Digital artwork market transaction method and system based on AI pricing transaction mechanism

Abstract

The invention discloses a digital artwork market transaction method and system based on an AI pricing transaction mechanism, and relates to the technical field of digital artwork markets, and the method comprises the following steps: S1, obtaining price element information composed of value element information, point information, quantity information, time information and price information issued by participants through AI artificial intelligence, the obtained price factor information is stored in a database table; according to the invention, an AI artificial intelligence culture and art data system is added to a long-term transaction system in a digital artwork market, and whether a transaction is made or not is automatically identified and judged by automatically screening and matching a generated price element sequence and digital evidence element information of a participant; the mobility of the digital artwork market is improved, the price stability and the balance of beautiful consumption are maintained, and the transaction timeliness is improved.

Description

Technical Field

[0001] The present invention relates to the field of digital artwork market technology, and specifically to a digital artwork market transaction method and system based on an AI pricing transaction mechanism. Background Art

[0002] The digital art market refers to digital art products designed based on the idea of ​​"one certificate, one object, and one source", and the market in which trading activities are carried out in a market-oriented manner in the digital network space. One certificate is a digital evidence generated based on digital copyright and value elements; one object is a standardized, limited, physical art product; one source is a fixed artwork entity and its corresponding cultural and artistic data resources, which is the origin of mining value elements and generating digital evidence. Due to the non-standard and complementary characteristics of artworks, the structure of the art market is complex, and the internal mechanism of value and price is always difficult to integrate and coexist. If traditional art transactions are directly introduced into the digital space market, the symbiosis between art prices and art values ​​has not been resolved, resulting in insufficient liquidity and more uncertainty in the art market. Now, through the discovery, management, and evaluation of art value based on AI artificial intelligence, the circulation cost of art has been reduced, the integration of price changes and value element changes has been improved, the balance of aesthetic education consumption has been improved, and the development of credible transactions in the art market has been promoted.

[0003] Therefore, we proposed a digital art market trading method and system based on AI pricing and trading mechanism to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a digital art market transaction method and system based on an AI pricing and transaction mechanism to solve the current market problems raised by the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The digital art market transaction method based on AI pricing transaction mechanism includes the following steps:

[0007] S1. Obtain price factor information consisting of value factor information, points information, quantity information, time information, and price information published by the participating entities through AI artificial intelligence, and store the obtained price factor information in a database table;

[0008] S2. Filtering the price factor information consisting of value factor information, points information, quantity information, time information and price information from the database table, and adding the filtered price factor information to the price factor sequence associated with pricing;

[0009] S3. Obtain the price factor information of the market participants through AI artificial intelligence, and match the price factor information of the participants with the price factor sequence associated with the pricing through AI artificial intelligence;

[0010] S4. When the price element of the participating entity is automatically matched with the value element associated with pricing in the sequence, the price element of the participating entity is automatically determined as a digital evidence element associated with pricing; when the price element of the participating entity is automatically determined as a digital evidence element, the digital evidence element of the participating entity is automatically processed as the transaction price.

[0011] As a further optimization scheme of the present invention, the element information, the filtered price element information, and the price element information of the participating entities all include the type of digital artwork, the number of digital artworks, digital evidence, value floating rate, points floating rate, purchase quantity, sale quantity, purchase price, sale price and transaction time.

[0012] The digital art market trading system based on AI pricing and trading mechanism includes initial module, screening module, matching module, generation module and transaction module;

[0013] The initial module is used to obtain the price factor information consisting of value factor information, point information, quantity information, time information, and price information released by the participating entity through AI artificial intelligence, and store the price factor information in the database table;

[0014] A screening module, used for screening price element information consisting of value element information, point information, quantity information, time information and price information from the database table, and adding the screened price element information to a price element sequence associated with pricing;

[0015] A matching module, which is used for AI to obtain the price factor information of market participants and perform AI matching between the price factor information of the participants and the price factor sequence;

[0016] A generation module, for automatically generating digital evidence of pricing elements for the price elements of the participating entities if the price elements of the participating entities are successfully matched with the value elements in the sequence;

[0017] The transaction module is used to automatically process the transaction price of the digital evidence elements of the participating entities if the price elements of the participating entities are automatically processed for digital evidence generation of pricing elements.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] The present invention adds the AI ​​artificial intelligence cultural and artistic data system to the medium- and long-term trading system of the digital artwork market. By automatically screening and matching the generated price element sequence and the digital evidence element information of the participating entities, it automatically identifies and determines whether a transaction has been completed, thereby improving the liquidity of the digital artwork market, maintaining price stability, and the balance of aesthetic consumption, and improving the timeliness of transactions.

[0020] The present invention provides a digital art market transaction method and system based on an AI artificial intelligence pricing and transaction mechanism, which improves the liquidity of the digital art market, maintains price stability, balances aesthetic consumption, and improves the timeliness of transactions.

[0021] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the digital artwork market transaction method based on the AI ​​pricing transaction mechanism of the present invention;

[0023] Figure 2 This is a schematic diagram of the relationship between the AI ​​artificial intelligence pricing mechanism of the present invention and other market participants;

[0024] Figure 3 This is the second schematic diagram of the relationship between the AI ​​artificial intelligence pricing mechanism of the present invention and other market participants;

[0025] Figure 4 is a schematic diagram of the conversion process of a physical artwork into a digital evidence in the second embodiment of the present invention;

[0026] Figure 5 is a schematic diagram of the digital transformation of a traditional enterprise in the third embodiment of the present invention;

[0027] Figure 6 is a schematic diagram of scene conversion in the fourth embodiment of the present invention;

[0028] Figure 7 is a schematic diagram of relationship definition of digital artworks in Embodiment 5 of the present invention;

[0029] Figure 8 It is a schematic diagram of the process of the trading system in the sixth embodiment of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Embodiment 1

[0032] See also Figure 1-Figure 3 , a digital art market transaction method based on AI pricing transaction mechanism, comprising the following steps:

[0033] S101. Obtain value factor information, point information, quantity information, time information, and price information published by participating entities through AI artificial intelligence to form price factor information, and store the price factor information in a database table.

[0034] Specifically, a digital art sharing supermarket is set up in the trading system, and the value factor information, point information, quantity information, time information, and price information of the participants in the digital art sharing supermarket are collected through AI artificial intelligence, constructed into a price factor information set, and stored in the system database.

[0035] S102, filtering price element information consisting of value element information, point information, quantity information, time information and price information from the database table, and adding the filtered price element information to a price element sequence associated with pricing.

[0036] Specifically, the value factor information after screening is clearly classified, and generally distinguishes between the ontological value factor information and the non-ontological value factor information. At this point, the price factors in the database have already possessed the utility of multi-level, reusable, and vector.

[0037] S103, obtaining price factor information of market participants through AI artificial intelligence, and matching the price factor information of the participants with the price factor sequence associated with the pricing through AI artificial intelligence;

[0038] Specifically, the filtered price elements are identified and matched with the associated standards, and the matched value element information is divided into two categories: material ontological value elements and spiritual ontological value elements. At the same time, AI artificial intelligence matching is performed with the previous digital evidence element information. According to the matching standards, the adapted price element information enters the automatic pricing database.

[0039] S104. When the price element of the participating entity is automatically matched with the value element associated with pricing in the sequence, the price element of the participating entity is automatically determined as a digital evidence element associated with pricing.

[0040] Specifically, when the type of digital artwork, the number of digital artworks, digital evidence, value floating rate, points floating rate, purchase quantity, sale quantity, purchase price, selling price and transaction time are all matched successfully, this information set is cast into the element information of digital evidence through blockchain technology to obtain the digital evidence corresponding to the digital artwork sharing supermarket.

[0041] S105. When the price element of the participating entity is determined to be automatically processed as a digital evidence element, the digital evidence element of the participating entity is automatically processed as a transaction price.

[0042] Embodiment 2

[0043] See also Figure 4 , the concept of artwork ontology is transformed into the artwork ontology, which fully and effectively solves the generation of value elements in the design of digital artworks. By transforming the artwork ontology to construct its digital assets, the qualitative value elements can be transformed into quantitative value elements through value discovery tools, and the ontological value elements can be separated. Based on the AI ​​function, the separated ontological value elements can be measured during data annotation, thereby providing information for the price element set and ensuring the generation of digital evidence.

[0044] The process of converting physical artworks into digital evidence:

[0045] Physical artworks: including porcelain, calligraphy and painting, jade, etc., as well as cultural data resources anchored to physical objects.

[0046] Artwork ontology: Solve the problem of constructing digital assets of artworks.

[0047] Value discovery: Use value discovery tools to solve real problems of artworks and convert qualitative value elements into quantitative value elements.

[0048] Value assessment: During the value assessment stage, solve the problems of price confirmation and data evaluation.

[0049] Ontological value elements: After value discovery and value assessment, the ontological value elements of the artwork are separated.

[0050] Data labeling: Data labeling is performed based on the ontological value elements. Each unit of ontological value element is equal to one label art data and multiple art data components.

[0051] Value management: Solve the problem of measuring the market value of consumer rights through value management.

[0052] Price factors: The results of data labeling form a set of price factors.

[0053] Digital evidence: The final set of price elements generated is used for digital evidence.

[0054] Embodiment 3

[0055] See also Figure 5 The tool effectiveness of digital evidence conversion is reflected in three aspects: data asset entry, digital equity value conversion, and corporate culture digital asset construction. Through the corresponding data generation middle-end management system, it is connected to the transaction system to realize transaction circulation in the digital space.

[0056] The inclusion of data assets in the balance sheet helps manufacturing companies to turn their data into assets, making them quantifiable and manageable resources. Through electronic bills of lading and electronic shopping vouchers, the value conversion of digital rights and interests is realized, enhancing the market appeal of products and customer experience. Through the digital processing of corporate cultural resources, the corporate image and market competitiveness are enhanced.

[0057] The application process of promoting the digital transformation of traditional enterprises through digital evidence technology is as follows:

[0058] First, various types of digital assets of commodities and cultural resources enter the "digital evidence storage" link.

[0059] After verification and processing of digital evidence, assets are further subdivided into two categories: "electronic delivery notes" and "electronic shopping vouchers".

[0060] Digital rights are then combined with corporate culture and the actual business of production-oriented enterprises to form products and services with unique value and competitiveness.

[0061] Finally, all data and operations are summarized in the art data generation middle platform management system, which is responsible for managing the data flow in the entire process to ensure the accuracy and real-time nature of the information.

[0062] Through the above process, the assetization of data, the visualization of rights and interests, and the digital asset construction of cultural resources can be realized, thereby promoting the transformation and development of traditional enterprises in the digital economy era.

[0063] Embodiment 4

[0064] See also Figure 6, the digital evidence contained in digital artworks has corresponding rights and interests, and is stored in the digital asset warehouse of the holder. When the holder wants to trade and circulate the digital evidence, it needs to be compared with the corresponding standards. The comparison is completed through AI combined with the value assessment vertical model. When the comparison results meet the standards, the rights and interests of digital evidence can be applied in consumption scenarios and life scenarios to realize the construction of data asset ecological scenarios.

[0065] Digital artwork - personalized digital asset package - standard application process in three scenarios:

[0066] Digital Artwork: A digital work with unique value and artistry.

[0067] Digital proof of ownership: Digital artworks come with certified proof of ownership, ensuring their authenticity and ownership.

[0068] Personalized digital asset package: Equity is integrated into a personalized digital asset package for holders to manage and utilize.

[0069] Standards: To ensure smooth transactions, clear standards are used to measure the quality and value of digital assets.

[0070] Through the value assessment vertical model, the value of digital assets is accurately assessed to ensure that they meet the set standards.

[0071] Digital product consumption scenarios: Once digital assets pass standard verification, they can enter the actual consumer market and become products that consumers can purchase and use.

[0072] Data rights life scenarios: Digital assets are integrated into daily life to provide users with a variety of services and conveniences.

[0073] Digital asset ecological scenario: Finally, all elements together constitute a complete digital asset ecosystem.

[0074] Embodiment 5

[0075] See also Figure 7 The digital artworks in the digital artwork sharing supermarket of the trading system are limited, and the wholesale price and retail price change with the transaction time, the floating rate of value elements, and the change of quantity, that is, the "three-point rule" method. The changes are always carried out around the unchanging values ​​reviewed by the trading mechanism, realizing the openness, fairness and justice of the AI ​​pricing mechanism.

[0076] The relationship between the price change of digital artworks in the trading system and quantity, value and time is:

[0077] Price fluctuations: As time goes by and the value changes, the wholesale price (Pn-1) and retail price (Vn-1) of digital artworks will change. As time goes by, the price curve shows an upward trend, which means that as time goes by, the scarcity of digital artworks increases, thus pushing up its price.

[0078] Value growth: The value of digital artworks (Vn) increases over time. As time goes by, participants will continue to promote the balance of art consumption and generate value elements, so that the structure of the ontological value elements contained in digital evidence will continue to be optimized, and the market recognition and collection value of digital artworks may increase.

[0079] Trading mechanism: Ensure the fairness and stability of transactions through core trading mechanisms or standards.

[0080] AI pricing mechanism: Determine the reasonable price of digital artworks through the pricing system to ensure the fairness of the price.

[0081] Embodiment 6

[0082] See also Figure 8 In the trading system, there are two main roles: wholesaler trading entities and retailer trading entities. After wholesalers wholesale digital artworks to retailers on the platform through corresponding mechanisms, retailers can buy and sell in the digital artwork sharing supermarket. There are two situations for the results of the transaction. One is to directly extract digital artworks, and the other is not to directly extract digital artworks after purchase, and entrust the corresponding warehouse for safekeeping at the same time. At this time, retailers who have not extracted digital artworks consume art through the "three-price mechanism" of value discovery, value management, and value evaluation, dig out more value element information, and screen and match these value element information based on AI functions, which become new price factors and new evidence of digital artworks to be sold.

[0083] The process of trading system:

[0084] Wholesale stage: Digital artworks are distributed by wholesalers to retailers on the platform.

[0085] Wholesale mechanism: supply mechanism and value element injection mechanism.

[0086] Retail stage: Retailers can buy and sell digital artworks in the digital artwork sharing supermarket.

[0087] There are two buying and selling situations: a. Withdrawing physical objects (category a): Retailers directly withdraw digital artworks from wholesalers. b. Not withdrawing physical objects (category b): Retailers do not withdraw digital artworks immediately, but store them in corresponding warehouses and consume and increase their value according to the mechanism.

[0088] Art consumption and value-added: For retailers who do not extract physical objects (category B), digital artworks are consumed and value-added through a three-price mechanism (value discovery, value management, and value assessment): a. Data labeling: Add data labels or annotations to digital artworks. b. Value element information: Collect and manage value element information about digital artworks. c. Automatic pricing mechanism: Calculate and adjust the matching selling price between the input selling price of digital artworks and the guide price range.

[0089] Circulation of value elements: Through the above-mentioned art consumption and value-added activities, new value elements generated will be recorded to form a new digital certificate. The new digital certificate will re-enter the market and continue to participate in transactions and circulation as part of the new digital artwork.

[0090] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A digital art market transaction method based on AI pricing and transaction mechanism, characterized in that: The following steps are involved: S1. Obtain price factor information consisting of value factor information, points information, quantity information, time information, and price information published by the participating entities through AI artificial intelligence, and store the obtained price factor information in a database table; S2. Filtering the price factor information consisting of value factor information, points information, quantity information, time information and price information from the database table, and adding the filtered price factor information to the price factor sequence associated with pricing; S3. Obtain the price factor information of the market participants through AI artificial intelligence, and match the price factor information of the participants with the price factor sequence associated with the pricing through AI artificial intelligence; S4. When the price element of the participating entity is automatically matched with the value element associated with pricing in the sequence, the price element of the participating entity is automatically determined as a digital evidence element associated with pricing; when the price element of the participating entity is automatically determined as a digital evidence element, the digital evidence element of the participating entity is automatically processed as the transaction price.

2. The digital artwork market transaction method based on AI pricing and transaction mechanism according to claim 1 is characterized by: The factor information, filtered price factor information, and price factor information of participating entities all include the type of digital artwork, the number of digital artworks, digital evidence, value floating rate, points floating rate, purchase quantity, sale quantity, purchase price, sale price, and transaction time.

3. A digital art market trading system based on AI pricing and trading mechanism, characterized by: It includes initial module, screening module, matching module, generation module and transaction module; The initial module is used to obtain the price factor information consisting of value factor information, point information, quantity information, time information, and price information released by the participating entity through AI artificial intelligence, and store the price factor information in the database table; A screening module, used for screening price element information consisting of value element information, point information, quantity information, time information and price information from the database table, and adding the screened price element information to a price element sequence associated with pricing; A matching module, which is used for AI to obtain the price factor information of market participants and perform AI matching between the price factor information of the participants and the price factor sequence; A generation module, for automatically generating digital evidence of pricing elements for the price elements of the participating entities if the price elements of the participating entities are successfully matched with the value elements in the sequence; The transaction module is used to automatically process the transaction price of the digital evidence elements of the participating entities if the price elements of the participating entities are automatically processed for digital evidence generation of pricing elements.

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