5.2 AI data collection and cleaning

Data is the lifeline of AI, and high-quality and diversified data is the prerequisite for training large models. Cosmic Cipher's advanced hardware configuration makes it an ideal data acquisition term

5.2.1 Distributed data cleaning network

This system plans to build a dynamic quality verification network based on multi-party games, hoping to ensure data credibility through a dual mechanism of economic incentives and algorithm verification, and form a three-level coordinated governance framework:

• Design of economic incentive mechanism

We plan to adopt the "contribution-verification-arbitration" three-rights separation model to establish a gradient reward system:

1. Data acquisition layer: Incentive edge nodes to upload original data, and set geographic distribution diversity coefficients to avoid excessive centralization of data sources.

2. Quality verification layer: Verification nodes need to pledge no less than a number of MMI tokens to participate in data annotation, develop dynamic adjustment algorithms based on confusion matrix, and trigger token penalty for error annotation.

3. Dispute arbitration layer: build a dual channel for expert committees and AI arbitration, start the on-chain arbitration process for data units with a verification difference value of >30%, develop an arbitration cost prediction model, and implement a gradient deduction of the pledge deposit for the malicious initiator.

• Technical implementation path

In the concept of technical teams and technical consultants, we plan to deploy the federal cleaning framework in phases, with technical verification routes including:

1. Edge computing layer: Deploy lightweight cleaning modules in the IoT terminal, integrate wavelet packet denoising algorithm (use db4 wavelet basis) and improved LOF abnormality detection model, and design a dynamic threshold adjustment mechanism to deal with equipment performance differences.

2. Collaborative verification layer: Develop consensus protocol based on improved PBFT, establish a field-specific verification node pool in vertical fields such as medical care and finance, set up a cross-industry data verification weight matrix to ensure the consistency of multimodal data quality annotations

3. Credited evidence layer: build a three-dimensional traceability system for the cleaning process, use the Hyperledger Fabric architecture to record data blood ties (Data Provenance), design a visual audit interface to support cleaning path playback, and develop a TEE hardware-assisted privacy computing module.

5.2.2 Data Asset Circulation Agreement Development Planning

In response to the liquidity dilemma of the data trading market, we plan to build a standardized value exchange agreement, focusing on breaking through the technical bottlenecks of asset rights confirmation and fragmented trading:

• Asset standardization

It is planned to develop cross-chain compatible metadata specifications, with core features including:

1. Multi-dimensional property rights packaging: design a nested NFT architecture, the basic layer stores data fingerprint hash (using the SHA3-512 algorithm), the extension layer embeds a smart contract interactive interface, and supports secondary creation copyright segmentation (such as music data can be split into sub-copyrights such as melody, rhythm, and tone).

2. Compliance framework: Develop a dynamic license management system, integrate regional scale boards such as GDPR and CCPA, and set up geofencing and aging fencing mechanism for data use.

3. Value traceability system: build a contributor weight map algorithm, develop a profit distribution model based on Shapley values ​​for data sets containing 100+ contributors, and reserve 5% of the derivative value to capture equity.

• Liquidity enhancement plan

Focus on breaking through the technical barriers of large-scale data set transactions and innovative design:

1. Fragmented trading engine: Develop an adaptive sharding algorithm, intelligently segment the storage unit according to the data type (text data is clustered by topic, time series data is divided by feature band), and a shard correlation evaluation model is established to prevent value loss.

2. Hybrid market making mechanism: design an AMM improvement plan based on curve binding. While deploying liquidity pools on the ETH main network, connect to the Chainlink oracle to obtain the quotation on the traditional data exchange, and develop an arbitrage balance module to maintain the price difference within 5%.

3. Micro payment channel: Build a status channel network to support millisecond-level micro-transaction settlement, and develop a "data-as-a-service" subscription model to meet the long-tail needs of the scientific research field, and support combination purchases according to feature dimensions (such as face data can be purchased separately)