Guqin brand value monitoring system and method based on dynamic multi-dimensional indexes

Abstract

The invention provides a seven-stringed plucked instrument brand value monitoring system and a seven-stringed plucked instrument brand value monitoring method based on a dynamic multi-dimensional index, and the system comprises four modules: 1, a data collection module which is used for obtaining multi-source heterogeneous data of a seven-stringed plucked instrument brand from the Internet, an industry database and user investigation; the dynamic index modeling module is used for setting monitoring indexes and weights according to the difference of three main bodies, namely a Guqin community, a hacker and a player, so as to form an independent index library; the self-adaptive calculation module is used for dynamically adjusting the weight in combination with industry events and introducing a culture scarcity coefficient Dc; and the visual report generation module is used for outputting a ranked radar map and a Guqin brand ecological niche competitiveness report of an optimization strategy. According to the method, commercial limitation of a traditional brand evaluation model is broken through, scientific quantitative evaluation of intangible cultural heritage brands is realized for the first time through three major innovations of a cultural scarcity coefficient, a dynamic weight mechanism and a subdivision domain index library, and effective technical support is provided for inheritance and marketization of Guqin culture.

Description

Technical Field

[0001] The present invention relates to the technical fields of brand management, data analysis and intangible cultural heritage protection, and particularly relates to a dynamic value monitoring system and method for the guqin cultural brand, which is applicable to the quantitative evaluation and optimization decision-making of the brand competitiveness of three types of entities: guqin societies, guqin makers, and performers. Background Art

[0002] Existing brand value evaluation models (such as the Interbrand model and the BrandZ model) are mostly designed based on the commercial consumption field and have the following defects: lack of cultural attributes: unable to quantify the cultural values such as the historical inheritance and technological scarcity of intangible cultural heritage brands; generalization in sub-domains: lacking targeted indicators for the differentiated needs of guqin industry societies and individual brands (guqin makers and performers); static data: relying on historical data and not dynamically adjusting weights in combination with industry cycles (such as cultural festivals and auction seasons). The Thousand Brands Value Index System V9.0 has continuously monitored the brand ecological competition positions in the intelligent building and smart home industries for more than 20 years and applied the data results, obtaining market recognition and achieving good results. The present invention is specially customized and optimized for the guqin industry based on the data model of the Thousand Brands Value Index System V9.0, and its core is the brand projection value measurement method under the brand ecology system. Summary of the Invention

[0003] The present invention provides a guqin brand value monitoring system and method based on dynamic multi-dimensional indicators, which solves the problems of difficult quantification of cultural values, poor adaptability in sub-domains, and insufficient data timeliness in the evaluation of traditional cultural brands.

[0004] The system architecture includes a data acquisition layer, an index modeling layer, a dynamic calculation layer, and a result output layer.

[0005] Data acquisition layer: Integrate publicly available Internet data (social media, auction records), industry databases (intangible cultural heritage certifications, museum collections), and user research data (collector evaluations, event participation).

[0006] The index modeling layer constructs three major monitoring models: society model: focusing on cultural dissemination power (member activity, frequency of elegant gatherings); guqin maker model: focusing on technological value (number of patents, auction premium rate); performer model: strengthening commercial conversion (number of performances, copyright income).

[0007] Dynamic calculation layer: Introduce AI algorithms to achieve adaptive weight adjustment. For example, when the intangible cultural heritage application for heritage status is successful, automatically increase the weight of the "historical inheritance" indicator to 1.2 times; during the cultural festival, temporarily increase the score proportion of "event exposure".

[0008] Result Output Layer: Generate the "Report on the Niche Competitiveness of Guqin Brands", including the total score ranking, radar chart of sub - dimensions, and optimization suggestions.

[0009] The core algorithms include the brand value index formula and the calculation formula of the cultural scarcity coefficient Dc.

[0010] Brand value index formula: V = ∑ (Wi×Si)+α⋅Dc from i = 1 to n.

[0011] Formula explanation: V: Total score of the brand value index, reflecting the comprehensive competitiveness of the brand in the industry; Wi: Dynamic weight of the i - th indicator (automatically adjusted according to industry events or cycles); Si: Standardized score of the i - th indicator (value after 0 - 1 standardization); Dc: Cultural scarcity coefficient, measuring the cultural uniqueness and historical value of the brand; α: Industry adjustment factor, with a default value of 0.15, used to balance the weights of cultural and commercial values.

[0012] Calculation formula of the cultural scarcity coefficient Dc: Dc = 0.3×L + 0.4×R + 0.3×M. Parameter definition: L: Intangible cultural heritage level (national level = 1, provincial level = 0.6, municipal level = 0.3); R: Reciprocal of the existing quantity (standardized to 0 - 1, the less the existing quantity, the higher the R value); M: Number of collections in museums / top - tier institutions (each collection = 0.1, with an upper limit of 3).

[0013] The beneficial effects of the present invention are to quantify the cultural value of Guqin: For the first time, intangible cultural heritage levels, teacher - disciple relationships, etc. are incorporated into the evaluation system, enhancing the ability to capitalize on Guqin brands. Precise adaptation in sub - fields: Differentiated indicators are designed for three types of entities, and the error rate is reduced by 46% compared with traditional models. Dynamic decision - making support: The weights are adjusted in real - time through AI, and the timeliness of response to industry events is increased by 70%. Brief Description of the Drawings

[0014] Figure 1 : System architecture diagram (showing data flow and module interaction) Figure 2 : Schematic diagram of the Guqin brand value index formula Figure 3 : Sample of the visualization report of the Guqin brand value index Detailed Implementation Modes

[0015] Example 1: Monitoring of luthier brands Data input: A luthier makes 5 Guqins annually, with an average auction price of 1 million yuan per piece, has provincial - level intangible cultural heritage certification, and cooperates with 2 museums.

[0016] Dynamic calculation: Weight distribution: Craft value (40%), market premium (30%), cultural endorsement (30%). Cultural scarcity coefficient Dc = 0.7 (provincial - level intangible cultural heritage + small existing quantity).

[0017] Output result: Brand value index V = 85.6 (top 10% in the industry), and it is recommended to "apply for national intangible cultural heritage to improve the Dc value".

[0018] Example 2: Community brand monitoring. Data input: A certain community holds 12 elegant gatherings annually, has 100,000 new media fans, and has won the "Advanced Unit for Intangible Cultural Heritage Protection". Dynamic calculation: During the cultural festival, the weight is adjusted: the weight of activity influence is increased from 15% to 25%. Calculate the score increase brought by the exposure increment. Output result: During the cultural festival, the brand value index increases by 22% and ranks among the top 5 in the industry.

Claims

1. A guqin brand value monitoring system, characterized in that: It includes data collection module, dynamic indicator modeling module, adaptive calculation module and visual report generation module. Data collection module: used to obtain multi-source heterogeneous data of guqin brands from Internet public platforms, industry databases and user surveys; dynamic indicator modeling module: differentiated monitoring indicators and weights are set according to the three main bodies of guqin societies, guqin makers and performers to form an independent indicator library; Adaptive calculation module: dynamically adjusts weights based on industry events and introduces the cultural scarcity coefficient Dc; Visual report generation module: outputs the "Guqin Brand Niche Competitiveness Report" containing total score rankings, radar charts of segmented dimensions and optimization strategies.

2. The system according to claim 1, characterized in that The calculation method of the cultural scarcity coefficient Dc is: Dc=0.3×L+0.4×R+0.3×M. Among them: L is the quantitative value of the intangible cultural heritage level (national level=1, provincial level=0.6, municipal level=0.3); R is the reciprocal of the number of surviving items, calculated by the standardized formula R=1−N / Nmax, N is the number of surviving items, Nmax is the maximum number of surviving items in the industry; M is the number of collections in museums or top cultural institutions, with each bed of collection assigned a value of 0.1, and an upper limit of 3.

3. The system according to claim 1, characterized in that The dynamic indicator modeling module includes the following segmented indicators: community model: member activity, frequency of gatherings, number of cross-border cooperation projects; luthier model: annual instrument production volume, auction premium rate, number of technical patents; performer model: number of commercial performances, streaming media playback volume, and copyright cooperation income.

4. The system according to claim 1, characterized in that The adaptive calculation module dynamically adjusts the weights through an event triggering mechanism, including: when a "successful intangible cultural heritage application" event is monitored, the LL value weight of the relevant brand is automatically increased to 1.2 times; during the cultural festival period, the score proportion of the "activity exposure" indicator is temporarily increased; if a brand has negative public opinion (such as craftsmanship fraud), the weight downgrade mechanism is triggered to reduce its market premium weight.

5. The system according to claim 1, characterized in that The data collection module further includes: a multi-source data cleaning unit: denoising and standardizing social media data (such as Weibo topic popularity and Tik Tok playback volume); an auction record parsing unit: extracting transaction prices, auction times, and collectors' evaluations from public data from auction houses such as Sotheby's and Christie's; a user survey interface: collecting data on collectors' recognition of the cultural value of a brand through a combination of online questionnaires and offline interviews.

6. A method for monitoring the brand value of guqin, characterized in that: The following steps are included: Step S1: Divide the monitoring subjects into three categories: societies, zither makers, and performers, and load the corresponding indicator library; Step S2: Collect multi-source data and clean them to generate a standardized data set; Step S3: Call the dynamic indicator model to calculate the initial brand value index; Step S4: Trigger the weight adaptive adjustment mechanism according to industry events and update the cultural scarcity coefficient Dc; Step S5: Generate a visual report to provide rankings, short board analysis and optimization strategies.

7. The method according to claim 6, characterized in that The weight adaptive adjustment mechanism in step S4 includes: using machine learning algorithms (such as random forests, gradient boosting trees) to predict the impact weights of industry events on indicators; dynamically updating the Wi value in the formula V=∑(Wi×Si)+α×Dc according to the prediction results; and retraining the algorithm model every quarter to adapt to changes in industry trends.

8. The system according to claim 1, characterized in that The visualization report generation module further supports: multi-dimensional comparison function: users can customize the comparison of different brands in cultural value, commercial value and other dimensions; historical trend analysis: generate a line graph of the brand value index changing over time, and mark the key event impact nodes; strategy simulator: users input hypothetical adjustment parameters (such as increasing the frequency of gatherings), and the system predicts the changes in the brand value index.

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