parameter estimation.

4. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 3 is characterized by: The financial management fusion decision-making unit includes a model building subunit, a parameter decision subunit, and a result formulation subunit; The model building subunit builds a fusion decision model of financial use and formation parameters based on multi-source connection analysis records and a system real-time modeling method to obtain a preliminary fusion decision model; The parameter decision subunit makes decisions on the model parameters based on the preliminary fusion decision model and the feature line difference and non-feature line difference technology to obtain the fusion decision model after the decision; The result formulation subunit formulates the overall financial management and forms a decision plan based on the fusion decision model after the decision and the decision analysis method to obtain the fusion decision result; The system real-time modeling method includes real-time simulation of financial flow and formation flow, the characteristic line difference and non-characteristic line difference technology are specifically the simplex method and the interior point method, and the decision analysis method is specifically multi-objective decision and sensitivity analysis.

5. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 4 is characterized by: The financial decision value assessment unit includes an effectiveness diagnosis subunit, an information-driven analysis subunit, and a decision plan implementation subunit; The effectiveness diagnosis subunit performs a preliminary financial value assessment on the financial decision based on the fusion decision result and the financial value assessment technology to obtain a decision financial value diagnosis record; The information-driven analysis subunit performs value analysis based on the decision-making financial value diagnosis record and the Gaussian kernel function and the SVM model to obtain an information-driven analysis record; The decision plan implementation subunit makes a second decision on the financial value of the financial decision based on the information-driven analysis record and Gaussian process cluster analysis to obtain a decision value record; The financial value assessment technology includes thermodynamic analysis and energy consumption analysis. The Gaussian kernel function and SVM model are specifically feature filtering and model fusion technology. The Gaussian process clustering analysis is specifically kernel function filtering and loss function decision technology.

6. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 5 is characterized by: The periodic record evaluation unit includes an information division subunit, a model fitting subunit, and an evaluation result obtaining subunit; The information division subunit divides the periodic record information into periodicity and change based on the decision value decision record, based on the periodic division model and the X-11 division method, to obtain the divided periodic record information; The model fitting subunit constructs a periodic self-clustering integrated moving average model based on the divided periodic record information and the self-clustering sliding average method and the periodic difference method to obtain a fitted periodic record model; The evaluation result obtaining subunit evaluates future financial needs based on the fitted period record model and the cycle evaluation and change synchronization technology to obtain a financial needs evaluation record; The periodic division model includes local weighted clustering technology, periodic element collection, and change denoising analysis. The self-clustering sliding average method is specifically the Taylor multiplier test and the regression function analysis method. The periodic difference method is specifically the periodic index denoising and the periodic period detection method. The cyclic evaluation includes real-time period window evaluation and point-by-point evaluation update. The change synchronization technology is specifically the characteristic line synchronization method and the exponential denoising synchronization.

7. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 6 is characterized by: The mapping real-time modeling unit includes a plan development subunit, a simulation experiment subunit, and an effect evaluation subunit; The plan development subunit, based on the financial needs assessment records, builds a model based on feedback loop analysis and variable dynamic change, and conducts system multimodal modeling to obtain a preliminary real-time decision plan; The simulation experiment subunit tests the effects of various plans based on the preliminary real-time decision plan and obtains simulation experiment results; The effect evaluation subunit evaluates the impact of the plan on value formation based on the simulation experiment results, multi-source loop diagram analysis and system behavior pattern testing, and obtains a real-time decision plan; The feedback loop analysis includes system boundary delimitation and variable detection; the variable dynamic change model construction includes flow management equations and real-time storage change analysis; the simulation includes random number acquisition and probability distribution fitting; the multi-source loop diagram analysis includes real-time system structure mapping and feedback loop detection; the system behavior pattern test includes behavior pattern matching and deviation change analysis.

8. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 7 is characterized by: The information parsing model unit includes an information flow analysis subunit, a risk analysis subunit, and a plan feedback subunit; The information flow analysis subunit performs real-time analysis of information flow according to the real-time decision plan, and applies HiveAPI to construct information pipeline and aggregate real-time information to obtain the analyzed information flow; The risk analysis subunit performs risk pattern matching and period window analysis based on the analyzed information flow and a complex risk analysis engine to obtain a risk analysis record; The plan feedback subunit provides real-time feedback and correction of the plan based on the risk analysis record, information-driven decision support method and reminder mode, and obtains real-time financial analysis record; The Hadoop technology includes distributed record storage, multi-copy fault tolerance mode and partition plan configuration; the Hive API includes streaming information analysis topology construction, state manager design and window aggregation; the complex risk analysis engine includes risk pattern definition and periodic window constraint configuration; the periodic window parsing specifically includes risk period record parsing and risk detection; the information-driven decision support method includes change parsing model and decision tree construction; the reminder mode includes abnormal pattern detection and real-time alarm triggering.

9. The multimodal AI-based business and financial fusion analysis and decision-making management system according to claim 8 is characterized by: The process analysis unit includes a record parsing subunit, a pattern detection subunit, and a decision result obtaining subunit; The record parsing subunit parses the financial usage information based on the real-time financial parsing record and record analysis technology to obtain the financial usage record parsing result; The pattern detection subunit explores the interaction pattern between financial use and formation process based on the ERP model according to the analysis results of financial use records, and obtains pattern exploration results; The decision result obtaining subunit updates the plan and decision strategy based on the pattern exploration result and the process decision method to obtain the process decision result; The record analysis technology includes risk record collection and record timing analysis; the ERP model includes dependency detection and dynamic fluctuation sequence analysis; and the process decision-making method includes cost-benefit analysis, process reconstruction technology and energy efficiency benchmark configuration; The real-time management unit includes a financial risk control sub-unit, a personalized adjustment sub-unit, and a management result implementation sub-unit; The financial risk control subunit controls the financial usage based on the process decision results and real-time information risk control technology to obtain real-time financial risk control records; The personalized adjustment subunit corrects financial usage based on the real-time financial risk control records and the personalized management model to obtain the financial personalized adjustment result; The management result implementation subunit implements real-time risk control and automatic adjustment of financial use based on the ratio-compression-decompression manager according to the financial personalized adjustment results, and obtains real-time management results; The real-time information risk control technology includes information flow capture and real-time value indicator calculation; the personalized management model includes real-time adjustment plan and feedback threshold setting; the ratio-compression-decompression manager includes a ratio manager, a compression manager, and a decompression manager.

10. A method for analyzing and managing business and financial integration based on multimodal AI, which is applied to the method for analyzing and managing business and financial integration based on multimodal AI as described in any one of claims 1 to 9, characterized in that: include: Based on financial usage and formation information, configure deep learning and testing to obtain multi-source connection analysis records; Based on the multi-source connection analysis records, the feature line and non-feature line differences are configured to obtain the fusion decision results; Based on the fusion decision results, configure information-driven machine learning and cluster analysis to obtain decision value records; Based on the decision value decision record and configuration cycle record analysis, the financial demand evaluation record is obtained; Based on the financial needs assessment records, configure the system multimodal modeling to obtain real-time decision plans; Based on real-time decision-making plans, configure real-time information analysis to obtain real-time financial analysis records; Based on real-time financial analysis records, configure process analysis to obtain process decision results; Based on the process decision results, configure real-time information flow analysis and personalized management to obtain real-time management results.

Images