Technical Architecture
Last updated
Last updated
PhyChain's technical architecture is based on the deep integration of core technologies and auxiliary modules, creating an innovative, efficient, and reliable decentralized computing ecosystem. The platform’s self-developed core components include the Distributed Resource Activation Protocol (DRA), EdgeFlow Acceleration Algorithm, and the Distributed Resource Management System (DRMS). Together, these technologies support PhyChain’s high-performance distributed computing network, addressing diverse demands such as AI model training, IoT real-time computation, and decentralized finance (DeFi). Additionally, with the collaboration of multiple auxiliary technologies, PhyChain has developed an efficient, transparent, and scalable technical ecosystem that propels decentralized networks to higher dimensions of development.
DRA is the core technology of PhyChain, designed to efficiently activate the computational resources of connected devices worldwide, transforming them into high-performance nodes within the decentralized computing network. By collecting real-time device status data—such as CPU usage, memory availability, network bandwidth, and battery levels—DRA can accurately evaluate the computational potential of each device and intelligently match it with task demands. The protocol employs reinforcement learning algorithms to dynamically allocate tasks, selecting optimal computing nodes to reduce latency, enhance efficiency, and ensure balanced resource loads across the network. All computational contributions and task completion data are recorded on the blockchain, and rewards are automatically distributed via smart contracts to ensure transparency, fairness, and efficiency throughout the process. DRA’s innovation enables ordinary devices such as smartphones, routers, and electric vehicle batteries to seamlessly join the computing network, unlocking latent value and providing users with new income opportunities while advancing the sustainability of decentralized networks.
· Dynamic Compute Discovery: By collecting real-time data, the DRA protocol monitors device performance, including CPU usage, memory availability, and network bandwidth, ensuring tasks are precisely matched to device capabilities.
· Intelligent Task Matching: Utilizing reinforcement learning algorithms, the DRA protocol selects the best computing nodes based on task demands, reducing latency and computational costs.
· Automated Reward Distribution: Each device's computational contribution is recorded on the blockchain, and rewards are distributed in real time via smart contracts, ensuring transparency and efficiency in the allocation process.
EdgeFlow is a high-efficiency acceleration algorithm specifically designed for distributed networks by PhyChain. It aims to maximize the computational potential of edge nodes while significantly reducing data transmission latency during task processing. With the widespread adoption of IoT devices and the rapid growth of decentralized networks, traditional computing models struggle to meet real-time and efficiency requirements. EdgeFlow provides an innovative solution.
EdgeFlow employs dynamic task decomposition techniques to divide complex computational tasks into multiple subtasks, assigning them directly to the edge nodes closest to the data source. This avoids bottlenecks associated with centralized servers. Additionally, the algorithm integrates efficient local caching mechanisms and predictive data scheduling models to shorten data transmission paths and enable collaborative resource sharing across multiple nodes, further improving computational efficiency.
Furthermore, EdgeFlow’s acceleration logic uses adaptive optimization techniques to dynamically adjust algorithm parameters based on changes in node conditions, network bandwidth, and task priorities. This ensures flexibility and efficiency in computing power allocation. All data processing is protected by end-to-end encryption, ensuring user data privacy and network security. Through EdgeFlow, PhyChain achieves seamless integration from cloud computing to edge computing, providing reliable technical support for AI model training, IoT real-time monitoring, and decentralized finance (DeFi), driving the application of decentralized networks across various industries.
· Multi-Layer Caching Technology: The algorithm sets up multi-level caching on the device side, reducing the number of network requests during task execution.
· Real-Time Data Compression: Dynamically compresses transmitted data at edge nodes, significantly reducing network bandwidth usage and improving overall operational efficiency.
· Predictive Scheduling Engine: Predicts task demands through big data analysis and pre-allocates node resources to optimize network load.
DRMS is the core resource management hub of PhyChain, specifically designed for distributed computing networks. It is responsible for the efficient allocation, optimization, and dynamic scheduling of computing resources globally. As a critical component connecting devices and tasks, DRMS leverages advanced algorithms and data analysis techniques to ensure that all network resources are fully utilized while meeting multi-scenario application demands.
DRMS operates based on real-time resource monitoring and predictive models. By collecting data on the computational status, geographical location, bandwidth conditions, and task requirements of devices in the network, it dynamically adjusts resource allocation strategies. Its intelligent scheduling algorithms can complete task distribution within milliseconds, prioritizing nodes that are closest and most capable, thereby minimizing data transmission latency and computational overhead. DRMS also integrates resource balancing modules to prevent overloading specific nodes, ensuring sustainable optimization and stable network operation.
After tasks are completed, DRMS records each node’s contributions via on-chain smart contracts and performs transparent reward distribution. Additionally, DRMS supports the dynamic expansion and contraction of resources, allowing users to flexibly adjust the scale of nodes based on workload changes, ensuring efficient operations under varying loads. These features provide strong technical support for AI model training, IoT applications, and DeFi scenarios with high computational demands, advancing the sustainable development of the distributed computing ecosystem.
DDDP is a data distribution protocol developed by PhyChain specifically for distributed networks. It aims to achieve large-scale data sharing and efficient storage through a combination of on-chain and off-chain mechanisms. Traditional data distribution relies on centralized storage systems, which face challenges such as high costs, low reliability, and centralized control. DDDP addresses these issues using blockchain and distributed storage technologies.
DDDP leverages blockchain to ensure data integrity and immutability. All metadata (e.g., task instructions and node information) is stored on-chain, providing transparent operational records and access control. Meanwhile, large-scale raw data (such as training data for AI models and data collected from IoT devices) is shared and distributed through off-chain distributed storage networks, reducing the burden on blockchain storage and optimizing overall performance.
The protocol’s intelligent routing algorithms dynamically select the best nodes for data distribution, ensuring low latency and high efficiency in data transmission. Additionally, DDDP incorporates data fragmentation and redundancy mechanisms to significantly enhance data storage reliability, ensuring that even if individual nodes go offline, overall data integrity remains unaffected. Through DDDP, PhyChain establishes a decentralized data distribution system that is secure, scalable, and efficient, providing strong data support for AI training, real-time monitoring, and cross-border payments.
The Node Optimization Engine (NOE) is an AI-driven module designed to optimize node performance in real-time, enhancing the operational efficiency and computational contributions of nodes within the distributed computing network. Variations in node performance and dynamic task demands present challenges for optimizing the network, and NOE is specifically developed to address this core issue.
NOE integrates AI-powered performance analysis algorithms and reinforcement learning models to monitor the state of each node in real-time, including CPU load, memory utilization, network latency, and battery levels. The system uses this data to generate optimization recommendations, supporting task allocation to maximize the utilization of computational resources.
Additionally, NOE employs adaptive optimization techniques to dynamically adjust task priorities or reallocate tasks when node performance declines or network congestion occurs, avoiding overloading individual nodes. By analyzing historical data, NOE can also provide users with personalized optimization plans, such as adjusting operating parameters or upgrading hardware configurations, to extend device lifespans and improve their long-term participation in the network.
The Trustless Identity Authentication System (TIAS) is a blockchain-based identity management solution developed by PhyChain to provide decentralized identity verification services for users and nodes. Traditional identity authentication relies on centralized institutions, posing risks of privacy breaches, inefficiencies, and high costs. TIAS eliminates these issues by implementing trustless authentication processes through blockchain technology.
Within the TIAS system, each user or device generates a Decentralized Identity (DID) upon initial network access. These identity records are stored on the blockchain and managed and verified by smart contracts, without the need for third-party institutions. TIAS leverages Zero-Knowledge Proofs (ZKPs) to ensure that identities can be verified without exposing users’ private data.
Additionally, TIAS supports multi-tiered identity permission management, dynamically adjusting access rights and operational scopes based on user roles (e.g., regular nodes, supernodes, or enterprise nodes). By integrating with PhyChain’s distributed computing protocols, TIAS also binds identities to node contributions and reward distributions, providing robust transparency and security for the network.
The Distributed Task Orchestration System (DTOS) is an automated task execution system designed for high-concurrency scenarios, ensuring smooth operation of large-scale task scheduling. As the complexity and concurrency of tasks in distributed networks continue to grow, traditional scheduling mechanisms struggle to meet demand. DTOS offers a smart task orchestration solution.
DTOS adopts a Directed Acyclic Graph (DAG) model to represent task dependencies, enabling the system to break down complex tasks into hierarchical subtasks and generate the most efficient execution paths. Reinforcement learning algorithms dynamically adjust task priorities to ensure that critical tasks are executed quickly while preventing resource blockage by lower-priority tasks.
In high-concurrency scenarios, DTOS’s parallel scheduling module distributes tasks across multiple computing nodes, fully leveraging the distributed nature of the network. DTOS also monitors task execution statuses in real-time, enabling rapid retries or reallocations for failed tasks, significantly improving task completion rates and network reliability.
DTOS supports transparent task execution management, allowing users to monitor task statuses, node allocations, and expected rewards in real-time through the PhyChain dApp. This transparency not only enhances the user experience but also provides enterprise users with robust task monitoring and analytics tools, enabling more efficient utilization of distributed computing resources.
PhyChain’s smart contract and security system is a core pillar of its decentralized network, ensuring the efficiency and transparency of task scheduling, execution, and reward distribution. The Smart Task Management Contract (STMC) automates the entire lifecycle of tasks, including task allocation, execution monitoring, and reward settlement. All task records are stored on the blockchain, offering high traceability and immutability, which allows users to clearly understand their computational contributions and reward shares, enhancing transparency and trust within the network.
In terms of security, PhyChain incorporates multi-layered security protocols to create a comprehensive protective framework:
· Quantum-Resistant Encryption: Anticipates future quantum computing threats and ensures long-term data security.
· End-to-End Encryption (E2EE): Provides comprehensive protection for user data during transmission.
· Automated Defense Mechanisms: Combines real-time threat detection with automated smart contract defenses to effectively prevent malicious nodes from intruding and leaking data.
By seamlessly integrating its smart contract and security systems, PhyChain achieves a high degree of automation and security for its computing network, providing users with a reliable and transparent decentralized computing ecosystem.
