A method and related device for realizing valuation using artificial intelligence technology

Abstract

The present invention relates to the field of valuation visualization technology, and in particular to a method and related device for realizing valuation using artificial intelligence technology. The method comprises the following steps: obtaining multi-source product data; performing multi-source data heterogeneous optimization on the multi-source product data to obtain heterogeneous optimized product data; performing point-by-point tensor decomposition processing on the multi-source product data to obtain multiple data node low-dimensional space vectors; constructing a holographic product data space based on multiple data node low-dimensional space vectors and heterogeneous optimized product data; performing multi-dimensional mining of product value factors on the holographic product data space to generate a product value trajectory curve; performing user demand analysis on the holographic product data space to generate multiple user group demand data; performing group demand prediction on multiple user group demand data to obtain product stage demand prediction data. The present invention realizes intuitive and efficient product valuation.

Description

Technical Field

[0001] The present invention relates to the field of valuation visualization technology, and in particular to a method for realizing valuation using artificial intelligence technology and related devices. Background Art

[0002] In the context of increasingly fierce competition among modern enterprises, product value assessment has become an indispensable part of corporate strategic decision-making. Traditional product valuation methods usually rely on financial data, market research and other means, lack a comprehensive consideration of the characteristics of the product itself, and it is difficult to objectively reflect the true value of the product. With the continuous development of artificial intelligence technology, companies are in urgent need of a more intelligent and visual product valuation method to help decision makers deeply understand the core value of the product. At present, corporate products are of various types and complex functions. A single value assessment indicator is difficult to fully capture the value characteristics of the product. At the same time, product value is also affected by external factors such as market demand and industry development. This complex correlation also brings challenges to traditional valuation methods. In order to solve these problems, it is urgent to develop an intelligent product valuation visualization method that can conduct in-depth analysis of the multi-dimensional characteristics of the product and present the core value of the product in an intuitive form. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a method and related devices for realizing valuation using artificial intelligence technology to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a method for realizing valuation using artificial intelligence technology, comprising the following steps:

[0005] Step S1: Acquire product multi-source data; perform multi-source data heterogeneous optimization on the product multi-source data to obtain heterogeneous optimized product data;

[0006] Step S2: Perform point-by-point tensor decomposition processing on the multi-source data of the product to obtain multiple data node low-dimensional space vectors; construct a holographic product data space based on the multiple data node low-dimensional space vectors and heterogeneous optimized product data;

[0007] Step S3: Perform multi-dimensional mining of product value factors on the holographic product data space to generate a product value trajectory curve;

[0008] Step S4: Performing user demand analysis on the holographic product data space to generate multiple user group demand data; performing group demand forecasting on the multiple user group demand data to obtain product stage demand forecasting data;

[0009] Step S5: Based on the product stage demand forecast data and the product value trajectory curve, multiple stage dependency mining is performed to construct a product full-cycle feature map;

[0010] Step S6: Perform dynamic valuation prediction based on the product full-cycle feature map, and perform dynamic visualization rendering to build a product valuation risk visualization model.

[0011] The present invention provides more comprehensive product information by integrating multi-source data, comprehensively understands all aspects of the product, heterogeneously optimizes product data to integrate information from different data sources, improves data integrity and quality, and provides a more reliable basis for subsequent analysis and prediction. It reduces the dimension and extracts the potential features of the data by point-by-point tensor decomposition, simplifies data expression while retaining key information, constructs a holographic product data space to convert product data into a visual spatial representation, understands the relationship and structure between product data, multi-dimensionally mines product value factors to reveal the source and change trend of product value, helps enterprises understand the core competitiveness of products, generates product value trajectory curves to intuitively display the evolution of product value, and provides a reference for product strategy and positioning. User demand analysis helps companies understand user preferences and needs, guides product design and marketing strategies, product stage demand forecasting data predicts market demand changes in advance, and provides support for product life cycle management and marketing promotion. Multiple stage dependency mining reveals the relationship between product demand and value, helping companies to formulate more effective product development strategies, and constructs a full-cycle feature map of the product to fully understand the characteristics and evolution trajectory of the product at different stages, providing support for product management and market positioning. Dynamic valuation forecasts help companies understand the future development trends and value changes of products, provide a reference for decision-making, and construct a product valuation risk visualization model to intuitively display the value risk of the product, helping companies to adjust strategies and risk management measures in a timely manner.

[0012] Preferably, step S1 comprises the following steps:

[0013] Step S11: Acquire product multi-source data, wherein the product multi-source data includes financial data, market data, and user behavior data;

[0014] Step S12: performing account classification standardization processing on the financial data to generate classification standard rules;

[0015] Step S13: Perform difference matching correction using classification standard rules to generate corrected financial data;

[0016] Step S14: Calculate the data boundary of the market data to generate a market data boundary range;

[0017] Step S15: filtering outliers according to the boundary range of the market data to obtain filtered market data;

[0018] Step S16: Perform multi-source data heterogeneity optimization on the corrected financial data, filtered market data, and user behavior data to obtain heterogeneous optimized product data.

[0019] The present invention provides a comprehensive product information perspective by combining financial data, market data and user behavior data, and deeply understands product operations and market performance. Multi-source data integration helps enterprises form a more comprehensive data portrait, providing more powerful support for subsequent analysis and prediction. Account classification standardization processing unifies the data format of financial data, improves data consistency and comparability, generates classification standard rules to standardize the financial data processing process, reduces data processing errors, and provides an accurate basis for subsequent analysis. Difference matching and correction discovers potential errors or inconsistencies in financial data, improves data accuracy and reliability, generates corrected financial data to eliminate data errors, and provides a more reliable financial data basis for subsequent analysis and prediction. Data boundary calculation Calculate and determine the scope and changing trend of market data to help companies understand market fluctuations. Generate market data boundary range to identify anomalies in market data and provide a reference for subsequent analysis and decision-making. Outlier filtering reduces noise and interference in market data, improves data quality and analysis accuracy, filters market data to highlight the key features and trends of market data, and provides clearer market information for subsequent analysis and forecasting. Multi-source data heterogeneous optimization integrates corrected financial data, clean market data and user behavior data to provide more comprehensive and high-quality product data. Heterogeneous optimization of product data provides companies with more accurate product value predictions and decision-making support, and provides a more reliable data foundation for product value prediction visualization.

[0020] Preferably, step S2 specifically comprises the following steps:

[0021] Step S21: Identify entity associations on product multi-source data and construct an entity relationship network;

[0022] Step S22: performing point-by-point tensor decomposition processing on the entity relationship network to obtain a plurality of data node low-dimensional space vectors;

[0023] Step S23: performing latent variable distribution mining on low-dimensional space vectors of multiple data nodes to generate node latent variable distribution data;

[0024] Step S24: performing a potential spatial association analysis on the node hidden variable distribution data based on the entity relationship network to generate implicit association relationships between nodes;

[0025] Step S25: Perform spatial holographic mapping of heterogeneous optimized product data based on implicit associations between nodes to construct a holographic product data space.

[0026] The present invention discovers the association relationship between entities in the data through entity association identification, helps enterprises understand the internal connection of product data, constructs an entity relationship network to form a relationship map between entities, and provides a structured data basis for subsequent analysis. The point-by-point tensor decomposition process converts the entity relationship network into a low-dimensional vector representation, retains the key features of the data and reduces the data dimension, obtains a simplified data representation of multiple data nodes in a low-dimensional space vector, extracts potential association information between entities, and provides a more effective data representation for subsequent analysis. The latent variable distribution mining discovers the potential patterns and distribution laws between data nodes, reveals the inherent structure of the data, and generates node latent variable distribution data to provide a deeper data understanding and potential association information for subsequent analysis. The latent space association analysis reveals the potential connections and association patterns between nodes, helps enterprises discover hidden relationships in the data, generates implicit association relationships between nodes to understand the complex relationship network between entities, and provides support for in-depth analysis and prediction of product data. The spatial holographic mapping converts the implicit association relationship between nodes into a visual spatial representation, helps enterprises understand the complex relationship of product data, constructs a holographic product data space to visualize product data, and provides intuitive support for enterprise product value prediction and decision-making, and promotes enterprise product management and innovative development.

[0027] Preferably, step S3 specifically comprises the following steps:

[0028] Step S31: multi-dimensional mining of product value factors is performed on the holographic product data space to extract multi-dimensional value influencing factors;

[0029] Step S32: Perform product time series iterative change analysis on the holographic product data space to generate product time series change data;

[0030] Step S33: performing iterative periodic calculation on the product time series change data to generate a product iteration cycle;

[0031] Step S34: Evolve the value trajectory based on multi-dimensional value influencing factors and product iteration cycles to generate a product value trajectory curve.

[0032] The present invention helps enterprises extract multiple key factors affecting product value from the holographic product data space through multi-dimensional mining, understand the value composition of the product more comprehensively, extract multi-dimensional value influencing factors to identify the key factors for product success, and provide a basis for product strategy formulation and optimization. The time series iterative change analysis reveals the temporal change trend and evolution trajectory of the product, helps enterprises understand the development history of the product, generates product time series change data to track the evolution process of the product, discovers the changing laws and trends of the product, and provides guidance for product improvement and optimization. Iterative periodic calculation determines the periodic characteristics of product evolution, helps enterprises grasp the opportunity of product update and optimization, generates product iteration cycle to plan product update and improvement plans, and improves product competitiveness and market adaptability. The value trajectory evolution combines multi-dimensional value influencing factors with the product iteration cycle, displays the evolution path of product value over time, and generates a product value trajectory curve to intuitively display the evolution process and change trend of the product, providing a reference for long-term prediction and management of product value.

[0033] Preferably, the specific steps of step S4 are:

[0034] Step S41: Analyze the user behavior characteristics of the holographic product data space to generate user behavior characteristic data;

[0035] Step S42: performing potential user group division processing based on user behavior feature data to generate multiple user groups;

[0036] Step S43: performing user demand analysis on multiple user groups to generate multiple user group demand data;

[0037] Step S44: performing group demand dynamic evolution analysis on the demand data of multiple user groups to generate dynamic demand data of different user groups;

[0038] Step S45: Perform stage demand forecasting on the dynamic demand data of different user groups, thereby obtaining product stage demand forecasting data.

[0039] The present invention helps enterprises understand the behavior and interaction methods of users on products, identify user preferences and habits, generate user behavior feature data to deeply understand user needs and behavior patterns, and provide guidance for product optimization and customization. The potential user group segmentation process divides users into different groups according to their behavior characteristics and attributes, helps enterprises to refine user management and positioning, generates multiple user groups to formulate personalized product strategies and marketing plans for the needs and preferences of different groups, reveals the demand characteristics and priorities of different user groups, helps enterprises understand the focus and expectations of users, generates multiple user group demand data to provide customized demand guidance for product design and function improvement, and improves product user satisfaction and market competitiveness. The demand dynamic evolution analysis tracks the demand change trends and evolution laws of different user groups, helps enterprises to adjust product strategies and services in a timely manner, generates dynamic demand data to grasp the changes in user needs, and provides support for flexible adjustment and updating of products. The stage demand forecast predicts the trend and law of future product demand according to the dynamic demand data of user groups, helps enterprises to formulate effective product planning and marketing strategies, and the product stage demand forecast data predicts market demand changes in advance, provides guidance for enterprise product research and development and promotion, and improves the market adaptability and competitiveness of products.

[0040] Preferably, the specific steps of step S5 are:

[0041] Step S51: Identify the mutation points of the product value trajectory curve to mark the mutation points of the trajectory curve;

[0042] Step S52: Quantitatively analyzing the evolution trend of the mutation points of the trajectory curve to generate the value trajectory evolution trend;

[0043] Step S53: performing multi-stage dependency mining on the value trajectory evolution trend based on the product stage demand forecast data to obtain multiple stage feature sub-graphs;

[0044] Step S54: Fusing the product feature graph with the feature sub-graphs of multiple stages to construct a full-cycle feature graph for the product.

[0045] The present invention helps enterprises discover important turning points and changing trends in the product value trajectory through trajectory mutation point identification, marks key moments and events, marks trajectory curve mutation points to highlight key nodes in product development, and provides important reference for subsequent analysis and decision-making. Evolution trend quantitative analysis quantifies the changing trend and impact of mutation points in the trajectory curve, helps enterprises understand the dynamic process of product value evolution, generates value trajectory evolution trends to reveal the development trend and law of product value, and provides data support for product strategy adjustment and optimization. Stage dependency mining helps enterprises discover the association and dependency relationship between different product stages, reveals the stage characteristics of product value evolution, obtains multiple stage feature sub-maps to deeply analyze the characteristics and evolution laws of different stages of the product, and provides a basis for the stage adjustment of product strategy. Product feature map fusion integrates and fuses the feature sub-maps of different stages to form a full-cycle product feature map, and constructs a full-cycle product feature map to comprehensively display the characteristics and evolution trends of products at different stages, providing support for enterprises to fully understand the development trajectory and market performance of products.

[0046] Preferably, the specific steps of step S6 are:

[0047] Step S61: performing a cross-time point product feature evaluation based on the product full-cycle feature map to generate product feature evaluation data;

[0048] Step S62: Perform dynamic valuation prediction on the product feature evaluation data, thereby generating product dynamic value prediction data;

[0049] Step S63: performing multi-scenario product risk trend analysis on the product dynamic value prediction data, thereby obtaining multi-scenario risk trend data;

[0050] Step S64: Dynamically visualize the product full-cycle feature map based on multi-scenario risk trend data to build a product valuation risk visualization model.

[0051] The present invention helps enterprises evaluate the changes and impacts of product features across different time points through cross-time product feature evaluation, comprehensively understands the evolution of product characteristics, generates product feature evaluation data to quantify the impact and change trend of product features, and provides data support for product development and optimization. Dynamic valuation prediction predicts the dynamic value changes of products based on product feature evaluation data, helps enterprises understand the fluctuations and trends of product value, generates product dynamic value prediction data to predict the future market performance and potential value of products, and guides enterprise product strategies and market activities. Multi-scenario product risk trend analysis helps enterprises evaluate the risks and challenges of products in different scenarios, respond to potential risks in advance, obtains multi-scenario risk trend data to identify the risks faced in product development, and formulates corresponding risk management strategies. Dynamic visualization rendering integrates multi-scenario risk trend data into the product full-cycle feature map, presents the valuation and risk dynamics of products in a visualized form, and constructs a product valuation risk visualization model. Enterprises intuitively understand the valuation situation and risk trends of products, providing an important reference for decision-making.

[0052] Preferably, the specific steps of step S63 are:

[0053] Sampling product application scenarios based on multi-source product data to obtain multiple product application scenarios;

[0054] Conduct time evolution analysis of external risk factors for multiple product application scenarios and obtain the evolution laws of multiple risk factors;

[0055] Conduct risk analysis on product dynamic value prediction data in different scenarios based on the evolution rules of multiple risk factors to obtain product risk data in different scenarios;

[0056] Quantify the risk level of product risk data in different scenarios to obtain multi-scenario product risk values;

[0057] Conduct risk propagation situation analysis based on multi-scenario product risk values ​​and generate multi-scenario risk propagation situation;

[0058] Risk trends in different scenarios are predicted for the risk propagation situation in multiple scenarios, so as to obtain risk trend data in multiple scenarios.

[0059] The present invention uses product application scenario sampling to understand the usage and application scope of products from multiple angles and scenarios, expand the cognition and understanding of products, and obtain multiple product application scenarios to help enterprises deeply explore the potential uses and market needs of products, and provide references for product positioning and optimization. The time evolution analysis of external risk factors helps enterprises understand the changes in the external environment and the evolution of risk factors in product application scenarios, respond to potential risks in a timely manner, and obtain risk scenarios predicted by the evolution of risk factors, providing guidance for product risk management and response strategies. Product risk analysis in different scenarios helps enterprises evaluate the risks and challenges faced by products in different situations, quantify the risk level of products, and obtain product risk data. Enterprises can formulate targeted risk management strategies to improve the response capabilities and Robustness, quantification of risk level quantifies product risks in different scenarios, helps companies compare and rank risk levels in different scenarios, and obtains multi-scenario product risk values. Companies focus on and manage high-risk scenarios to improve the efficiency and accuracy of risk management. Risk propagation situation analysis helps companies understand the spread trend and impact range of risks in different scenarios, respond to potential risk diffusion in a timely manner, and generate multi-scenario risk propagation situation companies to formulate comprehensive risk management strategies to reduce the adverse effects of risk propagation. Risk trend forecasting helps companies predict the evolution trend and development direction of risks in different scenarios, provides early warning and reference for risk management and decision-making, and obtains multi-scenario risk trend data. Companies adjust risk management strategies in a timely manner to respond to risk challenges and ensure the stable operation and development of products.

[0060] In this specification, a related device for realizing valuation using artificial intelligence technology is provided, which is used to execute the method for realizing valuation using artificial intelligence technology as described above, including:

[0061] The heterogeneous optimization module is used to obtain multi-source product data; perform heterogeneous optimization on the multi-source product data to obtain heterogeneous optimized product data;

[0062] The spatial mapping module is used to perform point-by-point tensor decomposition processing on multi-source product data to obtain multiple data node low-dimensional space vectors; and to construct a holographic product data space based on multiple data node low-dimensional space vectors and heterogeneous optimized product data;

[0063] The value trajectory module is used to conduct multi-dimensional mining of product value factors in the holographic product data space, thereby generating a product value trajectory curve;

[0064] The demand forecasting module is used to analyze user demand in the holographic product data space to generate demand data of multiple user groups; to forecast group demand for the demand data of multiple user groups, so as to obtain product stage demand forecast data;

[0065] The feature map module is used to mine multiple stages of dependencies based on product stage demand forecast data and product value trajectory curves, and build a full-cycle feature map of the product;

[0066] The visualization rendering module is used to make dynamic valuation predictions based on the product's full-cycle feature map, and to perform dynamic visualization rendering to build a product valuation risk visualization model.

[0067] The present invention integrates and optimizes product data from different sources and formats through a heterogeneous optimization module, improves the quality and availability of data, and obtains heterogeneous optimized product data to provide a high-quality data foundation for subsequent data processing and analysis, thereby enhancing the accuracy and reliability of product value prediction. The spatial mapping module helps map product data into a low-dimensional space, reduces the complexity and dimension of data, improves the efficiency of data processing and analysis, and constructs a holographic product data space to integrate all aspects of product data to form a more comprehensive and diversified perspective, providing more comprehensive data support for product value prediction. The value trajectory module helps enterprises deeply explore the value factors in product data, discover the core competitive advantages and market value of products, and generate a product value trajectory curve to intuitively display the evolution process and value change trend of products, providing guidance for enterprises to formulate value-driven product strategies, demand The prediction module helps enterprises gain in-depth understanding of the diversity and changing trends of user needs, guides product demand positioning and optimization, and obtains product stage demand forecast data to help enterprises adjust product strategies and development directions in a timely manner, and improve the fit between products and market demand. The feature map module helps enterprises understand the relationship and evolution law between product demand and value from a full-cycle perspective, helps product planning and management, builds a full-cycle feature map of the product to discover the characteristics and evolution trends of each stage of the product, and provides support for enterprises to formulate comprehensive product development strategies. The visualization rendering module helps enterprises intuitively display the valuation status and risk distribution of products, provides intuitive and easy-to-understand information support for decision makers, helps them formulate risk management strategies and product adjustment plans, and builds a product valuation risk visualization model. Enterprises can timely discover and respond to product risks, ensure the long-term and stable development of products, and improve the risk response capabilities of enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic diagram of the steps of a method for realizing valuation using artificial intelligence technology according to the present invention;

[0069] Figure 2 Detailed implementation flow chart of step S1;

[0070] Figure 3 Detailed implementation flow chart of step S2;

[0071] Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0072] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0073] The present application example provides a method and related device for realizing valuation using artificial intelligence technology. The execution subjects of the method and related device for realizing valuation using artificial intelligence technology include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of the present application, and the data processing platform includes but is not limited to: at least one of audio and image management system, information management system, and cloud data management system.

[0074] See also Figures 1 to 4 The present invention provides a method for realizing valuation using artificial intelligence technology, and the method for realizing valuation using artificial intelligence technology comprises the following steps:

[0075] Step S1: Acquire product multi-source data; perform multi-source data heterogeneous optimization on the product multi-source data to obtain heterogeneous optimized product data;

[0076] Step S2: Perform point-by-point tensor decomposition processing on the multi-source data of the product to obtain multiple data node low-dimensional space vectors; construct a holographic product data space based on the multiple data node low-dimensional space vectors and heterogeneous optimized product data;

[0077] Step S3: Perform multi-dimensional mining of product value factors on the holographic product data space to generate a product value trajectory curve;

[0078] Step S4: Performing user demand analysis on the holographic product data space to generate multiple user group demand data; performing group demand forecasting on the multiple user group demand data to obtain product stage demand forecasting data;

[0079] Step S5: Based on the product stage demand forecast data and the product value trajectory curve, multiple stage dependency mining is performed to construct a product full-cycle feature map;

[0080] Step S6: Perform dynamic valuation prediction based on the product full-cycle feature map, and perform dynamic visualization rendering to build a product valuation risk visualization model.

[0081] The present invention provides more comprehensive product information by integrating multi-source data, comprehensively understands all aspects of the product, heterogeneously optimizes product data to integrate information from different data sources, improves data integrity and quality, and provides a more reliable basis for subsequent analysis and prediction. It reduces the dimension and extracts the potential features of the data by point-by-point tensor decomposition, simplifies data expression while retaining key information, constructs a holographic product data space to convert product data into a visual spatial representation, understands the relationship and structure between product data, multi-dimensionally mines product value factors to reveal the source and change trend of product value, helps enterprises understand the core competitiveness of products, generates product value trajectory curves to intuitively display the evolution of product value, and provides a reference for product strategy and positioning. User demand analysis helps companies understand user preferences and needs, guides product design and marketing strategies, product stage demand forecasting data predicts market demand changes in advance, and provides support for product life cycle management and marketing promotion. Multiple stage dependency mining reveals the relationship between product demand and value, helping companies to formulate more effective product development strategies, and constructs a full-cycle feature map of the product to fully understand the characteristics and evolution trajectory of the product at different stages, providing support for product management and market positioning. Dynamic valuation forecasts help companies understand the future development trends and value changes of products, provide a reference for decision-making, and construct a product valuation risk visualization model to intuitively display the value risk of the product, helping companies to adjust strategies and risk management measures in a timely manner.

[0082] In the embodiment of the present invention, refer to Figure 1 , is a flow chart of the steps of a method for realizing valuation using artificial intelligence technology of the present invention. In this example, the steps of the method for realizing valuation using artificial intelligence technology include:

[0083] Step S1: Acquire product multi-source data; perform multi-source data heterogeneous optimization on the product multi-source data to obtain heterogeneous optimized product data;

[0084] In this embodiment, the required product-related data sources are determined, and a data collection plan is formulated, including a list of data sources, collection frequency, collection methods, etc. According to the plan, product-related raw data is obtained from various data sources through API, file export, web crawling, etc., and the obtained raw data is preliminarily processed to remove useless information to form preliminary product multi-source data. The differences in data structure, format, content, etc. of each data source are analyzed, and heterogeneous problems are identified. A data standardization plan is designed, including data model design, field mapping, format conversion, etc. According to the standardization plan, ETL, data cleaning and other technical means are used to convert and integrate the raw data, and the converted data is quality checked to ensure the integrity, accuracy, consistency, etc. of the data. The availability of the data is further enhanced according to needs, such as data desensitization and data completion, and the product multi-source data in a unified format that has been optimized for heterogeneity is output.

[0085] Step S2: Perform point-by-point tensor decomposition processing on the multi-source data of the product to obtain multiple data node low-dimensional space vectors; construct a holographic product data space based on the multiple data node low-dimensional space vectors and heterogeneous optimized product data;

[0086] In this embodiment, the heterogeneous optimized product data is represented as a multidimensional tensor, each dimension corresponds to a different data attribute (such as product category, attribute, feature, etc.), and the tensor is subjected to point-by-point singular value decomposition (SVD) processing to decompose it into multiple low-dimensional space vectors, each low-dimensional space vector corresponds to a data node, representing the projection of the node on a certain potential feature dimension, and the low-dimensional space vectors of multiple data nodes are output. The low-dimensional space vectors of each data node are combined into a high-dimensional feature vector as the coordinate of the data node in the holographic data space. Other attribute information in the heterogeneous optimized product data (such as product name, description, picture, etc.) is used to give rich semantic information to each data node in the holographic data space, and an index and query mechanism for the holographic product data space is constructed, so that relevant product information can be quickly searched and obtained in the space according to user needs, and the constructed holographic product data space is output.

[0087] Step S3: Perform multi-dimensional mining of product value factors on the holographic product data space to generate a product value trajectory curve;

[0088] In this embodiment, in combination with business needs, the main factors affecting product value, such as sales volume, profit, user evaluation, brand influence, etc., are determined. For each value factor, the relevant attributes and characteristics in the holographic product data space are analyzed, and valuable indicators are extracted. Technical means such as data mining and machine learning are used to establish a product value assessment model, and each value factor is quantitatively analyzed. The value factors of different types of products are compared and analyzed to find the commonalities and differences in product value, and the multi-dimensional analysis results of product value are output. The quantitative indicators of each product value factor are plotted as a curve over time / life cycle, and the trend characteristics of the curves of different value factors are analyzed. The laws and trends of product value changes are explored, and the curves of multiple value factors are superimposed to form a trajectory curve of the comprehensive value of the product. For different types of products, the characteristics of their value trajectory curves are compared and analyzed to find commonalities and differences, and the product value trajectory curve and its analysis report are output.

[0089] Step S4: Performing user demand analysis on the holographic product data space to generate multiple user group demand data; performing group demand forecasting on the multiple user group demand data to obtain product stage demand forecasting data;

[0090] In this embodiment, the target user group to be targeted is determined according to the product type and business scenario, and the user group is segmented from the dimensions of region, age, consumption habits, etc. The user behavior data contained in the holographic product data space, such as browsing records, purchase history, evaluation information, etc., is analyzed to find the characteristics and preferences of different user groups. Data mining technologies such as cluster analysis and association rule mining are used to divide users into multiple groups, and the demand characteristics of each group are extracted. In combination with the demographic characteristics and psychological characteristics of the user group, the demand characteristics are deeply analyzed to form a detailed description of the user group needs, and the demand data of multiple user groups, including group characteristics, demand preferences, usage, etc., are output. In combination with the product life cycle theory, the product is divided into different stages, such as the introduction stage, growth stage, maturity stage, decline stage, etc. For each product stage, the demand change trend of different user groups is analyzed, and the demand of each user group within a certain period of time in the future is predicted. The demand prediction results of each user group are summarized to obtain the overall demand prediction data of the product at different stages, and the change curve of the product demand prediction data is analyzed to identify key time nodes such as the peak period and the trough period of demand, and the demand prediction data of each stage of the product and the corresponding demand change trend analysis report are output.

[0091] Step S5: Based on the product stage demand forecast data and the product value trajectory curve, multiple stage dependency mining is performed to construct a product full-cycle feature map;

[0092] In this embodiment, according to the sales volume, revenue, cost and other indicators of the product, the value indicators of the product at each stage, such as gross profit margin, revenue growth rate, etc., are calculated, the value indicator data points of each stage are connected, the value trajectory curve of the product is drawn, the change trend of the value trajectory curve is analyzed, the key inflection points of the product life cycle are identified, and the value trajectory curve of the product's entire life cycle is output. For different stages such as the introduction period, growth period, maturity period and decline period of the product, the change characteristics of the demand forecast data and the value indicators are analyzed, and the key characteristic indicators of each stage, such as demand growth rate, gross profit margin, market share, etc., are extracted in combination with industry knowledge and expert experience. The characteristic indicators of different stages are compared and analyzed to find the correlation rules and dependencies between the stages. The analysis results are organized into a characteristic map of the product's entire cycle, including the characteristic indicators of each stage and their mutual dependencies. The characteristic indicators of each product stage and the dependencies between them are organized into a complete map. In the map, nodes are used to represent the characteristic indicators of each stage, and edges are used to represent the dependencies between them. Corresponding weights, directions and other attributes are added to the nodes and edges to represent the degree of correlation and influence direction between the characteristics. A complete product full-cycle characteristic map is output and visualized.

[0093] Step S6: Perform dynamic valuation prediction based on the product full-cycle feature map, and perform dynamic visualization rendering to build a product valuation risk visualization model.

[0094] In this embodiment, a dynamic valuation prediction model is established according to the attributes of each node (feature indicator) in the full-cycle feature map of the product and the dependencies between them. The model predicts the changes in the value indicators of the product in each future stage based on the input current state of the product (feature indicator value) and the association rules between the features. The model adopts machine learning, system dynamics and other methods to achieve the goal of accurately predicting product valuation through training and calibration. The model should be able to handle the nonlinear relationship between feature indicators and consider the impact of factors such as the external market environment on product valuation. The model outputs the valuation prediction results of the product in each future stage. For the dynamic valuation prediction results, its uncertainty and risk factors are analyzed, and the key risk indicators affecting product valuation are identified, such as market demand fluctuations, cost changes, and changes in the competitive landscape. A valuation risk assessment model is established to quantify the impact of these risk factors on product valuation. The valuation prediction results and risk assessment results are presented in a visual way to construct a product valuation risk visualization model. In the visualization model, intuitive methods such as colors and graphics are used to display the valuation level and risk status of the product at different development stages. A complete product valuation risk visualization model is output to provide decision makers with an intuitive basis for product valuation analysis.

[0095] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0096] Step S11: Acquire product multi-source data, wherein the product multi-source data includes financial statements, market data, and user behavior data;

[0097] Step S12: performing account classification standardization processing on the financial statements to generate classification standard rules;

[0098] Step S13: Use classification standard rules to perform difference matching correction and generate corrected financial statements;

[0099] Step S14: Calculate the data boundary of the market data to generate a market data boundary range;

[0100] Step S15: filtering outliers according to the boundary range of the market data to obtain filtered market data;

[0101] Step S16: Perform multi-source data heterogeneity optimization on the corrected financial statements, filtered market data, and user behavior data to obtain heterogeneous optimized product data.

[0102] In this embodiment, after obtaining the authorization of the enterprise, multi-source data of the product is obtained. Financial data can be obtained through the financial reports publicly disclosed by the enterprise, market data can be purchased from third-party market research institutions or collected by itself, and user behavior data can be obtained through website access logs, APP tracking points, etc., and a unified financial statement account classification standard is established. According to the classification standard, the specific accounts of the financial statements of each enterprise are mapped and classified, and classification standard rules are generated for the subsequent automatic classification of different financial statements. The generated classification standard rules are used to automatically match the financial statements of each enterprise, identify and correct the differences in the processing of similar accounts of different enterprises, and generate corrected unified financial statements. Data, analyze the historical change trends and normal fluctuation ranges of various market data, set the upper and lower boundary values ​​of various market data according to the statistical analysis results, generate the boundary range of market data, and compare the established market data boundary range to identify and eliminate outlier data points that exceed the boundary range to obtain market data filtered by outliers. The corrected financial statement data, filtered market data and user behavior data are integrated to analyze the correlation and differences between various data, identify heterogeneous problems that need to be optimized, and use data fusion, semantic mapping and other technologies to eliminate heterogeneous differences between data sources, generate final heterogeneous optimized product data, and provide a unified data foundation for subsequent analysis.

[0103] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0104] Step S21: Identify entity associations on product multi-source data and construct an entity relationship network;

[0105] Step S22: performing point-by-point tensor decomposition processing on the entity relationship network to obtain a plurality of data node low-dimensional space vectors;

[0106] Step S23: performing latent variable distribution mining on low-dimensional space vectors of multiple data nodes to generate node latent variable distribution data;

[0107] Step S24: performing a potential spatial association analysis on the node hidden variable distribution data based on the entity relationship network to generate implicit association relationships between nodes;

[0108] Step S25: Perform spatial holographic mapping of heterogeneous optimized product data based on implicit associations between nodes to construct a holographic product data space.

[0109] In this embodiment, various entity types, such as product, brand, user, channel, etc., are identified from the heterogeneous optimized product data, and entity recognition and relationship extraction technologies are used to construct a relationship network between these entities, such as product-brand, user-product, channel-product, etc. The constructed entity relationship network is sorted and optimized to ensure the accuracy of entities and their relationships. The constructed entity relationship network is represented as a high-dimensional tensor structure, and each dimension corresponds to a different entity type and relationship. The high-dimensional data tensor is decomposed into multiple low-dimensional space vectors, each of which represents the information of a potential feature dimension in the data, and each node ( The latent variable distribution of entities reflects the implicit features of nodes in each potential feature dimension, analyzes the similarity and correlation strength between nodes in each latent variable dimension, identifies the implicit correlation relationship between nodes, maps the implicit correlation relationship between nodes back to the original product data after heterogeneous optimization, and uses dimensionality reduction technology to embed product data into a low-dimensional holographic product data space, so that the relative position of the product in this space can reflect its inherent connection in each potential feature dimension. The constructed holographic product data space not only retains the main information of the original multi-source data, but also reveals the implicit correlation between products, providing richer and deeper data representation for subsequent analysis and application.

[0110] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0111] Step S31: multi-dimensional mining of product value factors is performed on the holographic product data space to extract multi-dimensional value influencing factors;

[0112] Step S32: Perform product time series iterative change analysis on the holographic product data space to generate product time series change data;

[0113] Step S33: performing iterative periodic calculation on the product time series change data to generate a product iteration cycle;

[0114] Step S34: Evolve the value trajectory based on multi-dimensional value influencing factors and product iteration cycles to generate a product value trajectory curve.

[0115] In this embodiment, principal component analysis, factor analysis and other methods are used to analyze various dimensions in the holographic product data space, and the main factors related to the product value are extracted. These factors include product performance indicators, user experience, market response and other dimensions. Through the analysis of these factors, the multi-dimensional key driving factors that affect the product value are excavated, and the various products in the holographic product data space are tracked in time series to analyze their change trajectories in the data space. The change trajectories reflect the iterative changes of the products over time, including the upgrade of product functions, changes in user evaluations, etc. These change trajectories are digitized to form product time series change data. For the product time series change data, the iteration cycle of the product is identified by the time series analysis method. For example, if a new version of the product is launched every 6 months, 6 months is identified as the iteration cycle of the product. The calculation of this iteration cycle provides a reference for future product planning. Combined with the obtained multi-dimensional value influencing factors and the obtained product iteration cycle, the value change trajectory of the product in the time dimension is analyzed. Through modeling and prediction, the value change curve of the product from research and development to the market is depicted. This value trajectory curve can reflect the value performance of the product at different stages and provide a basis for product planning, investment decisions, etc.

[0116] In this embodiment, step S4 includes the following steps:

[0117] Step S41: Analyze the user behavior characteristics of the holographic product data space to generate user behavior characteristic data;

[0118] Step S42: performing potential user group division processing based on user behavior feature data to generate multiple user groups;

[0119] Step S43: performing user demand analysis on multiple user groups to generate multiple user group demand data;

[0120] Step S44: performing group demand dynamic evolution analysis on the demand data of multiple user groups to generate dynamic demand data of different user groups;

[0121] Step S45: Perform stage demand forecasting on the dynamic demand data of different user groups, thereby obtaining product stage demand forecasting data.

[0122] In this embodiment, user behavior data in the holographic product data space are collected, including user browsing, searching, purchasing, evaluating and other behavior records. Data mining and machine learning technologies are used to analyze these user behavior data and extract user behavior characteristics. These characteristics include user browsing habits, purchasing tendencies, usage frequency and the like. These user behavior characteristics are organized into user behavior characteristic data. Based on the obtained user behavior characteristic data, users are classified by cluster analysis and other methods and divided into multiple user groups. Each user group represents a user group with similar behavior characteristics. Through this user group division, the characteristics and needs of different types of users can be understood more accurately. Based on the user behavior characteristic data, the demand characteristics of different user groups are analyzed to identify the user's needs for products in various aspects and extract the core needs of each user group. According to the user group demand data and combined with the product timeline, the changing trend of user demand in different periods is analyzed to identify the dynamic evolution law of user demand in the product life cycle and form dynamic demand data of different user groups in the time dimension. Based on the obtained dynamic demand data, the user demand at different stages in the future is predicted by using a time series prediction model to obtain product stage demand prediction data for different user groups. These prediction data can provide a basis for product planning, investment decisions and the like.

[0123] In this embodiment, the specific steps of step S5 are:

[0124] Step S51: Identify the mutation points of the product value trajectory curve to mark the mutation points of the trajectory curve;

[0125] Step S52: Quantitatively analyze the evolution trend of the mutation points of the trajectory curve to generate the value trajectory evolution trend;

[0126] Step S53: performing multi-stage dependency mining on the value trajectory evolution trend based on the product stage demand forecast data to obtain multiple stage feature sub-graphs;

[0127] Step S54: Fusing the product feature map with the feature sub-maps of multiple stages to construct a full-cycle feature map of the product.

[0128] In this embodiment, the value change data of the product throughout its life cycle is collected and sorted, a product value trajectory curve is constructed, the value trajectory curve is analyzed, and the mutation detection algorithm (such as CUSUM algorithm, Lepage test, etc.) is used to identify the mutation points on the trajectory curve. These mutation points mark the stage-by-stage changes in the product value during the time evolution process. These mutation points are marked on the value trajectory curve. According to the identified mutation points, the value change trends before and after the mutation points are analyzed. The evolution trend of the value trajectory before and after the mutation points is quantitatively described by using curve fitting, time series analysis and other methods to obtain the evolution trend characteristics of the value trajectory at different stages. Combined with the product stage demand forecast data, the impact of demand on the evolution of the value trajectory at different stages is analyzed. By using association rule mining, pattern recognition and other methods, characteristic sub-graphs of different stages are extracted from the value trajectory evolution trend. These characteristic sub-graphs reflect the key features of the product at different stages. Multiple stage characteristic sub-graphs are integrated, the logical relationship and time sequence between the features of each stage are analyzed, and the characteristic graph of the product throughout its life cycle is constructed. This full-cycle characteristic graph contains the key features of the product from birth to exit throughout its life cycle. This characteristic graph provides an important basis for subsequent product planning, design, optimization, etc.

[0129] In this embodiment, step S6 includes the following steps:

[0130] Step S61: performing a cross-time point product feature evaluation based on the product full-cycle feature map to generate product feature evaluation data;

[0131] Step S62: Perform dynamic valuation prediction on the product feature evaluation data, thereby generating product dynamic value prediction data;

[0132] Step S63: performing multi-scenario product risk trend analysis on the product dynamic value prediction data, thereby obtaining multi-scenario risk trend data;

[0133] Step S64: Dynamically visualize the product full-cycle feature map based on multi-scenario risk trend data to build a product valuation risk visualization model.

[0134] In this embodiment, the product full-cycle feature map is used to evaluate the characteristics of the product at different time points. The evaluation method adopts expert scoring, machine learning modeling, etc. to quantify and score each feature indicator. By comparing across time points, the changes in product characteristics at different stages are analyzed to form product feature evaluation data. Combined with the product feature evaluation data, a product value prediction model is established. Time series analysis, machine learning and other methods are used to dynamically model and predict the changes in product value at different time nodes, generate predicted value data of the product at each time node, and form a product dynamic value prediction matrix. Based on the product dynamic value prediction data, a variety of scenario assumptions (such as changes in market demand) are designed. , technological innovation, etc.), for these hypothetical scenarios, analyze the changing trend of product value at each time node, and evaluate its risk level, summarize the product risk trend data under multiple scenarios, and form a multi-scenario risk trend matrix for the entire product life cycle, integrate the value prediction data and risk trend data into the product full-cycle feature map, use dynamic visualization technology (such as D3.js, echarts, etc.), render and interactively design the product full-cycle feature map, and present the feature changes, value prediction trends and risk level changes of the product at different time nodes through visualization means, build a product valuation risk visualization model, and provide intuitive support for product innovation planning and decision-making.

[0135] In this embodiment, the specific steps of step S63 are:

[0136] Sampling product application scenarios based on multi-source product data to obtain multiple product application scenarios;

[0137] Conduct time evolution analysis of external risk factors for multiple product application scenarios and obtain the evolution laws of multiple risk factors;

[0138] Conduct risk analysis on product dynamic value prediction data in different scenarios based on the evolution rules of multiple risk factors to obtain product risk data in different scenarios;

[0139] Quantify the risk level of product risk data in different scenarios to obtain multi-scenario product risk values;

[0140] Conduct risk propagation situation analysis based on multi-scenario product risk values ​​and generate multi-scenario risk propagation situation;

[0141] Risk trends in different scenarios are predicted for the risk propagation situation in multiple scenarios, so as to obtain risk trend data in multiple scenarios.

[0142] In this embodiment, multi-source data related to the product are collected, including user needs, market trends, technological development, etc., and data mining methods such as cluster analysis and principal component analysis are used to identify different application scenarios of the product. For different application scenarios, their characteristics, usage scenarios, etc. are described to form multiple product application scenarios. For each product application scenario, external risk factors that affect the product, such as market competition and technological progress, are identified. Historical data of these risk factors are collected, and their time evolution trends are analyzed. Methods such as time series analysis and gray prediction are used to establish an evolution law model of risk factors. Different scenario assumptions are designed in combination with the obtained risk factor evolution laws. These scenario assumptions are brought into the constructed product dynamic value prediction model, and the changing trends of product value in different scenarios are analyzed to evaluate its risk level. Based on the product risk data of different scenarios, a multi-scenario product risk matrix is ​​formed. A quantitative indicator system is designed for the product risk data in different scenarios, including risk probability, risk loss, etc. The hierarchical analysis method, fuzzy comprehensive evaluation and other methods are used to score and integrate the product risks in each scenario to obtain the product risk values ​​in different scenarios. A correlation network model between product risk factors is constructed to simulate the propagation mechanism and propagation path between risk factors in different scenarios, analyze the risk propagation trend in the product system, generate a multi-scenario risk propagation trend, design different application scenario assumptions based on the obtained multi-scenario risk propagation trend, use trend prediction, scenario analysis and other methods to predict the changing trend of risk levels in each scenario, and summarize the product risk trend data in multiple application scenarios to provide a basis for subsequent risk management and control.

[0143] In this embodiment, a related device for implementing valuation using artificial intelligence technology is provided, which is used to execute the method for implementing valuation using artificial intelligence technology as described above, including:

[0144] The heterogeneous optimization module is used to obtain multi-source product data; perform heterogeneous optimization on the multi-source product data to obtain heterogeneous optimized product data;

[0145] The spatial mapping module is used to perform point-by-point tensor decomposition processing on multi-source product data to obtain multiple data node low-dimensional space vectors; and to construct a holographic product data space based on multiple data node low-dimensional space vectors and heterogeneous optimized product data;

[0146] The value trajectory module is used to conduct multi-dimensional mining of product value factors in the holographic product data space, thereby generating a product value trajectory curve;

[0147] The demand forecasting module is used to analyze user demand in the holographic product data space to generate demand data of multiple user groups; to forecast group demand for the demand data of multiple user groups, so as to obtain product stage demand forecast data;

[0148] The feature map module is used to mine multiple stages of dependencies based on product stage demand forecast data and product value trajectory curves, and build a full-cycle feature map of the product;

[0149] The visualization rendering module is used to make dynamic valuation predictions based on the product's full-cycle feature map, and to perform dynamic visualization rendering to build a product valuation risk visualization model.

[0150] The present invention integrates and optimizes product data from different sources and formats through a heterogeneous optimization module, improves the quality and availability of data, and obtains heterogeneous optimized product data to provide a high-quality data foundation for subsequent data processing and analysis, thereby enhancing the accuracy and reliability of product value prediction. The spatial mapping module helps map product data into a low-dimensional space, reduces the complexity and dimension of data, improves the efficiency of data processing and analysis, and constructs a holographic product data space to integrate all aspects of product data to form a more comprehensive and diversified perspective, providing more comprehensive data support for product value prediction. The value trajectory module helps enterprises deeply explore the value factors in product data, discover the core competitive advantages and market value of products, and generate a product value trajectory curve to intuitively display the evolution process and value change trend of products, providing guidance for enterprises to formulate value-driven product strategies, demand The prediction module helps enterprises gain in-depth understanding of the diversity and changing trends of user needs, guides product demand positioning and optimization, and obtains product stage demand forecast data to help enterprises adjust product strategies and development directions in a timely manner, and improve the fit between products and market demand. The feature map module helps enterprises understand the relationship and evolution law between product demand and value from a full-cycle perspective, helps product planning and management, builds a full-cycle feature map of the product to discover the characteristics and evolution trends of each stage of the product, and provides support for enterprises to formulate comprehensive product development strategies. The visualization rendering module helps enterprises intuitively display the valuation status and risk distribution of products, provides intuitive and easy-to-understand information support for decision makers, helps them formulate risk management strategies and product adjustment plans, and builds a product valuation risk visualization model. Enterprises can timely discover and respond to product risks, ensure the long-term and stable development of products, and improve the risk response capabilities of enterprises.

[0151] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0152] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for implementing valuation using artificial intelligence technology, characterized in that: The following steps are involved: Step S1: Acquire product multi-source data; perform multi-source data heterogeneous optimization on the product multi-source data to obtain heterogeneous optimized product data; Among them, the specific steps of step S1 are: Step S11: Acquire product multi-source data, wherein the product multi-source data includes financial data, market data, and user behavior data; Step S12: performing account classification standardization processing on the financial data to generate classification standard rules; Step S13: Perform difference matching correction using classification standard rules to generate corrected financial data; Step S14: Calculate the data boundary of the market data to generate a market data boundary range; Step S15: filtering outliers according to the boundary range of the market data to obtain filtered market data; Step S16: performing multi-source data heterogeneous optimization on the corrected financial data, filtered market data, and user behavior data to obtain heterogeneous optimized product data; Step S2: Perform point-by-point tensor decomposition processing on the multi-source data of the product to obtain multiple data node low-dimensional space vectors; construct a holographic product data space based on the multiple data node low-dimensional space vectors and heterogeneous optimized product data; Among them, the specific steps of step S2 are: Step S21: Identify entity associations on product multi-source data and construct an entity relationship network; Step S22: performing point-by-point tensor decomposition processing on the entity relationship network to obtain a plurality of data node low-dimensional space vectors; Step S23: performing latent variable distribution mining on low-dimensional space vectors of multiple data nodes to generate node latent variable distribution data; Step S24: performing a potential spatial association analysis on the node hidden variable distribution data based on the entity relationship network to generate implicit association relationships between nodes; Step S25: Performing spatial holographic mapping on the heterogeneous optimized product data based on the implicit association relationship between nodes to construct a holographic product data space; Step S3: Perform multi-dimensional mining of product value factors on the holographic product data space to generate a product value trajectory curve; Step S4: Performing user demand analysis on the holographic product data space to generate multiple user group demand data; performing group demand forecasting on the multiple user group demand data to obtain product stage demand forecasting data; The specific steps of step S4 are: Step S41: Analyze the user behavior characteristics of the holographic product data space to generate user behavior characteristic data; Step S42: performing potential user group division processing based on user behavior feature data to generate multiple user groups; Step S43: performing user demand analysis on multiple user groups to generate multiple user group demand data; Step S44: performing group demand dynamic evolution analysis on the demand data of multiple user groups to generate dynamic demand data of different user groups; Step S45: performing stage demand forecasting on the dynamic demand data of different user groups, thereby obtaining product stage demand forecasting data; Step S5: Based on the product stage demand forecast data and the product value trajectory curve, multiple stage dependency mining is performed to construct a product full-cycle feature map; Step S6: Perform dynamic valuation prediction based on the product full-cycle feature map, and perform dynamic visualization rendering to build a product valuation risk visualization model; The specific steps of step S6 are: Step S61: performing a cross-time point product feature evaluation based on the product full-cycle feature map to generate product feature evaluation data; Step S62: Perform dynamic valuation prediction on the product feature evaluation data, thereby generating product dynamic value prediction data; Step S63: performing multi-scenario product risk trend analysis on the product dynamic value prediction data, thereby obtaining multi-scenario risk trend data; Step S64: dynamically visualize and render the product full-cycle feature map based on the multi-scenario risk trend data to build a product valuation risk visualization model; The specific steps of step S63 are: Sampling product application scenarios based on multi-source product data to obtain multiple product application scenarios; Conduct time evolution analysis of external risk factors for multiple product application scenarios and obtain the evolution laws of multiple risk factors; Conduct risk analysis on product dynamic value prediction data in different scenarios based on the evolution rules of multiple risk factors to obtain product risk data in different scenarios; Quantify the risk level of product risk data in different scenarios to obtain multi-scenario product risk values; Conduct risk propagation situation analysis based on multi-scenario product risk values ​​and generate multi-scenario risk propagation situation; Risk trends in different scenarios are predicted for the risk propagation situation in multiple scenarios, so as to obtain risk trend data in multiple scenarios.

2. The method according to claim 1, characterized in that The specific steps of step S3 are: Step S31: multi-dimensional mining of product value factors is performed on the holographic product data space to extract multi-dimensional value influencing factors; Step S32: Perform product time series iterative change analysis on the holographic product data space to generate product time series change data; Step S33: performing iterative periodic calculation on the product time series change data to generate a product iteration cycle; Step S34: Evolve the value trajectory based on multi-dimensional value influencing factors and product iteration cycles to generate a product value trajectory curve.

3. The method for realizing valuation using artificial intelligence technology according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: Identify the mutation points of the product value trajectory curve to mark the mutation points of the trajectory curve; Step S52: Quantitatively analyze the evolution trend of the mutation points of the trajectory curve to generate the value trajectory evolution trend; Step S53: performing multi-stage dependency mining on the value trajectory evolution trend based on the product stage demand forecast data to obtain multiple stage feature sub-graphs; Step S54: Fusing the product feature map with the feature sub-maps of multiple stages to construct a full-cycle feature map of the product.

4. A device for realizing valuation using artificial intelligence technology, characterized in that: The method for implementing valuation using artificial intelligence technology as claimed in claim 1 comprises: The heterogeneous optimization module is used to obtain multi-source product data; perform heterogeneous optimization on the multi-source product data to obtain heterogeneous optimized product data; The spatial mapping module is used to perform point-by-point tensor decomposition processing on multi-source product data to obtain multiple data node low-dimensional space vectors; and to construct a holographic product data space based on multiple data node low-dimensional space vectors and heterogeneous optimized product data; The value trajectory module is used to conduct multi-dimensional mining of product value factors in the holographic product data space, thereby generating a product value trajectory curve; The demand forecasting module is used to analyze user demand in the holographic product data space to generate demand data of multiple user groups; to forecast group demand for the demand data of multiple user groups, so as to obtain product stage demand forecast data; The feature map module is used to mine multiple stages of dependencies based on product stage demand forecast data and product value trajectory curves, and build a full-cycle feature map of the product; The visualization rendering module is used to make dynamic valuation predictions based on the product's full-cycle feature map, and to perform dynamic visualization rendering to build a product valuation risk visualization model.

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