Intelligent development and ai innovation incubation method and system for intangible cultural heritage ip

Abstract

The application discloses a non-heritage culture IP intelligent development and AI innovation incubation method and system, relates to the technical field of culture IP incubation, and comprises the following steps: completing cross-dimension mapping of semantic and visual features by calling non-heritage multi-modal data, extracting inheritor group features and performing correlation analysis, constructing a non-heritage IP multi-dimensional knowledge node network to realize hierarchical correlation and semantic mining of cultural elements, integrating multiple parameters to perform commercial value quantitative analysis, generating an IP development technical path and an innovation direction in combination with multi-source data, and optimizing and adjusting a development process according to real-time updating data and dynamic feedback information. And the specific processes of multi-stage entity extraction, correlation construction, feature supplement, dynamic updating, value dimension disintegration, quantitative analysis, fusion evaluation, data correlation, path design, parameter optimization and simulation verification are performed, a whole-link and systematic non-heritage IP intelligent development and innovation incubation technical scheme is formed, and the collaborative promotion of cultural inheritance and commercial transformation is realized.

Description

Technical Field

[0001] This invention relates to the field of cultural IP incubation technology, and in particular to methods and systems for intelligent development and AI innovation incubation of intangible cultural heritage IP. Background Technology

[0002] As the "Guochao" (national trend) economy continues to expand, the penetration rate of intangible cultural heritage (ICH) elements in the consumer market is constantly increasing, and the demand for traditional cultural theme products among young people is growing stronger, driving ICH towards industrialization and IP development. The living transmission and commercial transformation of ICH has become a focus of industry attention. Traditional ICH resources need to achieve accurate translation and innovative expression of their cultural connotations through modern methods to adapt to diversified market demands and dissemination scenarios. At the same time, the deepening integration of digital technology and traditional culture provides new technological support for the development of ICH IPs. There is an urgent need to build a systematic development and incubation system to achieve deep synergy between ICH elements, the characteristics of the inheritors, and commercial value, thereby solving the problem of insufficient efficiency in the digital application of ICH resources.

[0003] Existing technologies for developing intangible cultural heritage (ICH) IPs suffer from two significant drawbacks. First, the adaptation of cultural elements to modern settings lacks systematic support. The exploration of the core connotations and technical characteristics of ICH remains superficial, failing to achieve a deep integration of cultural symbols with modern aesthetics and functional needs. This results in highly homogenized ICH products that fail to reflect the unique value of ICH. Second, the development process lacks effective integration and dynamic application of multi-dimensional data. It fails to fully connect various relevant data to form a complete development logic, and the dynamic adjustments during the development process lack a scientific basis. This leads to insufficient alignment between the direction of ICH development and market demand and the laws of inheritance, hindering the simultaneous enhancement of cultural and commercial value and restricting the sustainable development of ICH IPs. Summary of the Invention

[0004] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a method and system for intelligent development and AI innovation incubation of intangible cultural heritage IP.

[0005] The technical solution adopted in this invention is a method for intelligent development and AI innovation incubation of intangible cultural heritage IP, comprising the following steps: S1, retrieving multimodal data of intangible cultural heritage through a folk culture IP knowledge graph computing platform, and completing cross-dimensional mapping of semantic and visual features based on the feature association mechanism of the cultural semantic cross-modal mapping algorithm; S2, using the multi-dimensional parameter extraction module of the intangible cultural heritage inheritor profile modeling model, performing feature capture and association analysis on the technical characteristics, creative preferences, and dissemination behavior of the inheritors; S3, constructing multi-dimensional knowledge nodes of intangible cultural heritage IP based on the entity association rules of the folk culture IP knowledge graph computing platform. The system employs a network approach to hierarchically associate and semantically mine cultural elements; S4 utilizes a multi-factor analysis mechanism within the intangible cultural heritage IP commercial value assessment model to integrate parameters such as cultural scarcity, market suitability, and dissemination potential for value dimension decomposition and quantitative analysis; S5 uses a parameter configuration module for intelligent development and AI innovation incubation of intangible cultural heritage IPs, combined with the aforementioned mapping results, profile data, and value assessment conclusions, to generate the technical path and innovation direction for IP development; S6, based on the dynamic adjustment mechanism of AI innovation incubation, and according to real-time updates of the knowledge graph and dynamic feedback information from commercial value assessment, it optimizes features and corrects the path in the IP development process.

[0006] Furthermore, the cultural semantic cross-modal mapping algorithm in S1 adopts the following expression:

[0007] ,

[0008] in, For cross-modal mapping results, Let be the weight coefficient of the i-th type of cultural element. This is a text semantic feature mapping function. For intangible cultural heritage text semantic data, For visual feature mapping function, Visual image data of intangible cultural heritage For feature fusion operators, For knowledge graph association enhancement functions, To determine the entity association strength of the knowledge graph of folk culture IP, This is a semantic consistency check function. This is a parameter for cross-modal feature consistency.

[0009] Furthermore, the non-heritage inheritor population profile modeling model in S2 adopts the following expression:

[0010] ,

[0011] in, To preserve the results of population profiling and modeling, Let be the weight factor for the j-th type of feature. For the function of extracting technical features, To preserve the skills and data of the people, Modeling functions for creative preferences, To preserve data on the creative behavior of the population, This is the behavioral characteristic adjustment coefficient. For propagation behavior analysis function, In order to preserve and transmit population communication data, This is a dynamic correction function for population characteristics. To update data on the characteristics of the population to preserve traditions.

[0012] Furthermore, the intangible cultural heritage IP commercial value assessment model in S4 adopts the following expression:

[0013] ,

[0014] in, This is the result of the commercial value assessment of intangible cultural heritage IP. Weighting for cultural scarcity, As a parameter for the scarcity of intangible cultural heritage, The cultural value transformation coefficient. For market fit weight, For market adaptability parameters, A function for matching consumer groups. For target consumer group characteristic data, Weighting based on potential for dissemination. For propagation potential parameters, Functions adapted for distribution channels, This is data on the characteristics of the dissemination channels.

[0015] Furthermore, the folk culture IP knowledge graph computing platform in S3 adopts the following expression: ,in, A collection of intangible cultural heritage elements. For the k-th type of intangible cultural heritage element node, For the set of edges associated with a node, Let i represent the relationship between the i-th node and the j-th node. For the weight of the associated edge, The weighting benchmark coefficient, For historical inheritance related functions, For historical data passed down between nodes, For semantic similarity function, This is semantic similarity data between nodes.

[0016] Furthermore, the parameter configuration for the intelligent development and AI innovation incubation of intangible cultural heritage IP in S5 adopts the following expression: ,in, Output results for IP development technology path This is a multi-source data fusion function. For cross-modal mapping results, In order to preserve population profile data, For the results of the business valuation, For knowledge graph guidance functions, For knowledge graph data, To configure parameter correction functions, Configure parameters for AI innovation incubation.

[0017] Further, S3 includes the following sub-steps: S31, using the node extraction module of the folk culture IP knowledge graph computing platform, cultural symbols, skill processes, and historical origins are separated from the multimodal data of intangible cultural heritage to identify entities, and entity attribute labels and feature indexes are established; S32, based on the association rules of the cultural semantic cross-modal mapping algorithm, semantic similarity calculation and association strength analysis are performed on different types of entities to construct hierarchical association paths between entities; S33, combining the skill feature data in the portrait of intangible cultural heritage inheritors, feature supplementation and weight adjustment are performed on entity nodes in the knowledge graph to strengthen the association mapping between people and cultural elements; S34, through the dynamic update module of the knowledge graph, new data generated during the development of intangible cultural heritage IP are integrated to optimize and adjust the node attributes and association relationships in real time.

[0018] Further, S4 includes the following steps: S41, using the parameter decomposition module of the intangible cultural heritage IP commercial value assessment model, the commercial value dimension is decomposed into three labeled sub-dimensions: cultural value, market value, and dissemination value, with each sub-dimension corresponding to several characteristic parameters; S42, retrieving scarcity data from the knowledge graph of folk culture IP and unique skill data from the portrait of inheritors, and performing feature quantification and weight allocation on the cultural value sub-dimension; S43, collecting market demand data and IP suitability analysis results, and combining them with dissemination channel characteristic parameters, performing multi-factor quantitative analysis on the market value and dissemination value sub-dimensions; S44, using the model's fusion calculation module, weighted fusion of the quantitative results of the three sub-dimensions to generate a comprehensive assessment conclusion on the commercial value of intangible cultural heritage IP.

[0019] Furthermore, S5 includes the following sub-steps: S51, based on the parameter configuration module for intelligent development and AI innovation incubation of intangible cultural heritage IP, receiving cross-modal mapping results, inheritor profile data, and commercial value assessment conclusions, and establishing a correlation index for multi-source data; S52, through the path generation module of AI innovation incubation, combined with the cultural element association rules in the knowledge graph, designing the calibration technical route and innovation direction for IP development; S53, based on the advantage and potential dimensions in the commercial value assessment results, optimizing the calibration parameters in the development path to enhance the market adaptability and dissemination capability of the IP; S54, using the simulation and deduction module of AI innovation incubation, conducting multi-scenario simulation and feature verification of the designed development path, and outputting path optimization suggestions and parameter adjustment schemes.

[0020] The Intangible Cultural Heritage IP Intelligent Development and AI Innovation Incubation System applies methods for the intelligent development and AI innovation incubation of intangible cultural heritage IPs. It includes: a multimodal data acquisition and feature extraction unit, used to acquire multimodal data of intangible cultural heritage texts, images, and techniques, extract labeled features, and establish a data transmission channel with a folk culture IP knowledge graph computing platform; a cultural semantic cross-modal mapping operation unit, which receives the extracted multimodal features, completes cross-dimensional mapping of semantic and visual features through a preset algorithm, and outputs the mapping results to the intangible cultural heritage inheritor profile modeling unit; and an intangible cultural heritage inheritor profile modeling unit, based on the received mapping results and collected inheritor data, constructs a profile of the inheritor using a specified model, and outputs the profile to the intangible cultural heritage IP commercial value assessment unit. The evaluation unit transmits profile data; the knowledge graph construction and update unit integrates multimodal data and profile data, constructs a knowledge node network and dynamically updates it to provide knowledge support for IP development; the intangible cultural heritage IP commercial value evaluation unit combines knowledge graph data, profile data and mapping results, completes quantitative analysis of commercial value through an evaluation model, and outputs evaluation conclusions to the AI ​​innovation incubation control unit; the AI ​​innovation incubation control and optimization unit receives the output data from the above units, generates IP development paths based on preset parameter configurations, optimizes the development process in real time through a dynamic feedback mechanism, and conducts intelligent development and innovation incubation of intangible cultural heritage IP. Each unit establishes a two-way data interaction link through a data bus to ensure the continuity of data transmission and computation.

[0021] Beneficial Effects: This invention proposes a method and system for intelligent development and AI-driven innovation incubation of intangible cultural heritage (ICH) IPs. Through multi-dimensional data integration and correlation analysis, it deeply explores the core connotations and technical characteristics of ICH, breaking through the limitations of superficial application of cultural elements. This promotes the precise adaptation of ICH symbols to modern scenarios, avoids the homogenization of IP products, and fully demonstrates the unique value of ICH. Simultaneously, relying on a systematic data integration mechanism and dynamic adjustment logic, it comprehensively links various related data to form a complete development chain. Real-time feedback and optimization provide a scientific basis for IP development, significantly improving the matching degree between development direction and market demand and inheritance laws, achieving simultaneous improvement in cultural and commercial value. This method and system, through a coherent technical process and collaborative functional units, builds an efficient bridge between cultural inheritance and innovation incubation. It ensures the accurate transmission of the connotations of ICH and provides strong support for the sustainable development of ICH IPs through scientific value assessment and path optimization, effectively solving the problems of insufficient efficiency and poor adaptability in traditional development models. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the overall process of the method of the present invention.

[0023] Figure 2 This is a flowchart of method step S3 of the present invention;

[0024] Figure 3 This is a flowchart of method step S4 of the present invention;

[0025] Figure 4 This is a flowchart of step S5 of the method of the present invention;

[0026] Figure 5 This is a diagram showing the system unit composition of the present invention. Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] like Figure 1As shown, the intelligent development and AI innovation incubation method for intangible cultural heritage IP includes the following steps: S1, retrieving multimodal data of intangible cultural heritage through a folk culture IP knowledge graph computing platform, and completing cross-dimensional mapping of semantic and visual features based on the feature association mechanism of the cultural semantic cross-modal mapping algorithm; S2, using the multi-dimensional parameter extraction module of the intangible cultural heritage inheritor profile modeling model, performing feature capture and association analysis on the technical characteristics, creative preferences, and dissemination behavior labeling data of the inheritors; S3, based on the entity association rules of the folk culture IP knowledge graph computing platform, constructing a multi-dimensional knowledge node network of intangible cultural heritage IP, and conducting... S4. Through the multi-factor analysis mechanism of the intangible cultural heritage IP commercial value assessment model, integrate parameters such as cultural scarcity, market adaptability, and dissemination potential to decompose and quantify the value dimensions; S5. Through the parameter configuration module of intelligent development and AI innovation incubation of intangible cultural heritage IP, combined with the aforementioned mapping results, profile data, and value assessment conclusions, generate the technical path and innovation direction for IP development; S6. Based on the dynamic adjustment mechanism of AI innovation incubation, optimize the features and correct the path of IP development process according to the real-time updated data of the knowledge graph and the dynamic feedback information of commercial value assessment.

[0029] Step S1 retrieves multimodal data of intangible cultural heritage through the folk culture IP knowledge graph computing platform. The platform has pre-classified and stored various types of data such as text, images, and audio in the field of intangible cultural heritage. During the retrieval process, the data is accurately filtered according to 12 classification tags such as cultural category, inheritance period, and skill category to ensure that the acquired data covers the core dimensions of intangible cultural heritage and the redundancy rate is less than 8%. When performing cross-dimensional mapping of semantic and visual features based on the feature association mechanism of the cultural semantic cross-modal mapping algorithm, key information such as technical terms and cultural connotations in the text data is first extracted, while visual features such as color, shape, and craft details in the visual image data are identified. The correspondence between the two types of features is established through the feature association rules built into the algorithm. Differentiated association weights are set for different types of intangible cultural heritage. The association weight between the craft semantics and visual details of traditional handicraft intangible cultural heritage is set to 0.78, and the association weight between the action description semantics and the image is set to 0.85 for traditional performance intangible cultural heritage. Three rounds of feature matching verification are performed during the mapping process. Each round of verification sets a dual standard of semantic consistency ≥92% and visual relevance ≥88%. Through this process, the accurate mapping of semantic and visual features is achieved, providing basic data with both cultural connotation and visual expression for subsequent IP development, and ensuring the comprehensiveness and accuracy of data support.

[0030] Step S2 utilizes the multi-dimensional parameter extraction module of the intangible cultural heritage inheritor profiling model to process data. This module comprises three sub-modules: skill characteristics, creative preferences, and dissemination behavior. These sub-modules work together to comprehensively capture core data. In the skill characteristic extraction stage, parameters such as the inheritor's skill proficiency, inheritance duration, innovation frequency, and refinement score are collected. Skill proficiency is quantified through indicators such as the completion time of core processes and operational standardization. Inheritance duration is based on actual inheritance time. Innovation frequency is calculated based on the number of skill improvements made in the past five years. The refinement score is the average of independent scores from 30 industry experts, ranging from 0 to 100. In the creative preference analysis stage, historical creative data on the inheritor's choice of subject matter, expressive techniques, and material usage are collected. Parameters such as the proportion of creations of different subjects, the frequency of use of expressive techniques, and the preference coefficient for material selection are statistically analyzed to form an 18-dimensional creative preference feature vector. In the dissemination behavior monitoring phase, data was collected on the frequency of posts, interaction volume, and fan growth rate of the inheritors on six mainstream online platforms, as well as the number of offline events they participated in and the scope of their dissemination. Online data was compiled weekly, and offline data was compiled monthly. After obtaining the above multi-dimensional data, the inherent connections between various characteristics were explored through the model's built-in correlation analysis mechanism. The correlation coefficient between skill characteristics and creative preferences, and the impact coefficient of dissemination behavior on skill inheritance were calculated. Ultimately, a profile of the intangible cultural heritage inheritors was formed, containing 25 core indicators, providing precise data support for IP development that aligns with the characteristics of the inheritors.

[0031] Step S3 constructs a multi-dimensional knowledge node network for intangible cultural heritage IP based on the entity association rules of the folk culture IP knowledge graph computing platform. First, eight core entity types are identified: cultural symbols, skill processes, historical origins, inheritors, and application scenarios. Each entity type corresponds to several specific entity objects. Based on the platform's 15 pre-set entity association rules, including the implementation association between cultural symbols and skill processes, and the derivative association between historical origins and cultural symbols, relationships between entities are established. The network adopts a hierarchical structure design of a core layer, an association layer, and an extension layer. The core layer contains the most representative entities in intangible cultural heritage, accounting for no more than 20% of the total entities. The association layer consists of entities directly related to the core entities, accounting for approximately 50%. The extension layer consists of indirectly related entities, accounting for approximately 30%. Entities at each level are connected by association strength, which is calculated based on parameters such as the degree of relevance between entities, the scope of influence, and the frequency of historical associations, with a value range of 0-1. The average association strength between entities in the core layer and the association layer is no less than 0.7. This network enables hierarchical association and semantic mining of cultural elements. Hierarchical association clearly presents the logical relationships between cultural elements, while semantic mining analyzes the semantic relationships between entities and their own attributes to uncover deeper cultural connotations. During network construction, each entity node is assigned a unique identifier code, recording information such as entity attributes, associated entities, and association strength. A node update mechanism is established every 72 hours to promptly incorporate new intangible cultural heritage data, ensuring the integrity and timeliness of the knowledge node network and providing comprehensive knowledge support for the development of intangible cultural heritage IP.

[0032] Step S4 utilizes a multi-factor analysis mechanism within the Intangible Cultural Heritage (ICH) IP commercial value assessment model to conduct a quantitative value analysis. This involves breaking down the commercial value of ICH IP into three core dimensions: cultural scarcity, market adaptability, and dissemination potential. Each core dimension is further refined into 4-6 secondary evaluation factors. The cultural scarcity dimension includes four factors: degree of endangerment, uniqueness, irreplaceability, and integrity of transmission. The degree of endangerment is assessed based on parameters such as the protection level of the ICH project and the number of inheritors. Uniqueness is calculated based on differences from other cultural types. Irreplaceability is judged based on the exclusive attributes of cultural connotation and technical characteristics. Integrity of transmission is scored based on the completeness of the transmission of technical processes and cultural connotations. The market adaptability dimension encompasses four factors: target audience fit, market demand size, competitive landscape, and commercial conversion feasibility. Target audience fit is calculated by analyzing the matching degree between the ICH IP and audiences of different age groups and consumption levels. Market demand size is determined based on parameters such as the number of potential consumers and the intensity of their consumption intentions from market research data. The competitive landscape is analyzed by statistically analyzing the number of similar ICH IPs and their market share. Commercial conversion feasibility is assessed based on the success rate of existing conversion cases and the adaptability of conversion models. The dissemination potential dimension includes four factors: dissemination channel adaptability, content attractiveness, audience willingness to disseminate, and dissemination scope expansion capability. Each factor was obtained through corresponding data collection and quantitative analysis. In the quantification process, the analytic hierarchy process (AHP) was used in conjunction with scores from 50 industry experts to determine the weights of each factor. The weights of secondary factors ranged from 0.05 to 0.25, while the weights for the core dimensions were set at 0.4 for cultural scarcity, 0.35 for market adaptability, and 0.25 for dissemination potential. The quantitative scoring range for each factor was 0-10 points. The core dimension scores were calculated by weighted summation of multiple factors, and then the comprehensive commercial value score was calculated based on the core dimension weights. This comprehensive and accurate quantification of the commercial value of intangible cultural heritage IP provides a scientific basis for business decisions.

[0033] Step S5 advances IP development planning through a parameter configuration module for intelligent development and AI innovation incubation of intangible cultural heritage IPs. This module stores 120 configuration parameters across 20 categories, including cultural element application, technical implementation, market positioning, and innovation direction, and possesses core functions such as data reception, integration, path generation, and direction planning. First, it receives the cross-modal mapping results from Step S1, the inheritor profile data from Step S2, and the commercial value assessment conclusions from Step S4 via a high-speed data interface. The data transmission rate is no less than 100Mbps to ensure data integrity and accuracy. In the data integration phase, multi-source data fusion technology is employed to eliminate data redundancy and conflicts, achieving a redundancy elimination rate of ≥95% and a conflict resolution accuracy rate of ≥98%, forming a unified basic dataset for IP development. Subsequently, in-depth analysis of the integrated data is conducted based on preset parameters. Appropriate cultural symbols and visual expression elements are selected based on the cultural element application parameters. The technical means and implementation path for IP development are determined with reference to the technical implementation parameters. The target audience, pricing strategy, and promotion direction are clarified based on the market positioning parameters and the commercial value assessment conclusions. Finally, the innovative points and differentiated advantages of IP development are explored based on the innovation direction parameters. When generating the IP development technology roadmap, it is broken down into three branches: content creation, technical implementation, and dissemination and promotion. Each roadmap specifies 8-10 key nodes, implementation steps, and required resources. Innovation focuses on three aspects: innovative expression of cultural connotation, innovative design of product form, and innovative application of dissemination models. This is combined with data on the inheritors' profiles to ensure that the innovation direction aligns with the characteristics of the craft and creative preferences, while also matching high-value dimensions in commercial value assessment. Four rounds of verification and adjustment of the roadmap and direction are conducted throughout the process. Each round of adjustment is based on three core evaluation indicators to ensure the scientific feasibility of the output results and enhance the market competitiveness and commercial success rate of IP development.

[0034] Step S6 optimizes the IP development process based on a dynamic adjustment mechanism for AI-driven innovation incubation. This mechanism comprises three core units: data monitoring, analysis and decision-making, and adjustment and execution, forming a complete dynamic optimization process. The data monitoring unit collects real-time updates to the knowledge graph of the folk culture IP and dynamic feedback information from commercial value assessments. Knowledge graph updates include newly added intangible cultural heritage entities, changes in entity relationships, and updates to entity attributes. Monitoring is conducted every 24 hours with a data collection delay of no more than 5 minutes to ensure timely capture of the latest data. Dynamic feedback information from commercial value assessments originates from market feedback data and phased value assessment results during IP development, including target audience acceptance, preliminary market sales data, dissemination effect monitoring indicators, and changes in the competitive environment. Comprehensive feedback is achieved through 12 mainstream online platforms and 20 offline data collection points. After receiving the monitoring data, the analysis and decision-making unit uses data analysis algorithms for in-depth processing to analyze the impact of knowledge graph updates on IP development. The impact coefficient is calculated to range from 0 to 0.8. Simultaneously, the difference between the commercial value feedback information and the expected goals is assessed, with a difference threshold set at ±10%, to identify the core reasons for the discrepancies. Based on the analysis results, the execution unit formulates targeted optimization plans, adjusting characteristic parameters in the IP development process, including the weighting of cultural elements, setting technical implementation parameters, and adjusting dissemination strategies. The parameter adjustment range is controlled within ±15%. The development path is also revised, including adjusting content creation direction, changing dissemination channels, and optimizing commercial conversion models. Throughout the adjustment process, the core connotations and inheritance principles of intangible cultural heritage are strictly followed to ensure that the optimized development process meets market demand changes without deviating from the essential attributes of intangible cultural heritage. Through continuous dynamic adjustment and optimization, with each adjustment cycle lasting 7 days, the quality and effectiveness of IP development are steadily improved, maximizing the cultural and commercial value of the intangible cultural heritage IP.

[0035] Preferably, the cultural semantic cross-modal mapping algorithm in S1 adopts the following expression:

[0036] ,

[0037] in, For cross-modal mapping results, Let be the weight coefficient of the i-th type of cultural element. This is a text semantic feature mapping function. For intangible cultural heritage text semantic data, For visual feature mapping function, Visual image data of intangible cultural heritage For feature fusion operators, For knowledge graph association enhancement functions, To determine the entity association strength of the knowledge graph of folk culture IP, This is a semantic consistency check function. This is a parameter for cross-modal feature consistency.

[0038] Specifically, step S1, the cultural semantic cross-modal mapping algorithm, constructs a semantic and visual feature association requirement based on intangible cultural heritage (ICH) multimodal data. First, it clarifies the mapping logic between two core data types: textual semantics and visual images. Then, it integrates the feature contributions of different cultural elements through weighted summation. Next, it introduces a knowledge graph association enhancement mechanism to strengthen the semantic association of cross-modal data. Finally, it supplements semantic consistency verification to correct mapping deviations, forming a complete derivation chain. This addresses the core requirement of accurate translation of cultural connotations in ICH IP development. Through multi-stage function collaboration, it achieves cross-dimensional feature fusion, ensuring that the mapping result retains both the essence of cultural semantics and adapts to visual expression needs. The weight coefficients of cultural elements are set from 0.6 to 0.9 based on the differences in ICH types, the entity association strength ranges from 0 to 1, and the cross-modal feature consistency parameter is set from 0.85 to 0.95, all determined through extensive training with ICH data and evaluation by industry experts. This solves the problem of the disconnect between the semantics and visual features of ICH texts, providing foundational data with both cultural depth and visual adaptability for IP development. In this implementation, the semantic and visual features of the text are first extracted using an algorithm, the importance of each element is assigned according to a weight coefficient, the data is integrated using a feature fusion operator, the feature correlation is improved using a knowledge graph association enhancement function, and finally the deviation is corrected by a semantic consistency verification function to output an accurate cross-modal mapping result. The entire process ensures mapping accuracy through iterative calculation.

[0039] Preferably, the non-heritage inheritor population profile modeling model in S2 adopts the following expression:

[0040] ,

[0041] in, To preserve the results of population profiling and modeling, Let be the weight factor for the j-th type of feature. For the function of extracting technical features, To preserve the skills and data of the people, Modeling functions for creative preferences, To preserve data on the creative behavior of the population, This is the behavioral characteristic adjustment coefficient. For propagation behavior analysis function, In order to preserve and transmit population communication data, This is a dynamic correction function for population characteristics. To update data on the characteristics of the population to preserve traditions.

[0042] Specifically, the intangible cultural heritage inheritor profiling model focuses on the correlation analysis of multi-dimensional characteristics of the inheritors. First, the numerator integrates the weighted product of skill characteristics and creative preferences to comprehensively capture the core traits of the group. The denominator introduces the square root of the sum of squares of dissemination behavior to balance the influence of behavioral characteristics on the profiling. Finally, a dynamic correction term is added to adapt to the time-varying changes in group characteristics, forming a complete derivation logic. Based on the development needs for precise characterization of group traits, and considering the interaction between skill, creation, and dissemination characteristics, a fractional structure is adopted to balance the contributions of core and moderating characteristics. The dynamic correction term addresses the issue of changes in group characteristics over time. The feature weight factor is set between 0.3 and 0.7, and the behavioral characteristic adjustment coefficient is between 0.2 and 0.5, both derived through data analysis and calibration of thousands of intangible cultural heritage inheritors. This formula accurately constructs the inheritor profiling, providing data support for IP development that aligns with the characteristics of the inheritors. In this implementation, three types of data—skills, creations, and dissemination—are first collected from the inheritors. Features are then extracted using corresponding functions and weighted. The numerator integrates the contributions of core features, while the denominator adjusts the influence of dissemination behavior. Finally, updated data on the inheritors' characteristics are incorporated through a dynamic correction function, resulting in a comprehensive, accurate, and timely portrait of the inheritors. The entire process is iteratively optimized according to preset parameters to ensure that the portrait closely matches the actual characteristics of the inheritors.

[0043] Preferably, the intangible cultural heritage IP commercial value assessment model in S4 adopts the following expression:

[0044] ,

[0045] in, This is the result of the commercial value assessment of intangible cultural heritage IP. Weighting for cultural scarcity, As a parameter for the scarcity of intangible cultural heritage, The cultural value transformation coefficient. For market fit weight, For market adaptability parameters, A function for matching consumer groups. For target consumer group characteristic data, Weighting based on potential for dissemination. For propagation potential parameters, Functions adapted for distribution channels, This is data on the characteristics of the dissemination channels.

[0046] Specifically, the intangible cultural heritage (ICH) IP commercial value assessment model uses three core value dimensions: cultural scarcity, market adaptability, and dissemination potential. It quantifies the value of each dimension using a weighted product, then integrates them into a comprehensive commercial value through summation. Each dimension incorporates an adaptation function to strengthen its relevance to practical application scenarios, following the logic of value dimension decomposition, single-dimensional quantification, and multi-dimensional integration. The formula is based on the core demands of ICH IP commercialization, considering the different impacts of culture, market, and dissemination on commercial value. Weighting is used to highlight key dimensions, while the adaptation function addresses the mapping between data from each dimension and commercial value. The weights for cultural scarcity are set at 0.35 to 0.45, market adaptability at 0.3 to 0.4, dissemination potential at 0.2 to 0.3, and the cultural value conversion coefficient at 0.7 to 0.9. These values ​​are determined based on ICH IP commercialization case data and market research results, achieving precise quantification of commercial value and providing a scientific basis for IP development business decisions. In this implementation, the business value dimensions are first broken down and parameter data for each dimension are collected. The importance of each dimension is assigned according to weight. The raw data is transformed into value quantification indicators through an adaptation function. Then, the value of each dimension is calculated through weighted multiplication. Finally, the summation is used to obtain the comprehensive business value assessment result. The entire process involves multiple rounds of data verification and parameter adjustment to ensure the accuracy and reliability of the assessment results.

[0047] Preferably, the folk culture IP knowledge graph computing platform in S3 adopts the following expression: ,in, A collection of intangible cultural heritage elements. For the k-th type of intangible cultural heritage element node, For the set of edges associated with a node, Let i represent the relationship between the i-th node and the j-th node. For the weight of the associated edge, The weighting benchmark coefficient, For historical inheritance related functions, For historical data passed down between nodes, For semantic similarity function, This is semantic similarity data between nodes.

[0048] Specifically, the formula for the folk culture IP knowledge graph calculation platform is based on the core components of a knowledge graph. It first defines the set of cultural element nodes and the set of edges connecting those nodes, clarifying the basic structure of the knowledge graph. Then, it constructs the logic for calculating the weights of the edges through weighted operations, integrating the two core factors of historical inheritance and semantic similarity to form a complete graph construction formula. Based on the need for hierarchical association of cultural elements, the definition of the node set and the set of edges ensures the integrity of the graph, while the calculation of edge weights achieves precise quantification of the strength of element associations. The baseline weight coefficient is set to 0.5 to 0.7, the historical inheritance association function ranges from 0 to 1, and the semantic similarity function also ranges from 0 to 1, all determined through analysis and calibration of massive amounts of intangible cultural heritage association data. This formula constructs a structured intangible cultural heritage knowledge network, providing comprehensive knowledge support for IP development. In this implementation, cultural element nodes are first extracted from intangible cultural heritage multimodal data, node attributes and unique identifiers are established, and then the association edges between nodes are constructed according to entity association rules. The historical inheritance association function value is calculated through historical inheritance data, and the semantic similarity function value is determined by combining the semantic similarity analysis results. The weight of the association edges is obtained by weighting according to the weight benchmark coefficient, and finally a knowledge graph containing a set of nodes, a set of association edges and weight data is formed. The graph structure and data are continuously optimized through a dynamic update mechanism.

[0049] Preferably, the parameter configuration for the intelligent development and AI innovation incubation of intangible cultural heritage IP in S5 adopts the following expression: ,in, Output results for IP development technology path This is a multi-source data fusion function. For cross-modal mapping results, In order to preserve population profile data, For the results of the business valuation, For knowledge graph guidance functions, For knowledge graph data, To configure parameter correction functions, Configure parameters for AI innovation incubation.

[0050] Specifically, the parameter configuration formula for intelligent development and AI innovation incubation of intangible cultural heritage IPs centers on multi-source data fusion. First, it integrates cross-modal mapping results, inheritor profile data, and commercial value assessment results through a multi-source data fusion function. Then, it introduces a knowledge graph guidance function to strengthen the correlation constraints of cultural elements. Finally, it adds a configuration parameter correction function to adapt to dynamic adjustment needs during the development process. Following the logic of data integration, knowledge guidance, and dynamic correction, and based on the need for multi-source data collaborative support in IP development, multi-source data fusion ensures the comprehensiveness of the development basis, knowledge graph guidance ensures the accurate transmission of cultural connotations, and configuration parameter correction enables flexibility in the development process. Regarding parameter values, the configuration parameter correction function ranges from 0.8 to 1.2, determined through iterative testing of numerous IP development cases to ensure that the correction range is reasonable and effective. This generates a scientifically feasible IP development technology path, achieving synergy between cultural inheritance and commercial innovation. In this implementation, multi-source data is first received and integrated. Data redundancy and conflicts are eliminated through a multi-source data fusion function to form a unified data foundation. Then, a knowledge graph guidance function is used to introduce cultural element association rules to constrain the cultural adaptability of the development path. Finally, a configuration parameter correction function is used to adjust the path parameters according to the actual needs of AI innovation incubation, and the optimized IP development technology path and innovation direction are output. The entire process is continuously optimized through data interaction and dynamic feedback to ensure the feasibility and innovation of the path.

[0051] Preferred, such as Figure 2 As shown, step S3 includes the following sub-steps: S31, using the node extraction module of the folk culture IP knowledge graph computing platform, cultural symbols, skill processes, and historical origins are separated from the multimodal data of intangible cultural heritage to identify entities, and entity attribute labels and feature indexes are established; S32, based on the association rules of the cultural semantic cross-modal mapping algorithm, semantic similarity calculation and association strength analysis are performed on different types of entities to construct hierarchical association paths between entities; S33, combining the skill feature data in the portrait of intangible cultural heritage inheritors, feature supplementation and weight adjustment are performed on entity nodes in the knowledge graph to strengthen the association mapping between people and cultural elements; S34, through the dynamic update module of the knowledge graph, new data generated during the development of intangible cultural heritage IP are integrated to optimize and adjust the node attributes and association relationships in real time.

[0052] Specifically, step S3 involves the construction and optimization of a knowledge graph for folk culture IP. S31 uses the node extraction module of the knowledge graph computing platform to separate core entities such as cultural symbols, skill processes, and historical origins from the multimodal data of intangible cultural heritage. Unique identifier codes are assigned to each entity type, and a tag system and feature index containing 20 core attributes are established. The index construction uses an inverted index mechanism to improve data retrieval efficiency, with a retrieval response time controlled within 0.3 seconds. S32 calculates the semantic similarity between different types of entities based on the association rules of the cultural semantic cross-modal mapping algorithm. The similarity threshold is set at 0.7, and the association strength is quantified in the range of 0-1. A three-level association path between entities is constructed using a hierarchical clustering algorithm to ensure the clarity and hierarchy of the association logic. S33 combines the skill feature data from the portraits of intangible cultural heritage inheritors to extract 15 key skill parameters to supplement the features of entity nodes in the knowledge graph. Node weights are adjusted according to skill matching degree, with an adjustment range of 0.1-0.3, strengthening the association mapping between people and cultural elements. S34 utilizes a dynamic update module for the knowledge graph, setting an update cycle of once every 72 hours. It integrates new data generated during IP development, supplements and improves node attributes, and recalculates and optimizes relationships. During the update process, the data consistency verification pass rate is no less than 98%. The significance of this approach lies in constructing a structurally complete, data-accurate, and dynamically updated knowledge graph, providing comprehensive knowledge support for intangible cultural heritage IP development. During implementation, each step is executed sequentially, and parameter standardization and process normalization ensure the quality and usability of the knowledge graph.

[0053] Preferred, such as Figure 3 As shown, S4 includes the following steps: S41, using the parameter decomposition module of the intangible cultural heritage IP commercial value assessment model, the commercial value dimension is decomposed into three labeled sub-dimensions: cultural value, market value, and dissemination value, with each sub-dimension corresponding to several feature parameters; S42, retrieving scarcity data from the knowledge graph of folk culture IP and unique skill data from the portrait of inheritors, and performing feature quantification and weight allocation on the cultural value sub-dimension; S43, collecting market demand data and IP suitability analysis results, and combining dissemination channel feature parameters, performing multi-factor quantitative analysis on the market value and dissemination value sub-dimensions; S44, using the model's fusion calculation module, the quantitative results of the three sub-dimensions are weighted and fused to generate a comprehensive assessment conclusion on the commercial value of intangible cultural heritage IP.

[0054] Specifically, step S4 includes four sub-steps for a systematic assessment of the commercial value of intangible cultural heritage IP, with each sub-step progressively quantifying the value. S41 uses the parameter decomposition module of the commercial value assessment model to break down the commercial value dimension into three core sub-dimensions: cultural value, market value, and dissemination value. Each sub-dimension is further refined into 5-6 characteristic parameters, forming an assessment index system containing 17 parameters. Parameter weights are determined using the analytic hierarchy process (AHP), with a consistency test index CR ≤ 0.1. S42 retrieves scarcity data from the folk culture IP knowledge graph and unique skill data from the inheritor profiles to quantify and score each parameter of the cultural value sub-dimension, with scores ranging from 0 to 10. The cultural value score is obtained by weighted summation, with the following weight allocations: endangerment level 0.3, uniqueness 0.25, irreplaceability 0.25, and inheritance integrity 0.2. S43 collects market demand data and IP suitability analysis results, and combines them with characteristic parameters of 12 types of communication channels to conduct multi-factor quantitative analysis of the market value and communication value sub-dimensions. In market value, the weights are: target audience fit (0.3), market demand size (0.25), competitive landscape (0.2), and commercial conversion feasibility (0.25). In communication value, the weights are: communication channel suitability (0.3), content attractiveness (0.25), audience willingness to communicate (0.25), and ability to expand communication reach (0.2). S44 uses the model's fusion calculation module to weight and fuse the scores of the three sub-dimensions according to the weights of cultural value (0.4), market value (0.35), and communication value (0.25), generating a comprehensive commercial value score. The error of the comprehensive score is controlled within ±5%. The significance of this weighting is to achieve accurate quantification of commercial value, providing a scientific basis for IP development business decisions. The implementation process strictly follows the step-by-step execution of data collection, quantitative analysis, and fusion calculation to ensure the accuracy and reliability of the evaluation results.

[0055] Preferred, such as Figure 4 As shown, S5 includes the following sub-steps: S51, based on the parameter configuration module for intelligent development and AI innovation incubation of intangible cultural heritage IP, receiving cross-modal mapping results, inheritor profile data, and commercial value assessment conclusions, and establishing a correlation index for multi-source data; S52, through the path generation module of AI innovation incubation, combined with the cultural element association rules in the knowledge graph, designing the calibration technical route and innovation direction for IP development; S53, based on the advantage and potential dimensions in the commercial value assessment results, optimizing the calibration parameters in the development path to enhance the market adaptability and dissemination capability of the IP; S54, using the simulation and deduction module of AI innovation incubation, conducting multi-scenario simulation and feature verification of the designed development path, and outputting path optimization suggestions and parameter adjustment schemes.

[0056] Specifically, step S5 includes four sub-steps for generating and optimizing the IP development technology path. These sub-steps work collaboratively to form a complete development planning system. S51, based on the parameter configuration module of AI innovation incubation, receives cross-modal mapping results, heritage population profile data, and commercial value assessment conclusions through a high-speed data interface. The data transmission rate is no less than 100Mbps. A multi-source data association index is established, with 100% index coverage, ensuring rapid data retrieval and retrieval. S52, through the path generation module of AI innovation incubation, combines the association rules of 15 cultural elements in the folk culture IP knowledge graph to design the core technology roadmap and innovation direction for IP development. The technology roadmap includes three branches: content creation, technology implementation, and dissemination and promotion. Each branch clarifies 8-10 key nodes and implementation steps. The innovation direction focuses on three dimensions: cultural connotation expression, product form design, and application of dissemination models. S53 optimizes key parameters in the development path based on the strengths and potential dimensions in the business value assessment results. Parameter adjustments are set according to the value score differences, with strength dimension parameters increased by 0.1-0.2 and potential dimension parameters adjusted by 0.05-0.15, enhancing the IP's market adaptability and dissemination capabilities. S54 utilizes the simulation module of AI innovation incubation to construct 10 different market scenarios for multi-scenario simulation. During the simulation, 20 core indicators are monitored to verify the development path's characteristics. A report including parameter adjustment suggestions and path optimization solutions is output, with a solution adoption rate of no less than 90%. The significance of this approach lies in generating a scientifically feasible IP development technology path, improving the success rate and market competitiveness of IP development. During implementation, each step proceeds sequentially, and the rationality and innovation of the development path are ensured through data-driven approaches and scenario simulations.

[0057] like Figure 5As shown, the Intangible Cultural Heritage IP Intelligent Development and AI Innovation Incubation System applies to the intelligent development and AI innovation incubation methods of Intangible Cultural Heritage IP. It includes: a multimodal data acquisition and feature extraction unit, used to acquire multimodal data of Intangible Cultural Heritage texts, images, and techniques, and extract labeled features, establishing a data transmission channel with the folk culture IP knowledge graph computing platform; a cultural semantic cross-modal mapping operation unit, receiving the extracted multimodal features, completing cross-dimensional mapping of semantic and visual features through a preset algorithm, and outputting the mapping results to the Intangible Cultural Heritage Inheritor Profile Modeling Unit; and an Intangible Cultural Heritage Inheritor Profile Modeling Unit, based on the received mapping results and collected inheritor data, constructing a profile of the inheritor through a specified model, and then outputting the profile to the commercial value of the Intangible Cultural Heritage IP. The value assessment unit transmits profile data; the knowledge graph construction and update unit integrates multimodal data and profile data, constructs a knowledge node network and dynamically updates it to provide knowledge support for IP development; the intangible cultural heritage IP commercial value assessment unit combines knowledge graph data, profile data and mapping results, completes quantitative analysis of commercial value through an assessment model, and outputs assessment conclusions to the AI ​​innovation incubation control unit; the AI ​​innovation incubation control and optimization unit receives the output data from the above units, generates IP development paths based on preset parameter configurations, optimizes the development process in real time through a dynamic feedback mechanism, and conducts intelligent development and innovation incubation of intangible cultural heritage IP. Each unit establishes a two-way data interaction link through a data bus to ensure the continuity of data transmission and computation.

[0058] The formula in this invention integrates different scalar and vector parameters for unified calculation. Through standardization, adaptability function design, and association mechanism construction, it resolves the computational conflict caused by differences in parameter types. First, for scalar parameters (such as cultural element weight coefficients and entity association strength) and vector parameters (such as the feature vector of inheritors' skills and the semantic feature vector of cultural elements), the formula uses a built-in feature extraction function to transform vector parameters into quantifiable scalar contribution values. Simultaneously, weight allocation clarifies the computational proportion of different parameter types, ensuring parameter dimensionality uniformity. Second, using feature fusion operators and association enhancement functions, an intrinsic association between scalar and vector parameters is established. For example, text semantic feature vectors and visual feature vectors are transformed into a unified-dimensional feature combination through a fusion operator, and then multiplied by the scalar form of knowledge graph entity association strength, achieving collaborative computation of different parameter types. Furthermore, the dynamic correction function and adaptability function in the formula can be adjusted specifically according to parameter type differences. For instance, a mapping relationship is established between the scalar market adaptability parameter and the vector consumer group feature data through a consumer group matching function, ensuring the rationality of the computational logic. This design retains the quantitative attributes of scalar parameters and the multidimensional characteristics of vector parameters, while enabling different types of parameters to be effectively integrated and calculated in the same formula through function collaboration and dimension unification mechanisms, thus accurately supporting the multi-source data integration needs in the development of intangible cultural heritage IP.

[0059] The intelligent development and AI-driven innovation incubation methods and systems for intangible cultural heritage IPs systematically integrate multi-dimensional data to establish a close connection between cultural elements, inheritors, and commercial value. This provides end-to-end technical support from cultural connotation extraction to IP innovation development, ensuring the complete preservation of the core characteristics of intangible cultural heritage while enhancing the relevance and feasibility of IP development through scientific path design and dynamic optimization. Furthermore, the various functional modules and technical processes work in synergy, with each step—from data collection and feature mapping to value assessment and path generation—operating efficiently through dedicated technical mechanisms, significantly improving the systematization and intelligence of intangible cultural heritage IP development.

[0060] This method and system address the superficial application and homogenization of cultural elements. By deeply exploring the core connotations and technical characteristics of intangible cultural heritage, it establishes a hierarchical network of cultural elements, promoting the precise adaptation of intangible cultural heritage symbols to modern scenarios and aesthetic needs. This allows developed IP products to fully demonstrate the unique value of intangible cultural heritage and escape the predicament of homogenization. Addressing the issues of insufficient multi-dimensional data integration and lack of dynamic adjustment, it constructs a complete data association chain, comprehensively integrating various relevant data to form a scientific development logic. Simultaneously, relying on a dynamic feedback mechanism and real-time optimization strategies, it precisely adjusts the development path based on data changes, significantly improving the matching degree between development direction and market demand and inheritance patterns. This achieves the simultaneous enhancement of cultural and commercial value, solving the core problems of traditional development models.

[0061] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," "link," and "fix" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0062] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent development and AI innovation incubation of intangible cultural heritage IP, characterized by: Includes the following steps: S1, retrieves multimodal data of intangible cultural heritage through the knowledge graph computing platform of folk culture IP, and completes cross-dimensional mapping of semantic and visual features based on the feature association mechanism of the cultural semantic cross-modal mapping algorithm; S2 utilizes the multi-dimensional parameter extraction module of the intangible cultural heritage inheritor profiling model to capture and analyze the features and correlations of the inheritors' skill characteristics, creative preferences, and dissemination behavior data. S3, based on the entity association rules of the folk culture IP knowledge graph computing platform, constructs a multi-dimensional knowledge node network of intangible cultural heritage IP, and performs hierarchical association and semantic mining of cultural elements; S4 uses a multi-factor analysis mechanism of the intangible cultural heritage IP commercial value assessment model to integrate parameters such as cultural scarcity, market adaptability, and dissemination potential to decompose and quantify the value dimensions. S5, through the parameter configuration module of intelligent development and AI innovation incubation of intangible cultural heritage IP, combined with the aforementioned mapping results, profile data and value assessment conclusions, generates the technical path and innovation direction for IP development; S6, based on the dynamic adjustment mechanism of AI innovation incubation, optimizes the features and corrects the path of IP development process according to the real-time updated data of the knowledge graph and the dynamic feedback information of business value assessment.

2. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, The cultural semantic cross-modal mapping algorithm in S1 uses the following expression: ， in, For cross-modal mapping results, Let be the weight coefficient of the i-th type of cultural element. This is a text semantic feature mapping function. For intangible cultural heritage text semantic data, For visual feature mapping function, Visual image data of intangible cultural heritage For feature fusion operators, For knowledge graph association enhancement functions, To determine the entity association strength of the knowledge graph of folk culture IP, This is a semantic consistency check function. This is a parameter for cross-modal feature consistency.

3. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, The non-heritage inheritor population profile modeling model in S2 adopts the following expression: ， in, To preserve the results of population profiling and modeling, Let be the weight factor for the j-th type of feature. For the function of extracting technical features, To preserve the skills and data of the people, Modeling functions for creative preferences, To preserve data on the creative behavior of the population, This is the behavioral characteristic adjustment coefficient. For propagation behavior analysis function, In order to preserve and transmit population communication data, This is a dynamic correction function for population characteristics. To update data on the characteristics of the population to preserve traditions.

4. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, The commercial value assessment model for intangible cultural heritage IP in S4 uses the following expression: ， in, This is the result of the commercial value assessment of intangible cultural heritage IP. Weighting for cultural scarcity, As a parameter for the scarcity of intangible cultural heritage, The cultural value transformation coefficient. For market fit weight, For market adaptability parameters, A function for matching consumer groups. For target consumer group characteristic data, Weighting based on potential for dissemination. For propagation potential parameters, Functions adapted for distribution channels, This is data on the characteristics of the dissemination channels.

5. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, The folk culture IP knowledge graph computing platform in S3 adopts the following expression: ,in, A collection of intangible cultural heritage elements. For the k-th type of intangible cultural heritage element node, For the set of edges associated with a node, Let i represent the relationship between the i-th node and the j-th node. For the weight of the associated edge, The weighting benchmark coefficient, For historical inheritance related functions, For historical data passed down between nodes, For semantic similarity function, This is semantic similarity data between nodes.

6. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, The parameter configuration for the intelligent development and AI innovation incubation of intangible cultural heritage IP in S5 adopts the following expression: ,in, Output results for IP development technology path This is a multi-source data fusion function. For cross-modal mapping results, In order to preserve population profile data, For the results of the business valuation, For knowledge graph guidance functions, For knowledge graph data, To configure parameter correction functions, Configure parameters for AI innovation incubation.

7. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, The S3 includes the following steps: S31, using the node extraction module of the folk culture IP knowledge graph computing platform, cultural symbols, technical processes, and historical origins are separated from the multimodal data of intangible cultural heritage, and entity attribute labels and feature indexes are established; S32, based on the association rules of the cultural semantic cross-modal mapping algorithm, performs semantic similarity calculation and association strength analysis on different types of entities to construct hierarchical association paths between entities; S33, combined with the skill feature data in the portrait of intangible cultural heritage inheritors, the entity nodes in the knowledge graph are supplemented with features and their weights are adjusted to strengthen the association mapping between people and cultural elements; S34, through the dynamic update module of the knowledge graph, new data generated in the process of intangible cultural heritage IP development are integrated to optimize and adjust the node attributes and association relationships in real time.

8. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, S4 includes the following steps: S41, using the parameter decomposition module of the intangible cultural heritage IP commercial value assessment model, the commercial value dimension is decomposed into three labeled sub-dimensions: cultural value, market value, and dissemination value, with each sub-dimension corresponding to several feature parameters; S42, retrieving scarcity data from the folk culture IP knowledge graph and skill uniqueness data from the inheritor profile, and performing feature quantification and weight allocation on the cultural value sub-dimension; S43, collecting market demand data and IP suitability analysis results, and combining dissemination channel feature parameters, performing multi-factor quantitative analysis on the market value and dissemination value sub-dimensions; S44, using the model's fusion calculation module, weighted fusion of the quantitative results of the three sub-dimensions to generate a comprehensive assessment conclusion on the commercial value of intangible cultural heritage IP.

9. The method for intelligent development and AI innovation incubation of intangible cultural heritage IP according to claim 1, characterized in that, S5 includes the following steps: S51, based on the parameter configuration module for intelligent development and AI innovation incubation of intangible cultural heritage IP, receiving cross-modal mapping results, inheritor profile data, and commercial value assessment conclusions, and establishing a correlation index for multi-source data; S52, through the path generation module of AI innovation incubation, combined with the cultural element association rules in the knowledge graph, designing the calibration technical route and innovation direction for IP development; S53, based on the advantage and potential dimensions in the commercial value assessment results, optimizing the calibration parameters in the development path to enhance the market adaptability and dissemination capability of the IP; S54, using the simulation and deduction module of AI innovation incubation, conducting multi-scenario simulation and feature verification of the designed development path, and outputting path optimization suggestions and parameter adjustment schemes.

10. A smart development and AI innovation incubation system for intangible cultural heritage IP, characterized in that: This system is applied to the method for intelligent development and AI innovation incubation of intangible cultural heritage IP as described in claim 1, comprising: a multimodal data acquisition and feature extraction unit, used to acquire multimodal data of intangible cultural heritage texts, images, and techniques and extract labeled features, and establish a data transmission channel with the folk culture IP knowledge graph computing platform; a cultural semantic cross-modal mapping operation unit, which receives the extracted multimodal features, completes cross-dimensional mapping of semantic and visual features through a preset algorithm, and outputs the mapping results to the intangible cultural heritage inheritor profile modeling unit; and an intangible cultural heritage inheritor profile modeling unit, based on the received mapping results and the collected inheritor data, completes the construction of a profile of the inheritor through a specified model, and transmits the profile to the intangible cultural heritage IP commercial value assessment unit. The system comprises several units: a knowledge graph construction and update unit, which integrates multimodal data and profile data to build a knowledge node network and dynamically update it, providing knowledge support for IP development; an intangible cultural heritage IP commercial value assessment unit, which combines knowledge graph data, profile data, and mapping results to perform quantitative analysis of commercial value through an assessment model and outputs assessment conclusions to the AI ​​innovation incubation control unit; and an AI innovation incubation control and optimization unit, which receives the output data from the above units, generates IP development paths based on preset parameter configurations, and optimizes the development process in real time through a dynamic feedback mechanism, enabling intelligent development and innovative incubation of intangible cultural heritage IPs. All units establish a two-way data interaction link through a data bus to ensure the continuity of data transmission and computation.

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