Business and financial fusion analysis and decision support system based on enterprise

Abstract

The invention discloses an analysis and decision support system based on business and financial fusion of an enterprise, and relates to the technical field of enterprise informatization management, the system adopts a star-shaped mode to construct a unified data warehouse, and realizes accurate analysis of different business scenes of the enterprise by constructing a highly-customized analysis engine, and specifically, the system can be used for realizing accurate analysis of different business scenes of the enterprise. Aiming at a key scene of new product marketing, various data sources of market demands, historical sales data, market trends and promotion activities can be deeply mined and integrated, future market demands can be accurately predicted through an algorithm formula such as a market demand prediction algorithm, comprehensive influences of time decay, market trends and promotion activity factors are fully considered, and the market demands can be accurately predicted. Besides, by utilizing the VR / AR technology, a complex analysis result is presented to a decision maker in a visual and interactive manner, so that the decision maker can feel the actual effects of different decision schemes personally on the scene.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise information management, and in particular to an analysis and decision support system based on enterprise business-finance integration. Background Art

[0002] With the increasing complexity of corporate business and increasingly fierce market competition, companies are increasingly in need of accurate and efficient decision support systems. In particular, when faced with diverse business scenarios and ever-changing decision-making needs, how to quickly and accurately generate customized analysis reports and decision recommendations has become the key to improving companies' competitiveness and achieving sustainable development. Traditional data analysis methods often cannot meet this highly customized and real-time requirement. Therefore, new decision support systems based on big data and advanced artificial intelligence technologies have emerged.

[0003] Although traditional decision support systems have improved the efficiency and accuracy of data analysis to a certain extent, they still have many shortcomings in scenario analysis and decision-making recommendations. First, traditional systems often use standardized analysis templates and report formats, which makes it difficult to conduct highly customized analysis based on different business scenarios and decision-making needs. This results in the lack of pertinence and practicality of the generated reports and recommendations, and makes it difficult to directly apply them to the actual decision-making process. Secondly, traditional systems lack flexibility in data processing and are difficult to quickly adapt to market changes and business adjustments. In addition, traditional systems usually lack intuitive data display methods, and management finds it difficult to quickly obtain key information when viewing reports, which affects decision-making efficiency. Finally, traditional systems rarely combine cutting-edge technologies such as virtual reality (VR) and augmented reality (AR), and are unable to provide management with an immersive data analysis experience, limiting the depth and breadth of data analysis.

[0004] Therefore, a business-finance integration analysis and decision support system is developed. The system can automatically generate customized analysis reports and decision recommendations according to different business scenarios and decision-making needs, helping management to make decisions quickly and accurately. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and to provide a business-finance integration analysis and decision support system based on enterprises. It can deeply integrate big data, artificial intelligence, virtual reality and augmented reality technologies, aiming to solve the limitations of traditional decision support systems in scenario analysis and decision recommendations, and meet the diversity of different business scenarios and decision-making needs through highly customized analysis reports and accurate decision recommendations.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an enterprise business-finance integration analysis and decision support system, the system includes the following components:

[0007] Business and financial data integration module: uses the star model to build a unified data warehouse, extracts data from the enterprise's ERP, CRM, and SCM in real time through API interfaces, database links, and file transfers, and performs cleaning, conversion, and loading to achieve seamless docking and deep integration of business and financial data;

[0008] Customized analysis engine: Receive user inputs on scenarios and decision-making requirements for new product launches, market expansion, and cost control, form business scenario requirement documents, extract relevant data from the data warehouse based on the requirement documents, and perform cleaning, conversion, and integration to generate data sets. For each business scenario, select the corresponding algorithm formula based on the analysis objectives and key indicators, automatically generate customized data analysis reports based on the algorithm formula and real-time data, receive user feedback on the customized analysis reports, and optimize and improve the customized analysis engine based on the feedback results;

[0009] VR / AR immersive experience module: VR technology is used to build a three-dimensional virtual environment to present analysis results to decision makers in an intuitive and interactive way. At the same time, AR technology is used to overlay key data indicators on real scenes, allowing decision makers to experience the effect of decision implementation in person;

[0010] Intelligent decision-making recommendation module: Receives sales data, market trends, cost structures and customer feedback data analyzed by the customized analysis engine, and performs cleaning, denoising and formatting. According to the needs of product pricing, sales channel optimization and cost control, it extracts information features such as product characteristics, market demand, competitive situation and cost structure from the pre-processed data. It uses different types of algorithm formulas to calculate product pricing strategies, sales channel optimization and cost control plans, and presents the results to management in the form of charts using visualization technology.

[0011] Furthermore, the new product launch scenario in the customized analysis engine combines historical sales data, market trends, and promotional activity factors to predict future market demand through a market demand prediction algorithm. t is the actual market demand at time t, H t is the impact factor of historical sales data at time t, T t is the influencing factor of the market trend at time t, P t Calculate the moving average MVA of historical sales data as the impact factor of the promotion activity at time t n , the calculation formula is: Where n is the selected time period, D i Indicates the actual market demand at different time points;

[0012] Historical sales data impact factor H tIt is the ratio between the actual demand at the current time and the historical moving average, taking into account the time decay factor. The calculation formula is: Among them, α and β are weight coefficients, and t0 is the initial time point;

[0013] Market Trends t , assuming that the annual growth rate of market demand is r, the market trend impact factor calculation formula at time t is: Where m is the number of time periods in a year;

[0014] Promotional activity impact factor P t It is expressed as the ratio of demand during promotional activities to demand during non-promotional activities, taking into account the duration and decay factors of the promotional activities. The calculation formula is: Among them, D promo is the average demand during the promotion period, D non-promo is the average demand during the non-promotion period, γ and δ are weight coefficients, t prono The time when the promotion starts;

[0015] Taking into account historical sales data, market trends and promotional activities, the future market demand is calculated and the forecast formula is: Here, h is the time step of prediction.

[0016] Furthermore, the customized analysis engine calculates the cost expenditure and expected benefits under different decision-making schemes through formulas for market expansion scenarios, and calculates the indicators of net present value NPV and internal rate of return IRR. Assuming that the decision-making scheme has n time periods, t represents the time period, C t represents the cost expenditure at time t, R t represents the expected return at time t, w t represents the weight factor at time t, I represents the initial investment, and the adjusted cost calculation formula is: AC t =C t × t , the adjusted return calculation formula is: AR t =R t × t , the formula for calculating the net present value NPV is: Where r represents the discount rate, and the internal rate of return I RR is calculated as follows: Where x is the internal rate of return to be sought.

[0017] Furthermore, the customized analysis engine uses a comprehensive competitor analysis algorithm combined with social media monitoring and market research data for cost control scenarios to analyze competitors' market share, product strategy, and price change information. Let S represent the total amount of social media data, M represent the total amount of market research data, and R c represents the number of times a competitor is mentioned on social media, R m Indicates the number of times a competitor is mentioned in market research, P sc P represents the number of positive mentions of competitor product strategies on social media. sm represents the number of positive mentions of competitor product strategies in market research, V sc V represents the popularity of discussions on competitors’ price changes on social media. sm It represents the heat value of the feedback on the competitor's price changes in the market research and calculates the competitor mention rate TR. The formula is:

[0018] Calculate the product strategy index PSI, the formula is:

[0019] The formula for calculating the price change impact index PVI is:

[0020] Calculate the competitor comprehensive analysis index CAI, the formula is: CAI = α1 × TR + β1 × PSI + γ1

[0021] ×PVI, where α1, β1, and γ1 are weight coefficients.

[0022] Furthermore, the specific steps of generating a customized data analysis report in the customized analysis engine are:

[0023] (1) Extract data from the data warehouse through the preset data interface, clean and integrate it to form an analyzable data set;

[0024] (2) Select an algorithm formula based on the report requirements, input the data into the algorithm for analysis and extract the results;

[0025] (3) Design a report template based on user needs, and associate the data analysis results to be displayed with the corresponding elements in the report template;

[0026] (4) Use the visualization library to convert the results into charts and assemble optimization reports;

[0027] (5) Output the generated customized data analysis report in PDF format and distribute it to management via email, cloud storage sharing, and corporate intranet;

[0028] (6) Collect feedback and iteratively optimize the report generation process, algorithm performance, and template design based on the feedback.

[0029] Furthermore, the intelligent decision-making suggestion module predicts the optimal price point and price range for the product pricing strategy through a comprehensive pricing prediction algorithm. Let P c represents the product cost price, D represents the product demand, C represents the total product cost, M is the market competition intensity factor, Q represents the product quality factor, and I represents the market demand growth potential factor, taking into account the impact of cost and demand on price;

[0030] The cost calculation formula is: Among them, α2 and β2 are two constants;

[0031] The market competition intensity factor, product quality factor and market demand growth potential factor are introduced to adjust the price. The adjusted formula is: The price range is determined by the uncertainty and error margin of the parameters.

[0032] Furthermore, the intelligent decision-making suggestion module optimizes sales channels and identifies the most effective sales channels through the comprehensive scores of sales channels. Let S represent the comprehensive score of the sales channel, R represent the sales performance score of the sales channel, C represent the cost-effectiveness score of the sales channel, E represent the customer experience score of the sales channel, I represent the innovation potential score of the sales channel, P represent the market potential score of the sales channel, and W represent the sales performance score of the sales channel. r、 W c、 W e、 W i、 W p The weight coefficients of sales performance score, cost-effectiveness score, customer experience score, innovation potential score and market potential score are respectively satisfied. r +W c +W e +W i +W p =1;

[0033] Calculate the comprehensive score of the sales channel, the formula is: S = W r ×R+W c ×C+W e ×E+W i ×I+W p ×P.

[0034] Furthermore, the intelligent decision-making suggestion module formulates targeted cost control measures for the cost control scheme through the cost clustering algorithm. i represents the cost feature vector of the i-th product and service, d(P i ,P j ) represents two cost feature vectors Pi and P j The distance measure between them, T represents the distance threshold, which is used to determine whether products and services belong to the same cost-similar group, C k represents the kth cost similarity group, n represents the total number of products and services, N(C k ) represents the number of products and services in the kth cost-similar group, AVG(P,C k ) represents the average cost feature vector of the kth cost-similar group, where P is the dimension of the cost feature vector and the Euclidean distance is used as the distance metric;

[0035] The calculation formula is: Where P i,p and P j,p Represent the cost feature vector P i and P j The value in the pth dimension, for each product and service P i , calculate its distance d from all other products and services (P i ,P j ), where j = 1, 2, ..., n, there exists a k such that d(P i ,AVG(P,C k ))≤T, P i Join cost-similar group C k If there is no group that meets the conditions, a new cost-similar group C is created. k+1 , and P i Add it, for each cost similarity group C k , calculate its average cost feature vector;

[0036] The calculation formula is: For each cost-similar group C k , analyze its average cost characteristic vector, and formulate targeted cost control measures for dimensions with higher costs.

[0037] Furthermore, the intelligent decision suggestion module solves the cost minimization problem for the cost control scheme through the creative cost, and sets n different activities and decision variables, using x1, x2, ..., x n Indicates that each activity has a corresponding unit cost c1, c2, ..., c n , let f(x1,x2,…,x n ) represents a comprehensive benefit function, which comprehensively considers the impact of various factors on the overall benefit and defines the total cost function C as: Define the constraints as: g j (x1,x2,…,x n )≤b j, where j = 1, 2, ..., m, indicating that there are m different constraints, b j is the right-hand side of the constraint condition, introducing a penalty function P f , when the decision variable violates the constraint, the penalty function will increase the total cost. The penalty function is: P f =∑ j＝1 α 0j max(0,g j (x1,x2,…,x n )-b j ), where α 0j is the penalty coefficient, which indicates the severity of violating the jth constraint. The goal is to minimize the total cost and maximize the comprehensive benefit. The objective function is defined as: F(x1, x2, …, x n )=C+λ(1-f(x1,x2,…,x n ))+μP f , where λ and μ are weight coefficients used to balance cost minimization and comprehensive benefit maximization as well as to penalize the degree of violation of constraints.

[0038] Compared with the existing technology, this enterprise-finance integration analysis and decision support system has the following beneficial effects:

[0039] 1. The present invention realizes accurate analysis of different business scenarios of enterprises by constructing a highly customized analysis engine. Specifically, for the key scenario of new product launch, it can deeply mine and integrate multiple data sources such as market demand, historical sales data, market trends and promotional activities. Through algorithm formulas, such as market demand forecasting algorithm, it can accurately predict future market demand and fully consider the combined impact of time decay, market trends and promotional activities. In addition, by using VR / AR technology, complex analysis results are presented to decision makers in an intuitive and interactive way, allowing them to experience the actual effects of different decision-making plans in person, thereby greatly improving the scientificity and accuracy of decision-making.

[0040] 2. The present invention provides highly customized decision-making suggestions by analyzing the results of scenario-based analysis. In the decision-making of new product launch, it not only predicts future market demand, but also comprehensively considers factors such as cost and competitor situation, and gives detailed product pricing and sales channel selection suggestions. Specifically, through a comprehensive pricing prediction algorithm, the optimal price point and price range of the product are predicted, and the impact of market competition intensity, product quality and market demand growth potential factors on price are considered. At the same time, through the comprehensive score of the sales channel, the most effective sales channel is identified, providing enterprises with a clear channel selection direction.

[0041] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 It is a flow chart based on enterprise business-finance integration analysis and decision support system;

[0044] Figure 2 This is the flow chart of the business and financial data fusion module;

[0045] Figure 3 Flowchart of the intelligent decision-making suggestion module. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1:

[0048] This embodiment provides an analysis and decision support system based on the integration of business and finance of enterprises. The system integrates business and financial data integration, customized analysis, VR / AR immersive experience and intelligent decision-making suggestion modules. Through precise data processing and analysis, it can provide customized analysis reports and decision-making suggestions for various business scenarios such as new product launch, market expansion, and cost control. These reports and suggestions are not only based on real-time market data and historical trends, but also integrate competitor analysis and user demand forecasts, greatly improving the decision-making efficiency and accuracy of the enterprise. The introduction of VR / AR technology makes the decision-making process more intuitive and interactive, helping enterprises better understand and respond to market changes.

[0049] In the specific implementation, the business and financial data fusion module adopts a star-shaped model to design the data warehouse architecture with the fact table as the center and multiple dimension tables around it, which is convenient for data analysis and query, and identifies and integrates multi-source business system data, such as ERP, CRM, SCM and financial systems. It extracts business data in a variety of ways, cleans and preprocesses the data to ensure data quality and lay the foundation for subsequent analysis, implements the ETL process, accurately extracts data, converts and processes data according to business rules and financial logic in the data warehouse, and loads it into a unified data warehouse. It establishes a data quality monitoring mechanism to ensure high data quality, achieves seamless connection and deep integration of business and financial data, promotes cross-system and cross-departmental collaboration, provides data access and analysis interfaces, and provides flexible permissions and analysis tools for different user groups.

[0050] Customized analysis engine, working closely with business departments to clarify specific decision-making requirements for different business scenarios, such as new product launch, market expansion, and cost control, and define analysis objectives, key indicators, and expected results based on the requirements. Based on business needs, a business scenario requirement document is formed. According to the requirement document, relevant data is extracted from the data warehouse and data is cleaned, converted, and integrated to ensure that data quality meets analysis requirements. For each business scenario, a suitable analysis algorithm formula is selected for analysis.

[0051] For the new product launch scenario, historical sales data, market trends, and promotional factors are combined to predict future market demand through market demand forecasting algorithms. t is the actual market demand at time t, H t is the impact factor of historical sales data at time t, T t is the influencing factor of the market trend at time t, P t Calculate the moving average MVA of historical sales data as the impact factor of the promotion activity at time t n , where n is the selected time period, and the calculation formula is: Historical sales data impact factor H t It is the ratio between the actual demand at the current time and the historical moving average, taking into account the time decay factor. The calculation formula is: Among them, α and β are weight coefficients, t0 is the initial time point, and the market trend T t , assuming that the annual growth rate of market demand is r, the market trend impact factor calculation formula at time t is: Where m is the number of time periods in a year, and the promotion activity impact factor P t It is expressed as the ratio of demand during promotional activities to demand during non-promotional activities, taking into account the duration and decay factors of the promotional activities. The calculation formula is: Where D promo is the average demand during the promotion period, Dnon-promo is the average demand during the non-promotion period, γ and δ are weight coefficients, t prono The starting time of the promotion activity is taken into account. The future market demand is calculated by comprehensively considering historical sales data, market trends and promotion activity factors. The prediction formula is: Where h is the time step of prediction.

[0052] According to the market expansion scenario, the cost expenditure and expected income under different decision-making schemes are calculated through formulas, and the indicators of net present value (NPV) and internal rate of return (IRR) are calculated. Assume that the decision-making scheme has n time periods, and t represents the time period. C t represents the cost expenditure at time t, R t represents the expected return at time t, w t represents the weight factor at time t, which is used to consider the difference in importance of costs and benefits at different time points. I represents the initial investment. The adjusted cost calculation formula is: AC t =C t × t , the adjusted return calculation formula is: AR t =R t × t , the formula for calculating the net present value NPV is: Where r represents the discount rate, and the internal rate of return I RR is calculated as follows: Where x is the internal rate of return to be sought.

[0053] In the cost control scenario, the competitor comprehensive analysis algorithm is used in combination with social media monitoring and market research data to analyze competitors' market share, product strategy, and price change information. Let S represent the total amount of social media data, M represent the total amount of market research data, and R c represents the number of times a competitor is mentioned on social media, R m Indicates the number of times a competitor is mentioned in market research, P sc P represents the number of positive mentions of competitor product strategies on social media. sm represents the number of positive mentions of competitor product strategies in market research, V sc V represents the popularity of discussions on competitors’ price changes on social media. sm It represents the heat value of the feedback on the competitor's price changes in the market research and calculates the competitor mention rate TR. The formula is: This index reflects the degree to which competitors are mentioned in the overall data. The product strategy index PSI is calculated using the formula: This index measures the attention and recognition of competitors' product strategies in the data and calculates the price change impact index PVI. The formula is: This index reflects the degree of reaction caused by competitors' price changes in the data. The formula for calculating the competitor comprehensive analysis index CAI is: CAI = α1×TR+β1×PSI+γ1

[0054] ×PVI, where α1, β1, and γ1 are weight coefficients.

[0055] Extract data from the data warehouse through the preset data interface, clean and integrate it to form an analyzable data set, select algorithm formulas according to report requirements, design report templates according to user needs, associate the data analysis results to be displayed with the corresponding elements in the report template, use the visualization library to convert the results into charts and assemble optimized reports, output the generated customized data analysis report in PDF, and distribute it to management via email, cloud storage sharing and corporate intranet, collect feedback, and iteratively optimize the report generation process and template design based on the feedback.

[0056] The VR / AR immersive experience module uses VR technology to build a three-dimensional virtual environment based on decision-making needs and analysis results, integrates analysis results and key data indicators into the virtual environment, and designs rich interaction methods to enable decision makers to freely explore and operate in the virtual environment. Based on the decision makers' feedback and experience effects, the VR environment is continuously optimized, and the decision implementation process is simulated in the VR environment to display possible results and impacts. AR technology is used to identify specific objects and areas in real scenes, and key data indicators and analysis results are superimposed on the real scenes in the form of virtual information. AR interaction methods are designed to enable decision makers to interact with virtual information. Based on real-time data and analysis results, AR content is dynamically updated to ensure that decision makers can obtain the latest information at any time and make more accurate decisions based on it.

[0057] Collect analysis results from customized analysis engines, including historical sales data, cost structure, and market competition, and perform preprocessing operations such as cleaning, denoising, and formatting on these data to ensure data quality and consistency. Extract relevant features from the preprocessed data based on decision-making requirements for product pricing, sales channel optimization, and cost control. These features should reflect information on market demand, product characteristics, cost structure, and competitive situation.

[0058] The optimal price point and price range are predicted by a comprehensive pricing prediction algorithm for product pricing strategies. Let P represent the product cost price, D represent product demand, which is the actual sales quantity and market demand forecast value, C represent the total product cost, including production cost and marketing cost, M is the market competition intensity factor, which is determined according to the number of competitors and market saturation factors, and the value range is between 0 and 1. The closer to 0, the more intense the competition. Q represents the product quality factor, which is determined according to product quality rating and user satisfaction, and the value range is between 0 and 1. The closer to 1, the higher the quality. I represents the market demand growth potential factor, which is determined according to market trends and industry development prospects, and the value range is between 0 and 1. The closer to 1, the greater the growth potential. Considering the impact of cost and demand on price, the cost calculation formula is: Among them, α2 and β2 are two constants. The market competition intensity factor, product quality factor and market demand growth potential factor are introduced to adjust the price. The adjusted formula is: Parameters α and β are estimated by collecting a certain number of historical data points, including information on product price, cost, demand, market competition intensity, product quality and market demand growth potential. The price range is determined by the uncertainty and error range of the parameters.

[0059] The most effective sales channel is identified through the comprehensive score of the sales channel. Let S represent the comprehensive score of the sales channel, R the sales performance score of the sales channel, measured by sales volume and sales volume, C the cost-effectiveness score of the sales channel, considering the ratio of the cost and benefit of the sales channel, E the customer experience score of the sales channel, including customer satisfaction and loyalty, I the innovation potential score of the sales channel, evaluating the innovation ability of the sales channel in technology and marketing, P the market potential score of the sales channel, considering the growth potential and degree of competition in the market where the sales channel is located, and W r、 W c、 W e、 W i、 W p The weight coefficients of sales performance score, cost-effectiveness score, customer experience score, innovation potential score and market potential score are respectively, and they meet W r +W c +W e +W i +W p =1, the sales performance score R is calculated based on actual sales data, for example, sales target sales, the cost-effectiveness score C is determined by calculating the ratio of cost to benefit, the customer experience score E is obtained through customer satisfaction surveys, the innovation potential score I is evaluated based on the technical innovation and marketing innovation of the sales channel, and the market potential score P takes into account factors such as market growth rate and degree of competition to calculate the comprehensive score of the sales channel. The formula is: S = Wr ×R+W c ×C+W e ×E+W i ×I+W p ×P.

[0060] According to the cost control scheme, targeted cost control measures are formulated through cost clustering algorithm. i represents the cost feature vector of the i-th product and service, which contains multiple cost-related features, such as raw material cost, labor cost, and transportation cost. i ,P j ) represents two cost feature vectors P i and P j The distance measure between them, T represents the distance threshold, which is used to determine whether products and services belong to the same cost-similar group, C k represents the kth cost similarity group, n represents the total number of products and services, N(C k ) represents the number of products and services in the kth cost-similar group, AVG(P,C k ) represents the average cost feature vector of the kth cost-similar group, where P is the dimension of the cost feature vector, and the Euclidean distance is used as the distance measurement method. The calculation formula is: Where P i,p and P j,p Represent the cost feature vector P i and P j The value in the pth dimension, for each product and service P i , calculate its distance d from all other products and services (P i ,P j ), where j = 1, 2, ..., n, there exists a k such that d(P i ,AVG(P,C k ))≤T, P i Join cost-similar group C k If there is no group that meets the conditions, a new cost-similar group C is created. k+1 , and P i Add it, for each cost similarity group C k , calculate its average cost feature vector, the calculation formula is: For each cost-similar group C k , analyze its average cost characteristic vector, and formulate targeted cost control measures for dimensions with higher costs.

[0061] For the cost control scheme, we use creative cost to solve the cost minimization problem. There are n different activities and decision variables, and x1, x2, …, x nIndicates that each activity has a corresponding unit cost c1, c2, ..., c n The goal is to find the values ​​of these decision variables so that the total cost is minimized while satisfying a series of constraints. Let f(x1,x2,…,x n ) represents a comprehensive benefit function, which comprehensively considers the impact of various factors on the overall benefit and defines the total cost function C as: Define the constraints as: g j (x1,x2,…,x n )≤b j , where j = 1, 2, ..., m, indicating that there are m different constraints, b j is the right-hand side of the constraint condition, introducing a penalty function P f , when the decision variable violates the constraint, the penalty function will increase the total cost. The penalty function is: P f =

[0062] ∑ j＝1 α 0j max(0,g j (x1,x2,…,x n )-b j ), where α 0j is the penalty coefficient, which indicates the severity of violating the jth constraint. The goal is to minimize the total cost and maximize the comprehensive benefit. The objective function is defined as: F(x1, x2, …, x n )=C+λ(1-f(x1,x2,…,x n ))+μP f , where λ and μ are weight coefficients used to balance cost minimization and comprehensive benefit maximization as well as to penalize the degree of violation of constraints.

[0063] Based on the results, we perform parameter tuning, structural improvement, and algorithm selection optimization. We apply the optimized algorithm to business data to generate decision recommendations, and use visualization technology to present them to management in the form of charts. We collect feedback from management and actual execution effect data, and continuously optimize and improve the intelligent decision recommendation module.

[0064] Embodiment 2:

[0065] A business-finance integration analysis and decision support system in this embodiment focuses on a customized analysis engine as a core component, demonstrating its powerful application capabilities in different business scenarios.

[0066] The customized analysis engine receives specific business scenarios and decision-making requirements input by users to form a detailed requirements document. Then, the engine extracts relevant data from a unified data warehouse, cleans, converts and integrates it, and generates a data set suitable for analysis. For each business scenario, the engine selects and designs corresponding algorithm formulas based on analysis objectives and key indicators, automatically generates customized data analysis reports based on formulas and real-time data, and provides optimized decision recommendations.

[0067] In the scenario of new product launch, the customized analysis engine first combines historical sales data, market trends and promotional activities to predict future market demand through market demand forecasting algorithms. Specifically, the engine considers the moving average of historical sales data, the annual growth rate of market trends and the influencing factors of promotional activities, and comprehensively calculates the future market demand forecast value. In addition, the engine also analyzes competitors' product strategies, market share and price changes, and evaluates the market competition that new products may face. Based on these analyses, the engine provides companies with customized suggestions including product pricing, listing strategies and marketing plans to help companies successfully launch new products.

[0068] In the market expansion scenario, the customized analysis engine helps companies develop the best market expansion plan by evaluating the cost expenditure and expected benefits under different decision-making plans. The engine uses the net present value (NPV) and internal rate of return (IRR) financial indicators to conduct quantitative analysis of multiple plans. At the same time, the engine also considers the differences in costs and benefits at different time points, and adjusts the weight factors to reflect the impact of these differences on the overall decision. Based on the analysis results, the engine provides companies with customized decision-making recommendations including market expansion direction, investment scale and expected returns, helping companies achieve market share growth and improved profitability.

[0069] In the cost control scenario, the customized analysis engine uses a comprehensive competitor analysis algorithm combined with social media monitoring and market research data to deeply analyze competitors' cost structure and pricing strategy. By calculating competitor mention rates, product strategy indexes, and price change impact index indicators, the engine evaluates competitors' comprehensive competitive strength. Subsequently, the engine combines the company's own cost data and market demand conditions, and formulates targeted cost control measures through a cost clustering algorithm. These measures are aimed at reducing the company's key cost items of production costs, transportation costs, and labor costs, and improving overall profitability. At the same time, the engine also optimizes resource allocation through creative cost solving methods to achieve the dual goals of minimizing costs and maximizing comprehensive benefits.

[0070] To sum up, as the core component of the patent of this invention, the customized analysis engine has demonstrated its powerful data processing and analysis capabilities in different business scenarios. Through precise data analysis and customized decision-making recommendations, the system provides strong support for the development of the enterprise.

[0071] Embodiment three:

[0072] A system based on enterprise financial integration analysis and decision support in this embodiment focuses on demonstrating that the intelligent decision-making recommendation module provides strong support for the company's key decisions on product pricing, sales channel optimization and cost control by deeply integrating and analyzing data from a customized analysis engine. The intelligent decision-making recommendation module uses a comprehensive pricing prediction algorithm, combined with market competition intensity, product quality and market demand growth potential factors, to predict and recommend optimal price points and price ranges. By collecting and analyzing historical sales data, competitor pricing strategies and market trends, it dynamically adjusts pricing strategies to ensure that product pricing is both competitive and maximizes profits.

[0073] For the optimization of sales channels, the intelligent decision-making recommendation module uses a sales channel comprehensive scoring algorithm to comprehensively consider sales performance, cost-effectiveness, customer experience, innovation potential and market potential, and conducts a comprehensive evaluation of each sales channel. It can identify the most effective sales channel and adjust resource allocation accordingly to improve overall sales efficiency and customer satisfaction.

[0074] In terms of cost control, the intelligent decision-making recommendation module provides enterprises with targeted cost control measures through cost clustering algorithms and creative cost optimization methods. The cost clustering algorithm groups products and services according to cost characteristics, analyzes the average cost characteristics of each group, and formulates specific control measures for dimensions with higher costs. The creative cost optimization method solves the cost minimization problem and combines the comprehensive benefit function and penalty function to ensure that the total cost is minimized while meeting the constraints.

[0075] In summary, the intelligent decision-making recommendation module provides comprehensive and efficient decision-making support for enterprises through precise data analysis and scientific prediction, helping enterprises maintain their leading position in the fierce market competition.

[0076] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An enterprise-finance integration analysis and decision support system, characterized in that: The system consists of the following components: Business and financial data integration module: uses the star model to build a unified data warehouse, extracts data from the enterprise's ERP, CRM, and SCM in real time through API interfaces, database links, and file transfers, and performs cleaning, conversion, and loading to achieve seamless docking and deep integration of business and financial data; Customized analysis engine: Receive user inputs on scenarios and decision-making requirements for new product launches, market expansion, and cost control, form business scenario requirement documents, extract relevant data from the data warehouse based on the requirement documents, and perform cleaning, conversion, and integration to generate data sets. For each business scenario, select the corresponding algorithm formula based on the analysis objectives and key indicators, automatically generate customized data analysis reports based on the algorithm formula and real-time data, receive user feedback on the customized analysis reports, and optimize and improve the customized analysis engine based on the feedback results; VR / AR immersive experience module: VR technology is used to build a three-dimensional virtual environment to present analysis results to decision makers in an intuitive and interactive way. At the same time, AR technology is used to overlay key data indicators on real scenes, allowing decision makers to experience the effect of decision implementation in person; Intelligent decision-making recommendation module: Receives sales data, market trends, cost structures and customer feedback data analyzed by the customized analysis engine, and performs cleaning, denoising and formatting. According to the needs of product pricing, sales channel optimization and cost control, it extracts information features such as product characteristics, market demand, competitive situation and cost structure from the pre-processed data. It uses different types of algorithm formulas to calculate product pricing strategies, sales channel optimization and cost control plans, and presents the results to management in the form of charts using visualization technology.

2. According to claim 1, the enterprise financial integration analysis and decision support system is characterized by: The new product launch scenario in the customized analysis engine combines historical sales data, market trends, and promotional factors to predict future market demand through a market demand prediction algorithm. Let D t is the actual market demand at time t, H t is the impact factor of historical sales data at time t, T t is the influencing factor of the market trend at time t, P t Calculate the moving average MVA of historical sales data as the impact factor of the promotion activity at time t n , the calculation formula is: Where n is the selected time period, D i Indicates the actual market demand at different time points; Historical sales data impact factor H t It is the ratio between the actual demand at the current time and the historical moving average, taking into account the time decay factor. The calculation formula is: Among them, α and β are weight coefficients, and t0 is the initial time point; Market Trends t , assuming that the annual growth rate of market demand is r, the calculation formula for the market trend impact factor at time t is: Where m is the number of time periods in a year; Promotional activity impact factor P t It is expressed as the ratio of demand during promotional activities to demand during non-promotional activities, taking into account the duration and decay factors of the promotional activities. The calculation formula is: Among them, D promo is the average demand during the promotion period, D non-promo is the average demand during the non-promotion period, γ and δ are weight coefficients, t prono The time when the promotion starts; Taking into account historical sales data, market trends and promotional activities, the future market demand is calculated and the forecast formula is: Here, h is the time step of prediction.

3. According to claim 1, the enterprise financial integration analysis and decision support system is characterized by: The customized analysis engine uses formulas to calculate the cost expenditure and expected benefits under different decision-making schemes for market expansion scenarios, and calculates the indicators of net present value NPV and internal rate of return IRR. Assume that the decision-making scheme has n time periods, and t represents the time period. C t represents the cost expenditure at time t, R t represents the expected return at time t, w t represents the weight factor at time t, I represents the initial investment, and the adjusted cost calculation formula is: AC t =C t × t ; The adjusted earnings formula is: AR t =R t × t ; The formula for calculating the net present value (NPV) is: Where r represents the discount rate; To calculate the internal rate of return IRR, the formula is: Where x is the internal rate of return to be calculated.

4. According to claim 1, the enterprise financial integration analysis and decision support system is characterized by: The customized analysis engine uses a comprehensive competitor analysis algorithm combined with social media monitoring and market research data for cost control scenarios to analyze competitors' market share, product strategy, and price change information. Let S represent the total amount of social media data, M represent the total amount of market research data, and R c R is the number of times a competitor is mentioned on social media. m Indicates the number of times a competitor is mentioned in market research, P sc P represents the number of positive mentions of competitor product strategies on social media. sm represents the number of positive mentions of competitor product strategies in market research, V sc V represents the popularity of discussions on competitors’ price changes on social media. sm It represents the heat value of the feedback on the competitor's price changes in the market research and calculates the competitor mention rate TR. The formula is: Calculate the product strategy index PSI, the formula is: The formula for calculating the price change impact index PVI is: The formula for calculating the competitor comprehensive analysis index CAI is: CAI = α1×TR+β1×PSI+γ1×PVI, where α1, β1, and γ1 are weight coefficients.

5. According to claim 1, the enterprise financial integration analysis and decision support system is characterized by: The specific steps of generating a customized data analysis report in the customized analysis engine are as follows: (1) Extract data from the data warehouse through the preset data interface, clean and integrate it to form an analyzable data set; (2) Select an algorithm formula based on the report requirements, input the data into the algorithm for analysis and extract the results; (3) Design a report template based on user needs, and associate the data analysis results to be displayed with the corresponding elements in the report template; (4) Use the visualization library to convert the results into charts and assemble optimization reports; (5) Output the generated customized data analysis report in PDF format and distribute it to management via email, cloud storage sharing, and corporate intranet; (6) Collect feedback and iteratively optimize the report generation process and template design based on the feedback.

6. The enterprise-finance integration analysis and decision support system according to claim 1 is characterized in that: The intelligent decision-making suggestion module predicts the optimal price point and price range for the product pricing strategy through a comprehensive pricing prediction algorithm. Let P c represents product price, D represents product demand, C represents total product cost, M is market competition intensity factor, Q represents product quality factor, and I represents market demand growth potential factor, taking into account the impact of cost and demand on price; The cost calculation formula is: Among them, α2 and β2 are two constants; The market competition intensity factor, product quality factor and market demand growth potential factor are introduced to adjust the price. The adjusted formula is: The price range is determined by the uncertainty and error margin of the parameters.

7. The enterprise-finance integration analysis and decision support system according to claim 1 is characterized in that: The intelligent decision-making suggestion module optimizes sales channels and identifies the most effective sales channels through the comprehensive scores of sales channels. Let S represent the comprehensive score of the sales channel, R represent the sales performance score of the sales channel, C represent the cost-effectiveness score of the sales channel, E represent the customer experience score of the sales channel, I represent the innovation potential score of the sales channel, P represent the market potential score of the sales channel, and W represent the cost-effectiveness score of the sales channel. r、 W c、 W e、 W i、 W p The weight coefficients of sales performance score, cost-effectiveness score, customer experience score, innovation potential score and market potential score are respectively satisfied. r +W c +W e +W i +W p =1; Calculate the comprehensive score of the sales channel, the formula is: S = W r ×R+W c ×C+W e ×E+W i ×I+W p ×P.

8. The enterprise-finance integration analysis and decision support system according to claim 1 is characterized in that: The intelligent decision-making suggestion module formulates targeted cost control measures for the cost control scheme through the cost clustering algorithm. i represents the cost feature vector of the i-th product and service, d(P i ,P j ) represents two cost feature vectors P i and P j The distance measure between them, T represents the distance threshold, which is used to determine whether products and services belong to the same cost-similar group, C k represents the kth cost similarity group, n represents the total number of products and services, N(C k ) represents the number of products and services in the kth cost-similar group, AVG(P,C k ) represents the average cost feature vector of the kth cost-similar group, where P is the dimension of the cost feature vector and the Euclidean distance is used as the distance metric; The calculation formula is: Where P i,p and P j,p Represent the cost feature vector P i and P j The value in the pth dimension, for each product and service P i , calculate its distance d from all other products and services (P i ,P j ), where j = 1, 2, ..., n, there exists a k such that d(P i ,AVG(P,C k ))≤T, P i Join cost-similar group C k If there is no group that meets the conditions, a new cost-similar group C is created. k+1 , and P i Add it, for each cost similarity group C k , calculate its average cost feature vector; The calculation formula is: For each cost-similar group C k , analyze its average cost characteristic vector, and formulate targeted cost control measures for dimensions with higher costs.

9. The enterprise-finance integration analysis and decision support system according to claim 1 is characterized in that: The intelligent decision suggestion module solves the cost minimization problem for the cost control scheme through creative cost. There are n different activities and decision variables, and x1, x2, ..., x n Indicates that each activity has a corresponding unit cost c1, c2, ..., c n , let f(x1,x2,…,x n ) represents a comprehensive benefit function, which comprehensively considers the impact of various factors on the overall benefit and defines the total cost function C as: Define the constraints as: g j (x1,x2,…,x n )≤b j , where j = 1, 2, ..., m, indicating that there are m different constraints, b j is the right-hand side of the constraint condition, introducing a penalty function P f , when the decision variables violate the constraints, the penalty function will increase the total cost. The penalty function is: Among them, α 0j is the penalty coefficient, which indicates the severity of violating the jth constraint. The goal is to minimize the total cost and maximize the comprehensive benefit. The objective function is defined as: F(x1, x2, …, x n )=C+λ(1-f(x1,x2,…,x n ))+μP f , where λ and μ are weight coefficients used to balance cost minimization and comprehensive benefit maximization as well as to penalize the degree of violation of constraints.

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