The long-term Web3 data ecosystem has been constrained by two 'disconnection issues': the value of data assets is limited to 'direct usage returns', completely disconnected from their supporting derivative value (such as financial products developed based on data, scenario value-added services), where contributors are unable to share derivative dividends; the collaboration of ecological capabilities is 'one-time connection', requiring re-confirmation of data authorization and tool adaptation logic for each collaboration, with historical collaboration experiences not reusable, resulting in decreased efficiency as demand frequency increases. Chainbase breaks out of the mindset of 'single value' and 'single collaboration' through two innovative modules, allowing data value to 'follow the derivative link', enabling capability collaboration to 'remember historical experiences', reconstructing the value distribution and collaboration logic of the Web3 data ecosystem.
1. Data value derivative linkers: Allowing data to 'take a share of derivative value'.
The value logic of traditional data assets is 'use once, earn once'—the user's carbon data is used by institutions to develop carbon trading funds, with the management fees and revenue sharing all going to the institution, and the user only receiving the initial data authorization fee; DeFi platforms use user credit data to design credit products, where interest income is unrelated to data contributors. This disconnection of 'data creating derivative value, others enjoying the revenue' leads to a lack of long-term contribution motivation for data parties. Chainbase's 'data value derivative linkers' core is 'tracking derivative links and profit-sharing by contribution', allowing data value to cover the entire chain of 'direct usage + derivative value-added'.
The core operational logic of the linkers is divided into three steps:
1. On-chain tracking of derivative links: Each time data enters a new scenario, a connector automatically generates a 'derivative tracking label' to record the use of data (e.g., 'for carbon fund development', 'support DeFi credit model'), the type of derivative products (e.g., funds, credit, NFT pledge tools), and sources of income (e.g., management fees, interest, transaction fees). All information is recorded in real-time on-chain through smart contracts, forming an immutable 'derivative value link map', allowing data parties to check the downstream derivative paths of the data at any time.
2. Derivative contribution weight calculation: The linker has a built-in 'contribution evaluation model' that calculates weight based on the role of data in derivative products—if data is the core support of a derivative product (e.g., carbon fund pricing completely relies on carbon data), the contribution weight is set at 30%-50%; if it only plays a supporting role (e.g., supplementary credit reference for credit products), the weight is set at 5%-15%. Weight calculation relies on quantifiable indicators such as revenue structure and risk reduction extent of derivative products, avoiding subjective judgment.
3. Smart profit-sharing for derivative income: After the derivative product generates income, the smart contract automatically splits the income according to 'contribution weight' and transfers it to the data party's address. For example, if a carbon fund has an annual income of 1 million yuan and the data contribution weight is 40%, the data party can receive 400,000 yuan in profit-sharing each year; for a DeFi credit product with monthly interest income of 500,000 yuan and a data weight of 10%, the data party can receive 50,000 yuan in monthly shares. Profit-sharing does not require manual application, is executed immediately after income arrives, and the profit-sharing records are bound to the derivative link, traceable and verifiable.
This set of linkers completely breaks the disconnection between 'data value and derivative value': data is no longer 'one-time sold raw materials', but a 'long-term value partner' that can continuously share derivative dividends; institutions are also more willing to use high-quality data to develop derivative products due to transparent profit-sharing rules, forming a positive cycle of 'higher data quality → more derivative income → richer profit-sharing for data parties'.
2. Capability collaboration memory hub: Allowing capability collaboration to 'remember experiences and avoid detours'.
The pain point of traditional ecological capability collaboration is 'each collaboration is a new start'—the 'data authorization scope' and 'processing format standards' agreed upon by users and developers last time need to be re-communicated for the next collaboration; the 'profit-sharing ratio' and 'data usage monitoring rules' previously confirmed by institutions and data parties need to be renegotiated during the next connection. This 'memoryless' collaboration leads to decreased efficiency as demand frequency increases. Chainbase's 'capability collaboration memory hub' is centered on 'sinking historical collaboration experiences and automatically reusing adaptations', shifting collaboration from 'restarting each time' to 'continuous optimization'.
The core design of the hub is divided into two parts:
1. Collaborative memory tag system: After each collaboration is completed, the hub automatically tags the participants (users, developers, institutions) with a 'collaborative memory tag'—the tag includes key information such as 'data authorization scope (e.g., only open energy consumption fields), tool adaptation parameters (e.g., carbon data accounting standards), profit-sharing ratio, delivery time node', etc., and the tag is strongly bound to the participant's address and data asset ID, remaining valid even after the collaboration ends. For example, if user A collaborates with developer B on carbon data processing, the tag will record 'user A authorizes developer B for 3 months, processing standard as EU ETS'.
2. Dynamic reuse and optimization mechanism: When participants initiate similar collaborations again, the hub will automatically retrieve historical 'collaborative memory tags' to generate a 'reuse plan'—if the current demand has a similarity of ≥80% with historical demand (e.g., same carbon data processing, same EU scenario adaptation), the historical parameters can be reused with one click without re-communication; if the similarity is 50%-80% (e.g., processing standards change to UK ETS), the hub will automatically adjust the differential items (e.g., replacing compliance modules) while retaining the same parameters (e.g., authorization duration), reducing communication costs by 70%.
More critically, the hub will 'continuously optimize memory'—after each reuse, if the participants adjust parameters (e.g., changing the profit-sharing ratio from 20% to 25%), the hub will update the memory tag, prioritizing the latest parameters for the next collaboration. For instance, when institution C collaborates with user D for the first time, the profit-sharing ratio is 20%, and for the second time, it is adjusted to 25%, subsequent collaborations will automatically use the 25% ratio without repeated confirmation. This 'memory-based collaboration' improves the efficiency of repeated collaborations by over 80%, avoiding the internal friction of 'starting from scratch each time'.
3. Ecological support: Technical and incentive guarantees for linkage and memory realization.
The stable operation of the two innovative modules requires support from technical compatibility and incentive sustainability:
• Technical support: Adopt a 'multi-chain native tracking architecture' that supports derivative link tracking of mainstream public chains such as Ethereum, BSC, and Avalanche, synchronizing data without the need for cross-chain bridges; built-in 'compliance derivative verification' ensures that the data's derivative use complies with regional policies (such as prohibiting use in unauthorized high-risk financial products); stores collaborative tags through 'distributed memory nodes' to avoid memory loss due to single point failures, while ensuring that tags are tamper-proof.
• Incentive mechanism: 68% of the native tokens are used for 'derivative linkage incentives' and 'memory collaboration subsidies'—users participating in derivative products and receiving profit-sharing, as well as developers who frequently reuse memory tags (e.g., tags reused more than 100 times), can receive token rewards; institutions completing collaborations through the memory hub will be subsidized for connection costs based on efficiency (e.g., 1 hour to complete reuse vs. 24 hours); 17% is injected into the 'technology iteration fund' to optimize the derivative contribution model and memory tag system; only 15% is allocated to the team, locked for 4 years to avoid short-term cashing affecting ecological stability.
Summary: Chainbase's innovation is to make the Web3 data ecosystem 'profitable and efficient'.
The core innovation of Chainbase lies in solving the 'disconnection issue' neglected in the Web3 data ecosystem: using 'data value derivative linkers' to allow data parties to share derivative value, breaking the dilemma of 'contribution without long-term returns'; using 'capability collaboration memory hub' to allow the reuse of historical experiences in collaboration, breaking the inefficiency of 'every time being a new start'.
For users, data can continuously earn derivative profit-sharing without having to 'leave after selling data'; for developers, memory tags reduce repeated communication without needing to 're-confirm requirements each time'; for institutions, derivative links are transparent, and collaboration efficiency is high, without worrying about data parties not recognizing profit-sharing or fearing repeated adjustments in collaboration. This positioning of 'solving disconnection issues' makes Chainbase the 'value extension and efficiency enhancement infrastructure' of the Web3 data ecosystem—when data can share derivative value, and collaboration can remember experiences, the ecosystem can truly transition from 'short-term circulation' to 'long-term value-added', better connecting the derivative needs of the digital economy and the real economy.