In the Web3 data ecosystem, there exists a long-term "dual value disconnect" between data assets and ecological collaboration: the value of data assets is only anchored to their circulation revenue, completely disconnected from the derived value they support (such as returns from financial products developed based on data, scenario value-added returns), and data contributors cannot share the derived value; the capability adaptation of ecological roles only stays at the "surface functional connection," developer tools only adapt to data formats but do not embed core scenario logic, and users only authorize data but cannot participate in derived value decision-making, making it difficult to form deep collaboration. The core innovation of Chainbase lies in constructing a "value association tracing system + deep capability adaptation network," tightly binding data assets with derived value, upgrading ecological capabilities from surface connection to deep collaboration, and redefining the value boundaries and ecological collaboration depth of Web3 data assets.
I. Value Association Tracing System: From "Derived Disconnect" to "Strong Binding," Enabling Data Sharing of Full-Link Value
The value bottleneck of data assets lies in "unable to reach derived value links." In traditional models, the revenue of data assets comes only from direct invocation (e.g., one-time query payment), while the derived value they support—such as the revenue from DeFi credit products developed based on user credit data and the value-added of carbon financial tools designed based on carbon data—entirely belongs to the scenario party or platform, and data contributors are completely unable to participate in the distribution, leading to the imbalance of "data creates value, others enjoy profits." Chainbase constructs an association mechanism between data assets and derived value through "derived value chain tracing + dynamic profit-sharing protocols," allowing data contributors to share the full-link value.
Its technical core is the deep coupling of the "derived value map" and "association profit-sharing rules": the "derived value map" tracks the full-link application of data assets through smart contracts—recording what derived products data has been used to develop (such as DeFi credit, carbon futures tools), the sources of revenue from derived products (such as interest, transaction fees), and the contribution weight of data within them (such as the impact of credit data on credit risk control accounting for 40%), all information is recorded on-chain in real-time, forming an immutable "value association chain"; the "dynamic profit-sharing protocol" proportionally allocates derived value to data contributors based on contribution weights—e.g., if a certain DeFi credit product has a monthly revenue of $100,000 and the data contribution weight is 35%, the data provider can receive $35,000 in profit-sharing, and the profit-sharing synchronizes in real-time with the derived product revenue without the need for manual accounting.
This association tracing capability expands the value of data assets from "single circulation revenue" to "full-link derived value": in the green finance sector, carbon data can not only obtain direct invocation revenue but also share the value-added revenue of carbon trading funds developed based on it; in the DeFi sector, user credit data can participate in interest sharing of credit products. Unlike the "static profit-sharing" model, the core of value association tracing is "dynamic tracking of derived value and real-time profit-sharing based on contribution," resulting in a 2-3 times increase in comprehensive revenue for data assets and significantly enhancing contributor enthusiasm.
II. Deep Capability Adaptation Network: From "Surface Connection" to "Core Embedding," Enabling Deeper Ecological Capability Collaboration
The efficiency pain point of ecological collaboration lies in "capability adaptation not reaching core links." In traditional collaboration, developers' tools only complete surface adaptations such as "data format conversion," without embedding core logic of scenarios (such as risk control models for DeFi credit, core algorithms for carbon accounting); users only complete basic operations such as "data authorization," unable to participate in deep links such as derived value distribution decision-making and data usage supervision, leading collaboration to remain at "functional connection" rather than "value co-creation." Chainbase builds a "deep capability adaptation network" that embeds "core logic into interfaces + role rights and responsibilities extension agreements," allowing capability adaptation to penetrate core scenarios and value decision-making links.
The core design of the network is "opening of core scenario modules" and "expansion of role rights and responsibilities": "opening of core scenario modules" allows developers to directly embed tools into core scenario logic—such as DeFi protocols opening "risk control model interfaces," where developers' credit data processing tools can directly connect to the model, and the output risk score directly impacts credit limit determinations, rather than merely providing data raw materials; carbon trading platforms open "accounting algorithm interfaces," where developers' carbon data processing tools can participate in the accounting process, optimizing the precision of carbon asset pricing. "Expansion of role rights and responsibilities" grants users more deep-level permissions—users can participate in adjusting derived value profit-sharing ratios through on-chain voting, can view real-time records of data usage in derived products, and if data is found to be used beyond authorized scenarios, they can trigger contract freezing of data usage rights.
This deep adaptation upgrades ecological collaboration from "functional assistance" to "value co-creation": developers' tools are no longer "peripheral assistance tools" but key components of core scenario operations; users are no longer "passive data providers" but participants in value decision-making. For example, a certain developer's credit data tool, after being embedded in the DeFi risk control model, reduces the credit bad debt rate by 28%, and the tool developer, in addition to the basic invocation fee, can also receive a 15% share of the derived credit revenue; users can increase their own revenue by voting to raise the derived profit-sharing ratio from 30% to 35%. Unlike "surface adaptation," the core of deep adaptation is "capability embedded in core, rights and responsibilities extended to decision-making," improving the value creation efficiency of ecological collaboration by more than three times.
III. Association-Adaptation Dual Co-Creation Cycle: From "One-Way Value" to "Two-Way Gains," Allowing Ecological Continuous Value Increase
The long-term vitality of the ecology lies in the "mutual promotion of value association and deep adaptation." Chainbase constructs the "association-adaptation dual co-creation cycle mechanism," where the derived revenue brought by value association tracing attracts more roles to participate in deep adaptation; deep adaptation enhances the value of derived products and the accuracy of data contributions, which in turn feeds back into the profit-sharing scale of value association, forming a positive cycle of "the more association profit-sharing there is → the more active deep adaptation → the higher the derived value → the richer the association profit-sharing."
The core of the mechanism is "derived contribution rewards" and "deep adaptation bonuses": if data assets contribute a high weight in derived value (e.g., ≥40%), data providers can receive an additional reward of 20%-30% of derived revenue; if developers' tools are deeply embedded in core scenario modules (e.g., directly impacting risk control and pricing), the tool invocation fees and derived profit-sharing ratios can increase by 15%-25%; if users actively participate in derived value decision-making (e.g., voting, supervision), they can earn "decision contribution points," which can be exchanged for additional profit-sharing rights. For example, if a certain carbon data contributes a weight of 45% in the carbon fund, the data provider receives a 25% additional reward; if the developer's carbon accounting tool is embedded in the pricing core module, the profit-sharing ratio increases by 20%.
Native tokens support the cycle: 78% of tokens are used for "associated profit-sharing subsidies" and "deep adaptation incentives," specifically rewarding data assets with high contribution weight, core-embedded tools, and proactive decision-making users; 18% of data invocation fees are injected into the "dual co-creation fund" for the research and development of derived value tracing technology and support for the opening of core scenario modules. Under this mechanism, the motivation of ecological roles shifts from "short-term functional connection" to "long-term value co-creation," synchronously expanding the value boundaries of data assets and the depth of ecological collaboration.
Summary and Prediction: From "Surface Disconnect" to "Deep Co-Creation," Leading the Value Upgrade of the Data Ecosystem
The core breakthrough of Chainbase lies in using "value association tracing" to solve the imbalance pain point of "derived value disconnect" of data assets, breaking the efficiency bottleneck of ecological "surface collaboration" with "deep capability adaptation," ultimately achieving ecological value co-creation through a dual co-creation cycle. The key innovation of this model is upgrading the logic of the data ecosystem from "single link value creation" to "full-link value association and deep-level capability co-creation," which not only expands the value boundaries of data assets but also enhances the depth and stickiness of ecological collaboration.
In the future, Chainbase is expected to promote industry transformation in three aspects: first, AI deepening derived value tracing, using AI models to analyze the implicit contributions of data in derived products in real-time (such as the impact of user growth driven indirectly by data on derived revenue), making association tracing more precise; second, deep adaptation expansion across industries, extending the network to the real economy (such as deep embedding of industrial data into intelligent manufacturing production optimization modules, sharing derived revenue from production efficiency improvements); third, outputting industry associations and adaptation standards, its derived value tracing protocols and core module opening specifications may become universal standards, driving the industry from "surface disconnect" to "deep co-creation."
It is foreseeable that Chainbase's "value association tracing + deep capability adaptation" logic will drive Web3 data assets into a new stage of "full-link value sharing and deep collaborative co-creation," allowing data assets to truly become a value co-creation carrier connecting the digital ecosystem and the real industry, and also letting the Web3 ecosystem shift from "functional collaboration" to "deep value co-creation."
