Currently, the application of Web3 on-chain data has long been limited to the 'self-circulation of the crypto asset ecosystem'—data predominantly revolves around token trading, NFT circulation, DeFi staking, etc., with almost no connection to the real economy (supply chain, consumption, public welfare, agriculture, etc.). This situation of 'self-circulation in the crypto circle' has led to two core contradictions: on the one hand, the real economy faces pain points of 'data opacity, difficult traceability, and low collaborative efficiency' (such as the difficulty in identifying the authenticity of supply chain goods, the lack of interoperability of consumption points across brands, and the unclear destination of public welfare donation funds), yet it is challenging to leverage the advantages of 'immutability and traceability' offered by Web3 on-chain data; on the other hand, Web3 on-chain data lacks support from real economic scenarios, limiting its value boundaries and making it difficult to achieve breakthroughs 'from virtual to reality.' Traditional on-chain tools only focus on the crypto ecosystem and lack the capability for 'on-chain data to integrate with real economic systems,' becoming the core barrier to collaboration between the two. Bubblemaps' core breakthrough lies in constructing a collaborative system of 'on-chain data standardization - real system API integration - scenario-based value transformation,' allowing on-chain data to leave the crypto circle and deeply integrate into high-frequency scenarios of the real economy, solving pain points in the real economy while expanding the value boundaries of Web3 data, achieving 'Web3 empowering reality, and reality feeding back Web3' bidirectional win-win.
I. The Three Core Pain Points of On-chain Data and Real Economy Collaboration
The division between on-chain data and the real economy fundamentally stems from a triple misalignment of 'technical logic, data standards, and value demands,' making effective collaboration difficult. The specific manifestations of this challenge are three major pain points:
(1) Technical Logic Misalignment: On-chain Data and Real System 'Interface Incompatibility'
Web3 on-chain data is stored based on a decentralized architecture of blockchain (such as blocks, hashes, smart contracts), while real economic systems (e.g., enterprise ERP, supply chain management systems, public welfare donation platforms) often adopt centralized database architectures. The technical logic and data formats of the two have essential differences: on-chain data's 'immutability' relies on distributed node verification, while real systems' 'efficient querying' relies on centralized indexing; on-chain data exists in 'hash values and transaction records' form, while real systems store data in 'structured forms and order numbers.' This technical misalignment leads to 'on-chain data not being able to directly integrate into real systems'—for example, on-chain traceability data of products in the supply chain (such as production location, logistics nodes, quality inspection records) cannot be directly imported into the enterprise's ERP system for production scheduling; on-chain transfer records of public welfare donations cannot be synchronized to the donor inquiry interface of the charity platform, making it difficult for real systems to leverage the advantages of on-chain data.
(2) Data Standard Misalignment: On-chain Data and Real Needs 'Definitions Differ'
The standards of Web3 on-chain data mostly revolve around 'crypto asset interactions' (such as the 'amount, address, block time' of token transfers), while the definitions, dimensions, and precision requirements of data in real economic scenarios are completely different: supply chain scenarios require 'product SKU, production batch, logistics order number, quality inspection report number' and other real attribute data, while on-chain lacks corresponding standardized fields; consumption points scenarios require 'points validity period, redemption ratio, cross-brand rules,' and the generalized 'token balance' field on-chain cannot cover this; public welfare scenarios require 'donation project numbers, beneficiary information, fund usage details,' and on-chain can only record 'transfer amount and address,' unable to associate with real information. This 'data standard misalignment' means that even when on-chain data integrates into real systems, it cannot function due to 'information mismatch'—for example, enterprises cannot judge the compliance of a certain batch of products through on-chain 'token transfer records,' and donors cannot confirm through 'on-chain transfer hashes' whether funds are used for designated public welfare projects.
(3) Value Demand Misalignment: On-chain Data and Real Value 'Goals Do Not Match'
The core value of Web3 on-chain data focuses on 'trustworthy interactions of crypto assets' (such as tamper-proof and anti-double-spending), while the core needs of the real economy are 'efficiency improvement, cost reduction, and trust enhancement': enterprises hope to use on-chain data to lower supply chain traceability costs (e.g., reducing manual verification stages), rather than focusing on 'whether the block hash is correct'; consumers hope to confirm the authenticity of products through on-chain data (e.g., scanning to view traceability records), rather than learning 'how to query transactions in a block explorer'; donors hope to track the flow of funds through on-chain data (e.g., whether funds are used for poverty alleviation), rather than just seeing 'funds transferred to the charity organization address.' This 'value goal mismatch' leads to on-chain data being difficult to accept even when integrated into real scenarios due to 'poor user experience and low practical value,' making it hard to form long-term collaboration.
II. The Core Implementation of Collaborative Connector: From 'Technical Integration' to 'Value Realization'
Bubblemaps' collaborative system is not a simple 'technical grafting,' but solves the 'technical, standard, and value' misalignment through a three-layer architecture of 'data standardization layer, system integration layer, and scenario application layer,' achieving deep collaboration between on-chain data and the real economy.
(1) Data Standardization Layer: Building a 'Universal Data Dictionary for On-chain and Real'
The core of solving data standard misalignment is to establish a universal language for 'on-chain data and real needs.' Bubblemaps collaborates with enterprises in the real economy (supply chain, consumption, public welfare), Web3 technology teams, and industry associations to jointly formulate the 'on-chain-real-world collaborative data dictionary,' defining key data in core scenarios in a 'bidirectional definition'—clarifying how on-chain data maps to fields understandable by the real world and how real data can be standardized on-chain.
Taking three high-frequency scenarios as examples:
• Supply Chain Scenarios: Defining 'Product On-chain Traceability Data Models,' which include 'real fields' (product SKU, production batch, production factory code, quality inspection report number, logistics order number) and 'on-chain fields' (hash values of the corresponding data, on-chain node addresses, timestamps, modification records), ensuring that enterprise ERP systems can query on-chain traceability hashes through 'product SKU' and can also standardize the 'quality inspection report' to generate hash values on-chain;
• Consumption Points Scenarios: Establishing 'on-chain points data standards,' including 'real business fields' (points type, validity period, redemption ratio, applicable brands) and 'on-chain technical fields' (points token contract address, balance, lock-up status, transfer permissions), ensuring that when redeeming points across brands, the points data of brand A can interface with brand B's system through on-chain standard fields;
• Public Welfare Scenarios: Designing 'On-chain Public Welfare Donation Data Specifications,' which include 'real public welfare fields' (project number, beneficiary ID, fund usage categories, executing agency) and 'on-chain transaction fields' (donation amount, donor address, fund flow records, smart contract execution logs), ensuring that donors can associate on-chain fund records through 'project numbers' to track the flow of funds.
The data dictionary adopts a 'dynamic iteration mechanism' that updates through the process of 'industry proposal - technical review - pilot verification - formal inclusion,' ensuring that standards always match the needs of the real economy and the development of Web3 technology. It currently covers more than 20 sub-scenarios in four major fields: supply chain, consumption, public welfare, and agriculture, becoming a 'universal language' for the collaboration of on-chain and real-world data.
(2) System Integration Layer: API + Smart Contracts Achieve Bidirectional Interaction between 'On-chain and Real'
The key to solving the misalignment of technical logic is to establish a bidirectional interactive channel between 'on-chain data and real systems.' Bubblemaps develops the 'on-chain and real-world collaborative API gateway' and 'scenario-based smart contract templates' to achieve seamless integration between the two—supporting real systems to call on-chain data and allowing real data to be standardized on-chain while ensuring the security and efficiency of data interaction.
1. On-chain and Real Collaborative API Gateway: The 'Translator' for Real Systems to Call On-chain Data
The API gateway has three core capabilities: 'data format conversion, permission control, real-time inquiry':
• Format Conversion: Automatically converting the inquiry request from the real system (e.g., 'query the on-chain traceability data of the product with SKU ABC123') into a query instruction recognizable on-chain (e.g., 'query block data associated with hash value SKU=ABC123'), and converting the returned on-chain data such as hash, timestamp, etc., into 'production records and logistics information' that can be displayed by the real system;
• Permission Control: Based on the permission rules in the 'data dictionary,' controlling the access range of the real system to on-chain data—e.g., enterprise ERP systems can query 'the complete traceability data of their own produced goods,' while ordinary consumers can only query 'public traceability information of the product (production location, quality inspection results),' avoiding sensitive data leaks;
• Real-time Inquiry: Connecting to mainstream public chain node services (such as Ethereum Infura, Polygon Alchemy), reducing the query delay of on-chain data to 'seconds,' ensuring that the user experience of the real system (such as consumers scanning codes to check authenticity) is not affected.
For example, an ERP system of a supply chain company queries 'on-chain data for product SKU=XYZ789' through the API gateway, which automatically converts the request and calls on-chain nodes, returning 'the production factory code (F001), quality inspection report hash (0xabc...), logistics node records' for the product. The ERP system directly imports this data for compliance review without manual intervention.
2. Scenario-based Smart Contract Templates: 'Standardized Tools' for Real Data to Go On-chain
For different real economic scenarios, Bubblemaps provides 'out-of-the-box' smart contract templates that support rapid on-chain data entry according to 'data dictionary' standards, without requiring enterprises to develop contracts themselves:
• Supply Chain Traceability Contract Template: Enterprises only need to fill in 'Product SKU, Production Batch, Quality Inspection Report URL' and other real data, with the contract automatically generating corresponding data hash values on-chain and recording 'on-chain organization, timestamp', while supporting subsequent data addition from logistics nodes and sales nodes;
• Consumption Points Contract Template: Brand parties deploy points token contracts through templates, with built-in 'points validity period, redemption ratio' and other real business logic, supporting points issuance, redemption, and cross-brand transfer (subject to authorization from the other brand). Real systems can query points data in the contract through the API gateway.
• Public Welfare Donation Contract Template: After charity organizations deploy the contract, donation funds are directly transferred to the contract address (rather than the charity organization’s own account), and the contract automatically records 'donor, amount, project number,' controlling fund flow according to preset rules (such as 'funds can only be transferred to the designated address of the beneficiary organization'). The real public welfare platform can query the fund usage records within the contract through API.
Smart contract templates have 'low-code features,' allowing enterprises to deploy and call them through a visual interface without requiring specialized blockchain development capabilities, significantly lowering the technical threshold and costs for real data to go on-chain.
(3) Scenario Application Layer: On-chain Data Resolves Core Pain Points in the Real Economy
Technical integration and data standards are just the basics; the ultimate value must be realized through 'scenario-based applications'—Bubblemaps creates 'on-chain data-driven solutions' for high-frequency scenarios in the real economy, allowing on-chain data to genuinely address real pain points and create visible value.
1. Supply Chain Scenarios: On-chain Data Achieves 'Full Link Reliable Traceability'
In the real economy, the core pain point of supply chain traceability is 'data is easily tampered with, and verification of links is difficult'—product production records may be forged by companies, logistics node data may be tampered with, making it difficult for consumers to confirm the authenticity of products. Bubblemaps' solution achieves reliable traceability through 'complete on-chain data recording + real system collaboration':
• Data on-chain: Production enterprises use 'Supply Chain Traceability Contract Templates' to put the product's 'production records (raw material sources, production time), quality inspection reports' on-chain; logistics companies call the contract through the API gateway, adding 'logistics order number, transportation nodes, receipt records'; sales terminals record 'sales stores, sales time' through the contract, forming 'production - logistics - sales' complete on-chain data;
• Real Inquiry: Enterprise ERP systems query on-chain data via API gateways, verifying whether the goods provided by suppliers are compliant (e.g., whether they come from designated production factories); when consumers scan codes to inquire, offline inquiry tools (e.g., brand mini-programs) call on-chain data via API to display 'complete traceability information,' marking 'data has been on-chain, and is immutable,' solving the authenticity determination problem;
• Efficiency Improvement: After integration, a food company reduced the manual verification stage of supply chain traceability from '5' to '1' (only needing to verify on-chain data), with verification costs reduced by 60%; the proportion of consumers scanning codes to check authenticity increased from '15%' to '60%', significantly enhancing brand trust.
2. Consumption Scenarios: On-chain Points Realize 'Cross-brand Interoperability and Trustworthy Redemption'
In the real economy, the core pain point of consumption points is 'high brand barriers and multiple redemption restrictions'—points from brand A cannot be used at brand B, the rules for points expiration are opaque, and consumer points are highly idle. Bubblemaps' 'on-chain points solution' breaks brand barriers:
• Standardized On-chain: Multiple brands deploy points tokens through 'on-chain points contract templates,' unifying standards for 'validity period and value anchoring (e.g., 1 point = 0.1 yuan)' according to 'data dictionary';
• Cross-brand Interoperability: Brands authorize 'cross-brand redemption of points' through smart contracts, allowing consumers to transfer points earned from spending at brand A to brand B's points system (according to a unified redemption ratio). Real POS machines or online stores can query on-chain points balances through the API, supporting direct deductions for consumption;
• Transparent Management: Records of 'issuance, usage, and expiration' of points are all placed on-chain, allowing consumers to view points details and validity periods through the brand app by calling on-chain data, avoiding 'backroom dealings.' After a retail alliance integrated, the cross-brand points redemption rate increased by 50%, and consumer points idleness decreased by 40%, with the alliance's overall sales increasing by 15%.
3. Public Welfare Scenarios: On-chain Data Achieves 'Full Process Transparency of Donation Funds'
In the real economy, the core pain point of public welfare donations is 'unclear fund allocation and lack of transparency in execution'—donors find it difficult to confirm whether funds are used for designated projects, and the fund usage of charitable organizations lacks supervision, which can easily lead to a crisis of trust. Bubblemaps' 'on-chain public welfare solution' addresses the transparency challenge:
• Funds on-chain: Donors directly transfer funds through the 'Public Welfare Donation Contract,' and the funds enter the contract address (rather than the charity organization’s account), with the contract automatically recording 'donor, amount, project number';
• Process Control: Charity organizations need to submit 'fund usage applications' (including beneficiary, amount, purpose), which, after being approved through on-chain voting (with donor representatives participating), automatically transfers funds to the beneficiary organization’s address via smart contract, with fund flow records being updated on-chain in real time.
• Transparent Inquiry: Donors can call on-chain data through the public welfare platform to check whether their donated funds have been transferred to the designated project, whether the aided organization has received them, and the details of fund usage. They can also verify contract execution logs through a blockchain explorer.
• Trust Enhancement: After a charity organization integrated, the re-donation rate of donors increased from '20%' to '45%', and the complaint rate regarding fund usage dropped from '15%' to '0,' with fundraising efficiency of public welfare projects improving by 30%.
III. The Value of Collaborative Ecology: Bidirectional Win-Win between Web3 and the Real Economy
Bubblemaps' 'on-chain and real-world collaborative system' is not a one-way 'Web3 empowering reality,' but forms a 'bidirectional win-win' ecological value:
For the real economy, on-chain data brings 'efficiency improvement, cost reduction, and trust enhancement'—supply chain companies reduce the costs of traceability verification, consumption brands enhance user stickiness, and public welfare organizations boost donation trust. These are core demands of the real economy; at the same time, the real economy provides Web3 with 'massive real scenarios,' allowing on-chain data to no longer be limited to the crypto ecosystem, achieving value landing 'from virtual to reality.'
For the Web3 ecosystem, the integration of the real economy brings 'data volume growth, value boundary expansion, and user base enlargement'—massive on-chain data generated from supply chain, consumption, and public welfare scenarios enriches the Web3 data ecosystem; the real-world application of on-chain data allows Web3 to move from 'niche crypto circle' to 'mainstream real economy users.' For example, consumers do not need to hold crypto assets when scanning codes to check traceability, thereby indirectly engaging with Web3 technology and bringing incremental users to the Web3 ecosystem.
From a social perspective, the collaboration between on-chain data and the real economy promotes the development of 'trustworthy business, transparent public welfare, and safe consumption'—the reliable traceability of the supply chain reduces counterfeit goods, the interchange of consumption points enhances user benefits, and the transparency of public welfare increases social trust. These all align with the long-term needs of social development, allowing Web3 technology to truly serve social value.
Conclusion
The ultimate value of Web3 never lies in 'creating a virtual ecosystem independent of reality,' but in 'empowering reality with technology, allowing reality to feed back technology.' Bubblemaps' 'on-chain and real-world collaborative connector' captures this core logic, allowing on-chain data to escape the 'self-circulation' of the crypto circle and deeply integrate into the 'main battlefield' of the real economy. When on-chain data can solve the traceability pain points of the supply chain, the points redemption challenges of consumption, and the transparency issues of public welfare, and when the real economy can leverage Web3 technology to enhance efficiency and trust, Web3 can truly break through the label of 'niche technology' and become an important force in promoting socio-economic development. This is not a fictional future vision, but a necessity based on current pain points in the real economy and the practical implementation of Web3 technology, marking the inevitable shift of the Web3 industry from 'technology-driven' to 'value-driven'.@Bubblemaps.io
