In the Web3 data ecosystem, there are two 'bottleneck' issues that seem small yet slow down the entire pace: first, the 'contribution in the scene cannot be clearly calculated'; a certain piece of carbon data helps an institution reduce risk, and a certain piece of DeFi data improves trading efficiency, but how much it contributes and how much profit should be shared is all based on 'guesswork', ultimately resulting in a 'confused account'; second, capability adaptation often faces 'sudden interruptions'; when scene demands switch from single-chain to multi-chain and compliance policies introduce new clauses, developers are unprepared, and users cannot keep up, causing adaptations to abruptly 'slam on the brakes', requiring several days to readjust. Chainbase does not engage in complex concepts but focuses on 'clarifying contributions' and 'preparing plans', using two core modules to clarify the 'confused accounts' and transform 'sudden stops' into 'smooth transitions', enabling the Web3 data ecosystem to run more steadily and quickly.

One, data value attribution compass: turning 'confused contributions' into 'transparent accounts'

In the traditional data ecosystem, 'data contributions' are a messy account—institutions claim data is worth only 10% returns, while data providers feel entitled to 30%, yet there's no evidence; developers claim processing doubles data value, but scene providers don't agree and have no basis. The problem lies in 'the inability to quantify the specific roles of data in scenes': does it reduce risk by 5% or 15%? Does it improve efficiency by 10% or 20%? Lacking these key pieces of information, profit-sharing can only be 'negotiated', and if negotiations fail, they just drag on. Chainbase's 'data value attribution compass' focuses on 'multi-dimensional quantification of contributions and on-chain evidence of attribution', ensuring that every contribution is backed by data.

The core operational logic of the compass consists of three steps:

1. Attribution dimension breakdown: First, break down the data's contribution in the scene into 'quantifiable dimensions'—for instance, in a DeFi scenario, dividing it into 'risk reduction coefficient' (the proportion of bad debts reduced by the data) and 'funding efficiency improvement proportion' (the proportion of speed increase in fund turnover due to the data); in a carbon scenario, dividing it into 'carbon pricing accuracy' (the extent to which the data reduces carbon price error) and 'compliance approval rate improvement' (the proportion of institutions passing compliance audits due to the data). Each dimension corresponds to specific indicators, avoiding 'vague descriptions'.

2. Real-time data collection and calculation: The compass collects scene data in real-time through on-chain nodes, such as changes in the bad debt rate of DeFi protocols and the pricing error values of carbon transactions, and then uses algorithms to calculate the 'contribution proportion' of the data—assuming that after a certain data is integrated, the protocol's bad debt rate drops from 8% to 5%, a total risk reduction of 3 percentage points, where the data contributes 2 percentage points, then the 'risk reduction coefficient' is 67%; if the corresponding profit-sharing is 100,000 yuan, the data provider can receive 67% of that, amounting to 67,000 yuan.

3. Attribution results on-chain evidence: All collected raw data, calculation processes, and contribution proportions will be recorded on-chain in real-time to form 'attribution reports', which data providers, developers, and scene providers can all access. For example, if the contribution proportion is 67%, it can be clearly seen that it is calculated from a '2 percentage point reduction in bad debt rate', not 'determined arbitrarily'; if subsequent scene revenues increase, the attribution results will also be updated accordingly, and the profit-sharing ratio will adjust without needing to renegotiate.

This set of compasses has completely changed the situation of 'confused contributions': data providers no longer need to 'guess their returns'; they can clearly prove their value with attribution reports; scene providers no longer need to 'worry about data providers asking for sky-high prices'; quantified results serve as the basis for profit sharing; developers' processing contributions can also be quantified (for example, the contribution ratio of processed data rises from 40% to 60%), avoiding 'working for nothing'. The clarity of data value attribution has improved from the original 30% to over 95%, and profit-sharing disputes have decreased by 80%.

Two, capability adaptation plan library: transforming 'sudden stops in adaptations' into 'smooth transitions'

Capability adaptations in the ecosystem often resemble 'a car without brakes suddenly encountering an obstacle'—a scenario that originally used single-chain data suddenly needs to switch to multi-chain, and the developer's tools are unprepared, forcing them to stop adaptation and modify code; compliance policies add cross-border clauses, requiring changes to the user data authorization process, leading to halting integration and adjustments. This kind of 'sudden stop' not only slows down the scene landing but also wastes previous adaptation costs. Chainbase's 'capability adaptation plan library' focuses on 'preparing plans in advance and quickly invoking changes', allowing adaptations to proceed without interruptions and facilitating smooth transitions.

The core design of the plan library consists of two parts:

1. High-frequency change scenario plan reserves: For frequently changing demands in the ecosystem (such as chain type switching, compliance policy updates, and scenario function upgrades), develop 'standardized plans' in advance—

◦ Chain switching plans: Reserve multi-chain data parsing modules and cross-chain format conversion plugins, for example, switching from Ethereum to BSC, directly invoking the BSC parsing module without needing to redevelop;

◦ Compliance update plans: Reserve compliance modules by region (such as EU MiCA, US CCPA, UK UK ETS); when policies change, directly load the corresponding module without requiring users to modify the authorization process;

◦ Function upgrade plans: For example, when a scene upgrades from 'data querying' to 'data staking', reserve staking evaluation modules and risk control plugins, allowing direct integration with existing data without needing to start from scratch.

2. Dynamic plan matching and invocation: When the scene requirements change, the algorithm in the plan library will automatically identify the 'type of change' (such as chain switching, compliance updates), match the corresponding plan, and complete the invocation within 10 minutes— for example, if an organization suddenly needs to access Solana chain data, the algorithm matches the 'Solana chain parsing plan'. After developers load the plugin in the developer tools, they can support Solana data processing within 1 hour, without stopping the adaptation for 3 days; if compliance policies add a Japan FSA clause, the 'Japan compliance module plan' will be matched, and the user data authorization process will automatically adapt without needing to reconfirm.

More importantly, the plan library will 'continue to update': upon monitoring new changing trends (such as an increase in the usage of a certain type of public chain or the introduction of new compliance policies), corresponding plans will be developed in advance—for example, predicting that a certain scenario will use Aptos chain data, the Aptos parsing plan will be prepared in advance, allowing for direct invocation when the scene actually switches. This kind of 'prepared adaptation' has reduced the adaptation interruption rate from the original 60% to below 10%, shortening the scene landing cycle by 90%, without needing to slam on the brakes for 'sudden changes'.

Three, ecosystem support: Technology and incentives enable 'clear calculations and smooth transitions' to take place

Two core modules must operate long-term, relying on technical support and incentive pull:

• Technical assurance: Utilizing a 'real-time response architecture', plan invocation and attribution calculations can be completed within 10 minutes, maintaining adaptation pace; supporting mainstream public chains and compliance systems, enabling access without changing ecosystems; using 'distributed nodes' to store attribution data and plans, avoiding service interruptions due to single points of failure while ensuring data and plans are immutable.

• Incentive mechanism: 70% of the native token is used for 'attribution incentives' and 'plan subsidies'—data providers who contribute high-quality attribution data (such as helping to accurately calculate contribution ratios) and developers who develop high-frequency calling plans (such as chain switching plans called over 100 times a month) can receive token rewards; institutions avoid abrupt stops in adaptations using the plan library and are subsidized based on implementation efficiency; 15% is injected into the 'technical fund' for optimizing attribution algorithms and updating the plan library; only 15% is allocated to the team, locked for 4 years, avoiding short-term cashing out.

Summary: The core of Chainbase is to make the Web3 ecosystem 'clear and smooth'

Chainbase does not engage in complex innovative concepts but focuses on two practical issues in the Web3 data ecosystem: using the 'data value attribution compass' to clarify the 'confused contributions' to ensure fair profit sharing; using the 'capability adaptation plan library' to transform 'sudden stops in adaptations' into 'smooth transitions' for efficiency improvements.

For data providers, contributions can be quantified, and returns are guaranteed, eliminating the need to 'suffer losses'; for developers, changes have plans, no need to repeatedly modify code, no more 'wasted efforts'; for institutions, profit sharing has no disputes, adaptations run smoothly, no more 'time wasted'. This positioning of 'solving practical troubles' has made Chainbase the 'stabilizer' of the Web3 data ecosystem—when contributions are clear and adaptations are smooth, the ecosystem can truly shift from 'chaotic and stuttering' to 'stable and efficient', and better meet the needs of the digital economy and the real economy.@Chainbase Official #Chainbase