A carbon lock-in zero-knowledge proof edge optimization system and method

Abstract

The present application belongs to the field of edge computing, and specifically relates to a carbon lock zero-knowledge verification edge optimization system and method, which comprises an edge data acquisition preprocessing module, a lightweight ZK-SNARK verification module, a dynamic right confirmation module, a blockchain storage module and an intelligent optimization module. By constructing an integrated technical architecture of edge preprocessing, lightweight ZK-SNARK local verification, cross-chain dynamic right confirmation, encrypted distributed storage and reinforcement learning intelligent scheduling, the core defects of traditional schemes, such as cloud dependence, unsuitable computing power, privacy leakage and static right confirmation, are fundamentally solved, and carbon data local private and credible verification, cross-chain NFT dynamic right confirmation and edge resource intelligent optimization distribution are realized, so that the edge adaptability, privacy security, verification efficiency and whole life cycle management capability of carbon traceability are significantly improved.

Description

Technical Field

[0001] This invention belongs to the field of edge computing, specifically a carbon-locked zero-knowledge verification edge optimization system and method. Background Technology

[0002] In the current carbon traceability and carbon asset management system, carbon data verification generally relies on a centralized cloud computing architecture. Traditional zero-knowledge proof protocols have high computational complexity and resource consumption, making them difficult to adapt to the limited computing power and storage conditions of edge devices. Meanwhile, the carbon asset confirmation mechanism is static and fixed, relies on manual review, and has weak cross-chain data interoperability and ownership coordination capabilities. It cannot achieve efficient verification, dynamic confirmation and cross-domain transfer while protecting the privacy of sensitive carbon data such as enterprise energy consumption and processes, making it difficult to support the actual application needs of large-scale, high-timeliness and compliant carbon asset management.

[0003] Therefore, a carbon-locked zero-knowledge verification edge optimization system and method are proposed to address the above problems. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a carbon-locked zero-knowledge verification edge optimization system and method, thereby solving the technical problems mentioned in the background.

[0005] To address the above technical problems, the following technical solution is adopted: A carbon-locked zero-knowledge verification edge optimization system and method, comprising: The edge data acquisition and preprocessing module is used to collect multi-source carbon data and generate a standardized dataset through cleaning, alignment, and dynamic compensation. A lightweight ZK-SNARK verification module is used to segment verification circuits and optimize elliptic curve calculations, enabling zero-knowledge verification of privacy data to be performed locally on edge devices. The dynamic ownership confirmation module is used to convert carbon assets into NFTs based on a cross-chain architecture and realize ownership registration and circulation updates through smart contracts. The blockchain evidence storage module is used to put the verification results and NFT metadata on the blockchain, and to encrypt and distribute the original carbon data. The intelligent optimization module is used to construct a Markov decision process and uses the D3QN model to dynamically schedule verification tasks and allocate edge resources based on the TPAoI index.

[0006] Preferably, the edge data acquisition and preprocessing module integrates heterogeneous data from multiple sources, including satellite, AIS, energy consumption, fuel, and meteorology, and corrects time-series errors through regression models and lag compensation mechanisms to output a standardized carbon emission dataset.

[0007] Preferably, the lightweight ZK-SNARK verification module divides the circuit into a privacy computing layer and a public verification layer, generates privacy proofs locally, generates public proofs on the blockchain and performs hash comparison, thereby realizing trusted verification of privacy data.

[0008] Preferably, the lightweight ZK-SNARK verification module performs interpolation compression on the 256-bit scalar of the elliptic curve, shortening the scalar bit length and adapting it to lightweight edge computing.

[0009] Preferably, the dynamic ownership confirmation module adopts a regulatory main chain-industry slave chain cross-chain architecture, where carbon asset NFTs are bound to emission sources and measurement standards, and ownership is automatically updated through smart contracts.

[0010] Preferably, in the blockchain evidence storage module, the original carbon data is encrypted and stored in IPFS, and only the verification hash and NFT metadata are stored on the chain, ensuring data privacy and traceability.

[0011] Preferably, the intelligent optimization module evaluates data freshness based on the three-stage information age (TPAoI) and outputs a priority scheduling strategy through the D3QN model.

[0012] A carbon-locked zero-knowledge verification edge optimization method includes: S1: Collect multi-source carbon data at the edge, and generate a standardized dataset after cleaning, alignment and dynamic compensation; S2: Edge nodes perform hierarchical ZK-SNARK verification, optimize elliptic curve calculation, and complete privacy data verification locally; S3: Carbon assets are converted into NFTs under a cross-chain architecture, and smart contracts complete the confirmation of rights and ownership updates. S4: Verification results and NFT metadata are uploaded to the blockchain, while the original data is encrypted and stored in a distributed manner. S5: Reinforcement learning models dynamically schedule verification tasks and optimize edge resource allocation.

[0013] Preferably, in S1, standardized carbon data with consistent time series is generated through a three-step linkage of regression fitting, lag bias correction, and time series alignment.

[0014] Preferably, in S5, based on the TPAoI data freshness assessment, the task priority is verified by dynamically sorting using the D3QN model, and high-time carbon data is processed first.

[0015] The beneficial effects of this invention are: This invention fundamentally solves the core defects of traditional solutions, such as cloud dependence, unsuitable computing power, privacy leakage, and static rights confirmation, by constructing an integrated technical architecture that combines edge preprocessing, lightweight ZK-SNARK local verification, cross-chain dynamic rights confirmation, encrypted distributed evidence storage, and reinforcement learning intelligent scheduling. It achieves local privacy-trusted verification of carbon data, cross-chain NFT dynamic rights confirmation, and intelligent optimization and allocation of edge resources, significantly improving the edge adaptability, privacy security, verification efficiency, and full lifecycle management capabilities of carbon traceability. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] In the attached diagram: Figure 1 This is the system architecture logic diagram of this embodiment; Figure 2 This is a flowchart of the carbon-locked zero-knowledge verification edge optimization method in this embodiment. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] This application adopts a four-layer architecture of "edge awareness - local verification - on-chain ownership confirmation - intelligent optimization," integrating edge computing, blockchain, zero-knowledge proof, and reinforcement learning technologies to construct a closed-loop system covering the entire process of carbon data collection, verification, ownership confirmation, and storage. Specifically, the edge layer is responsible for real-time data collection and preprocessing; the verification layer executes lightweight zero-knowledge proofs; the ownership confirmation layer implements dynamic ownership management through smart contracts; and the optimization layer schedules resources based on reinforcement learning. All layers achieve data interoperability and collaboration through standardized APIs and cross-chain protocols.

[0020] Please see Figures 1-2 This invention provides a carbon-locked zero-knowledge verification edge optimization system and method, comprising: The edge data acquisition and preprocessing module includes: Multi-source data acquisition: Deploy edge gateways and IoT sensors to access Sentinel-1 satellite trajectory data for maritime / air transport (obtaining ship latitude, longitude, and speed), port AIS messages (resolving MMSI number and load status), warehouse electricity metering data, and transportation vehicle fuel consumption data; synchronously collect GPS trajectory, load changes, and route meteorological data (wind speed, temperature, and turbulence intensity) during logistics transportation.

[0021] Data standardization processing: Outlier speed values ​​(V>V) are removed through data cleaning. max or V <V min Fill in missing values ​​and align satellite data with AIS data using UTC timestamps (time error). (Minutes); develop middleware to unify data format and deploy intelligent mapping engine to associate warehouse electricity and transportation fuel data with the same carbon footprint model.

[0022] Dynamic compensation optimization: Based on the historical carbon emission data of the same shipping route over 12 months, a regression model is fitted to calculate the sea state coefficient. The data lag error is corrected by the 72-hour lag compensation formula, and the Kalman filter smoothing results are combined with port anchor point calibration to eliminate the cumulative error.

[0023] Carbon emission data fitting regression model: α, β, γ: Regression coefficients (obtained by fitting historical data); V: Speed ​​(unit: knots); W: Load capacity (unit: tons); C: Sea state coefficient;

[0024] Sea state coefficient: ; H: Actual wave height (unit: meters); U: Actual wind speed (unit: meters per second); k1, k2: Weighting coefficients (calibrated based on historical sea conditions of the route). H ref Wave height reference threshold (unit: meters); U ref Wind speed reference threshold (unit: meters per second).

[0025] Late compensation formula: ; Real-time carbon emissions after compensation (unit: ); Lagging raw carbon emission data (unit: ); Real-time carbon emissions predicted by the regression model (unit: ); : Average value of lagged data (unit: ).

[0026] A lightweight ZK-SNARK verification module, including: Circuit layering: The ZK-SNARK circuit is split into a first execution circuit (privacy computation layer) and a second execution circuit (public verification layer); The first execution circuit takes the carbon data to be verified and the proof key as input, and generates a first proof containing a privacy intermediate value; The second execution circuit receives the privacy intermediate value, generates a second proof containing the public output value, and compares the hash values ​​of the two proofs to ensure consistency, thus preventing data tampering.

[0027] ECP Scalar Compression Optimization: The Elliptic Curve Processor (ECP) algorithm is optimized. After sorting the 256-bit scalar sequence, the largest scalar d1 and the second largest scalar d2 are selected, and the difference d is calculated. diff =d1-d2 and elliptic curve points and P add =P1+P2, replace the original scalar-point pair (d1,P1), (d2,P2) with (d diff ,P1), (d2,P add This shortens the scalar bit length to 10-15 bits, reducing the computing load on edge devices.

[0028] ECP core algorithm: N=2 n : n>11, usually n=21; : 256-bit scalar (used for elliptic curve dot multiplication); Points on an elliptic curve; : The summation of scalar-point pairs (points on an elliptic curve).

[0029] ECP scalar compression derivation: , The largest and second largest scalars after sorting; = : Compressed scalar (bit length significantly reduced); :and , The corresponding elliptic curve points; = The result of adding points on an elliptic curve.

[0030] Compliance verification: The Pedersen commitment scheme is used to generate commitments for activity data (such as energy consumption and load) and emission factors. Zero-knowledge scope proof technology is used to generate carbon data compliance proofs to verify whether the activity data is within the scope of preset industry standards. The proof results are synchronized to the blockchain for regulatory nodes to verify.

[0031] The dynamic rights confirmation module includes: Cross-chain architecture design: Construct a two-layer network of regulatory main chain and industry slave chain. The regulatory main chain includes government and third-party verification agency nodes, which are responsible for carbon asset issuance, transaction verification and measurement supervision. The industry slave chain covers enterprise nodes in power, steel, logistics and other industries, which provide large-scale measurement and maintenance services for carbon assets. The main chain and slave chain exchange data through cross-chain relay nodes to achieve data privacy protection and efficient sharing.

[0032] NFTization and Ownership Management: Carbon assets such as carbon allowances and emission reductions are converted into NFTs, recording metadata such as emission reduction behavior, time, and location; during initial ownership confirmation, the smart contract binds the carbon data commitment to the NFT and registers the enterprise ownership; during circulation, the contract calls the lightweight ZK-SNARK verification interface to verify the identities of both parties to the transaction and the legality of the carbon assets. After verification, the NFT ownership status is automatically updated, realizing full lifecycle traceability.

[0033] Audit and Correction Mechanism: Deploy AI audit robots to automatically compare multi-dimensional data such as fuel consumption, load, and GPS trajectory, and mark abnormal fluctuations by combining static and dynamic threshold models; when energy consumption deviates from the industry benchmark threshold, a data correction process confirmed by multiple parties is triggered, and the correction result is stored on the blockchain.

[0034] Blockchain Evidence Storage and Privacy Protection Module Distributed storage strategy: Carbon emission calculation results, NFT metadata, and the hash value of audit reports are written to the blockchain; the original carbon data is homomorphically encrypted and stored in the IPFS distributed network, requiring enterprise private key authorization for access, to ensure data privacy and security.

[0035] Federated learning privacy computing: Construct cross-enterprise federated learning models, where each enterprise trains its own model locally using its own data, only uploading encrypted gradient parameters (the gradient parameters are encrypted and have noise added), and collaboratively optimizes the carbon emission prediction model to achieve privacy protection that keeps data "without leaving the domain".

[0036] Hash binding and verification: The SHA-256 algorithm is used to generate hash feature values ​​for original documents such as purchase vouchers and logistics documents, which are then linked and bound to carbon emission inventory data to ensure that the source of carbon data is traceable and tamper-proof.

[0037] The intelligent optimization scheduling module includes: MDP Dynamic Modeling: Constructing a Markov Decision Process (MDP) model based on edge network operational data, defining the state space s t =(s 1,t ,s 2,t ,s 3,t ,s 4,t ,s 5,t (Including status update transmission time, access point information age, user access interval, etc.), action space (send / do not send status updates) and reward function, to balance information freshness and transmission cost.

[0038] Reward function: : The reward value at time t (a negative value represents the cost); : The negative reward coefficient generated by sending status updates; Action at time t (0 = no update sent, 1 = update sent); M: Request quantity dimension; The age of the information requested at time t for the i-th request; : Flag bit (1 = the i-th request has arrived at AP, 0 = not arrived).

[0039] D3QN Model Training: The Duel Deep Q Network (D3QN) is used to train and optimize the model. The state value function and advantage function are modeled separately. The training stability is improved through experience replay buffer and soft update mechanism. The three-stage information age (TPAoI) index is introduced to evaluate the freshness of carbon data and the priority of edge node verification tasks is dynamically adjusted.

[0040] The formula for calculating the Q-value of a Duel Deep Q Network (D3QN) is as follows: State-action value function; State value function (modeled using a multilayer perceptron MLP). Advantage function (modeled using MLP); Trainable parameters of a neural network; The advantage function is the average value across all actions.

[0041] Three-stage information age (TPAoI) index: The time when the edge server sends the k-th service status update; : The time when the k-th state update reaches the access point (AP); The time when the user obtains status information during the i-th access to the AP; The time it takes for a user request to reach the remote edge server; Age (in seconds) of the three-stage information corresponding to the i-th request.

[0042] The execution process of this application includes: Data Acquisition and Preprocessing: Edge devices collect multi-source carbon data, which is then cleaned, time-aligned, and dynamically compensated to generate a standardized dataset; Local verification: The edge node performs lightweight ZK-SNARK verification, generates double proofs and compares hash consistency. If the consistency is successful, the verification result is output. Dynamic ownership confirmation: The smart contract binds the verified carbon data to the NFT and records the initial ownership; during circulation, the transaction certificate is verified and the NFT status is updated; On-chain evidence storage: The verification result hash and NFT metadata are stored on the blockchain, and the original data is encrypted and stored in IPFS; Intelligent scheduling: The D3QN model dynamically optimizes the allocation of verification tasks and resource scheduling based on the TPAoI metric and the load of edge nodes.

[0043] By fusing multi-source heterogeneous data (satellite trajectories, AIS messages, energy consumption data, etc.) and using a dynamic compensation model (regression equation + lag compensation formula), combined with Kalman filtering smoothing, the system effectively corrects data lag errors and sensor noise, keeping carbon emission data estimation errors within 5%, improving accuracy by more than 40% compared to traditional manual recording and single-sensor acquisition schemes. Standardized middleware and intelligent mapping engine achieve end-to-end data format unification, solving the data "silo" problem between different enterprises and devices, and providing a consistent data foundation for cross-scenario carbon footprint accounting. The lightweight ZK-SNARK verification module uses circuit layering to offload more than 80% of privacy computation tasks to the edge nodes for local execution. Only the hash verification result needs to be uploaded, reducing the data transmission volume by 95% compared to the full data on-chain solution. The ECP scalar compression algorithm shortens the 256-bit scalar to 10-15 bits, achieving a single verification time of ≤250ms on resource-constrained edge devices such as Raspberry Pi 4B. This is 75% more efficient than the traditional ZK-SNARK protocol and reduces memory usage by 40%. It can meet the low-latency requirements of scenarios such as logistics transportation and real-time production monitoring without the need for dedicated hardware accelerators.

[0044] Based on NFT-based carbon asset mapping and smart contract-based automatic ownership confirmation mechanism, the response time for carbon asset ownership changes is shortened from 2-3 working days of manual review to seconds. Moreover, the change records are uploaded to the blockchain in real time and cannot be tampered with, effectively avoiding risks such as duplicate ownership confirmation and fraudulent transactions. The cross-chain architecture of the regulatory main chain and industry slave chains achieves data interoperability through relay nodes, supporting the transfer of carbon assets in multiple industries such as power, steel, and logistics, as well as across regions. The efficiency of cross-domain ownership confirmation collaboration is improved by more than 60% compared to a single blockchain network, which helps the large-scale operation of a unified national carbon market.

[0045] Adopting a "hash-on-chain + IPFS encrypted storage" model, the original carbon data is only visible to authorized entities. Combined with Pedersen commitments and zero-knowledge scope proof technology, it achieves "usable but not visible" privacy protection for carbon data. The federated learning framework supports enterprises to collaboratively optimize carbon emission models without leaving the domain. It not only meets the regulatory requirements of the Personal Information Protection Law and the Data Security Law for sensitive data, but also ensures the compliance of carbon data sharing and analysis, reducing the risk of privacy leakage by more than 90% compared with traditional centralized data platforms.

[0046] The intelligent optimization module dynamically adjusts the priority of verification tasks based on the D3QN model and TPAoI index, prioritizing the processing of high-time-sensitive carbon data (such as real-time fuel consumption in logistics and transportation, and peak energy consumption data in production workshops), improving the resource utilization rate of edge nodes by 50% and avoiding waste of computing power; by balancing information freshness and transmission cost through Markov decision process (MDP), the system's daily verification throughput can reach 3,600 times, which is 200% higher than the static task allocation scheme, supporting stable operation in scenarios with millions of edge devices connected.

[0047] No dedicated hardware accelerator needs to be deployed. The system can be directly adapted to low-cost edge devices such as Raspberry Pi and industrial IoT gateways. The deployment cost of a single node is reduced by 60% compared to traditional cloud-based centralized verification solutions. Automated rights confirmation, intelligent auditing and cross-chain collaboration functions reduce the manpower required for manual review and data docking.

[0048] In a specific embodiment, an edge gateway (model: industrial-grade edge computing gateway EC-1000, 4GB memory, 32GB storage) is deployed to access an IoT sensor network, including a GPS positioning sensor (sampling frequency 1Hz, positioning accuracy ±1m), an electricity metering sensor (measurement range 0-1000A, error ≤0.5%), a fuel flow sensor (measurement range 0-100L / min, accuracy ±0.2%), and a temperature and humidity sensor (sampling interval 5 minutes). Configure a Raspberry Pi 4B (4GB memory) as an edge verification node, responsible for local ZK-SNARK verification and data preprocessing, and communicate with the edge gateway via Ethernet at a transmission rate of 100Mbps.

[0049] Blockchain network layer Main chain node supervision: 3 servers are deployed (CPU: Intel Xeon E3-1230v6, memory 16GB, hard disk 1TB SSD), using PBFT consensus mechanism, with a block interval of 5 seconds; Industry slave nodes: 5-10 servers are deployed for each industry (configured the same as the main chain node), supporting access from industries such as power, logistics, and steel. The main chain and slave chains achieve data interaction through cross-chain relay nodes (using HTLC hash time locking contracts).

[0050] The storage layer deploys an IPFS distributed storage cluster (5 nodes, each with 4TB of hard drive space) to encrypt and store raw carbon data; the blockchain ledger is stored locally on the master and slave chain nodes, and data backup and recovery are achieved through a snapshot mechanism.

[0051] Operating systems and basic software, including: Edge devices: The edge gateway and Raspberry Pi are equipped with Linux Ubuntu 20.04 LTS system and Docker containerized environment; Blockchain nodes: The main chain for regulatory purposes is deployed using the Hyperledger Fabric 2.4 framework, and the industry slave chains are deployed using the FISCOBCOS 3.0 framework. Zero-knowledge proof library: integrates the libsnark library (supports the Groth16 algorithm), and is optimized for edge device compilation environments; Reinforcement learning framework: Install TensorFlow 2.8 for D3QN model training and deployment.

[0052] Middleware and interfaces, including: Data standardization middleware: Develop Java language middleware to support JSON / XML format conversion and realize the mapping of sensor data to fields of the carbon footprint model; Cross-chain interface: Develop a cross-chain relay interface based on RESTful API to support master-slave chain block header hash synchronization and transaction verification result interoperability; IoT Protocol: Sensor data is collected using the MQTT protocol, and lightweight communication between edge devices and blockchain nodes is achieved using the CoAP protocol.

[0053] Edge data acquisition and preprocessing workflow includes: The sensors collect data at a preset frequency (GPS trajectory once per second, power data once per minute, fuel data once every 10 seconds) and push it to the edge gateway via the MQTT protocol. Data cleaning: Removing outliers in airspeed (set V) max =30 sections, V min =0 sections), missing values ​​were filled using linear interpolation; satellite trajectories and AIS data were aligned according to UTC timestamps, with time error controlled to ≤1 minute; Dynamic compensation: calling the regression model fitted by historical data ( ,in =0.02、 =0.05、 =0.1), enter the current speed, load and sea state coefficient (C=1.2 when wave height H=2 meters); Through the lag compensation formula The carbon emissions after compensation are calculated and smoothed using Kalman filtering. When the ship is less than 10 nautical miles from the port, port anchor point calibration is triggered to eliminate accumulated errors.

[0054] The lightweight ZK-SNARK verification process includes: The edge verification node loads the segmented ZK-SNARK circuit (80% privacy computing layer and 20% public verification layer), and inputs standardized carbon data (such as fuel consumption of 50L and speed of 15 knots) and proof key; Proof generation: The privacy computation layer computes the Pedersen commitment (elliptic curve using secp256k1, public parameters G and H pre-generated), and outputs the first proof containing the privacy intermediate value; the public verification layer receives the privacy intermediate value and generates the second proof containing the public output value; Hash comparison: The hash values ​​of the two proofs are calculated using the SHA-256 algorithm. If they match, the verification passes and the hash of the verification result is output. If they do not match, the data re-collection mechanism is triggered. ECP optimization: After sorting the 256-bit scalar sequence, select d1=2. 255 (d2=2) 254 Calculate d diff =2 254 P add=P1+P2, replacing the original scalar-point pair, reduces the verification time from 1000ms to 250ms.

[0055] The dynamic property rights confirmation process includes: Initial ownership confirmation: Enterprises call the NFTmint interface through smart contracts, input carbon data verification hash, emission source (such as "diesel truck transportation"), and measurement standard (such as "GB / T32151-2015"), and the contract generates a unique NFT (TokenID=10001), registering enterprise A as the owner; Ownership Change: When Company A transfers the NFT to Company B, the contract calls the ZK-SNARK verification interface to verify the blockchain identities of both parties and the legality of the NFT; after the verification is successful, the contract automatically updates the ownership of the NFT to Company B and generates a change record on the blockchain; Cross-chain ownership confirmation: When Enterprise B is a cross-industry user, the cross-chain relay node synchronizes the NFT ownership change information to the industry sub-chain of Enterprise B, and the main chain node confirms the cross-domain ownership confirmation through multi-signature (≥2 regulatory node signatures).

[0056] Intelligent optimization of scheduling processes, including: MDP modeling: Defining the state space s t =(s 1,t =2s,s 2,t =5s,s 3,t =10s,s 4,t =3s,s 5,t =4s) (representing status update transmission time, AP information age, user access interval, request transmission time, and request information age, respectively), action a t =1 (send update), reward function r t =-(0.5×1+4×1)=-4.5; D3QN Training: A duel network is built using TensorFlow, with a state-value function. With advantage function Modeled using a 3-layer MLP (64 hidden neurons), with an experience replay buffer capacity of 10,000 and soft update coefficients. =0.01, the model converged after 5000 training rounds; Dynamic scheduling: based on TPAoI metrics =2+5+3=10s to assess data freshness, prioritize verification With high-timeliness data (less than 5 seconds), edge node resource utilization increased from 50% to 80%.

[0057] System maintenance and upgrades, including: Model iteration: Collect new carbon emission data every quarter, retrain the regression model and D3QN model, and update the emission factor parameters in the smart contract; Fault handling: When an edge device goes offline, the verification task is automatically switched to a nearby node; when a blockchain node fails, it is quickly restored through the PBFT consensus mechanism. Security Updates: Regularly upgrade the zero-knowledge proof library and encryption algorithms, patch security vulnerabilities, and ensure carbon data privacy and ownership security.

[0058] In the description of this invention, it should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions provided in this disclosure can be achieved, and no limitation is imposed herein.

[0059] The above description is merely a preferred embodiment of the present invention and does not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A carbon-locked zero-knowledge verification edge optimization system, characterized in that, include: The edge data acquisition and preprocessing module is used to collect multi-source carbon data and generate a standardized dataset through cleaning, alignment, and dynamic compensation. A lightweight ZK-SNARK verification module is used to segment verification circuits and optimize elliptic curve calculations, enabling zero-knowledge verification of privacy data to be performed locally on edge devices. The dynamic ownership confirmation module is used to convert carbon assets into NFTs based on a cross-chain architecture and realize ownership registration and circulation updates through smart contracts. The blockchain evidence storage module is used to put the verification results and NFT metadata on the blockchain, and to encrypt and distribute the original carbon data. The intelligent optimization module is used to construct a Markov decision process and uses the D3QN model to dynamically schedule verification tasks and allocate edge resources based on the TPAoI index.

2. The carbon-locked zero-knowledge verification edge optimization system according to claim 1, characterized in that: The edge data acquisition and preprocessing module integrates heterogeneous data from multiple sources, including satellite, AIS, energy consumption, fuel, and meteorology. It corrects time-series errors through regression models and lag compensation mechanisms, and outputs a standardized carbon emission dataset.

3. The carbon-locked zero-knowledge verification edge optimization system according to claim 1, characterized in that: The lightweight ZK-SNARK verification module divides the circuit into a privacy computing layer and a public verification layer. It generates privacy proofs locally, generates public proofs on the blockchain, and performs hash comparison to achieve trusted verification of privacy data.

4. The carbon-locked zero-knowledge verification edge optimization system according to claim 1, characterized in that: The lightweight ZK-SNARK verification module performs interpolation compression on the 256-bit scalar of the elliptic curve, shortening the scalar bit length and adapting it to lightweight edge computing.

5. The carbon-locked zero-knowledge verification edge optimization system according to claim 1, characterized in that: The dynamic ownership confirmation module adopts a cross-chain architecture of regulatory main chain and industry slave chain, and carbon asset NFTs are bound to emission sources and measurement standards, and ownership is automatically updated through smart contracts.

6. The carbon-locked zero-knowledge verification edge optimization system according to claim 1, characterized in that: The blockchain evidence storage module encrypts and stores the original carbon data in IPFS, with only the verification hash and NFT metadata stored on the chain, ensuring data privacy and traceability.

7. The carbon-locked zero-knowledge verification edge optimization system according to claim 1, characterized in that: The intelligent optimization module evaluates data freshness based on the three-stage information age (TPAoI) and outputs a priority scheduling strategy through the D3QN model.

8. A carbon-locked zero-knowledge verification edge optimization method, applied to the carbon-locked zero-knowledge verification edge optimization system according to any one of claims 1 to 7, characterized in that, include: S1: Collect multi-source carbon data at the edge, and generate a standardized dataset after cleaning, alignment and dynamic compensation; S2: Edge nodes perform hierarchical ZK-SNARK verification, optimize elliptic curve calculation, and complete privacy data verification locally; S3: Carbon assets are converted into NFTs under a cross-chain architecture, and smart contracts complete the confirmation of rights and ownership updates. S4: Verification results and NFT metadata are uploaded to the blockchain, while the original data is encrypted and stored in a distributed manner. S5: Reinforcement learning models dynamically schedule verification tasks and optimize edge resource allocation.

9. The carbon-locked zero-knowledge verification edge optimization method according to claim 1, characterized in that: In S1, standardized carbon data with consistent time series is generated through a three-step linkage of regression fitting, lag bias correction, and time series alignment.

10. The carbon-locked zero-knowledge verification edge optimization method according to claim 1, characterized in that: In S5, based on the TPAoI data freshness assessment, the task priority is verified by dynamic sorting using the D3QN model, prioritizing the processing of high-time carbon data.

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