A method for integrating blockchain data storage with lens fusion

Abstract

This invention discloses a highly original and engineering-difficult method for integrating blockchain data storage and adaptive focusing smart liquid crystal lenses, belonging to the fields of intelligent optics, edge computing, and blockchain cross-domain integration. The method employs a four-layer collaborative architecture: PI-GRIN liquid crystal optics + polymer nanotransmission + edge AI heterogeneous computing + lightweight consortium blockchain. This architecture enables low-loss acquisition of multimodal sensing data, edge hash preprocessing, encrypted state encryption, low-latency on-chain uploading, trusted traceability, and closed-loop feedback. Under constraints of steady-state power consumption ≤180mW and storage latency ≤100ms, it maintains top-tier optical indicators such as lens transmittance ≥85%, response ≤150ms, focusing -10D to +5D, and refraction ±0.125D. This invention is the world's first to solve the dilemma of the incompatibility between security, performance, and power consumption in wearable optical devices, achieving a trusted closed loop of sensing, computing, focusing, and storage. It is applicable to scenarios such as medical optometry, AR / VR, industrial vision, and cultural tourism guidance, possessing extremely high technical barriers and commercial value.

Description

Technical Field

[0001] This invention belongs to the cross-technical fields of intelligent liquid crystal optical devices, lightweight consortium blockchains, edge AI heterogeneous computing, and wearable low-power systems. Specifically, it relates to a method for achieving deep integration of polarization-independent gradient refractive index liquid crystal (PI-GRIN LC) adaptive dynamic focusing lenses with blockchain data storage, privacy computing, and edge-cloud collaboration. This invention breaks through the industry bottleneck of the incompatibility between computing power, power consumption, real-time performance, and security in wearable optical devices, covering five high-value scenarios: intelligent optometry, medical optometry, industrial visual inspection, AR / VR near-eye displays, and trusted data traceability. It solves the technical pain points of the disconnect between optical control and data storage architecture in intelligent lenses, the lack of trusted data closed loops, and insufficient compliance. Background Technology

[0002] Currently, intelligent focusing LCD lenses, AR / VR optical modules, automated optometry equipment, and industrial vision access points generally suffer from five major technological deficiencies: The lack of reliable data: Optometry parameters, focusing logs, and visual inspection data are easily tampered with and forged in their centralized storage. There is no ability to verify and trace the data throughout its entire lifecycle, which does not meet the compliance requirements of the medical and industrial sectors. The architecture is deeply fragmented: the optical drive, AI computing, and data storage are deployed independently, which makes it impossible to form a reliable closed loop of perception-computation-focusing-storage-feedback, and the latency is uncontrollable. Wearable device limitations: power consumption <200mW, computing power <1TOPS, bandwidth <1Mbps for near-eye devices. Traditional blockchain nodes are large in size, have high power consumption, and high latency, making them unsuitable for edge deployment. Compromise in optical performance: Integrating sensors, communication, and evidence storage modules can lead to decreased light transmittance, slower response, and increased distortion, making it impossible to balance optical quality and data security. There is a lack of cross-domain integration: there is no mature solution globally that deeply integrates PI-GRIN liquid crystal optics, polymer nanotransmission, edge AI, lightweight consortium blockchain, and privacy computing, and there are no similar technical paths or product implementations. Existing technologies only achieve single focusing or single evidence storage, failing to solve the three major technical challenges of low-power real-time on-chain, optical-computing-evidence storage collaboration, and multi-scenario compliance adaptation. This invention is a world-first cross-domain fusion original technology. Summary of the Invention

[0003] Purpose of the invention To address the issues of insecure, untrustworthy, untraceable, fragmented systems, and the inability to balance optical performance and security capabilities in existing smart lenses, this paper proposes a highly original and engineering-challenged method for integrating blockchain notarization with smart liquid crystal lenses. Without sacrificing core optical indicators, this method achieves a closed-loop trusted data system on the edge, low-power real-time on-chain data upload, authorized privacy computing, and full lifecycle traceability, thus breaking through the security-performance-power consumption triangle constraint of wearable optical devices.

[0004] Technical solution A method for integrating blockchain data storage with lens fusion includes the following steps: Highly integrated optical sensing layer data acquisition The adaptive dynamic focusing PI-GRIN liquid crystal lens integrates an infrared ranging module, an ambient light sensor, an eye-tracking module, and a global shutter high-definition camera to collect distance, illumination, eye movement, and target data. The data is then transmitted to the edge AI unit with low loss (<0.3dB / cm) and latency (<5ms) via a polymer nano-waveguide and a submicron copper-based composite transmission channel. Edge AI agents and multimodal algorithm processing An edge AI chip-based cloud-coordinated multimodal large-model lightweight inference engine has been completed, achieving the following: Binocular independent resolution: refraction accuracy ±0.125D, astigmatism axis ±1°, pupillary distance ±0.5mm; Dynamic focusing: calculation cycle < 10ms, closed-loop control cycle < 20ms; Hash preprocessing: SHA-256 parallel hashing, data compression ratio >90%, feature extraction time <8ms; Privacy Computation: Local differential privacy encryption reduces ciphertext data size by more than 95%. Lightweight consortium blockchain evidence storage access Employing a medical / industrial grade consortium blockchain (Hyperledger Fabric / FISCO BCOS trimmed version), a three-tier node architecture of endpoint-network-chain is constructed: Lens end: Ultralight node, only signature + hash on-chain, storage <64MB, power consumption <30mW; Nearest gateway: Light node, completes consensus forwarding, latency <40ms; Cloud-based: Full nodes, permanent evidence storage, regulatory auditing, and cross-chain interoperability. The data uploaded to the blockchain consists of a hash digest and a cryptographic feature value, while the original data is stored locally. On-chain evidence storage and trusted traceability The consortium blockchain adopts the PBFT / Raft consensus mechanism, with write latency <60ms and total notarization latency ≤100ms; it generates a four-dimensional immutable certificate consisting of block height + transaction hash + timestamp + unique device ID, and supports: Verification: Hash comparison completed within 0.1 seconds; Traceability: Multi-dimensional backtracking by time, device, user, and scenario; Authorization: Privacy-preserving access based on zero-knowledge proofs (ZKP), with no original data disclosure. Closed-loop feedback and intelligent linkage On-chain historical data is processed using cryptographic computation and then transmitted back to the AI ​​unit to achieve the following: Personalized adaptive focusing; Vision trend analysis and rehabilitation monitoring; Industrial quality inspection defect tracing and AR navigation integration of virtual and real worlds.

[0005] Key innovation points World's first four-chain integrated architecture For the first time, it has achieved deep integration of PI-GRIN liquid crystal optics, polymer nanotransmission, edge AI heterogeneous computing, and lightweight consortium blockchain, building an original system of "optics as node, focusing as on-chain, and perception as evidence storage". Breaking the Limits of Wearable Technology Under extreme conditions of power consumption ≤200mW, volume ≤1.2cm³, and bandwidth ≤1Mbps, it achieves trusted evidence storage with latency ≤100ms, making it the only feasible solution in the industry. Optical-Evidence Decoupling Without Compromise The evidence storage system does not consume optical drive resources, and its core indicators such as transmittance, response time, focusing accuracy, and distortion remain unaffected, resolving a long-standing technical challenge in the industry. Multimodal AI + Privacy Computing Collaboration The edge model features a lightweight compression ratio of over 90%, and employs differential privacy and zero-knowledge proof dual encryption to achieve compliant data storage that is "usable but not visible." Full-scenario cross-domain adaptation capability A single architecture covers medical optometry, AR / VR, industrial vision, cultural tourism guidance, and quality inspection and traceability, with leading global technology versatility and barriers to entry.

[0006] Core technical indicators Optical core: transmittance ≥85%; focusing range -10D to +5D; response time ≤150ms; distortion <0.5%; no polarizer required; operating temperature range -20℃ to +70℃. Computing power and power consumption: Edge AI computing power 0.5-1 TOPS; steady-state power consumption ≤180mW; peak power consumption ≤220mW; battery life ≥18 hours. Evidence storage performance: On-chain latency ≤100ms; consensus latency ≤60ms; verification time ≤0.1s; data tampering success rate <10⁻² 4 . Accuracy indicators: Refraction ±0.125D; Distance measurement ±0.01mm; Eye movement positioning <0.5°; Visual detection accuracy 0.01mm. Material transfer Nanochannel loss < 0.3 dB / cm; transmission delay < 5 ms; MTBF > 50,000 hours.

[0007] Technological originality Unique technical approach: PI-GRIN liquid crystal lens + lightweight consortium blockchain + end-side hash preprocessing integrated method without any patents or literature disclosure. Unique architecture: Deeply coupled three layers of optical sensing, edge computing, and blockchain evidence storage, rather than a simple superposition. Breakthrough in Constraints: The combination of low power consumption, small size, and real-time blockchain integration in wearable devices breaks through the physical limits of the industry. Original compliance capabilities It simultaneously meets medical data security standards, industrial traceability standards, and personal information protection laws.

[0008] Technical difficulty and complexity The difficulty of cross-domain integration is that it requires expertise in five major fields: liquid crystal optics, materials science, AI, blockchain, and embedded low power consumption, which presents extremely high talent and technical barriers. System coordination difficulty: optical drive, AI inference, on-chain consensus, data encryption, multi-threaded hard real-time scheduling, jitter <1ms. Extreme engineering challenges: miniaturization, low power consumption, high reliability, anti-interference, and wide temperature range adaptability. Security compliance difficulty A four-layer security system: immutability, privacy protection, authorized auditing, and cross-chain interoperability.

[0009] Feasibility Mature hardware: PI-GRIN liquid crystal lenses, micro sensors, and edge AI chips are all in mass production. Mature software: The lightweight consortium blockchain, edge AI inference, and hash encryption all have mature kernels that can be customized. Integrated feasibility: nanometer-scale transmission, low-power drive, and miniaturized structure enable mass production. Scenario verification: Prototype and small-batch tests have been completed on optometry glasses, AR glasses, and industrial vision systems, and all indicators have met the standards. Costs are controllable: the incremental cost per unit after batch production is less than 50 yuan, making it suitable for large-scale commercialization.

[0010] Beneficial effects Trusted data foundation: Blockchain-based evidence storage is tamper-proof, verifiable, and traceable, meeting medical / industrial compliance requirements. Top-tier optical performance: high light transmittance, fast response, wide focus range, low distortion, and no polarizer limitations. Optimal wearable experience: ultra-low power consumption, long battery life, miniaturization, and seamless use. The technological barriers are extremely high: it involves cross-domain applications, requires a high degree of innovation, is subject to strong constraints, and is difficult to replicate. It has enormous commercial value: it can serve as a core AI / AR solution, medical optometry equipment, industrial vision gateway, and trusted data service. Attached Figure Description Figure 1: System architecture for the fusion of blockchain data storage and adaptive focusing lens Figure 2: Method Flowchart Figure 3: Schematic diagram of data closed loop in multiple scenarios. Detailed Implementation

[0011] Example 1: Quick Automatic Optometry and Metering Glasses The lens collects eye parameters, and the AI ​​agent completes the refraction in 10 seconds with an accuracy of ±0.125D; The optometry data is encrypted using hashing and differential privacy encryption before being stored on the blockchain as evidence. Users / doctors can verify and trace the data using hash values; The data was used for vision rehabilitation monitoring, improving efficiency by 80%. Example 2: AI / AR Glasses Core Lens Supplier Solution Adapts to AR scene switching between virtual and real, real-time focus adjustment and on-chain recording; Optical parameters and usage logs are used for quality traceability. Annual supply of ≥100,000 units, with evidence supporting trustworthy supply chain management. Example 3: Industrial Robot Vision Inspection Entry Point As the optical entry point for robot vision, it automatically focuses and collects appearance defects; The detection accuracy is 0.01mm, and the data uploaded to the blockchain is tamper-proof. Testing efficiency has been improved by 40%, supporting quality control and traceability. Example 4: Smart Glasses for Cultural Tourism AR Navigation The camera automatically adjusts focus based on the distance to the attraction, and AR narration is simultaneously uploaded to the blockchain. User browsing history and navigation logs are stored as evidence to protect privacy; Serving ≥1 million tourists annually.

Claims

1. A method for implementing blockchain data storage and lens fusion, characterized in that, This is based on a deep integration of four layers: polarization-independent gradient refractive index liquid crystal PI-GRIN LC, polymer nanotransport channels, edge AI heterogeneous computing, and lightweight consortium blockchain, including: 1) Multimodal sensing data at the lens end is transmitted via a nanometer transmission channel with low loss (<0.3dB / cm) and low latency (<5ms); 2) Edge AI completes multimodal computation, SHA-256 hash preprocessing, data compression ratio >90%, and local differential privacy encryption; 3) Ultralight node power consumption <30mW; Encrypted hash data signature on the chain, with a storage latency ≤100ms; 4) Realize verification, traceability, and authorization privacy computation based on on-chain four-dimensional credentials; 5) On-chain data is transmitted in a dense state to form a hard real-time closed loop of perception-computation-focusing-evidence storage-feedback.

2. The method according to claim 1, characterized in that, The PI-GRIN liquid crystal lens has the following characteristics: light transmittance ≥85%, focusing range -10D to +5D, response time ≤150ms, distortion <0.5%, no polarizer required, and stable operation in a temperature range of -20℃ to +70℃.

3. The method according to claim 1, characterized in that, The polymer nanomaterial is an optical waveguide + submicron copper-based composite channel that supports parallel transmission of optical and electrical signals with a loss of <0.3dB / cm, a delay of <5ms, and an MTBF of >50,000 hours.

4. The method according to claim 1, characterized in that, The edge AI unit has a computing power of 0.5-1 TOPS, power consumption of <80mW, supports binocular independent calculation, 240Hz eye tracking, global shutter visual multimodal input, refraction accuracy of ±0.125D, and focus calculation cycle of <10ms.

5. The method according to claim 1, characterized in that, The lightweight consortium blockchain employs a three-tier node architecture (end-network-chain), with ultra-lightweight nodes having storage of <64MB, consensus algorithms of PBFT / Raft, consensus latency of ≤60ms, and a data tampering success rate of <10⁻². 4 .

6. The method according to claim 1, characterized in that, The system's steady-state power consumption is ≤180mW, battery life is ≥18 hours, certificate storage latency is ≤100ms, verification time is ≤0.1s, and real-time scheduling jitter is <1ms.

7. The method according to claim 1, characterized in that, The evidence storage uses hash digests and zero-knowledge proof authorization, and the original data is stored locally, satisfying medical compliance, industrial traceability, and personal information protection laws.

8. The method according to claim 1, characterized in that, Applicable scenarios include: rapid automatic optometry glasses, AI / AR near-eye displays, industrial robot vision entry points, cultural tourism AR navigation, and medical vision rehabilitation monitoring.

9. A blockchain-integrated smart lens system, characterized in that, include: The PI-GRIN adaptive focusing liquid crystal lens, polymer nanotransmission module, multimodal sensing unit, edge AI unit, lightweight consortium chain unit, low-power driving unit, and dense state computing unit are used to execute the method described in any one of claims 1-8.

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