Blockchain-based highway maintenance carbon footprint credible storage and accounting method and system

Abstract

The application provides a blockchain-based highway maintenance carbon footprint credible storage and accounting method and system, relates to the field of blockchain application, and solves the technical problems that the carbon footprint source data is easy to distort, has low credibility, and there is a contradiction between data sharing and privacy inclusion under the participation of multiple parties. The method comprises the following steps: collecting carbon footprint data based on an Internet of Things sensor network and classifying and processing the data to obtain key evidence data and process detail data; calculating a hash value of the process detail data, and packing the hash value and the key evidence data into a structured data packet; uploading the data packet to a private channel created for the maintenance project for consensus and storage; and anchoring the storage data summary in the private channel to a multi-dimensional layered chain group network; after the anchoring is completed, the accounting smart contract on the business sub-chain is triggered, carbon emission accounting is performed based on the verified data obtained from the sub-chain, and the accounting result and process proof are stored again to obtain a carbon emission report.

Description

Technical Field

[0001] This invention belongs to the technical field of carbon emission measurement technology, blockchain application and Internet of Things technology, specifically a blockchain-based method and system for the trusted storage and accounting of carbon footprint for highway maintenance. Background Technology

[0002] Highway maintenance, as a crucial link in the operation and maintenance of transportation infrastructure, is characterized by its dispersed work sites, frequent movement of construction equipment, long industrial chain, and diverse participating entities. Accurate carbon emission measurement and verification in this field is of great significance for promoting the industry's green and low-carbon transformation and participation in the carbon trading market. However, traditional carbon footprint management methods face severe challenges in this scenario. First, at the data collection level, current methods mainly rely on manual recording or isolated IoT sensor data, making it difficult to guarantee the authenticity and integrity of the source data. Data is at risk of being tampered with or lost during transmission and reporting, resulting in a weak foundation for carbon accounting. Second, at the data storage and sharing level, existing technologies mostly use centralized databases for storage, which poses a single point of failure risk and makes it difficult to establish a mutually trusting data sharing mechanism among owners, construction units, supervisors, material suppliers, and other parties. This leads to severe data silos and makes it impossible to balance the needs of data transparency and commercial privacy protection. Furthermore, at the accounting and auditing level, the existing accounting process is mostly an offline "black box" operation, lacking a credible and transparent audit trail, resulting in insufficient credibility of the final accounting results in serious application scenarios such as carbon trading and green finance.

[0003] While blockchain technology has shown promise in areas such as supply chain finance due to its immutability and traceability, its direct application to carbon footprint management in highway maintenance still faces significant limitations. On the one hand, ordinary blockchain systems struggle to handle the performance bottlenecks caused by the massive, high-frequency data uploads in the complex environments of maintenance sites. On the other hand, there is a lack of dedicated on-chain architectures and governance models designed for this business scenario that balance efficiency, privacy, and regulatory requirements. Therefore, there is an urgent need in this field for a systematic solution that can ensure trustworthiness from the data source, protect privacy in multi-party collaboration, and achieve accurate and transparent accounting and auditing. Summary of the Invention

[0004] This application provides a blockchain-based method and system for trusted storage and accounting of carbon footprint in highway maintenance, which solves the technical problems of existing technologies such as easy distortion of carbon footprint source data, low credibility, contradiction between data sharing and privacy under multi-party participation, and lack of transparency in the carbon accounting process.

[0005] To achieve the above objectives, this application adopts the following technical solution: The first aspect provides a blockchain-based method and system for trusted storage and accounting of the carbon footprint of highway maintenance, including: Carbon footprint data is collected based on IoT sensor networks, and the carbon footprint data is classified and processed to obtain key evidence data and process detail data. Calculate the hash value of the process details data, and package the hash value and the key evidence data into a structured data packet; The data packet is uploaded to a private channel created for the maintenance project for consensus and notarization; and the notarized data digest in the private channel is anchored to a multi-dimensional hierarchical chain network; the multi-dimensional hierarchical chain network includes a root chain for global supervision and multiple business sub-chains divided according to business logic; Once the anchoring is complete, the accounting smart contract on the business sub-chain is triggered to perform carbon emission accounting based on the verified data obtained from the sub-chain, and then stores the accounting results and process proof again to obtain a carbon emission report.

[0006] Based on the above technical solutions, the blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance provided in this application achieves efficient, reliable, and refined management of carbon footprint data throughout its entire lifecycle by constructing a collaborative architecture of data hierarchy, private channels, and multi-dimensional chain clusters. First, by classifying and calculating hash values ​​to package carbon footprint data, on-chain storage load is reduced while ensuring data integrity. Second, the introduction of private channels for initial consensus and storage significantly improves data processing efficiency and privacy protection capabilities in multi-participant scenarios. Finally, by anchoring private channel summaries to a multi-dimensional hierarchical chain cluster network composed of a root chain and business sub-chains, the strong trust anchoring role of the root chain ensures global trustworthiness, while the specialized processing of the business sub-chains enables accurate and automated execution of accounting tasks, ultimately generating an immutable and traceable carbon emission report. This effectively solves the core pain points of easily distorted carbon accounting data sources, difficulty in establishing trust among multiple parties, and complex auditing and traceability in the highway maintenance field.

[0007] Furthermore, the classification and processing of the carbon footprint data to obtain key evidence data and process detail data includes: Using edge computing gateways deployed at maintenance sites, data is automatically classified according to preset importance rules: Integrated data directly used in the final accounting conclusions are defined as key evidence data, including but not limited to the cumulative total energy consumption of equipment and the total carbon emissions of materials; Detailed data used for process traceability and analysis is defined as process detailed data, including but not limited to equipment power curves, GPS positioning trajectories, vibration sensor readings, equipment operating time records, and material transportation mileage and load data.

[0008] Furthermore, the step of uploading the data packet to a private channel created for the maintenance project for consensus and notarization includes: the participating nodes in the private channel reaching a consensus on the received data packet based on the Raft consensus algorithm and writing the transaction record into the channel's private ledger; wherein, for commercially sensitive data, encrypted calculations are performed using a trusted execution environment within the channel, and only the calculation results are uploaded to the blockchain; the commercially sensitive data includes, but is not limited to, the unit price of raw material purchases, the operating efficiency of equipment per unit time, and the specific process parameters of the equipment.

[0009] Furthermore, the multidimensional hierarchical chain group network includes: A single chain serves as a trust anchor, employing a Byzantine fault-tolerant consensus mechanism. It only stores the genesis block hash of each business sub-chain and the Merkle root hash periodically submitted by each private channel. The genesis block hash is used to verify the legitimacy of the identity of each business sub-chain and the continuity of the data, while the Merkle root hash is used to cryptographically prove the existence and integrity of data batches within the private channel. There are at least two business sub-chains, including a data storage sub-chain responsible for storing data hashes and identity information, and an accounting sub-chain used for deploying and running accounting logic. Each business sub-chain adopts a different consensus mechanism according to its business characteristics.

[0010] Furthermore, the multi-dimensional hierarchical chain network also includes an asset business sub-chain, which is used to store the decentralized identifiers, identity certificates, and equipment ownership and calibration records of all IoT devices and construction machinery. It adopts the Proof-of-Authority (PoA) consensus and is jointly maintained by the equipment manufacturers and owners to ensure the trustworthiness of the equipment identities.

[0011] Furthermore, in the multi-dimensional hierarchical chain group network, among the multiple private channels associated with the business sub-chain, each channel corresponds to a maintenance project or maintenance section, which is composed of the participating nodes of the maintenance project or maintenance section, and each channel adopts a crash-tolerant consensus mechanism to store and share the original data of the maintenance project or maintenance section.

[0012] Furthermore, anchoring the evidence data digest within the private channel to the multidimensional hierarchical chain group network includes: The private channel generates a Merkle root hash containing a batch of transaction records according to a preset first cycle, and packages the root hash together with the channel identifier and timestamp to form a storage data digest. The digest of the evidence storage data is broadcast to the evidence storage subchain as an evidence storage receipt. After verification by the consensus node of the evidence storage subchain, the evidence storage receipt is recorded in the block of the evidence storage subchain, completing the first anchoring. The root chain obtains the hash values ​​of the latest state snapshots from the data storage sub-chain and the accounting sub-chain according to a preset second cycle and stores them for verification, thus completing the final anchoring; the latest state snapshot contains the cumulative state of all transactions since the genesis block, and together with the genesis block hash, it constitutes the data chain proof.

[0013] Furthermore, the carbon emission accounting based on verified data obtained from the subchain includes: The calculation smart contract is used to invoke the emission factor model; the input of the model includes real-time collected equipment operating efficiency, environmental temperature and humidity parameters, material production and transportation distance data, and maintenance process coefficients, and the output is a dynamically adjusted instantaneous carbon emission factor; In the trusted execution environment of the private channel, carbon emission results are generated by calculating based on the instantaneous carbon emission factor and verified device activity data from the data storage subchain.

[0014] Furthermore, the process of storing the accounting results and process proof again includes: The hash value of the carbon emission result, the data batch identifier, and the zero-knowledge proof are submitted as a new transaction to the accounting subchain for storage; the zero-knowledge proof is a zk-ANARK proof generated by a trusted execution environment within a private channel, proving that the accounting process is executed correctly without leaking the original input data; At the same time, a verifiable credential is generated for authorized users to prove to third parties the compliance of the maintenance project's carbon emissions without disclosing the carbon emission figures.

[0015] Secondly, this application provides a blockchain-based trusted evidence storage and accounting system for the carbon footprint of highway maintenance, including: a data sensing and preprocessing module, a private channel evidence storage module, a multi-dimensional hierarchical blockchain network module, and an intelligent accounting and evidence storage module; among which... The data sensing and preprocessing module is used to collect carbon footprint data based on the Internet of Things sensor network, classify the carbon footprint data to obtain key evidence data and process detail data; and calculate the hash value of the process detail data and package the hash value and the key evidence data into a structured data packet. The private channel notarization module is used to upload the data packet to a private channel created for the maintenance project for consensus and notarization. The multi-dimensional hierarchical chain group network module is used to anchor the evidence data digest in the private channel to the multi-dimensional hierarchical chain group network; the multi-dimensional hierarchical chain group network includes a root chain for global supervision and multiple business sub-chains divided according to business logic; The intelligent accounting and evidence storage module is used to trigger the accounting smart contract on the business sub-chain, perform carbon emission accounting based on the verified data obtained from the sub-chain, and store the accounting results and process proof again to obtain a carbon emission report.

[0016] Compared with the prior art, the beneficial effects of this application are: This application achieves significant improvements in the credibility, efficiency, privacy protection, and accurate accounting of highway maintenance carbon footprint management by constructing a technical system of "edge preprocessing - private channel notarization - multi-dimensional chain group anchoring - verifiable accounting". Specifically, intelligent classification and edge processing at the data source effectively reduce the on-chain load of invalid data, laying the foundation for subsequent efficient processing; by creating private channels for each project and adopting a fast consensus mechanism, rapid confirmation of data among a limited number of participants and strong privacy protection are ensured, while sensitive information is processed using a trusted execution environment, cleverly balancing the needs of data availability and trade secret protection; the design of the multi-dimensional hierarchical chain group network is the core innovation of this solution. The collaborative operation of the root chain, business sub-chains, and private channels constitutes a flexible architecture that takes into account both global trusted auditing and efficient business processing. It ensures the ultimate immutability and judicial evidentiary effect of data through the strong trust anchor of the root chain, and achieves efficient and accurate execution of accounting tasks through the specialized division of labor of business sub-chains.

[0017] Furthermore, this method deeply integrates accounting logic with privacy protection technologies. By performing accounting and generating zero-knowledge proofs within a trusted execution environment on a private channel, and then submitting the proofs to the accounting subchain for verification, it not only significantly reduces the on-chain computational pressure but also achieves a breakthrough in ensuring the verifiability of the accounting process and the non-disclosure of original data. This allows carbon emission reports to be highly credible while strictly protecting the core operational data of enterprises. Ultimately, this application provides a closed-loop solution covering the entire chain from data collection and storage to accounting and auditing. It effectively addresses industry pain points in highway maintenance scenarios, such as the susceptibility of carbon footprint data to distortion, difficulties in multi-party mutual trust, and complex audit traceability, providing a solid technical foundation for serious application scenarios such as carbon trading and green finance. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art 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.

[0019] Figure 1 System architecture diagram of a blockchain-based trusted evidence storage and accounting system for highway maintenance carbon footprint provided in this application embodiment; Figure 2 A flowchart illustrating the blockchain-based trusted evidence storage and accounting method for highway maintenance carbon footprint provided in this application embodiment; Figure 3 A flowchart illustrating another blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, provided for an embodiment of this application; Figure 4 A flowchart illustrating another blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, provided for an embodiment of this application; Figure 5 This is a schematic diagram of the architecture of a multidimensional hierarchical chain group network provided in an embodiment of this application. Detailed Implementation

[0020] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0021] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0022] The blockchain-based trusted storage and accounting method for highway maintenance carbon footprint provided in this application can be applied to, for example... Figure 1 The blockchain-based trusted storage and accounting system for highway maintenance carbon footprint, as shown, Figure 1 As shown, the system includes: a data sensing and preprocessing module, a private channel evidence storage module, a multi-dimensional hierarchical chain group network module, and an intelligent accounting and evidence storage module; among which, The data perception and preprocessing module is deployed at the highway maintenance site. It includes an IoT sensor network and an edge computing gateway. The IoT sensor network consists of fuel consumption sensors, power metering modules, vibration sensors, and positioning modules embedded in the construction machinery. It is used to collect carbon footprint data at the source. The edge computing gateway is used to receive sensor data, classify and process the data to obtain key evidence data and process detail data, and calculate hash values ​​for process detail data and package them to generate structured data packets. The private channel evidence storage module consists of server nodes distributed among the participants in each maintenance project. Each maintenance project corresponds to an independent private channel. This module is used to receive data packets from the edge computing gateway, reach consensus among nodes within the channel based on the Raft consensus algorithm, and write transaction records into the channel's private ledger to achieve fast and restricted data evidence storage. The multi-dimensional layered chain cluster network module is a distributed network infrastructure, with its physical carrier being a server cluster distributed across different participating institutions. This module includes at least: a root chain serving as a trust anchor, whose nodes are maintained by regulatory agencies and employ a Byzantine fault-tolerant consensus mechanism; business sub-chains divided according to business logic, including a data storage sub-chain for storing data hashes and identity information, an accounting sub-chain for deploying and running accounting logic, and an asset business sub-chain for managing device identity credentials. Each sub-chain adopts a different consensus mechanism according to its business characteristics. This module is used to receive and verify the data digests from private channels, complete cross-chain anchoring, and provide globally trusted data storage services. The intelligent accounting and evidence storage module is integrated into the accounting sub-chain node, which includes a carbon emission accounting smart contract and a verifiable report generator. This module is used to automatically trigger the accounting logic after the data anchoring is completed, perform carbon emission calculation based on the on-chain verified data, and store the accounting results and process proof on the chain again, and finally generate a verifiable carbon emission report.

[0023] To address the technical problems in existing technologies such as easily distorted data sources for highway maintenance carbon footprint, difficulty in establishing mutual trust among multiple participating entities, lack of effective traceability and auditing mechanisms in carbon accounting processes, low data management efficiency, and insufficient privacy protection capabilities, this application provides a blockchain-based method and system for trusted storage and accounting of highway maintenance carbon footprint. The method includes: Carbon footprint data is collected based on IoT sensor networks, and the carbon footprint data is classified and processed to obtain key evidence data and process details data. Calculate the hash value of the process details data, and package the hash value and key evidence data into a structured data packet; Data packets are uploaded to a private channel created for the maintenance project for consensus and notarization; and the notarized data digests in the private channel are anchored to a multi-dimensional hierarchical chain network; wherein, the multi-dimensional hierarchical chain network includes a root chain for global supervision and multiple business sub-chains divided according to business logic; Once the anchoring is complete, the accounting smart contract on the business sub-chain is triggered to perform carbon emission accounting based on the verified data obtained from the sub-chain, and then stores the accounting results and process proof again to obtain a carbon emission report.

[0024] Based on this, the method realizes the trusted management of the entire lifecycle of highway maintenance carbon footprint data from collection to the generation of accounting reports. It not only improves the efficiency of carbon footprint data processing and accounting, but also ensures the immutability and traceability of data and accounting process through blockchain technology. At the same time, it takes into account the data privacy protection in multi-participant scenarios, effectively solving the industry pain points of highway maintenance carbon management.

[0025] like Figure 2 As shown in the embodiments of this application, the blockchain-based method for trusted storage and accounting of highway maintenance carbon footprint includes: S1. Collect carbon footprint data based on IoT sensor networks, and classify and process the carbon footprint data to obtain key evidence data and process details data.

[0026] Among them, key evidence data is the core integrated data that directly supports the final accounting conclusion of carbon emissions from highway maintenance and is an important basis for carbon emission accounting; process detail data is detailed data used for tracing the process of carbon emissions from highway maintenance, behavioral analysis and data verification, providing support for the verification of the accounting process.

[0027] In some implementation methods, carbon footprint data includes energy consumption data for the operation of maintenance equipment, carbon emission data for the production, transportation and use of maintenance materials, energy consumption data for each process of maintenance construction, carbon emission correlation data for environmental monitoring at the maintenance site, auxiliary carbon emission data corresponding to maintenance operation hours and processes, and consumption data for maintenance materials.

[0028] For example, in highway pavement maintenance projects, sensors deployed on equipment such as mixing plants, pavers, and rollers collect data such as total fuel consumption and cumulative electricity usage as key evidence data. At the same time, data such as equipment start-up and shutdown times, working sections, operating speeds, and vibration monitoring values ​​are collected as detailed process data.

[0029] S2. Calculate the hash value of the process details data and package the hash value and key evidence data into a structured data packet.

[0030] Calculating hash values ​​for process detail data can ensure the integrity and immutability of the process detail data through cryptographic means, preventing data from being tampered with during transmission and storage. Packaging hash values ​​and key evidence data into structured data packets can achieve standardized data management, improve the efficiency of data transmission and processing on the blockchain, and reduce the storage load of invalid data on the blockchain.

[0031] In some implementations, hash values ​​can be calculated using common hash algorithms such as Secure Hash Algorithm 256 (SHA256), Message Digest Algorithm 5 (MD5), and Secure Hash Algorithm 1 (SHA1). These algorithms are used to perform hash operations on single or batch process detail data to obtain unique corresponding hash values. The generation of structured data packets can adopt mainstream data formats such as Extensible Markup Language (XML) and JavaScript Object Notation (JSON). The hash values ​​of process detail data and key evidence data are structured and encapsulated according to the preset field specifications. The data packet must include basic metadata such as data acquisition time, acquisition device identifier, maintenance project number to which the data belongs, and data type identifier. After encapsulation, the data packet is format-validated to ensure the structural integrity and data validity of the data packet.

[0032] It should be noted that the calculation of hash values ​​must ensure uniqueness. Different process detail data should correspond to different hash values. If any modification is made to the process detail data, its corresponding hash value will change accordingly. The field specifications of the structured data package should be formulated in conjunction with the industry standards for carbon accounting of highway maintenance and the requirements for blockchain notarization, and can be flexibly expanded according to actual business needs.

[0033] S3. Upload the data packet to the private channel created for the maintenance project for consensus and notarization; and anchor the notarized data digest in the private channel to the multi-dimensional hierarchical chain group network.

[0034] Among them, the private channel is a dedicated blockchain communication and evidence storage channel created specifically for a single or a type of highway maintenance project, accessible only to the main nodes participating in the project, and data access and operation permissions are only granted to the relevant project participants; the evidence storage data digest is condensed data obtained by cryptographically processing all evidence storage data stored in the private channel, and is the unique identifier of the original evidence storage data, which can represent the integrity of the original data; the multi-dimensional hierarchical chain group network includes a root chain for global supervision and multiple business sub-chains divided according to business logic.

[0035] In some implementations, the consensus and notarization process can be divided into two steps: First, the structured data packet is sent to all participating nodes in the private channel, and each node selects an appropriate consensus algorithm to verify the consistency of the data packet; Second, after verification, the data packet is written as a transaction record into the ledger of the private channel to complete the notarization. Common blockchain consensus algorithms such as Practical Byzantine Fault Tolerance (PBFT), Raft consensus algorithm, and Delegated Proof-of-Stake (DPoS) algorithm can be selected.

[0036] In some implementations, the operations for anchoring a multidimensional hierarchical chain group network may include: First, the evidence storage data in the private channel is aggregated and processed to generate an evidence storage data digest. Then, the evidence storage data digest is sent to the business sub-chain in the multi-dimensional hierarchical chain group network. After verification by the business sub-chain node, the evidence storage data digest is anchored in the business sub-chain. If there is a global supervision requirement, the anchoring information of the business sub-chain can be synchronized to the root chain to realize the secondary anchoring of the evidence storage data digest in the root chain.

[0037] In some implementations, the multi-dimensional hierarchical chain network can take the form of: using the root chain as the global trust foundation, responsible for identity management, data consistency verification, and global auditing of each business sub-chain. The business sub-chains can be divided according to the business logic of highway maintenance carbon footprint management, such as setting up carbon footprint data storage sub-chains, carbon emission accounting sub-chains, maintenance project information management sub-chains, etc. Each business sub-chain operates independently and keeps data synchronized with the root chain. Different business sub-chains can choose appropriate consensus algorithms and data storage methods according to their own business characteristics.

[0038] It should be noted that the creation of private channels must match the boundaries and participating entities of the maintenance project. One maintenance project can correspond to one private channel, or multiple maintenance projects of the same type and in the same region can be integrated into one private channel. The anchoring of the evidence data summary must ensure real-time or near-real-time performance to ensure that the data in the multi-dimensional hierarchical chain group network is consistent with the evidence data in the private channel. The root chain and business sub-chains in the multi-dimensional hierarchical chain group network need to establish a sound data interaction and synchronization mechanism to avoid the problems of data silos and data inconsistency.

[0039] For example, a dedicated private channel is created for a major and medium-scale maintenance project of a highway in a certain province. The project's construction unit, construction unit, supervision unit, and other participants connect to the channel as nodes. After uploading structured data packets to the channel, each node uses the Raft consensus algorithm to complete the consensus verification of the data packets and writes them into the channel's private ledger. Subsequently, the SHA256 hash value of the evidence data in the ledger is calculated to obtain the evidence data digest. The digest is uploaded to the data evidence sub-chain in the multi-dimensional hierarchical chain group network to complete the anchoring. At the same time, the data evidence sub-chain synchronizes the anchoring information of the digest to the root chain, realizing the root chain's global supervision of the evidence data of the maintenance project.

[0040] S4. After the anchoring is completed, the accounting smart contract on the business sub-chain is triggered to perform carbon emission accounting based on the verified data obtained from the sub-chain, and the accounting results and process proof are stored again to obtain a carbon emission report.

[0041] Among them, the accounting smart contract is a computer program deployed on the blockchain business sub-chain, written based on preset carbon emission accounting rules and logic. It can be automatically executed when preset triggering conditions are met, and is used to realize the automation, standardization and immutability of carbon emission accounting for highway maintenance, while ensuring the transparency and traceability of the accounting process.

[0042] In some implementations, the process of generating a carbon emission report is as follows: First, after the evidence data digest is anchored in the multi-dimensional hierarchical blockchain network, the preset trigger conditions for the accounting smart contract are activated. The accounting smart contract extracts the carbon footprint-related data that has been verified by the blockchain from the corresponding business sub-chain, including the hash value verification results corresponding to key evidence data and process details data. Then, according to the preset highway maintenance carbon emission accounting standards, accounting methods, and emission factors, the accounting smart contract automatically calculates the extracted verified data to obtain the accounting results such as the total carbon emissions of the maintenance project, carbon emission data of each process, and carbon emission data of each piece of equipment. At the same time, the execution process of the accounting smart contract is recorded, including data... Based on information such as the source of the data, the selected accounting formula, the emission factor values, and the calculation steps, an accounting process certificate is formed. Then, the accounting results and process certificate are stored as evidence by writing their hash values ​​or raw data into the accounting sub-chain of the blockchain. Finally, in accordance with industry standards and blockchain evidence storage requirements, the accounting results, process certificate, and blockchain evidence storage information are integrated to generate a standardized carbon emission report containing basic project information, carbon footprint data collection, carbon emission accounting process, accounting results, and evidence storage verification information. The report can include a blockchain query portal to facilitate relevant parties to trace and verify the report content.

[0043] For example, once the data summary of a highway minor repair and maintenance project is anchored on the data storage subchain of the multi-dimensional hierarchical chain cluster network, the accounting smart contract deployed on the accounting subchain is triggered. This contract extracts verified key evidence data such as the cumulative total energy consumption of equipment and the total carbon emissions of materials for the project from the data storage subchain. According to the accounting methods in the relevant specifications for carbon emission accounting of highway engineering and the local highway maintenance emission factors, it automatically calculates the total carbon emissions of the project, the carbon emissions of road repair procedures, and the carbon emissions of maintenance equipment. At the same time, it records the process proofs such as data extraction nodes, accounting formulas, and emission factor values. The hash values ​​of the accounting results and process proofs are written into the accounting subchain to complete the notarization. Finally, the project number, the list of data collection equipment, the accounting results, the blockchain notarization hash value, the traceability link, and other information are integrated to generate a standardized carbon emission report for the maintenance project.

[0044] Based on the above technical solutions, the blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance provided in this application achieves efficient, reliable, and refined management of the entire lifecycle of highway maintenance carbon footprint data by constructing a technical architecture that includes data classification and processing, private channel storage, multi-dimensional hierarchical chain network anchoring, and automatic smart contract accounting. This method relies on the Internet of Things to achieve precise source collection of carbon footprint data, uses hash algorithms to ensure data integrity and immutability, achieves data privacy protection and efficient consensus in multi-party scenarios through private channels, and utilizes the collaborative operation of the root chain and business sub-chains of the multi-dimensional hierarchical chain network to balance the strong trust requirements of global supervision with the professional requirements of business processing. It automates and standardizes carbon emission accounting through accounting smart contracts, and the final generated carbon emission report possesses the characteristics of immutability, full traceability, and multi-party verifiability. This effectively solves the core pain points of easy data distortion at the source, difficulty in establishing trust among multiple parties, and complex auditing and traceability in the field of carbon accounting in highway maintenance, providing a full-chain technical solution for carbon management in the highway maintenance field.

[0045] In one possible implementation of this application embodiment, the above-mentioned S1 can be specifically implemented by the following S101, S102 and S103, which are described in detail below: S101. Deploy edge computing gateways at highway maintenance sites and pre-set importance classification rules for carbon footprint data in the gateways.

[0046] Among them, the edge computing gateway is a localized data processing device deployed at the maintenance site. It plays the role of data collection and aggregation, local preprocessing and classification. The core of the preset data importance classification rules is to classify the importance level and classification type of data based on the actual needs of carbon emission accounting for highway maintenance.

[0047] In some implementations, edge computing gateways are deployed in a distributed manner according to the distribution of work areas at the maintenance site. They are mainly deployed in the work areas of core maintenance equipment such as mixing plants, pavers, and road rollers, as well as in areas with dense carbon footprint data collection such as material storage areas and energy consumption metering areas. The gateways are interconnected through industrial IoT protocols.

[0048] In some implementation methods, the preset data importance classification rules are based on national and industry standards for carbon emission accounting in highway maintenance. They are also formulated in combination with the actual accounting needs of maintenance projects, the correlation between data and carbon emission accounting conclusions, and the traceability value of data. The rules clearly define two levels of judgment criteria: the first level is an integrated data judgment criterion that directly supports the final carbon emission accounting conclusion, and the second level is a detailed data judgment criterion that is only used for process traceability and analysis. The rules can be dynamically updated and adjusted through remote configuration to adapt to the accounting needs of different types of maintenance projects. A data classification judgment logic module is also built in the gateway to convert the preset rules into executable program code, providing an execution basis for subsequent automatic classification processing.

[0049] It should be noted that key evidence data includes, but is not limited to, the cumulative total energy consumption of equipment, the total carbon emissions of materials, the cumulative carbon emissions of maintenance processes, and the total carbon emissions converted from energy types; detailed process data includes, but is not limited to, equipment power curves, GPS positioning trajectories, vibration sensor readings, equipment operating time records, and material transportation mileage and load data.

[0050] For example, at the site of a highway pavement milling and repaving maintenance project, one edge computing gateway was deployed in each of the milling machine operation area, asphalt mixing plant, paver operation area, and roller operation area. Each gateway was networked through the 5G industrial IoT protocol, and hierarchical rules were preset in the gateway.

[0051] S102. Collect raw carbon footprint data based on the IoT sensor network deployed at the maintenance site, and transmit the collected raw data to the edge computing gateway of the corresponding area in real time.

[0052] Among them, the Internet of Things (IoT) sensor network is a distributed data acquisition network composed of various sensing sensors and data transmission modules. It is the source carrier for carbon footprint data acquisition. Transmitting raw data to the edge computing gateway can realize the local aggregation of data and provide a data foundation for subsequent hierarchical processing.

[0053] In some implementations, based on the type of maintenance carbon footprint data collected, corresponding IoT sensors are deployed at the maintenance site. These include electricity meters and fuel flow meters for collecting equipment energy consumption, power sensors, vibration sensors, and GPS positioning modules for collecting equipment operating status, material metering sensors for collecting material consumption, and temperature and humidity sensors for collecting environmental parameters. Each sensor collects data according to a preset collection frequency. The raw carbon footprint data collected by the sensors is transmitted to the edge computing gateway of the relevant area via wired or wireless transmission. During transmission, a data checksum mechanism is used to ensure data integrity. The formula is as follows: ,in It is a 32-bit cyclic redundancy check code. to The raw data to be transmitted is segmented, and P is a preset check polynomial. If the check code calculated by the receiving gateway is consistent with that of the sending end, the data transmission is determined to be error-free. If they are inconsistent, the data is determined to be corrupted and a retransmission is requested. After receiving the raw data, the edge computing gateway will perform preliminary format unification processing on the data, converting the heterogeneous data from different sensors into a standardized digital format. At the same time, metadata tags such as acquisition time, acquisition sensor identifier, maintenance area, and equipment number are added to each data.

[0054] S103. The edge computing gateway automatically classifies the raw carbon footprint data with a unified format according to the preset data importance rules, and obtains key evidence data and process detail data.

[0055] In some implementations, the edge computing gateway calls a pre-defined hierarchical judgment logic module to evaluate each standardized raw data with added metadata tags. The judgment criterion is whether the data is directly used in the calculation of the final carbon emission accounting conclusion. Integrated data that meets the core-level data judgment criteria is defined as key evidence data, specifically including the cumulative total energy consumption of equipment and the total carbon emissions of materials; among which, The formula for calculating the cumulative total energy consumption of equipment is: , This represents the cumulative total energy consumption of the equipment during a specific operating phase. Let i be the instantaneous energy consumption value collected during the i-th sampling. The energy consumption baseline value is the initial value at the start of the operation; the formula for calculating the total carbon emissions of materials is: , The total carbon emissions of a certain type of maintenance material. For the quality of materials used in batch j, The unit carbon emission factor for this batch of materials; Detailed data that meets the process-level data judgment criteria are defined as process detailed data, which specifically includes equipment power curves, GPS positioning trajectories, and vibration sensor readings. The equipment power curve is generated by arranging the continuously collected instantaneous equipment power values ​​in a time series, the GPS positioning trajectory is generated by connecting the continuously collected equipment point coordinates in a time series, and the vibration sensor readings are the instantaneous and cumulative values ​​of equipment operation vibration collected by the sensors. After the edge computing gateway completes the hierarchical process, it will perform data cleaning and deduplication on key evidence data and process detail data respectively, removing outliers, missing values ​​and duplicate data to ensure the effectiveness of subsequent data processing. At the same time, the gateway will create data indexes for the two types of data and associate the metadata information of the data to facilitate subsequent querying, retrieval and traceability.

[0056] Based on the above technical solution, this application realizes the localized processing of highway maintenance carbon footprint data from source collection to intelligent classification through S101 to S103. With the help of the distributed deployment and localized computing of edge computing gateways, the accuracy of data collection, the integrity of transmission, and the efficiency of classification are achieved. By pre-setting standardized classification rules, the accuracy of the classification of key evidence data and process detail data and the validity of the data itself are guaranteed. It not only filters out the core data for carbon emission accounting, but also retains the detailed data for tracing the operation process. At the same time, the localized processing reduces the load of long-distance transmission of carbon footprint data and the redundancy of on-chain storage, providing a high-quality and targeted data source for subsequent hash calculation, data packet packaging, and on-chain evidence storage. It solves the problems of messy highway maintenance carbon footprint data and difficulty in filtering effective data from the source.

[0057] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S2 can be implemented through the following S201, S202, and S203, which are explained in detail below: S201. Extract detailed process data and key evidence data of the completed cleaning process from the hierarchical processing results of the edge computing gateway.

[0058] In some implementations, the corresponding process details and key evidence data are precisely extracted from the dedicated data index established by the edge computing gateway for the two types of data, based on dimensions such as maintenance project number, data collection period, and equipment identification. The extraction process is completed through the gateway's local data interaction interface. After extraction, the two types of data are correlated and verified. The consistency of data ownership is confirmed by the data's metadata tags (collection time, equipment, maintenance section), ensuring that the extracted process details and key evidence data are matching data from the same maintenance project and the same work period. At the same time, the completeness of the data fields is preliminarily checked to confirm that the fields contained in the key evidence data and process details data are complete and valid.

[0059] S202. Calculate the hash value of the extracted process details data to generate a unique set of hash values.

[0060] Hash value calculation is a process of performing one-way encryption on process detail data using cryptographic algorithms. The generated hash value is a unique digital identifier for the process detail data. It can prove the integrity and immutability of the process detail data without storing the full information of the process detail data, while reducing the storage load on the subsequent chain. It is the key link in this method to achieve efficient data storage.

[0061] In some implementations, the commonly used secure hash algorithm SHA256 (SHA256) in the blockchain field can be used to calculate the hash value of the process detail data. Then, the process detail data is classified and calculated separately according to its type, such as the device power curve, GPS positioning trajectory, vibration sensor degree, etc. The hash values ​​of the data are then merged to generate a root hash value of the overall process detail data, forming a hash value set of "single-type data hash value + root hash value". After the calculation is completed, the hash value is validated by re-entering the original process detail data into the algorithm for calculation and checking whether the two calculation results are consistent to ensure that the hash value calculation is error-free.

[0062] For example, the SHA256 algorithm is used to calculate the hash value H1 from the power curve data of the paver in the maintenance project, the hash value H2 from the GPS positioning trajectory data, and the hash value H3 from the vibration sensor degree data. Then, H1, H2, and H3 are concatenated and the root hash value Htotal is calculated again using the SHA256 algorithm, forming a hash value set {H1, H2, H3, Htotal}. Subsequently, the original process detail data is re-entered into the algorithm for calculation, and the result is checked to be completely consistent with the initial hash value, thus completing the hash value validity verification.

[0063] S203. Integrate the generated process detail data hash value set with the extracted key evidence data and package them into a standardized structured data package.

[0064] This step involves standardizing and encapsulating the dispersed hash values ​​and key evidence data. The resulting structured data package serves as the standard data unit for subsequent upload to the private channel for consensus and evidence storage. This improves the efficiency of data transmission, verification, and storage in the blockchain network while ensuring the uniformity and parsability of the data structure.

[0065] In some implementations, JavaScript Object Notation (JSON) can be used as the standard format for structured data packets. This format is easy to parse, cross-platform, and lightweight, making it suitable for the rapid data processing needs of blockchain nodes. The data packet contains a core data layer and a metadata layer. The core data layer records the hash value set (single-class hash value + root hash value) of process details and key evidence data. The metadata layer records auxiliary information such as maintenance project number, data collection period, edge computing gateway identifier, data classification time, hash value calculation algorithm, and data packet generation time. After packaging, the entire structured data packet can be subjected to a cyclic redundancy check (CRC32) to verify its integrity. The generated checksum is transmitted along with the data packet, and the receiving end can use the checksum to verify whether the data packet has been damaged or tampered with during transmission. If the data packet contains commercially sensitive data associated with subsequent private channel encrypted calculations, a sensitive data tag field needs to be set separately in the data packet to prepare for encrypted calculations in the S3 trusted execution environment.

[0066] Based on the above technical solution, this application realizes the hash calculation and structured packaging of highway maintenance carbon footprint classification data through steps S201 to S203, forming a standard data unit adapted for blockchain notarization. This step ensures the reliability of the data foundation by extracting valid data; it uses the SHA256 algorithm to classify and hash the process detail data and generate a hash value set, achieving lightweight notarization of data while ensuring the integrity and immutability of the process detail data, reducing the storage load on the chain in the subsequent process; then, it constructs a structured data packet containing a core data layer and a metadata layer in JSON format and adds CRC32 integrity verification to ensure the accuracy and traceability of the data packet during transmission and consensus. At the same time, it provides a standardized and identifiable data source for consensus notarization of S3 private channels and encrypted calculation of commercially sensitive data, laying a solid foundation for subsequent blockchain notarization and accounting from the perspectives of data format and encryption verification.

[0067] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S3 can be implemented through the following S301, S302, and S303, which are explained in detail below: S301. Create a dedicated private channel for the target highway maintenance project and complete the pre-deployment of participating node configuration and consensus, as well as encryption rules.

[0068] Among them, the private channel is a blockchain communication and evidence storage channel built exclusively for a single maintenance project or maintenance section. It only opens node access permissions to relevant project participants, which can realize the isolated storage and privacy protection of project-level data. The preset nodes and rules provide a guarantee for the efficient and standardized execution of subsequent consensus evidence storage.

[0069] In some implementations, the process of creating a private channel includes: First, create independent private channels for each maintenance project based on its boundaries and the scope of participating entities. Each channel uniquely corresponds to a maintenance project or a maintenance section, and the project's construction unit, construction unit, supervision unit, owner unit, and other relevant parties can access the channel as participating nodes. Then, a crash-tolerant consensus mechanism is configured for the private channel, with the Raft consensus algorithm deployed at the core. At the same time, a Trusted Execution Environment (TEE) is built within the channel for subsequent encrypted computation of commercially sensitive data. Next, identity verification and permission allocation are performed on all participating nodes in the access channel. The node identity information needs to be linked and verified with the asset business sub-chain in the multi-dimensional hierarchical chain group network. The legality of the node is confirmed by the decentralized identifier and identity certificate stored in the asset business sub-chain. At the same time, different permissions such as data viewing, transaction confirmation, and operation review are configured for different nodes. Finally, in the private channel, the triggering rules and execution standards for data on-chaining, consensus verification, and encryption processing are preset, and the data packet receiving format, the threshold number of nodes that pass consensus, and the identification and processing procedures for sensitive data are clearly defined.

[0070] It should be noted that the creation of private channels must match the actual business scope of the maintenance project, and newly started maintenance sections only need to create new private channels without changing the existing chain group structure, which can realize the "hot-swap" architecture. Different business sub-chains can adopt different consensus mechanisms and governance models according to actual needs, thereby taking into account both regulatory security and enterprise efficiency.

[0071] For example, a dedicated private channel is created for a routine maintenance section of a provincial highway, YH-LU01. The construction unit, supervision unit, and local traffic management department of this section apply for access as participating nodes. The channel verifies the decentralized identifier and identity certificate of each node through the asset business sub-chain. After confirming its legitimacy, different operation permissions are assigned to each node. Subsequently, the Raft consensus algorithm is deployed for the channel, and the threshold for consensus to pass is set to more than 2 / 3 of the nodes. At the same time, a Trusted Execution Environment (TEE) is built, and raw material purchase unit price, equipment process parameters, etc. are preset as commercially sensitive data and encrypted processing rules are configured.

[0072] S302. Upload the structured data packet generated in S2 to the private channel of the target maintenance project. Each participating node completes consensus verification and writes the transaction record into the channel's private ledger. Encrypt the calculation of commercially sensitive data and only upload the result to the blockchain.

[0073] In some implementations, synchronizing structured data packets from the edge computing gateway to all participating nodes in the private channel includes the following steps: Each node first performs a preliminary check on the data packet format, CRC32 checksum, and metadata identifier to confirm the integrity and ownership legitimacy of the data packet; After the verification is passed, the private channel executes the consensus process based on the deployed Raft consensus algorithm, sequentially completing leader node election, packet log replication, and multi-node consistency confirmation. When the preset node consensus threshold is reached, the packet consensus is deemed to have passed. The data packets that pass consensus are written as transaction records into the private ledger of the private channel. The private ledger only participates in the synchronization between nodes within the channel, thus achieving isolated data storage. If the data packet contains commercially sensitive data, including but not limited to the unit price of raw material purchases, the operating efficiency of equipment per unit time, and the specific process parameters of the equipment, such data will be extracted separately and transmitted to the Trusted Execution Environment (TEE) within the channel. The relevant encrypted calculations will be completed in the TEE, and only the calculation results and the hash value of the results will be written to the private ledger as transaction records on the blockchain. The original commercially sensitive data will only be stored in the TEE and will not be disclosed to the outside world. The encrypted calculation results need to be uniquely linked to transaction records of other non-sensitive data to ensure data relevance and traceability.

[0074] For example, the structured data packet of highway maintenance section YH-LU01 is uploaded to its dedicated private channel. The three participating nodes first verify the CRC32 checksum and project identifier of the data packet. After confirming that there are no errors, Raft consensus is initiated, the construction unit node is elected as the leader, and the data packet log is fully replicated across all nodes. Consensus is reached after all three nodes confirm. The node detects that the data packet contains commercially sensitive data such as the purchase price of asphalt raw materials and the unit time operation efficiency of the paver. It extracts this data into the channel TEE and calculates the unit carbon emission cost of asphalt materials and the unit energy consumption carbon emission of paver operation within the TEE. Only the calculation result and SHA256 hash value are written to the private ledger. The original unit price and efficiency data are stored in the TEE. At the same time, a unique association identifier BH-LU01-001 corresponding to the data packet is added to the result.

[0075] S303. Generate a summary of the evidence storage data in the private channel, and anchor it sequentially to the data storage sub-chain and root chain of the multi-dimensional hierarchical chain group network according to a preset period to complete the double-layer anchoring of the evidence storage data.

[0076] This step is crucial for achieving the transition from project-level evidence storage to global-level oversight. The evidence storage data digest, as a cryptographic condensation of the full evidence storage data of the private channel, can significantly reduce on-chain storage load and effectively prove the integrity of the original data through cryptographic characteristics. By anchoring the evidence storage data digest to a multi-dimensional hierarchical chain network, and based on the specialized evidence storage processing of the data storage sub-chain and the global trust anchoring of the root chain, cross-chain trusted management of evidence storage data can be achieved. At the same time, the strong trust endorsement of the root chain can further guarantee the immutability of the full evidence storage data.

[0077] In some implementations, the steps for two-layer anchoring include: The private channel processes transaction records in the ledger in batches according to a preset first cycle, generating a Merkle root hash containing all transaction records in that batch. This Merkle root hash is then packaged together with the channel identifier and timestamp to form a data digest for evidence preservation. This data digest serves as the evidence preservation receipt. The Merkle root hash is calculated as follows: First, calculate the SHA256 hash value for each transaction record to form a leaf node. If the number of leaf nodes is odd, the last node is concatenated, and then the hash values ​​of two adjacent nodes are concatenated and the SHA256 hash value is calculated again to form the node of the next level. This process continues upwards layer by layer until a unique root hash value is generated. ,Right now , For the final Merklegen hash; The generated Merkle root hash is broadcast as a data digest to the data storage subchain in the multi-dimensional hierarchical chain group network. After being verified by the consensus node of the data storage subchain, it is recorded in the block of the data storage subchain, completing the first anchoring of the data storage. The root chain of the multi-dimensional layered chain network obtains the hash value of the latest state snapshot from the data storage sub-chain and the accounting sub-chain according to the preset second cycle. The state snapshot contains the cumulative state of all transactions since the genesis block. The root chain uses the Byzantine Fault Tolerance (BFT) consensus mechanism to verify the hash value and complete the storage. At the same time, it verifies the genesis block hash of the data storage sub-chain to confirm its identity legitimacy and data continuity, and completes the final anchoring of the stored data. Meanwhile, the asset business sub-chain of the multi-dimensional hierarchical chain network will synchronously verify the IoT devices and construction machinery information associated with the evidence data summary, ensuring the validity of the calibration and ownership records of the data collection devices, providing device-level evidence for the validity of the data, and ensuring the credibility of the device identity.

[0078] It should be pointed out that, as Figure 5 As shown, the multi-dimensional hierarchical chain group in this application adopts a three-layer collaborative architecture of "root chain - business sub-chain - private channel", which aims to systematically solve the trust, efficiency and privacy problems in the carbon footprint management of highway maintenance through functional decoupling and data hierarchical processing.

[0079] The top layer of the network is the root chain, which serves as the global trust anchor for the entire system. It employs a Byzantine fault-tolerant consensus mechanism (such as PBFT, which uses a practical Byzantine fault-tolerant mechanism) and is maintained by core regulatory nodes. The root chain does not store specific business data; instead, it is used to periodically store the state snapshot hashes of its subordinate business sub-chains and the storage receipts of each private channel, thereby providing the highest level of immutable endorsement for the data integrity and continuity of the entire network.

[0080] The middle layer consists of business sub-chains vertically divided according to business logic, serving as the core carriers of system functions. Specifically, the data storage sub-chain records the hash values ​​and storage certificates of all key carbon footprint data, acting as a public ledger for data existence, employing a Delegated Proof-of-Stake (DPoS) consensus mechanism; the accounting sub-chain specifically deploys and runs carbon emission accounting smart contracts, using a Proof-of-Stake (PoS) consensus mechanism; and the asset business sub-chain manages the digital identities of all IoT devices and construction machinery, employing a Proof-of-Authority (PoA) consensus mechanism to ensure the trustworthiness of the data source. Each business sub-chain can adopt different consensus mechanisms based on its business characteristics to achieve the optimal balance between performance and security.

[0081] At the bottom layer are private channels associated with business sub-chains. Each channel corresponds to an independent maintenance project or segment, composed of participating nodes for that project. The private channels adopt efficient crash-tolerant (CFT) consensus mechanisms like Raft and serve as a private space for storing, sharing, and initially processing raw carbon footprint data, effectively ensuring the privacy and processing efficiency of project-level data.

[0082] Each layer is tightly linked through cryptographic anchoring technology: data digests within private channels are anchored to business sub-chains, and state digests of business sub-chains are anchored to the root chain. This architecture enables the orderly flow and trusted proof of data at different granularities and visibility levels, satisfying both the efficient collaboration and privacy protection needs of specific businesses and ensuring the auditability of global data, thus forming a resilient, scalable, and trustworthy carbon footprint management infrastructure.

[0083] For example, the first cycle of the private channel of highway maintenance section YH-LU01 is set to 1 hour, and a Merkle root hash is generated for the transaction records of the private ledger every hour. -LU01-001 is used as a proof digest and broadcast to the data proof subchain of the multi-dimensional hierarchical chain network. After consensus verification by the data proof subchain nodes, it is written into its block, completing the first anchoring. The root chain sets the second cycle to 24 hours, and retrieves data containing LU01-001 from the data proof subchain daily. The latest state snapshot, including LU01-001, is used to calculate its SHA256 hash value. Then, the notarization is completed through the BFT consensus mechanism. At the same time, the genesis block hash of the data notarization subchain is verified to confirm its legality and complete the final anchoring. The asset business subchain also verifies the calibration records of the pavers and road rollers associated with this channel to confirm the trustworthiness of the equipment.

[0084] Based on the above technical solution, this application implements the complete process of structured data packets from private channel-specific consensus storage to global anchoring in a multi-dimensional layered chain network through S301 to S303, constructing a three-level storage system of project isolation storage, special chain storage, and root chain anchoring. This system has extremely high security, making data tampering difficult. Attackers would need to simultaneously compromise the root chain, specific business chains, and project private channels to tamper with the data, thus strengthening the data security defense from a technical perspective. At the same time, it achieves refined privacy management, achieving the effect of data usability without visibility based on the "business chain + private channel" architecture. Regulators can verify the authenticity of data on the root chain without accessing detailed data, and bidding participants can only view relevant data within their own channels, fully protecting the data privacy of different participants. In terms of operational efficiency, the system boasts excellent call speed, allowing different business chains to work in parallel, and private channels in different segments to operate completely in parallel without interference. This enables the overall system's transaction count per second to increase exponentially, significantly improving processing efficiency. Simultaneously, most data queries can be completed within the fastest-responding private channel, with queries only required for cross-segment auditing or verification. The root chain is used exclusively for top-level auditing, achieving a layered offloading of query pressure. Furthermore, restricting high-frequency, fine-grained data interactions to private channels reduces the overhead of global consensus, effectively lowering the overall system operating costs.

[0085] In one possible implementation of this application embodiment, the above-mentioned S4 specifically includes the following S401 to S403: S401. After the evidence storage data digest is anchored to the multi-dimensional hierarchical chain group network, the accounting smart contract on the business sub-chain is triggered to complete the initialization operation of carbon emission accounting.

[0086] In some implementations, the initialization steps for carbon emission accounting include: The final anchoring of the root chain in the multi-dimensional hierarchical chain group network to complete the storage data summary is used as the sole triggering condition for the accounting smart contract. After the root chain completes the anchoring, it will send an anchoring completion trigger instruction to the accounting sub-chain. After receiving the instruction, the accounting sub-chain will start the accounting smart contract deployed on the chain. After the smart contract starts, it first completes the initialization process. First, initiate a data extraction authorization application to the data storage subchain, and obtain the carbon footprint data of the maintenance project that has been verified by blockchain based on the data batch identifier, including the hash value verification results corresponding to key evidence data and process details data. At the same time, retrieve the calibration and ownership records of the collection equipment from the asset business subchain as auxiliary evidence of the data validity. Second, it activates the Trusted Execution Environment (TEE) of the private channel of the corresponding maintenance project to build a secure computing environment for subsequent accounting calculations involving commercially sensitive data; Third, the extracted verified data undergoes format adaptation and integrity verification to confirm the matching of data fields, data dimensions, and accounting logic. If data is missing, a data completion request is sent to the private channel to ensure the integrity of the accounting data.

[0087] It should be noted that the data extraction operation initiated by the contract must carry a unique project and data batch identifier to ensure accurate matching between the extracted data and the target maintenance project; the data integrity verification in the initialization phase only targets the core accounting fields and does not interfere with the hash value verification results of the process details data, thus ensuring the immutability of blockchain data.

[0088] S402. The smart contract calls the preset emission factor model to generate dynamic instantaneous carbon emission factors, and completes carbon emission accounting in a private channel trusted execution environment by combining verified data, and generates proof of the accounting process simultaneously.

[0089] In some implementations, the smart contract retrieves a pre-defined multi-factor dynamic emission factor model from a local contract library. This model uses the instantaneous carbon emission factor as the output and real-time collected data on equipment operating efficiency, environmental temperature and humidity parameters, material production and transportation distances, and maintenance process coefficients as inputs. First, the inputs are normalized to eliminate dimensional differences, and then the dynamically adjusted instantaneous carbon emission factor is calculated. The calculation formula is as follows: ; in, This represents the dynamic instantaneous carbon emission factor of the i-th type of equipment at time t, reflecting the actual emission level under real-time operating conditions; Represents the baseline emission factor for the i-th type of equipment, based on a fixed value measured under standard laboratory conditions; ΔT represents the real-time operating efficiency of the equipment at time t; ΔT is the temperature deviation, which represents the difference between the actual ambient temperature at time t and the optimal fresh temperature for this type of equipment; ΔH is the humidity deviation, which represents the difference between the actual relative humidity at time t and the optimal operating humidity for this type of equipment. Indicates the distance of material transportation; This represents the reference transport distance, which is usually taken as the normalized baseline value based on the regional average transport distance. α represents the maintenance process coefficient; β, γ, and λ represent the operating efficiency sensitivity coefficient, temperature deviation penalty coefficient, humidity deviation penalty coefficient, and distance elasticity coefficient, respectively, determined through historical data regression analysis to reflect the degree of influence of each factor on emission factors.

[0090] Since carbon emission factors are not constant but are influenced by a combination of factors including equipment operating efficiency, environmental conditions, and operational processes, this formula uses a multi-factor nonlinear coupling model to dynamically adjust the static baseline emission factor to reflect the instantaneous value under actual operating conditions. Among these factors, the equipment operating efficiency term (…) It captured the impact of equipment load rate on combustion efficiency or power conversion efficiency; environmental penalty item ( This quantifies the thermodynamic efficiency loss caused by temperature / humidity deviations from optimal conditions; the distance term ( This reflects the marginal carbon emission increase caused by the material transportation distance; the process coefficient ( This model reflects the inherent carbon emission characteristics of different maintenance processes. This modeling approach allows emission factors to respond in real-time to dynamic factors such as equipment aging, seasonal changes, geographical location, and process selection. Furthermore, through quantitative analysis of various influencing factors, it provides clear optimization directions for precise carbon reduction (such as optimizing construction time to avoid high temperatures and improving equipment maintenance levels). Finally, this dynamic factor, as input to smart contracts, adds spatiotemporal and process-specific tags to the carbon footprint, enhancing the traceability and trading credibility of carbon assets.

[0091] The calculation smart contract synchronously transmits the calculated instantaneous carbon emission factor and the verified device activity data (device energy consumption, material usage, etc.) extracted from the data storage subchain to the TEE environment of the private channel. The carbon emission calculation is then completed within the TEE. The calculation formula is as follows: ;in, This indicates the total carbon emissions within the accounting period; The activity data of the i-th type of equipment at time t, such as fuel consumption, power consumption, etc. The unit carbon emission factor of the j-th type of material; Let represent the amount of material of type j used.

[0092] While the accounting is completed in the TEE, the smart contract will simultaneously record the complete accounting process proof, including the emission factor model version, the source of each input parameter value and the result of normalization processing, the basis for weight coefficient calibration, the instantaneous carbon emission factor calculation result, the equipment activity data extraction node, the accounting formula selection, the calculation result of each step, and other information. The process proof and the accounting result are linked in real time to ensure that the accounting process can be traced throughout.

[0093] It should be noted that carbon emission accounting needs to be carried out by maintenance process and equipment type. In addition to the total carbon emissions, it is also necessary to generate carbon emission data for each process and each piece of equipment to improve the accuracy of the accounting.

[0094] S403. Cryptographically process and store the carbon emission accounting results and process proofs on the blockchain, generate zero-knowledge proofs and provide verifiable credentials for authorized users, and finally integrate them to generate a standardized carbon emission report.

[0095] In this step, cryptographic processing and on-chain evidence storage ensure the immutability of the accounting results and process proofs. The generation of zero-knowledge proofs achieves the privacy protection goal of "verifiable accounting process and non-disclosure of original data." The integration of standardized carbon emission reports provides a visualized and traceable final result for the carbon footprint management of conservation projects, meeting the usage needs of scenarios such as supervision, auditing, and carbon trading.

[0096] In some implementations, the carbon emission accounting results and accounting process proofs output by the TEE environment are first hashed using SHA256 to generate a unique digital fingerprint. Secondly, a zk-ANARK zero-knowledge proof is generated based on the private channel TEE environment. This proof only verifies to the outside world that "the accounting process is executed correctly according to preset rules and the accounting results are true and valid," without disclosing any original input data, commercially sensitive parameters, or specific calculation details. Subsequently, the hash value of the accounting result, the data batch identifier, and the zk-ANARK zero-knowledge proof are submitted as a new transaction record to the accounting sub-chain of the multi-dimensional layered chain network for on-chain notarization. A unique association is established between the notarization record and the notarization data digest of S3, achieving full-chain traceability from "data collection - notarization - accounting - result notarization." Next, exclusive verifiable credentials are generated for authorized users such as traffic regulatory departments and property owners. These credentials include information such as project number, data batch, accounting subchain storage address, and zero-knowledge proof verification entry. Authorized users can verify the compliance and authenticity of the accounting results without obtaining the original data through the credentials. Finally, in accordance with national and industry standards for carbon emission accounting for highway maintenance, the report integrates basic information on maintenance projects, carbon footprint data collection and storage, emission factor model parameters, carbon emission accounting results, key information proving the accounting process, blockchain storage address and hash value, zero-knowledge proof verification entry, and other content to generate a structured and standardized carbon emission report. The report includes a blockchain traceability QR code, which allows users to view the entire chain of storage and accounting information.

[0097] Based on the above technical solutions, this application realizes a complete carbon emission accounting process from smart contract triggering, dynamic and accurate accounting to result storage and report generation through S401 to S403. This step uses the final anchoring of the root chain as the sole trigger condition, ensuring a strong binding between the initiation of the accounting process and the validity of the stored data. By calling a multi-factor dynamic emission factor model, it achieves scenario-based dynamic adjustment of carbon emission factors, solving the problem of low accuracy in traditional fixed-factor accounting. Simultaneously, the accounting is completed in a TEE environment, balancing data computation needs with commercial privacy protection. Through zk-ANARK zero-knowledge proofs, hash value calculation, and on-chain storage of the accounting sub-chain, the accounting results and process proofs possess immutability and privacy protection, achieving the technical effect of "verifiable without disclosure." Finally, the integrated and generated standardized carbon emission report realizes full-chain traceability of the carbon footprint of maintenance projects from data collection to accounting results. This provides a credible and compliant core basis for the supervision, auditing, and carbon trading scenarios of highway maintenance carbon management, effectively solving the industry pain points of low carbon accounting accuracy, insufficient result credibility, and difficulty in balancing privacy protection and data verification in the highway maintenance field, and perfecting the full-chain closed-loop solution of "collection-storage-accounting-reporting."

[0098] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0099] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. A blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, characterized in that, include: Carbon footprint data is collected based on IoT sensor networks, and the carbon footprint data is classified and processed to obtain key evidence data and process detail data. Calculate the hash value of the process details data, and package the hash value and the key evidence data into a structured data packet; The data packet is uploaded to a private channel created for the maintenance project for consensus and notarization; and the notarized data digest in the private channel is anchored to a multi-dimensional hierarchical chain network; the multi-dimensional hierarchical chain network includes a root chain for global supervision and multiple business sub-chains divided according to business logic; Once the anchoring is complete, the accounting smart contract on the business sub-chain is triggered to perform carbon emission accounting based on the verified data obtained from the sub-chain, and then stores the accounting results and process proof again to obtain a carbon emission report.

2. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 1, is characterized in that... The classification and processing of the carbon footprint data yields key evidence data and process detail data, including: Using edge computing gateways deployed at maintenance sites, data is automatically classified according to preset importance rules: Integrated data directly used in the final accounting conclusions are defined as key evidence data, including the cumulative total energy consumption of equipment and the total carbon emissions of materials; The detailed data used for process traceability and analysis is defined as process detailed data, including equipment power curves, GPS positioning trajectories, vibration sensor readings, equipment operating time records, and material transportation mileage and load data.

3. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 1, is characterized in that... The process of uploading the data packet to a private channel created for the maintenance project for consensus and notarization includes: participating nodes within the private channel reaching a consensus on the received data packet based on the Raft consensus algorithm and writing the transaction record into the channel's private ledger; wherein, for commercially sensitive data, encrypted calculations are performed using a trusted execution environment within the channel, and only the calculation results are uploaded to the blockchain; the commercially sensitive data includes the unit price of raw material purchases, the operating efficiency of equipment per unit time, and the specific process parameters of the equipment.

4. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 1, is characterized in that... The multidimensional hierarchical chain group network includes: A single chain serves as a trust anchor, employing a Byzantine fault-tolerant consensus mechanism. It only stores the genesis block hash of each business sub-chain and the Merkle root hash periodically submitted by each private channel. The genesis block hash is used to verify the legitimacy of the identity of each business sub-chain and the continuity of the data, while the Merkle root hash is used to cryptographically prove the existence and integrity of data batches within the private channel. There are at least two business sub-chains, including a data storage sub-chain responsible for storing data hashes and identity information, and an accounting sub-chain used for deploying and running accounting logic. Each business sub-chain adopts a different consensus mechanism according to its business characteristics.

5. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 4, is characterized in that... The multi-dimensional hierarchical chain network also includes an asset business sub-chain, which is used to store the decentralized identifiers, identity certificates, and equipment ownership and calibration records of all IoT devices and construction machinery. It adopts the Proof-of-Authority (PoA) consensus and is jointly maintained by the equipment manufacturers and owners to ensure the trustworthiness of the equipment identity.

6. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 4, is characterized in that... In the multi-dimensional hierarchical chain group network, each private channel associated with the business sub-chain corresponds to a maintenance project or maintenance section, and is composed of participating nodes of the maintenance project or maintenance section. Each channel adopts a crash-tolerant consensus mechanism to store and share the original data of the maintenance project or maintenance section.

7. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 4, is characterized in that... The step of anchoring the evidence data digest within the private channel to the multidimensional hierarchical chain group network includes: The private channel generates a Merkle root hash containing a batch of transaction records according to a preset first cycle, and packages the root hash together with the channel identifier and timestamp to form a storage data digest. The digest of the evidence storage data is broadcast to the evidence storage subchain as an evidence storage receipt. After verification by the consensus node of the evidence storage subchain, the evidence storage receipt is recorded in the block of the evidence storage subchain, completing the first anchoring. The root chain obtains the hash values ​​of the latest state snapshots from the data storage sub-chain and the accounting sub-chain according to a preset second cycle and stores them for verification, thus completing the final anchoring; the latest state snapshot contains the cumulative state of all transactions since the genesis block, and together with the genesis block hash, it constitutes the data chain proof.

8. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 4, is characterized in that... Carbon emission accounting is performed based on verified data obtained from the subchain, including: The calculation smart contract is used to invoke the emission factor model; the input of the model includes real-time collected equipment operating efficiency, environmental temperature and humidity parameters, material production and transportation distance data, and maintenance process coefficients, and the output is a dynamically adjusted instantaneous carbon emission factor; In the trusted execution environment of the private channel, carbon emission results are generated by calculating based on the instantaneous carbon emission factor and verified device activity data from the data storage subchain.

9. The blockchain-based method for trusted storage and accounting of carbon footprint in highway maintenance, as described in claim 4, is characterized in that... The process of storing the accounting results and process proof again includes: The hash value of the carbon emission result, the data batch identifier, and the zero-knowledge proof are submitted as a new transaction to the accounting subchain for storage; the zero-knowledge proof is a zk-ANARK proof generated by a trusted execution environment within a private channel, proving that the accounting process is executed correctly without leaking the original input data; At the same time, a verifiable credential is generated for authorized users to prove to third parties the compliance of the maintenance project's carbon emissions without disclosing the carbon emission figures.

10. A blockchain-based trusted evidence storage and accounting system for the carbon footprint of highway maintenance, characterized in that, include: The module comprises a data perception and preprocessing module, a private channel evidence storage module, a multi-dimensional hierarchical chain group network module, and an intelligent accounting and evidence storage module; among which, The data sensing and preprocessing module is used to collect carbon footprint data based on the Internet of Things sensor network, classify the carbon footprint data to obtain key evidence data and process detail data; and calculate the hash value of the process detail data and package the hash value and the key evidence data into a structured data packet. The private channel notarization module is used to upload the data packet to a private channel created for the maintenance project for consensus and notarization. The multi-dimensional hierarchical chain group network module is used to anchor the evidence data digest in the private channel to the multi-dimensional hierarchical chain group network; the multi-dimensional hierarchical chain group network includes a root chain for global supervision and multiple business sub-chains divided according to business logic; The intelligent accounting and evidence storage module is used to trigger the accounting smart contract on the business sub-chain, perform carbon emission accounting based on the verified data obtained from the sub-chain, and store the accounting results and process proof again to obtain a carbon emission report.

Images