๐ฆ 4. Other Independent Systems
Last updated
Last updated
DID
LMTLS' Decentralized Identity (DID) adheres to the W3C DID standard, with the registry designed to align with the Ethereum ERC1056 Specification. This approach merges on-chain and off-chain processes to register and manage identity information, reducing the strain on the blockchain network while enhancing its dependability.
LMTLS ecosystem members are categorized into three groups: Alliance, Influencer, and General User. Alliance and Influencer, who require a strong identity and reputation, have their information recorded and transparently disclosed to LMTLS General Users. On the other hand, General Users have minimal identity information recorded on-chain, with personal information kept off-chain to comply with laws regarding anonymity and personal data protection. This approach is necessary due to the inefficiency and difficulty of recording all user information on-chain in a service with millions of General Users like LMTLS, given the nature of blockchain technology.
The LMTLS Decentralized Identity (DID) System offers user-friendly experience to participants, even without any blockchain knowledge. With the Server Wallet, it provides an easy-to-use interface that enables the registration and management of ID and personal information, similar to general web services, without requiring any additional knowledge of DID. However, the LMTLS DID System allows only the server wallet's signature to manage one's identity and credentials, preserving the self-sovereign identity model, a key difference from traditional digital identity solutions.
The Big Data AI Analysis System
The Big Data AI Analysis System is designed to analyze the blockchain and extract information related to LXP earned by participants through A2E activities on the LMTLS platform. This system collects, processes, models, and interprets community participant data while ensuring anonymity, to provide digital target marketing services.
The system involves the following main processes:
Data Collection: Collects blockchain blocks containing LXP earned by participants and historical information about using them.
Data Processing: Refines the collected data, converts it into the required form, and makes it suitable for analysis.
Modeling: Uses AI and machine learning algorithms to generate various analytical models such as prediction, classification, and clustering.
Consequence Interpretation: Interprets the results to uncover insights into behavior patterns, preferences, and tendencies of participants.
Results Utilization: The analysis results are utilized to develop digital marketing services and recommend customized recommendations to community participants.
Payment System
The payment system offered by the LMTLS platform supports conventional payment methods such as credit card and mobile payments, as well as payments made using LIX.
Users can utilize LXP received as rewards, which are convertible to LIX, as a payment option for various services provided by the LMTLS platform. These LXP payments are settled in the liquidity pool.
