A blockchain-based bamboo product life cycle carbon footprint traceability method

Abstract

This invention discloses a blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle, belonging to the field of carbon footprint traceability management. It includes the creation of a digital twin of the carbon footprint, high-precision estimation of carbon emissions in stages, multi-chain layered on-chain evidence storage, multi-party trusted verification of data based on privacy computing, and smart contract-driven visualization and feedback. By integrating multi-source data collection from sensors, RFID, QR codes, etc., it achieves a digital mapping of the entire process of products from production, processing, transportation to recycling. Machine learning models are used to intelligently calculate carbon emissions at each stage, and the multi-chain structure of the blockchain ensures data immutability. Combining zero-knowledge proofs and federated learning, it achieves compliance and privacy protection for carbon footprint data. The system supports automatic early warning of key indicators, green incentives, and carbon trading settlement, enhancing the transparency and credibility of carbon management and promoting green manufacturing and sustainable development.

Description

Technical Field

[0001] This invention belongs to the field of carbon footprint traceability management, and more specifically relates to a blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire life cycle. Background Technology

[0002] In the context of global green development, carbon footprint management is increasingly becoming a crucial foundation for the bamboo and wood products industry to achieve sustainable transformation and participate in international trade. Traditional bamboo and wood product production, transportation, sales, and recycling involve multi-entity collaboration and cross-regional transfers. Fragmented carbon emission data distribution, difficulty in ensuring its authenticity, and cumbersome process traceability severely restrict the accurate calculation and compliant disclosure of carbon footprints. Currently, most common carbon footprint traceability models rely on centralized databases, which suffer from the problems of data tampering, difficulty in achieving multi-party trust, and difficulty in achieving full-process transparency. At the same time, with the rise of green certification, carbon trading, and environmental finance, the demand for secure sharing of carbon emission data and minimal privacy disclosure is constantly increasing across all stages of the industry. Traditional IT solutions face significant challenges in protecting commercial sensitivity and improving distributed collaboration efficiency.

[0003] Furthermore, current carbon footprint data collection and verification processes largely rely on manual aggregation and post-event spot checks, lacking automation and real-time feedback. This makes it difficult to support the high-reliability and high-efficiency carbon data required for policymaking, consumer choices, and corporate green management. Particularly when carbon data flows across organizations, regions, and stages, achieving clear data on-chain notarization with defined responsibilities, ensuring compliant and transparent data flow, and reducing the cost and complexity of carbon footprint traceability and verification have become critical technical challenges. Meanwhile, the rapid development of cutting-edge information technologies such as privacy computing, blockchain, and smart contracts in recent years has provided new technological pathways for building a collaborative, distributed, low-infringement, and automated carbon footprint management system.

[0004] Therefore, developing a technical method that integrates blockchain's multi-chain layered structure, privacy computing, and smart contract mechanisms to achieve efficient collection, layered on-chain storage, multi-party trusted verification, and automatic incentive feedback of carbon footprint data throughout the entire life cycle of bamboo and wood products has become a key technological requirement for improving the green compliance level of the bamboo and wood products industry, strengthening its international market competitiveness, and promoting the industry's low-carbon upgrading. Summary of the Invention

[0005] This invention addresses the technical challenges of existing carbon footprint traceability processes for bamboo and wood products, including fragmented data collection, low accuracy in carbon emission estimation, insufficient data reliability and security, and difficulties in efficient collaboration and dynamic feedback throughout the traceability process. It proposes a blockchain-based method for carbon footprint traceability across the entire lifecycle of bamboo and wood products. This method aims to achieve highly reliable, tamper-proof, and dynamically mapped carbon footprint data for each stage of bamboo and wood product development, from production, transportation, processing, and sales to waste disposal. It improves the accuracy of carbon emission accounting and the security of multi-party collaborative verification, while ensuring efficient traceability, visualization, and intelligent feedback of carbon footprint information on the blockchain. This solves the core technical bottlenecks in the industry's green and low-carbon transformation process, such as difficulties in achieving seamless carbon footprint traceability, compliance, and incentives.

[0006] To achieve the above objectives, the present invention employs the following technical solution: the method comprising: Create a digital twin of the carbon footprint of bamboo and wood products, establish a digital twin of the carbon footprint of each bamboo and wood product, and collect original data from multiple stages of production, transportation and processing to achieve dynamic digital mapping of the entire life cycle of bamboo and wood products. High-precision phased carbon emission estimation: For each key stage in the life cycle of bamboo and wood products, a machine learning-driven life cycle carbon emission intelligent estimation model is used to calculate the phased carbon footprint. The blockchain multi-chain layered traceability structure design and on-chain evidence storage introduce a multi-chain (main chain + side chain) layered evidence storage and traceability architecture, and adopt a cross-chain data consistency evidence aggregation algorithm to ensure the immutability of multi-source carbon footprint information in the whole process of traceability and the efficiency of on-chain access. The carbon footprint data is verified by multiple trusted parties based on privacy computing, and a privacy computing mechanism based on a combination of zero-knowledge proof and federated learning is adopted. Carbon footprint visualization and feedback decision-making driven by smart contracts: Real-time visualization and source tracing feedback of key indicators throughout the entire life cycle of carbon footprint are achieved through smart contracts.

[0007] In one approach, the creation of a digital twin of the carbon footprint of bamboo and wood products includes: using a smart sensor module as an information acquisition front-end, combining RFID, QR code, temperature and humidity sensors and positioning devices to acquire attribute data of production, transportation, processing and other processes in real time; performing multi-modal information reliable mapping on the acquired raw data stream, and using a data fusion mechanism to map multi-source heterogeneous data into the state of the digital twin of the carbon footprint of bamboo and wood products. The mapping process incorporates a blockchain zero-knowledge random challenge mechanism to compare hash digests of multi-source data, ensuring the credibility of the mapping results. Distributed storage redundancy enables the multi-source data of each digital twin's state to be traceable, and the digital twin's state is updated in real time at each stage of the bamboo and wood product lifecycle.

[0008] In one approach, the phased high-precision carbon emission estimation includes: continuously integrating environmental and behavioral data collected from production, logistics, processing, sales, and waste disposal into the carbon footprint accounting system, and calculating the carbon footprint of each key stage through a machine learning-driven lifecycle carbon emission intelligent estimation model.

[0009] In one scheme, the blockchain multi-chain hierarchical traceability structure design and on-chain evidence storage include: adopting a multi-chain architecture, with the main chain as the core traceability and evidence storage carrier, and the side chains responsible for the diversion and management of carbon footprint data at different stages or types. Data from different sources is encrypted, mapped, and layered onto the blockchain according to data sensitivity and traceability thresholds. The original data on each chain is stored using a hash digest array to ensure immutability. A cross-chain data consistency evidence aggregation algorithm is used to collect and aggregate carbon footprint information stored on the main chain and each side chain to generate a globally consistent certificate, and the verification process is triggered through a smart contract. Sidechains periodically synchronize digests and status information through a cross-chain communication protocol. Any on-chain traceability access ensures controlled flow and consistency verification of sensitive data within a layered architecture.

[0010] In one approach, the multi-party trusted verification of carbon footprint data based on privacy computing includes: combining zero-knowledge proof technology with a federated learning architecture to construct a multi-party participatory and differentially protected data verification framework. Data owners can use zero-knowledge proof protocols to securely demonstrate the integrity and compliance of their carbon footprint data to verifiers without revealing the underlying raw data, and can upload the proof to a sidechain or compliant node for verification. Each enterprise node jointly trains the carbon emission accounting model through a federated learning mechanism, independently maintains training parameters and samples locally, and adopts a periodic synchronization parameter synchronization mechanism to improve global consistency and resistance to tampering. In high-security scenarios such as regulatory environments, a secure multi-party computation algorithm is used to collaboratively and encryptedly calculate carbon emission compliance ranges. Each node then uses zero-knowledge proofs to verify the authenticity and compliance of the calculation process, ensuring the security and privacy protection of data compliance verification.

[0011] In one solution, the smart contract-driven carbon footprint visualization and feedback decision-making includes: automatically monitoring carbon footprint data in each stage of production, transportation, sales and recycling through smart contracts; after capturing changes in key parameters, triggering automatic response processes for green incentives, subsidies and early warnings based on preset multi-dimensional rules; and connecting to a visualization interface to generate multi-level carbon emission performance displays. The contract automatically distributes green points, subsidies, or certificates, promptly pushes early warnings when critical events are detected, and supports carbon trading settlement, environmental tax algorithms, and a closed loop of corporate ecological rating feedback.

[0012] In one approach, the lifecycle carbon emission intelligent estimation model embeds an environmental context adaptive adjustment factor, which can dynamically adjust the carbon emission weights based on real-time collected environmental parameters, thereby improving the timeliness and accuracy of carbon footprint estimation results. After detecting energy consumption anomalies, logistics path changes, and carbon emission critical events in the data stream, it automatically optimizes the model parameters and provides real-time early warnings for carbon emission hotspots.

[0013] Beneficial effects of this invention: This invention achieves highly reliable, accurate, and efficient management of the carbon footprint of bamboo and wood products throughout their entire lifecycle by introducing advanced technologies such as blockchain-based layered traceability, multimodal data fusion, machine learning-based intelligent carbon emission estimation, privacy computing, and smart contracts. Compared with existing technologies, this invention has several beneficial effects: First, the construction of a digital twin based on multi-source heterogeneous data significantly improves the comprehensiveness and timeliness of carbon footprint data, supporting accurate tracking of each product throughout its entire lifecycle.

[0014] Secondly, by leveraging machine learning models and dynamic adaptation of environmental factors, high-precision phased carbon emission accounting is achieved, enhancing the scientific validity and decision-making value of carbon footprint data. Thirdly, the multi-chain layered architecture of the blockchain, combined with cross-chain consensus algorithms, effectively ensures the immutability of data from multiple parties, full-process traceability, and efficient collaboration in on-chain storage and retrieval. The application of privacy computing mechanisms greatly enhances the compliance, confidentiality, and multi-party credible verification capabilities of carbon footprint data. Finally, the smart contract-driven visualization and feedback system enables real-time information display and green incentives throughout the entire carbon footprint process, helping to encourage enterprises and the upstream and downstream ecosystem to actively participate in carbon reduction and continuous green innovation. Attached Figure Description

[0015] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0016] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0017] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0018] like Figure 1 As shown, a blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle includes: Step 1: Create a digital twin of the carbon footprint of bamboo and wood products First, a digital twin of the carbon footprint is created for each bamboo and wood product. This twin is not simply a unique identifier, but rather combines data collected from multiple stages of production, transportation, and processing by smart sensors (such as RFID and QR codes). Through a data fusion algorithm—multi-modal information trusted mapping—the barriers between the physical and data worlds are broken down, enabling dynamic digital mapping of the entire lifecycle of bamboo and wood products. This lays a high-quality data foundation for subsequent distributed and trusted traceability.

[0019] The creation of a digital twin of the carbon footprint of each bamboo and wood product requires close integration with the physical world. Intelligent sensor modules are used as the information acquisition front-end, combined with RFID, QR codes, temperature and humidity sensors, and positioning devices to acquire attribute data from production, transportation, and processing stages in real time. These raw data streams often exhibit multimodal and heterogeneous characteristics, such as binary codes, image sequences, and time-series sensor values. To effectively map these scattered and disorganized data into a unified digital twin, a multimodal information trusted mapping (MPIM) algorithm is innovatively introduced as the core data fusion mechanism. Let the collected raw data stream be denoted as... Each of them If a source is represented as a class of sources (such as RFID, image, environmental sensors, etc.), the algorithm uses a trusted weighted mapping function. Transforming D into a digital twin state S can be mathematically expressed as: in, These are weighting coefficients, automatically and dynamically adjusted based on the reliability of the data source. This represents a multi-modal data normalization processing function, employing convolutional feature extraction for images and hash mapping for binary labels. To improve the reliability of the fusion results, a blockchain zero-knowledge random challenge mechanism is introduced into the mapping process: the system periodically randomly selects a portion of the data. The hash digest is compared with the original and mapped features, and automatic adjustment is made if mapping distortion occurs. Or recalculate The complete mapping process utilizes distributed storage redundancy to ensure that the multi-source data of each digital twin's state S is traceable on the blockchain. Whenever bamboo and wood products enter a new lifecycle stage, the system updates D in real time and re-generates S, forming a dynamically evolving digital twin. In this way, regardless of how the physical scenario changes, the corresponding digital twin can reflect the true and complete key characteristics of the entire lifecycle carbon footprint, achieving an efficient closed loop with the physical entity and greatly enhancing the data foundation reliability of subsequent traceability processes.

[0020] Step 2: High-precision estimation of carbon emissions in stages Next, a machine learning-driven intelligent lifecycle carbon emission estimation model is used to calculate the carbon footprint for each key stage in the lifecycle of bamboo and wood products. This model incorporates an environmental context adaptive adjustment factor, which dynamically adjusts the carbon emission weights based on real-time environmental parameters (energy consumption fluctuations, changes in logistics routes, and carbon emission critical events), improving the timeliness and accuracy of carbon footprint estimation and enabling real-time early warning of carbon emission hotspots.

[0021] High-precision, phased estimation of lifecycle carbon emissions requires the continuous integration of environmental and behavioral data from the production, logistics, processing, sales, and even waste disposal of bamboo and wood products into the carbon footprint accounting system. At each stage, a data matrix is ​​collected... Each of them These represent features across different dimensions, such as production energy consumption, transportation distance, processing steps, and on-site environment. By constructing machine learning regression models, such as those based on Gradient Boosting Tree (GBDT) or deep neural network structures, phased carbon emissions can be estimated. The formula is described as follows: ,in This is the carbon emission mapping function learned by the model. In implementation, historical samples and real-time environmental parameters are fed into the model, and an adaptive adjustment factor based on the environmental context is embedded within the model. , used to dynamically sense environmental variables. From environmental data After adaptive function Therefore, the overall carbon emission weighting adjustment formula is: in Depending on the actual application, it can be dynamically optimized using linear weighting or more complex environment-sensitive layers (such as RNN recurrent structures).

[0022] Anomalies in energy consumption and deviations from carbon emission thresholds were detected in the data stream, leading to improvements. Weights are used to achieve a highly sensitive response to carbon footprint. Label supervision is employed during model training, with prior actual carbon emission values ​​as input. Compared with the predicted value Optimize the loss function The algorithm periodically updates environmental adaptation parameters to achieve hot zone early warning. It also updates the phased carbon emission result sequence. Using cluster analysis and anomaly detection mechanisms, when a certain stage Exceeding the average or threshold throughout the entire life cycle triggers a carbon emission warning signal. The model automatically embeds newly collected data streams and environmental context at each node of the bamboo and wood product life cycle, accurately adjusting the carbon footprint estimation. This adapts to dynamic scenarios while ensuring high reliability of the prediction results, greatly improving the intelligence and practicality of carbon emission management.

[0023] Step 3: Design of a multi-chain layered traceability structure and on-chain evidence storage. After the efficient generation of carbon footprint data, a multi-chain (main chain + side chain) hierarchical evidence storage and traceability architecture is introduced. Data from different stages and sources is distributed and encrypted on the main chain and multiple side chains according to sensitivity and traceability thresholds. The Cross-Chain Consistent Evidence Aggregation (CCPA) algorithm is adopted to ensure the immutability of multi-source carbon footprint information and the efficiency of on-chain access throughout the entire traceability process, while taking into account both data privacy and the need for fully controllable traceability.

[0024] The carbon footprint data of bamboo and wood products throughout their entire lifecycle must be securely, hierarchically, and controllably stored and traced on-chain after generation. To this end, a multi-chain architecture is designed, with the main chain serving as the core traceability and evidence storage carrier, and side chains responsible for the distribution and management of data at different stages or types. According to sensitivity and traceability threshold They are categorized separately and mapped to either the main chain or extended sidechains. The mapping formula is as follows: ,in This represents a chain (main chain or side chain). For the original data... It employs distributed encryption and hash digest arrays. This ensures immutability. Data consistency across different chains relies on the innovative cross-chain proof aggregation algorithm CCPA (Cross-Chain Proof Aggregation), whose core function is to store evidence of data across different chains. Collection, via aggregation functions The mathematical expression for generating a globally consistent credential G is: in, This indicates an aggregation of blockchain signature products, where M represents the number of participating sidechains. This global credential is linked to the main chain's notarization database, and access is triggered by a smart contract-driven verification process. Users requesting traceability can verify this. To ensure data consistency, no original sensitive data needs to be exposed throughout the process, thus protecting privacy while maintaining traceability accuracy. Sidechains periodically synchronize partial summaries and status information via cross-chain communication protocols (such as Cosmos IBC or custom lightweight protocols). If a data update triggers, a new aggregate certificate is automatically generated, synchronizing the on-chain state with the main chain and all sidechains. All on-chain access and traceability are completed within a layered architecture, with controlled flow of sensitive data. The entire carbon footprint and its origin are traceable, and consistency verification does not require exposing core original data. The entire process is connected by distributed smart contracts, with each chain node implementing fault tolerance and redundancy, efficiently maintaining the security and integrity of the carbon footprint traceability ecosystem.

[0025] Step 4: Multi-party trusted verification of carbon footprint data based on privacy computing For different types of participants (consumers, regulators, and enterprises), a privacy-preserving computation mechanism based on zero-knowledge proofs and federated learning is adopted. Data owners can securely demonstrate the true integrity and compliance of their carbon footprint to others without exposing the underlying raw data, providing a strong data compliance foundation for subsequent scenarios such as carbon trading and green certification.

[0026] When faced with diverse carbon footprint data verification needs, traditional centralized disclosure methods are insufficient to guarantee data security and business privacy. There is an urgent need to embed privacy computing mechanisms to ensure that different stakeholders can reliably verify the authenticity and compliance of carbon footprint data without having access to the underlying sensitive data details.

[0027] To address this, zero-knowledge proof technology is organically integrated with a federated learning architecture to construct a data verification framework that features multi-party participation, differential protection, and flexible adaptation. It is assumed that the carbon footprint data owner possesses the local plaintext data. Corresponding to the total carbon emissions to be verified (f represents the local carbon emission estimation model), and the verification parties (such as consumers, regulators, and financial institutions) need to verify it. Authenticity: At this point, the owner generates a proof using a zero-knowledge proof protocol, such as zk-SNARK. Under the condition that there is no data leakage, prove For the reason Calculated according to standard procedures, i.e. and It will be made public. In the specific protocol process, the data holder runs a local carbon emission model f, extracts privacy features, encodes them into a zK circuit, and generates... And upload the sidechain or compliant node; validators only need to verify the connection. and This ensures consistency without requiring access to the source data, significantly reducing data compliance risks.

[0028] To further enhance the global consistency and tamper resistance of multi-source data, a federated learning mechanism is introduced. All relevant enterprise nodes jointly train the carbon emission accounting model. Each maintains its own training parameters locally. The global model parameters are periodically synchronized using the FedAvg algorithm with the samples. ,Right now After the model is released, each company can use its own parameters to estimate its carbon footprint, and then generate a local zero-knowledge proof to upload to the verification network. For regulatory processes with higher security levels, secure multi-party computation (MPC) can also be used for collaborative encrypted summation. Output the global carbon emission compliance range, and each node proves through zk-SNARK that the cryptographic summation step has not been maliciously interfered with.

[0029] Throughout the multi-party verification process, when accessing the main chain or multi-chain for evidence storage, the accessing party (such as certification authorities or public users) only needs to consult the aggregated zero-knowledge proof sequence. With model parameters The consistent assertion ensures that no one can see the enterprise-level raw data. This architecture enables credible verification of carbon footprints under "minimal disclosure," maintaining basic compliance while activating the flow of trust in carbon trading and green finance scenarios.

[0030] Step 5: Visualization and Feedback Decision-Making of Carbon Footprint Driven by Smart Contracts Finally, an automated rules engine driven by smart contracts enables real-time visualization and traceability feedback of key indicators throughout the entire carbon footprint lifecycle. This mechanism supports the automatic triggering of green incentives, subsidies, and early warnings for enterprises based on carbon footprint performance, while providing multi-dimensional and reliable decision support for policymaking, market supervision, and consumer environmental decision-making. The entire process ensures efficient collaboration among all parties, integrating carbon footprint management into the entire bamboo and wood products industry chain ecosystem.

[0031] In the context of intelligent carbon footprint management, smart contracts become the core driver for automated triggering and feedback throughout the entire product lifecycle. Carbon data generated at each stage of production, transportation, sales, and recycling is uploaded to the blockchain via a multi-chain architecture. Smart contracts monitor changes in key parameters in real time, capturing carbon indicators and their dynamics at each stage. The contracts incorporate multi-dimensional rules, such as automatic response mechanisms for carbon emission thresholds, year-on-year trends, and green behavior incentive standards. Upon entering the decision-making feedback process, the contract parses trusted carbon footprint summaries extracted from the main chain and side chains, calling visualization interfaces to generate dynamic process flows, carbon emission hotspot distribution, early warning maps, and other multi-layered displays, allowing enterprises, regulatory agencies, and consumers to intuitively understand the carbon performance throughout the product's lifecycle. Whenever the carbon footprint exceeds the standard, the contract automatically issues green points, subsidies, or certificates to the enterprise's account. If potential risks or critical events are detected, early warnings are immediately pushed, serving as real-time data references for policymakers and market regulators. In the feedback loop, the contract also triggers carbon trading settlement, environmental tax algorithms, and enterprise ecological rating processes, ensuring the traceability and immutability of all incentives and penalties through on-chain notarization.

[0032] Example: 1. Scene Introduction A bamboo and wood furniture company (A) produces a batch of customized bamboo chairs, spanning five major stages: production, processing, transportation, sales, and waste disposal. The company collects and calculates the carbon footprint data of the bamboo chairs in real time and in stages, uses a blockchain multi-chain structure for evidence storage and traceability, and achieves carbon footprint visualization and automated feedback under the drive of smart contracts.

[0033] 2. Front-end data acquisition process Company A assigns a unique RFID tag and QR code to each bamboo chair during the raw material procurement process, combining this with data collected from temperature, humidity, and positioning sensors. Each data point is then fused using multiple modes to create a digital twin of the bamboo chair.

[0034] Table 1: Example of Raw Data Collection (Production Stage) The data stream generates a twin state through a blockchain random challenge mechanism (the system automatically issues challenge codes and returns hash digests), and updates and redundantly stores the data in real time at each stage.

[0035] 3. Intelligent estimation of carbon footprint in stages Using machine learning models, Company A calculates carbon emissions at each stage of processing, logistics, sales and recycling, and embeds environmental context adaptive factors to optimize timeliness and accuracy.

[0036] Table 2: Examples of phased carbon emission data The model is corrected in real time, automatically alerts to abnormal logistics routes / critical carbon emission events, and optimizes parameters.

[0037] 4. Blockchain multi-chain layered on-chain evidence storage process The main chain stores global traceability information, while side chains distribute phased data, with each data point encrypted and mapped onto the chain based on its sensitivity. A digest of all original parameters is stored using a hash array, and cross-chain evidence aggregation is triggered by a smart contract for consistency verification.

[0038] Table 3: Examples of Main Chain and Side Chain Data Structures Each sidechain regularly synchronizes summaries, sensitive data flows under control, and the main chain aggregates and generates unique proofs, supporting on-chain queries and regulatory verification.

[0039] 5. Multi-party trusted data verification and privacy protection Company A, together with Company B (logistics company) and Company C (sales company), participated in federated learning, using zero-knowledge proofs to demonstrate the integrity of their carbon footprint. Company B trains its local transportation carbon emission model with parameters synchronized periodically. Company C uses a zero-knowledge proof protocol to upload proof of compliance with its sales carbon footprint to the sidechain; Regulatory nodes use a secure multi-party computation algorithm to jointly verify the carbon emission compliance range.

[0040] Table 4: Summary of Multi-Party Validation Data 6. Visualization and Automated Feedback Driven by Smart Contracts The system automatically distributes green credits and subsidies according to preset rules; changes in carbon footprint data trigger real-time visualization and push alerts. Company A receives carbon credits and green incentives, and an automatic alert is issued when there are anomalies in the transportation process.

[0041] Table 5: Smart Contract Feedback Results This embodiment details a method for tracing the carbon footprint of bamboo and wood furniture throughout its entire lifecycle: front-end data collection from sensors is mapped to a twin, carbon emissions are intelligently estimated in stages, and the data is encrypted and stored in layers through a multi-chain blockchain; collaboration among enterprise nodes enables multi-party trusted verification through privacy computing and zero-knowledge proofs, smart contracts automatically trigger incentives and warnings, and carbon footprint information is visualized in real time, supporting on-chain environmental supervision and carbon trading.

[0042] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0043] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle, characterized in that: The method includes: Create a digital twin of the carbon footprint of bamboo and wood products, establish a digital twin of the carbon footprint of each bamboo and wood product, and collect original data from multiple stages of production, transportation and processing to achieve dynamic digital mapping of the entire life cycle of bamboo and wood products. High-precision phased carbon emission estimation: For each key stage in the life cycle of bamboo and wood products, a machine learning-driven life cycle carbon emission intelligent estimation model is used to calculate the phased carbon footprint. The blockchain multi-chain layered traceability structure design and on-chain evidence storage introduce a multi-chain (main chain + side chain) layered evidence storage and traceability architecture, and adopt a cross-chain data consistency evidence aggregation algorithm to ensure the immutability of multi-source carbon footprint information in the whole process of traceability and the efficiency of on-chain access. The carbon footprint data is verified by multiple trusted parties based on privacy computing, and a privacy computing mechanism based on a combination of zero-knowledge proof and federated learning is adopted. Carbon footprint visualization and feedback decision-making driven by smart contracts: Real-time visualization and source tracing feedback of key indicators throughout the entire life cycle of carbon footprint are achieved through smart contracts.

2. The blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle, as described in claim 1, is characterized in that: The creation of a digital twin of the carbon footprint of bamboo and wood products includes: using a smart sensor module as an information acquisition front end, combining RFID, QR code, temperature and humidity sensors and positioning devices to acquire attribute data of production, transportation and processing in real time; performing multi-modal information reliable mapping on the acquired raw data stream, and using a data fusion mechanism to map multi-source heterogeneous data into the state of the digital twin of the carbon footprint of bamboo and wood products. The mapping process incorporates a blockchain zero-knowledge random challenge mechanism to compare hash digests of multi-source data, ensuring the credibility of the mapping results. Distributed storage redundancy enables the multi-source data of each digital twin's state to be traceable, and the digital twin's state is updated in real time at each stage of the bamboo and wood product lifecycle.

3. The method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle based on blockchain, as described in claim 1, is characterized in that: The aforementioned high-precision estimation of phased carbon emissions includes: continuously integrating environmental and behavioral data collected from production, logistics, processing, sales, and waste disposal into the carbon footprint accounting system, and calculating the carbon footprint of each key stage through a machine learning-driven life cycle carbon emission intelligent estimation model.

4. The blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle, as described in claim 1, is characterized in that: The aforementioned blockchain multi-chain layered traceability structure design and on-chain evidence storage includes: adopting a multi-chain architecture, with the main chain serving as the core traceability and evidence storage carrier, and side chains responsible for the diversion and management of carbon footprint data at different stages or types; Data from different sources is encrypted, mapped, and layered onto the blockchain according to data sensitivity and traceability thresholds. The original data on each chain is stored using a hash digest array to ensure immutability. A cross-chain data consistency evidence aggregation algorithm is used to collect and aggregate carbon footprint information stored on the main chain and each side chain to generate a globally consistent certificate, and the verification process is triggered through a smart contract. Sidechains periodically synchronize digests and status information through a cross-chain communication protocol. Any on-chain traceability access ensures controlled flow and consistency verification of sensitive data within a layered architecture.

5. The blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle, as described in claim 1, is characterized in that: The aforementioned privacy-preserving computation-based multi-party trusted verification of carbon footprint data includes: combining zero-knowledge proof technology with federated learning architecture to construct a data verification framework with multi-party participation and differential protection; Data owners can use zero-knowledge proof protocols to securely demonstrate the integrity and compliance of their carbon footprint data to verifiers without revealing the underlying raw data, and can upload the proof to a sidechain or compliant node for verification. Each enterprise node jointly trains the carbon emission accounting model through a federated learning mechanism, independently maintains training parameters and samples locally, and adopts a periodic synchronization parameter synchronization mechanism to improve global consistency and resistance to tampering. In high-security scenarios, a secure multi-party computation algorithm is used to collaboratively and encryptedly calculate the carbon emission compliance range. Each node then uses zero-knowledge proofs to verify the authenticity and compliance of the calculation process, ensuring the security and privacy protection of data compliance verification.

6. The method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle based on blockchain, as described in claim 1, is characterized in that: The aforementioned smart contract-driven carbon footprint visualization and feedback decision-making includes: automatically monitoring carbon footprint data in each stage of production, transportation, sales and recycling through smart contracts; after capturing changes in key parameters, triggering automatic response processes for green incentives, subsidies and early warnings based on preset multi-dimensional rules; and connecting to a visualization interface to generate multi-level carbon emission performance displays. The contract automatically distributes green points, subsidies, or certificates, promptly pushes early warnings when critical events are detected, and supports carbon trading settlement, environmental tax algorithms, and a closed loop of corporate ecological rating feedback.

7. A blockchain-based method for tracing the carbon footprint of bamboo and wood products throughout their entire lifecycle, as described in claim 3, is characterized in that: The aforementioned lifecycle carbon emission intelligent estimation model embeds an environmental context adaptive adjustment factor, which can dynamically adjust the carbon emission weights based on real-time collected environmental parameters, thereby improving the timeliness and accuracy of carbon footprint estimation results. After detecting energy consumption anomalies, logistics path changes, and carbon emission critical events in the data stream, it automatically optimizes the model parameters and provides real-time early warnings for carbon emission hotspots.

Images