📲Technical Architecture
To fully leverage the characteristics of blockchain, the system is designed to include various layers: data layer, model layer, oracle layer, bidding layer, incentive layer, and application layer. This structure is essential for building the trading and incentive components on top of the traditional AI computing power trading platform.
Platform Data Architecture
Data Layer
The data layer is a critical component responsible for storing and managing invocation data from all computing power node providers and users. Within this data layer, various information is encompassed, including but not limited to:
Service types provided by computing power node providers, such as inference or rendering, encompassing different types of computing power services.
Graphics card and CPU types offered, including specific models, performance parameters, and detailed information.
Supported algorithm models by computing power node providers, ensuring users can choose appropriate models for computation as needed.
Availability of computing power, including information on whether there are currently idle computing resources and estimated response times.
Geographic location information of computing power node providers, which may be crucial for specific requirements such as data privacy and regulatory compliance.
Effective management and utilization of this data enable better optimization and scheduling of computing power resources, thereby improving overall service efficiency and quality to meet the diverse needs of users for computing power services.
Model Layer
The model layer serves as the core component of the entire system, bearing significant functions and responsibilities. Within the model layer, AI algorithms deeply analyze and process information from various dimensions provided by the data layer to achieve the following objectives:
Determine the optimal computing power path through comprehensive evaluation of data from computing power node providers and user invocations, ensuring users can obtain required computational resources most effectively.
Find computing power paths that offer both the lowest prices and highest quality based on factors such as cost and service quality, providing users with more competitive options.
Offer recommendations and solutions for bidding and computing power scheduling in different scenarios, assisting users in flexibly adjusting the allocation and utilization of computing power resources according to their needs.
Conduct real-time optimization and adjustments to adapt to evolving computing power demands and market environments, ensuring the system can fully utilize computing power resources and provide stable and reliable services.
Through precise calculations and intelligent analysis in the model layer, the system can better address complex challenges in computing power management, achieve maximized resource utilization, and optimize service enhancements, bringing more efficient and flexible computing power service experiences to users and computing power node providers.
Oracle Layer
The oracle layer undertakes crucial functions and responsibilities within the entire system architecture. Its task is to ensure the reliability and legality of external data to support various operations and decisions within the system. Specifically, the main responsibilities of the oracle layer include but are not limited to the following aspects:
Data filtering and optimization: The oracle layer rigorously filters and optimizes external input data to ensure accuracy, completeness, and security, preventing false information or malicious attacks from affecting the system.
Data approval and validation: Approved and verified data undergoes filtering processes to ensure the legitimacy and trustworthiness of its sources, facilitating decision-making and operations within various modules of the system based on reliable data.
Data pushed to the blockchain: Validated data is pushed to the corresponding positions on the blockchain so that other components such as the model layer and bidding layer can easily read and use it, achieving information sharing and transparency.
Maintaining data security: The oracle layer implements security measures to ensure the security of external data during transmission and storage, guarding against the risks of data leakage and tampering, and maintaining the stability and reliability of the entire system.
Through the effective operation of the oracle layer, the entire blockchain system can better address the challenges posed by external data, establish trust and transparency, ensure the fairness and efficiency of computing power services, and promote the development and active participation of users.
Bidding Layer
The bidding layer achieves automation and intelligent management of user bidding information and computing power resource allocation through smart contract models. Within the bidding layer, the system provides various bidding modes, including but not limited to the following:
Automated bidding using AMM: Utilizing the automated market maker (AMM) mechanism, prices and resource allocations are dynamically adjusted based on market supply and demand to ensure efficient resource utilization and price fairness.
Order book bidding mode: Based on the order book method, intelligent matching and resource allocation are carried out according to the priority and conditions of user-submitted orders, achieving personalized computing power service responses.
Dutch auction: Adopting the Dutch auction method, prices start high and gradually decrease until a price is accepted by a node provider, making resource allocation more efficient and fair.
Through smart contract technology, the system automatically selects the most suitable smart contract to execute the corresponding bidding mode, thereby effectively aligning user bidding requests with node provider bidding responses and rationalizing computing resource allocation. Users can choose different bidding modes according to their needs, while node providers bid through corresponding modes. Ultimately, transactions are executed by smart contracts, ensuring the fairness and transparency of the bidding process and providing users and node providers with an efficient and secure computing power service trading ecosystem.
Incentive Layer
The incentive layer aims to reduce platform transaction idle rates, increase transaction rates, and promote cooperation and win-win situations between node providers and users through token incentives. Specifically, the main functions of the incentive layer include:
Node provider incentives: Incentivizing node providers with token rewards to encourage their participation in computing power transactions and the provision of high-quality services to meet user demands.
User incentives: Providing token incentives to computing power demanders to encourage them to select lower-priced, more quickly matched computing power resources, thereby increasing the platform's transaction activity and efficiency.
Win-win mechanism: Constructing a mutually supportive and cooperative ecosystem through incentive measures, allowing node providers and users to maximize their interests.
Through the design and implementation of the incentive layer, computing power trading platforms can effectively stimulate the enthusiasm and participation of participants, increase overall transaction activity, and drive the healthy development of the platform. Additionally, incentive mechanisms also help establish trust and cooperation, promote the formation of community consensus, and lay the foundation for the platform's sustainable development.
Application Layer
The primary task of the application layer is to provide a user-friendly and easy-to-use user interface (UI) for node providers and users to facilitate their operations and transactions. Additionally, the application layer also provides APIs and SDKs for DePin devices and developers to easily develop and access the platform. Specifically, the functions of the application layer include:
UI design: Designing an intuitive and easy-to-use UI interface, allowing users to easily browse computing power resources, submit bidding requests, view transaction records, etc., enhancing user experience and transaction efficiency.
API and SDK support: Providing rich APIs and SDK interfaces so that DePin devices and developers can develop customized applications or integrate services with the tools and resources provided by the platform, achieving a more diverse range of application scenarios.
Customized features: Continuously optimizing and improving application layer functions based on user needs and feedback, meeting personalized user demands, and increasing user stickiness and platform activity.
Through the efforts of the application layer, computing power trading platforms can provide more convenient and flexible services, attract more node providers and users to participate, and promote the innovation and development of DePin devices and developers. This diversified application layer design helps expand the platform's influence and coverage, driving the healthy development and growth of the entire ecosystem.
AI Data Model Architecture
We utilize the Vector Blockchain Library and RAG indexing enhancement to train our AI personality models.
Vector Blockchain Library
The Vector Blockchain Library is an innovative technology that utilizes vectorization techniques to transform various data on the blockchain into a form that machines can easily understand and process. This transformation process enables data to be efficiently stored and organized, forming a vector blockchain library. This database has unique advantages in handling and analyzing large amounts of unstructured data, which is challenging in traditional database systems.
Vectorization is a method of transforming data into vector representations, enabling machines to process and analyze more effectively. In the vector blockchain library, this technique is applied to convert transaction records, smart contract information, and other blockchain data into numerical vectors. These vectors can be efficiently stored in the vector database, enabling fast data retrieval and analysis.
A vector database is a database system specifically designed for storing and retrieving vector data, utilizing advanced indexing structures and algorithms to efficiently process vector data. Compared to traditional relational databases and key-value databases, vector databases have significant advantages in handling unstructured data and large-scale datasets.
By combining blockchain technology with vector databases, the vector blockchain library brings new possibilities to the blockchain field. Firstly, it enhances the availability and scalability of blockchain data, enabling blockchain systems to better support complex smart contracts and decentralized applications. Secondly, it enables blockchain data to be more widely applied in fields such as machine learning and artificial intelligence, opening up new avenues for innovation and application of blockchain technology. In summary, the vector blockchain library is a technology with enormous potential, bringing more innovation and value to the blockchain field.
Working Principle:
The system is based on vector space theory, storing blockchain data in a three-dimensional vector space.
Data storage and querying are conducted through vector operations such as addition, subtraction, and multiplication.
Each vector represents a set of entity attributes, which can include any on-chain data.
Efficient vector space indexing and similarity calculation algorithms are utilized to achieve fast querying and analysis of on-chain data.
Blockchain Retrieval Enhanced RAG (RAG) is an advanced tool that enables a deeper understanding of blockchain data and converts the semantics and contextual information of user's natural language data into a large model for blockchain indexing. This model mainly consists of the following key components:
Illusion Phenomenon Handling:
Utilizing the immutable nature of blockchain to ensure the accuracy and transparency of model outputs. To achieve real-time detection and correction of illusion phenomena, we have established a monitoring mechanism based on smart contracts.
Dynamic Training Data Updates:
Storing model parameters on the blockchain to support dynamic dataset updates and model retraining. At the same time, leveraging decentralized storage features to achieve secure sharing and updating of training information.
Domain Knowledge Expansion:
Utilizing blockchain cross-chain technology to introduce knowledge graphs from different domains, enriching the model's domain knowledge. To improve the model's performance in specific domains, we have created smart contracts to automatically execute domain knowledge updates and integration.
Secure Training Data Interaction:
Utilizing blockchain encryption algorithms to protect the privacy of sensitive training data. Additionally, establishing access control mechanisms based on smart contracts to ensure the secure transmission and storage of training data.
Incentive Model
In a decentralized AI blockchain indexing system, there are four different roles that collectively ensure the protocol's normal operation and maintain the security of the entire computing power network through appropriate incentive mechanisms. Here are detailed descriptions of these four roles:
Node Incentives:
Firstly, users need to purchase specific node NFTs. Once purchased and held, it signifies that users own corresponding computing power. Next, users can utilize this computing power to participate in the platform's profit calculation, thereby receiving corresponding rewards. Additionally, users can choose to stake a certain amount of tokens to earn higher rewards. However, it's important to note that if users provide false computing power information or engage in other violations, their staked tokens will be deducted.
AI Model Validators Incentives:
Within the Reflection platform, there exists a group of specialized AI model validators whose primary responsibility is to ensure the integrity and transparency of the network. These validators utilize advanced AI models to inspect and verify the computational outputs of other nodes, identifying and penalizing nodes that may engage in cheating or provide false information. Upon detection of improper behavior, validators execute punitive measures, imposing sanctions on violating nodes. As an incentive, validators receive a portion of tokens from the staking of penalized nodes as a reward. Through this decentralized "witch-hunting" mechanism, Reflection ensures the authenticity and effectiveness of its computational network while providing reasonable rewards to validators who honestly and effectively execute their tasks.
User Behavior Incentives:
To encourage users to participate more actively and use the platform, the platform introduces a user behavior incentive system. When users create and actively use virtual AI personality systems on the platform, they will receive a certain amount of tokens as rewards. This incentive mechanism aims to encourage users to participate more actively in the platform, thereby increasing its activity and utility.
User Decentralized Computing Power Incentives:
The platform also allows users to contribute their idle computing power from personal computers (PCs) and smartphones to the platform. Through this method, users can not only earn token rewards using these idle resources but also provide more computational power for the platform's operation. This decentralized computing power contribution method not only improves resource utilization but also enhances the stability and scalability of the platform.
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