Multi-category traditional culture brand value dynamic monitoring system and method

Abstract

The invention provides a multi-category traditional culture brand value dynamic monitoring system and method, and the system is characterized in that the system comprises a category factor library module which stores core indexes and unique parameters of all fields; the dynamic index modeling module is used for selecting index combinations according to categories and setting initial weights; the event response engine is used for identifying category-specific events and triggering weight adjustment; the culture scarcity calculation module is used for generating a Dc value according to a category adaptation formula; and the visual report module is used for outputting a cross-category comparative analysis result. According to the method, a category factor and dynamic response double-engine model is put forward for the first time, the field barrier of traditional cultural brand evaluation is broken through, and scientific and standardized transition from a single field to multiple categories is achieved. By defining an extensible category factor library and an adaptive algorithm, core technical support is provided for cultural capitalization and internationalization in the fields of Chinese classical musical instruments, porcelain, calligraphy, traditional Chinese paintings and the like.

Description

Technical Field

[0001] The present invention relates to the fields of cultural brand management, data science and intangible cultural heritage protection technology, and specifically to a dynamic value monitoring system and method suitable for traditional cultural brands of multiple categories such as Chinese traditional musical instruments, ink paintings, Chinese paintings, calligraphy, porcelain, purple clay teapots, pottery, bronzes, and sculptures. By introducing category factors to achieve cross-domain adaptation, the problems of difficulty in quantifying cultural attributes, poor adaptability to industry differences, and insufficient data timeliness in traditional cultural brand value assessment are solved. Background Art

[0002] The existing brand value assessment model has the following limitations: field generalization: it does not distinguish the core value dimensions of different traditional cultural categories (such as porcelain and calligraphy); static indicators: it is unable to dynamically adjust the weights in response to industry-specific events (such as porcelain auction season and calligraphy biennale); lack of cultural scarcity: it ignores key cultural asset attributes such as intangible cultural heritage, surviving quantity, and unique craftsmanship. Summary of the invention

[0003] The present invention provides a system and method for dynamically monitoring the brand value of traditional culture in multiple categories, which realizes cross-domain precise evaluation through a category factor library and a dynamic weight mechanism, and promotes the assetization and internationalization of traditional cultural brands.

[0004] The system architecture in the technical solution includes data collection layer, indicator modeling layer, dynamic calculation layer, and result output layer.

[0005] The data collection layer integrates multi-source data, including auction records, intangible cultural heritage databases, social media, museum archives, and industry exhibition data; Category Adaptive Collection: Customized crawler rules for different fields (such as capturing kiln data in the porcelain field and capturing auction house inscription records in the calligraphy field).

[0006] The indicator modeling layer establishes a category factor library to define the core indicators in each field. For example, the core indicators of porcelain are firing technology, glaze color uniqueness, and kiln history. The unique parameter is the kiln temperature control error (within ±10℃=0.9); while the core indicators of purple clay pots are the scarcity of clay materials, the inheritance of craftsmen, and the air permeability test. The unique parameter is the aging time (years). The dynamic indicator model also selects indicator combinations according to categories (such as bronze ware focuses on "historical dating" and "inscription research", and purple clay pots focus on "scarcity of clay materials" and "inheritance of craftsmen").

[0007] The calculation method of the dynamic calculation layer is as follows: The cultural scarcity coefficient (Dc) is based on the general formula, and the parameter weights are adjusted by category. For example, the Dc calculation formula for porcelain is: Dc=0.4L+0.3R+0.2M+0.1K (K=kiln history score), the Dc calculation formula for calligraphy is: Dc=0.5L+0.3R+0.2T (T=authority of inscriptions and postscripts); the Dc calculation formula for purple clay teapots is: Dc=0.3L+0.4C+0.3M (C=standardized value of aging time). The event response engine is used to identify category-specific events (such as the Jingdezhen International Ceramics Expo) and trigger weight adjustments.

[0008] The result output layer will generate a "Multi-category Brand Niche Competitiveness Report", including total score ranking, category comparison radar chart, and optimization strategy.

[0009] The core algorithm in the technical solution is mainly the multi-category value index formula: V=∑i=1n(Wi×Si)+α⋅Dc+β⋅Pf.

[0010] In this formula, Pf refers to the category adjustment factor (the default value is 0-0.2, which is set according to the complexity of the field, such as Pf=0.2 for bronze ware and Pf=0.1 for calligraphy); V is the total score of the brand value index, which reflects the comprehensive competitiveness of the brand in the industry; Wi: the dynamic weight of the i-th indicator (automatically adjusted according to industry events or cycles); Si is the standardized score of the i-th indicator (0-1 standardized value); Dc is the cultural scarcity coefficient, which measures the cultural uniqueness and historical value of the brand; α is the industry adjustment factor, with a default value of 0.15, which is used to balance the weights of culture and commercial value.

[0011] The calculation formula of cultural scarcity coefficient Dc is: Dc=0.3×L+0.4×R+0.3×M. Parameter definition: L is the level of intangible cultural heritage (national level=1, provincial level=0.6, municipal level=0.3); R is the reciprocal of the number of surviving items (standardized to 0-1, the fewer the number of surviving items, the higher the R value); M is the number of collections of museums / top institutions (each collection=0.1, upper limit 3).

[0012] The present invention has cross-domain versatility: "one framework, multiple adaptations" are realized through the category factor library, covering 10+ traditional cultural fields; in-depth cultural quantification: unique parameters (such as porcelain kiln temperature, calligraphy inscriptions) accurately reflect field characteristics; dynamic scene response: the event engine supports automatic response to major events in various fields, improving timeliness by 60%.

[0013] This invention proposes the "category factor + dynamic response" dual-engine model for the first time, breaking through the field barriers of traditional cultural brand evaluation and achieving a scientific and standardized transition from a single field to multiple categories. By defining an extensible category factor library and adaptive algorithms, it provides core technical support for the cultural assetization and internationalization of Chinese classical musical instruments, porcelain, calligraphy, Chinese painting and other fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 :Architecture diagram of multi-category traditional cultural brand value monitoring system

[0015] Figure 2 : Schematic diagram of the category factor library configuration interface DETAILED DESCRIPTION

[0016] Example 1: Porcelain brand monitoring (a kiln in Jingdezhen). Data input: 200 years of kiln history (K=0.9), national level of intangible cultural heritage (L=1), inverse of the number of surviving pieces R=0.7. Dynamic calculation: Dc=0.4×1+0.3×0.7+0.2×0.3+0.1×0.9=0.82; V=(craft score 0.8×0.4+glaze score 0.9×0.3+...)+0.15×0.82+0.2=0.91; Output result: industry ranking TOP 5%, it is recommended to "participate in the International Ceramics Expo to increase exposure weight".

[0017] Example 2: Calligraphy brand monitoring (a certain calligrapher). Event trigger: The calligraphy work was selected for the Palace Museum special exhibition, and the system automatically increased the weight of "authority of inscriptions" to 1.3 times. Total score increased: from 0.85 to 0.89, entering the top 10% of the industry.

[0018] Example 3: Luthier brand monitoring. Data input: A luthier made 5 luthiers per year, with an average auction price of 1 million yuan, and obtained provincial intangible cultural heritage certification, and cooperated with 2 museums. Dynamic calculation: Weight distribution: craft value (40%), market premium (30%), cultural endorsement (30%). Cultural scarcity coefficient Dc=0.7 (provincial intangible cultural heritage + small number of surviving products). Output result: V=0.86 (top 10% in the industry), it is recommended to "apply for national intangible cultural heritage to increase Dc value".

Claims

1. A multi-category traditional cultural brand value monitoring system, characterized by The first is the category factor library module: it stores the core indicators and unique parameters of each field, and supports the configuration of more than 10 categories such as porcelain, calligraphy, purple clay teapots, and bronze ware; The second is a dynamic indicator modeling module: load the corresponding indicator combination according to the selected category and set the initial weight; The third is an event response engine: it identifies category-specific events (such as porcelain kiln fire festivals and calligraphy biennials) and triggers dynamic weight adjustments; The fourth is a cultural scarcity calculation module: the cultural scarcity coefficient Dc is generated according to the category adaptation formula; The fifth is a visual report module: outputs cross-category comparative analysis results and optimization strategies.

2. The system according to claim 1, characterized in that Each category in the category factor library needs to define core indicators, unique parameters, and event response rules. Core indicators: no less than 3 common evaluation dimensions (such as craftsmanship value, market premium); unique parameters: at least 2 field-specific parameters (such as porcelain "kiln temperature control error", calligraphy "inscription authority score"); event response rules: at least 1 mapping relationship between industry events and weight adjustment.

3. The system according to claim 1, characterized in that The weight adjustment method of the event response engine is: automatically generate the weight coefficient Wi′ according to the event type (academic publication, exhibition activities, policy changes): Wi′=Wi×(1+γ⋅Eimpact). Among them, γ is the event impact coefficient (0.1-0.5), and Eimpact is calculated by the level of the institution involved in the event (national level=1, provincial level=0.6) and the amount of media coverage.

4. The system according to claim 1, characterized in that The calculation formula of the cultural scarcity coefficient Dc is dynamically adapted according to the category: Porcelain: Dc=0.4L+0.3R+0.2M+0.1K, where K is the kiln history score; Calligraphy: Dc=0.5L+0.3R+0.2T, where T is the authority of the inscription; Zisha teapot: Dc=0.3L+0.4C+0.3M, where C is the standardized value of the clay aging time.

5. The system according to claim 1, characterized in that The data collection layer includes a multi-source crawler engine, a blockchain evidence interface, and a data cleaning module. The multi-source crawler engine: customizes crawler rules for different categories (such as capturing kiln temperature records in the porcelain field and parsing inscription texts in the calligraphy field); the blockchain evidence interface: stores key data such as the number of existing items and the path of circulation on the chain to ensure that they cannot be tampered with; the data cleaning module: uses natural language processing (NLP) technology to clean unstructured data (such as auction house description texts).

6. The system according to claim 1, characterized in that Provide a graphical configuration interface for users: add / edit categories: customize core indicators, unique parameters and Dc formulas; set event rules: bind industry events and weight adjustment actions (such as "During the Jingdezhen Ceramics Expo, the firing technology weight is +15%"); real-time preview effect: verify the rationality of the configuration through simulation calculation.

7. The system according to claim 1, characterized in that The core algorithm is the multi-category value index formula: V=∑(Wi×Si)+α⋅Dc+β⋅Pf (i ranges from 1 to n).

8. The system according to claim 1, characterized in that The following security mechanisms are integrated: Data encryption: AES-256 encryption is used to store sensitive data (such as auction transaction prices and collector information); Permission classification: set three levels of access rights for administrators, brands, and the public, and limit access to core algorithms and raw data; Audit log: record all data operations and configuration changes, and support traceability and accountability.

9. The system according to claim 1, characterized in that Supports access to third-party data sources through API interfaces and plug-in mechanisms: such as museum collection databases and real-time transaction records of international auction houses; expansion of new categories: users can add new areas such as carvings and lacquerware without modifying the underlying code; integration of AI models: embedding deep learning algorithms (such as LSTM) to predict future brand value trends.

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