Every project within our platform possesses a knowledge base, consisting of various knowledge sets (KS). A Knowledge Set is a shareable entity designed to be utilized across multiple projects. Our platform features a dynamic knowledge marketplace, offering a wide range of KSs across diverse fields such as medical, chemical, blockchain, and more. These KSs are accessible to numerous users and projects, open to reviews and feedback.
Users on our platform have the flexibility to purchase, sell, or use KSs within the marketplace. In addition to this, we have implemented a reward system to recognize and incentivize the creation of outstanding KSs.
Data Validators: These are parties or individuals responsible for data validation work. They verify the truth of data, ensuring its authenticity, legality, and overall reliability within the platform.
KS Providers: These are individuals who supply valuable data to the platform. They play a crucial role in enriching the knowledge base and contributing to the diversity of available knowledge sets.
Simple Logic (Deprecated V1)
In our platform, Validators have the authority to define the specific token reward allocation for each training session, denoted as the "REWARD_UNIT."
Users engage in training activities by inputting various forms of data, including website links, PDF documents, and textual content. Upon completion of training, the reward is computed based on the formula: REWARD_UNIT multiplied by the total count of data items successfully trained.
Users can conveniently view their earned rewards displayed on their profile pages. A seamless withdrawal system has been implemented, allowing users to initiate transactions at their discretion.
[Additional details regarding withdrawal periods, minimum and maximum withdrawal amounts, as well as specific withdrawal logic can be incorporated here as per the platform's operational guidelines.]
Structured Reward Mechanism
The structured reward mechanism ensures a fair and transparent process, empowering both trainers and administrators within our training ecosystem. Users are rewarded based on the formula:
Reward Amount = User Trained Data Count × Reward Unit
Applying Reward Points by Importance or Value of Training Data
To further enhance the incentive structure, we propose implementing a system where reward points are assigned based on the importance or value of the training data. These points can be categorized according to the type or kind of data, and this information can be shared with users publicly.
This categorization allows users to understand the significance of their contributions and encourages the creation and utilization of high-value training data sets.
This incentive mechanism aims to foster a thriving knowledge-sharing community, where contributors are duly rewarded for their efforts and the platform's overall knowledge base continues to expand and evolve.