Supply chain carbon data credible management method based on block chain

Abstract

The invention relates to the technical field of data processing, and relates to a blockchain-based supply chain carbon data credible management method, which is characterized in that energy consumption of supplier production equipment is acquired in real time through deploying an edge computing gateway, and supplier production data is acquired in real time through the edge computing gateway deployed on the supplier production equipment; the real-time working condition parameters of the logistics transportation carrier are obtained in combination with the vehicle-mounted terminal, the material disassembly data of the recovery processing link are integrated, and a multi-source data acquisition system of the whole link of the supply chain is constructed. And the block chain network carries out credible processing on the data, a regional carbon emission factor in the industry reference database is called to carry out deviation verification, the intelligent contract engine executes an audit decision based on a preset compliance rule, and when it is detected that the energy consumption deviates from a reference threshold value, a data correction process of multi-party signature confirmation is triggered. According to the method, full-link credible evidence storage and dynamic optimization of the carbon data are realized, and the supply chain carbon emission accounting precision and the cross-border auditing efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a trusted management method for supply chain carbon data based on blockchain. Background Art

[0002] There are technical bottlenecks in the existing supply chain carbon data management, such as data islands in multiple links and the lack of a verification mechanism, resulting in insufficient accuracy of carbon emission accounting throughout the life cycle. Existing methods rely on manual collection of scattered supplier energy consumption data and static transportation parameters, and it is difficult to integrate heterogeneous data sources such as raw material procurement traceability information, energy efficiency parameters of production equipment, and dynamic working conditions of logistics carriers in real time, resulting in calculation deviations of carbon emission factors. The data storage formats of each node in the supply chain are not unified, and there is a lack of a cross-link traceability verification mechanism, making it impossible to effectively verify the relevance between supplier life cycle assessment data and actual production energy consumption. In carbon emission accounting for logistics transportation, fixed emission factors are mostly used, without dynamic correction in combination with real-time route optimization and load changes. The audit node algorithm lacks a collaborative verification mechanism for abnormal detection of transportation trajectories and analysis of energy consumption fluctuations in warehouses. The application of blockchain technology is mostly limited to data storage and evidence, and has not solved technical problems such as standardization integration of multi-source data, dynamic monitoring and early warning, and adaptation of cross-border audit rules, restricting the full-link trusted control of the supply chain carbon footprint. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides a trusted management method for supply chain carbon data based on blockchain, and the technical problem to be solved is the problem of low carbon emission accounting accuracy, insufficient audit efficiency, and lack of a trusted traceability system caused by difficulties in integrating multi-source heterogeneous data, lack of cross-link traceability verification, and imperfect dynamic monitoring mechanism in the existing supply chain carbon data management.

[0004] To solve the above technical problems, the specific technical solutions of the present invention are as follows: The trusted management method for supply chain carbon data based on blockchain provided by the present invention includes: Step S101, obtaining data of all links in the supply chain. The data of all links in the supply chain includes supplier production data, manufacturing process data, logistics transportation data, and recycling and processing data. Among them, the supplier production data is used to collect the energy consumption in real time through an edge computing gateway deployed on the supplier production equipment. The manufacturing process data includes energy equipment sensor data and process improvement records in the factory. The logistics transportation data is used to obtain the real-time working condition parameters of the transportation vehicle through an on-vehicle terminal. The recycling and processing data includes the recovery rate of disassembled materials of the product; Step S102: Input the data of all links in the supply chain into the blockchain network for trust processing. Generate a data digest including spatio-temporal features through the data standardization module, and write the data digest into the blockchain distributed ledger. Among them, the following processing is performed on the supplier production data: Call the regional carbon emission factors in the industry benchmark database to perform data deviation verification, and generate an on-chain record including a verification identifier; Combine the manufacturing process data with the equipment operation parameters to construct a dynamic emission factor model, and verify the logical relevance between the process improvement record and the energy consumption through the dynamic emission factor model. Step S103: Input the on-chain record into the intelligent contract execution engine to make an audit decision based on the preset carbon emission compliance rules. Among them, when it is detected that the verification identifier corresponding to the energy consumption deviates from the industry benchmark threshold, trigger the generation process of the data correction instruction, and perform multi-party signature confirmation through the blockchain consensus node. Step S104: Generate a supply chain optimization instruction according to the result of the audit decision, and feedback the supply chain optimization instruction to the management-side execution device. The supply chain optimization instruction includes supplier evaluation parameters and transportation path planning strategies dynamically adjusted based on the anomaly detection result, forming a low-carbon supply chain management closed-loop system that runs through data collection, verification, and decision-making.

[0005] Collect power consumption data at a set sampling frequency through the edge computing gateway deployed on the supplier production equipment, and realize real-time acquisition of multi-source data in combination with the industrial Internet of Things protocol. The data standardization module calls the carbon emission factors in the industry benchmark database to convert the original energy consumption into the standard carbon emission equivalent value. The dynamic emission factor model dynamically adjusts the calculation logic according to the equipment operation state parameters. When it is detected that there is a significant deviation between the process improvement record and the energy consumption change, generate an on-chain record with a verification identifier. The intelligent contract engine triggers the data correction process based on the preset rules, and the audit node uses an encryption algorithm to perform multi-party verification on the correction instruction, and finally generates a supplier evaluation parameter adjustment instruction and feedbacks it to the management side.

[0006] Further, in the method for trusted management of supply chain carbon data based on blockchain of the present invention, step S102 further includes: Call the raw material procurement database of the third-party certification agency through the blockchain oracle interface, extract the hash digest of the original procurement voucher, and generate a hash feature value including a timestamp; Input the hash feature value and the bill of materials data in the life cycle assessment report into the zero-knowledge proof verification module to verify the relevance and consistency between the procurement voucher and the carbon emission inventory; Generate a blockchain data block including a trust identifier according to the verification result, and the trust identifier is used to update the integrity assessment status of the supplier production data.

[0007] In the processing of supplier production data, the blockchain oracle interface accesses the procurement database of silicon material suppliers in region C, and performs SHA-256 hash operation on the procurement orders to generate 128-bit feature values. The zero-knowledge proof module performs relevance verification on the feature values and the LCA reports provided by the certification institutions in region D. When the energy consumption deviation exceeds the threshold, a data block with a trusted identifier is generated. This identifier triggers the supplier status update process in the supply chain management system, realizing the dynamic matching of procurement traceability data and actual production energy consumption.

[0008] Further, in the method for trusted management of supply chain carbon data based on blockchain according to the present invention, step S102 includes Access the waste disposal blockchain of the supervision platform through the cross-chain relay node to obtain the digitally signed electronic consignment note data; Extract the time-series consumption data of the chemical inventory management system at the production end, and calculate the dynamic deviation rate between the time-series consumption data and the disposal volume of the electronic consignment note; When the dynamic deviation rate exceeds the preset threshold, trigger the smart contract to lock the on-chain status of the carbon emission calculation result for the corresponding production batch.

[0009] The compliance verification of the manufacturing process is implemented in the chemical plant in region E. The cross-chain relay node accesses the hazardous waste disposal blockchain of the Department of Ecology and Environment in region F to obtain the electronic consignment note data signed by the national cryptographic algorithm. The chemical inventory management system synchronizes the consumption data every 15 minutes. When the deviation between the monthly consumption volume and the disposal volume of dichloromethane is detected to reach 4.3%, the smart contract automatically locks the carbon emission calculation result for this batch. The dynamic emission factor model generates a correction coefficient by combining the boiler thermal efficiency parameter of 78%. The boiler thermal efficiency of 78% represents the proportion of effective thermal energy in the total fuel energy during the fuel combustion process. The higher the thermal efficiency, the higher the actual energy utilization rate per unit of fuel, the lower the fuel consumption under the same output, and the corresponding carbon emissions should be less. The dynamic emission factor model dynamically corrects the industry benchmark emission factor (based on the ideal thermal efficiency or industry average) by introducing the real-time boiler thermal efficiency parameter to reflect the impact of the actual operation state of the equipment on carbon emissions, writes it into the Hyperledger Fabric chain code and triggers an audit alarm.

[0010] Further, in the method for trusted management of supply chain carbon data based on blockchain according to the present invention, step S102 includes: Based on the starting or ending coordinates of the transportation task and the vehicle load parameter, call the path planning algorithm to generate the benchmark carbon emission value of the theoretically optimal transportation path; Obtain the real-time working condition data of the actual transportation trajectory through in-vehicle sensors, and calculate the incremental difference between the actual carbon emissions and the benchmark carbon emission value; When the incremental difference exceeds the preset path deviation threshold in the smart contract, an abnormal transportation event record is created in the blockchain ledger, and a suspension execution instruction for the on-chain freight settlement contract is triggered.

[0011] The logistics anomaly detection is applied to the transportation task of auto parts from Region G to Region H. The path planning engine generates a benchmark carbon emission value of 2.1 tons of CO based on parameters such as a load of 28 tons and a distance of 850 kilometers. 2 The actual path deviation of 37 kilometers collected in real time by the in-vehicle OBD terminal triggers a smart contract warning. The blockchain ledger creates an abnormal event record including the transportation number GL2024-07, and synchronously freezes the on-chain freight settlement contract. The edge computing node calls the historical load data to verify the transportation rationality, and the corrected data is synchronized to the regulatory platform in Region I through the cross-chain protocol.

[0012] Furthermore, the blockchain-based trusted management method for supply chain carbon data of the present invention further includes: parsing the energy consumption log data of port handling equipment by an edge computing node, and extracting the speed and cargo capacity parameters in the ship AIS trajectory data; Synchronously collecting the GPS trajectory data and load change parameters of road transport vehicles, and the freight formation information and energy consumption monitoring data of railway transport; Based on the speed and cargo capacity parameters, a carbon emission calculation model for the sea transportation segment is constructed, and the real-time load, path optimization parameters of road transport and the energy consumption coefficient of railway transport are integrated to generate a carbon emission integration model covering multimodal transport; Extract the carbon emission intensity characteristics of sea, road and railway transport, and map them to the transportation identifier through a preset coding rule; Generate a carbon emission feature vector including ship identifiers, road transport batch codes and railway freight waybills; Cross-chain hash bind the carbon emission feature vector with the blockchain data of the road, railway and sea transportation segments, and generate a carbon footprint tracking map covering multiple transportation modes of road, railway and sea through a smart contract.

[0013] Furthermore, in the blockchain-based trusted management method for supply chain carbon data of the present invention, the step S103 includes: According to the metal element content parameters marked in the product design bill of materials, calculate the theoretical recovery threshold based on the metallurgical recovery rate formula; Input the actual extraction amount reported by the recycler and the theoretical recovery threshold into the blockchain smart contract for deviation comparison; When it is detected that the actual extraction amount exceeds the theoretical threshold and the continuous duration exceeds the preset period, activate the supplier performance deduction rule module in the smart contract, and send an alarm instruction including the abnormal batch identifier to the blockchain node with regulatory authority.

[0014] The recycling process audit is carried out at the battery recycling plant in Region M. Based on the theoretical threshold of 85 kg of cobalt content in the bill of materials, the smart contract compares the actual extraction amount of 92 kg reported by the recycler. When the overlimit persists for 8 hours, the supplier performance deduction rule is activated and an alarm instruction is sent to the regulatory node in Region N. The temperature and humidity data collected by the environmental sensors are bound to the disassembly batches, and a credibility score associated with the timestamp is generated and written into the verification parameter library. When the score is lower than 70 points, the data review process is triggered.

[0015] Furthermore, the blockchain-based trusted management method for supply chain carbon data of the present invention further includes: collecting the monitoring data of the warehouse temperature and humidity sensors within a preset time window, and generating a time series of environmental parameters bound to the product recycling batch code; Collecting the monitoring data of the warehouse temperature and humidity sensors within a preset time window is mainly based on the following technical purposes: Achieving environmental compliance in the recycling process: Temperature and humidity are key parameters affecting the quality of material recycling. For example, excessive humidity during battery disassembly may cause metal corrosion, and abnormal temperature may accelerate chemical decomposition, affecting the recovery rate. Through continuous monitoring in a preset time window (such as non-operation periods), it can be verified whether the recycling operations are carried out under compliant environmental conditions, avoiding material loss or pollution risks caused by environmental anomalies.

[0016] Verifying the credibility of recycling data: After binding the temperature and humidity data to the recycling batch code to generate a time series, combined with an anomaly detection model (such as an LSTM model) trained with historical data, abnormal energy consumption fluctuations during non-operation periods can be identified. For example, if the temperature and humidity sensors show that the warehouse is airtight but the energy consumption suddenly increases during the early morning hours, it may indicate unauthorized equipment operation or data tampering, thus triggering a decrease in the data credibility score and initiating a review process.

[0017] Supporting full-chain traceability: The time series of environmental parameters provides a complete "environmental fingerprint" for each recycling batch. When the actual recycling amount deviates from the theoretical threshold (such as abnormal cobalt extraction), the corresponding batch's environmental data can be traced back to determine whether the temperature and humidity affect the metallurgical process efficiency or whether there is human intervention (such as false reporting of the recycling amount), providing an objective basis for auditing.

[0018] Optimizing energy consumption analysis: Temperature and humidity are directly related to warehouse energy consumption (such as the power consumption of the air conditioning system). By analyzing the correlation between the environmental data and energy consumption within a preset time window, the carbon emission calculation model in the recycling link can be dynamically calibrated, avoiding carbon accounting deviations caused by abnormal operation of environmental conditioning equipment and improving the accuracy of supply chain carbon footprint tracking.

[0019] The temperature and humidity data of the temperature and humidity sensor are cross - chain bound with the recycling process data through the blockchain smart contract, forming an immutable "environment - energy consumption - recycling" multi - dimensional evidence chain. For example, when the actual recycling volume exceeds the threshold and the environmental data is abnormal, the smart contract automatically deducts the supplier performance score and sends an encrypted alarm to the regulatory node, realizing a full - automatic closed - loop management from environmental monitoring to audit decision - making.

[0020] Based on the energy consumption anomaly detection model trained with historical recycling data, the environmental parameter time - series sequence is used to identify the energy consumption fluctuation characteristics during non - operating periods. The energy consumption fluctuation characteristics are associated and matched with the timestamps of the recycling process data, and a credibility score is generated and written into the verification parameter library of the blockchain smart contract.

[0021] The energy consumption fluctuation analysis is realized through LoRa sensors deployed in the warehouse in area O, and the environmental data during non - operating periods is collected every 5 minutes. The LSTM model trained with historical recycling data identifies the abnormal energy consumption characteristics from 1:00 to 5:00 in the early morning. When the fluctuation amplitude exceeds 30% of the baseline value, an auxiliary verification factor is generated. After this factor is matched with the timestamp of the recycling process data, the supplier credit rating parameters in the smart contract are updated.

[0022] Furthermore, in the method for trusted management of supply - chain carbon data based on blockchain of the present invention, step S103 includes: setting at least two consensus nodes with auditing functions in the blockchain network, and configuring the node certificates to include carbon emission auditing permission identifiers. When the data correction instruction involves the update of the industry benchmark threshold, the corrected data features are extracted to generate a hash value to be signed. The consensus nodes are used to call the elliptic curve digital signature algorithm to jointly sign and verify the hash value, and the signature result is anchored to the blockchain transaction block header.

[0023] The multi - party signature process configures two auditing nodes in the blockchain network in area P, and the node certificates include auditing permission identifiers issued by the SM2 algorithm. When the industry benchmark threshold is updated from 0.55 kgCO 2 / kWh to 0.51 kgCO 2 / kWh, the corrected data features are extracted to generate a hash value to be signed. The auditing nodes call the SM9 algorithm for joint signature verification, and the signature digest is written into the block header and synchronized to the shard chain in area Q, and the transaction confirmation time is compressed within 2 seconds.

[0024] Furthermore, the method for trusted management of supply - chain carbon data based on blockchain of the present invention further includes: calculating the percentage deviation rate of the carbon emissions of each node from the industry benchmark value based on the real - time carbon emission data of each link in the supply chain. Identify the characteristics of regional concentrated abnormal distribution through spatial cluster analysis of the percentage deviation rate between the carbon emissions of each node and the industry benchmark value by using a geographic information system; Write the production capacity adjustment suggestions including regional identification into the intelligent contract execution queue, and the intelligent contract execution queue triggers the update of supplier evaluation parameters according to the first-in-first-out rule.

[0025] The production capacity adjustment strategy is implemented based on the geographic information system of Region R, and the percentage deviation rate between the carbon emissions of each node in the supply chain and the industry benchmark is calculated. Spatial cluster analysis identifies that 3 suppliers in Region S have a deviation of more than 15%, and generates production capacity compression suggestions to be written into the intelligent contract queue. The queue triggers the update of evaluation parameters according to the FIFO rule, and performs the supplier grading adjustment operation every 24 hours.

[0026] Furthermore, for the blockchain-based trusted management method of supply chain carbon data of the present invention, the construction of the dynamic emission factor model includes: Collect the thermal efficiency value of production equipment, the energy consumption coefficient of transportation vehicles, and the conversion rate of recycled materials through the industrial Internet of Things interface to generate a standardized parameter data set; Input the standardized parameter data set into the genetic algorithm optimization engine, and perform chromosome crossover and mutation iterations under the emission constraint conditions that meet the GHG Protocol Scope 1-3; Conduct compliance voting verification on the generated candidate emission factor set through the blockchain consensus nodes, and select the parameter combination with a voting passing rate exceeding the preset threshold to update the coefficients of the dynamic emission factor model.

[0027] The construction of the dynamic emission factor is implemented in Region T. The thermal efficiency value of the stamping equipment is 82%, the energy consumption coefficient of the transportation vehicle is 0.35 L / km, and the conversion rate of recycled materials is 91% are collected through the OPC UA interface. The genetic algorithm optimization engine iteratively generates a candidate factor set under the constraints of the GHG Protocol Scope 1-3. The blockchain consensus nodes conduct compliance voting on the candidate set, and the parameter combination with a voting passing rate exceeding 75% updates the coefficients of the dynamic emission factor model, and the optimization process data is written into the quantum-resistant on-chain log.

[0028] During the construction of the dynamic emission factor model, the industrial Internet of Things interface collects in real time multi-source parameters such as the thermal efficiency value of 83% of stamping equipment in Region A, the energy consumption coefficient of 0.38 L / km of transport trucks in Region B, and the material conversion rate of 89% of the recycling production line in Region C, and generates a standardized data set to input into the genetic algorithm optimization engine. This engine simulates the biological evolution mechanism, encodes the candidate solutions of carbon emission factors into chromosome gene sequences, and iteratively generates a candidate factor set through the roulette wheel selection strategy and two-point crossover mutation operation under the composite constraint conditions that meet the GHG Protocol Scope 1 (direct emissions), Scope 2 (indirect emissions from purchased energy), and Scope 3 (other indirect emissions in the supply chain). In each iteration process, the fitness function calculates the comprehensive score of the candidate factor in the energy efficiency verification scenario of transport vehicles and the thermal loss simulation scenario of production equipment, eliminates invalid solutions that deviate more than 20% from the emission constraint boundary, and finally outputs the parameter combination optimized through 50 generations of evolution to submit to the blockchain consensus node for compliance voting verification.

[0029] Advantages of the present invention; The present invention realizes the isolated storage and cross-chain synchronous verification of multi-regional data in the supply chain through the blockchain sharding chain technology, effectively solving the problem of carbon emission accounting deviation caused by data islands in existing methods. The dynamic emission factor model combines equipment operation parameters to correct the calculation logic in real time, improving the accuracy of process improvement effect verification. The intelligent contract engine triggers anomaly detection based on industry benchmark thresholds and enhances the credibility of data correction through a multi-party signature confirmation mechanism. The edge computing node collaborates with the machine learning model to achieve intelligent identification of transport path deviation correction and energy consumption fluctuations in recycling processing. The genetic algorithm optimization engine generates a candidate emission factor set under the multi-scope constraints of the GHG Protocol, combines the blockchain consensus verification mechanism to dynamically update model parameters, and optimizes the accuracy of supply chain carbon footprint tracking. The linkage between the geographic information system spatial clustering analysis and the intelligent contract execution queue realizes the rapid positioning of regional carbon emission anomalies and the precise implementation of production capacity adjustment strategies, forming a trusted management closed-loop of carbon data covering the entire life cycle. Description of the drawings

[0030] In order to more clearly illustrate the technical solutions of the present invention, the following will briefly introduce the drawings required for use in the embodiments. Obviously, for those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on the drawings.

[0031] Figure 1 It is a flowchart of the trusted management method for supply chain carbon data based on blockchain provided by an embodiment of the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention. The technical solutions provided by the embodiments of the present invention are described in detail below with reference to the drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0033] In a first aspect, please refer to Figure 1 , the present invention provides a method for trusted management of supply chain carbon data based on blockchain, including: Step S101: Obtain data for all links of the supply chain. The data for all links of the supply chain includes supplier production data, manufacturing process data, logistics transportation data, and recycling and processing data. Among them, the supplier production data collects the energy consumption in real time through an edge computing gateway deployed on the supplier production equipment. The manufacturing process data includes sensor data of energy equipment in the factory and process improvement records. The logistics transportation data obtains real-time operating condition parameters of the transportation vehicle through a vehicle-mounted terminal. The recycling and processing data includes the product disassembly material recovery rate. The edge computing gateway establishes a communication connection with the data acquisition terminal of the supplier production equipment through an industrial Internet of Things protocol to collect the consumption data of energy media such as electricity, gas, and steam in real time. The gateway is built with a data preprocessing module to perform range conversion and unit unification processing on the original signal, eliminate outliers caused by sensor drift or communication interference, and generate a structured energy consumption data stream including device identifiers and timestamps. The energy consumption data stream is uploaded to the edge node of the blockchain network through an encrypted transmission channel to form a data input source that is spatio-temporally aligned with the manufacturing process data.

[0034] The manufacturing process data integrates the output of the sensor network of the energy equipment in the factory, including the air compressor power curve, boiler thermal efficiency parameter, and cooling system circulation volume time series record. The process improvement records are extracted through the enterprise resource planning system interface, and the record content covers the equipment transformation time node, technical parameter adjustment amount, and energy efficiency test report summary. The data fusion module version-correlates the sensor data with the process records to construct a dynamic data set reflecting the relationship between equipment status and process evolution, as the input condition for subsequent carbon emission factor model training.

[0035] Logistics transportation data collects real-time operating condition parameters of the transport vehicle through multi-modal sensors of the on-vehicle terminal, including engine speed, instantaneous fuel consumption, and load change curve. The on-vehicle terminal uploads data in a hybrid mode of event-triggered and periodic polling. When detecting events such as rapid acceleration, overloading, or route deviation, it preferentially transmits high-priority data packets. The transportation data stream collaborates with the roadside unit through the near-field communication protocol to complement the data missing segments caused by insufficient network coverage and form continuous spatio-temporal trajectory information.

[0036] Recycling and processing data obtains the classification recovery rate of metal components during the product disassembly process through the radio frequency identification system and weighing device deployed on the disassembly production line. The disassembly batch code and material recovery rate data are associated with the product life cycle assessment report through the blockchain oracle interface to generate a traceable data unit including material flow and recovery carbon footprint. The recycling data is aligned with the carbon emission records in the production and transportation links based on a unified time benchmark to form a carbon data topology network covering all links of the supply chain.

[0037] Step S102, input the data of all links of the supply chain into the blockchain network for trust processing. Generate a data summary including spatio-temporal features through the data standardization module, and write the data summary into the blockchain distributed ledger. Among them, the following processing is performed on the supplier production data: call the regional carbon emission factor in the industry benchmark database for data deviation verification, and generate a chain record including a verification identifier; combine the manufacturing process data with the equipment operation parameters to construct a dynamic emission factor model, and verify the logical relevance between the process improvement record and the energy consumption through the dynamic emission factor model. The blockchain network performs format parsing and semantic alignment on the input all-link data through the data standardization module, and uniformly converts the energy consumption unit in the supplier production data into the standard carbon emission equivalent. The spatio-temporal feature extraction engine generates a structured data summary including the regional code and time window identifier based on the device geographical location coordinates and data collection timestamp. The data summary generates a unique summary identifier through the collision-resistant hash function, and is written into the distributed ledger of the blockchain after being bound to the meta-information of the original data.

[0038] The trust processing of the supplier production data calls the API interface of the industry benchmark database to dynamically match the grid carbon emission factor and production process type characteristics in the region where the supplier is located. The data deviation verification module uses a sliding window statistical model to calculate the dynamic deviation rate of the real-time energy consumption data from the benchmark value, and generates a chain record including the abnormal level and verification timestamp when the deviation rate exceeds the preset threshold. The encoding rule of the verification identifier integrates the blockchain transaction hash and the supplier identity information to form traceable verification status metadata.

[0039] The processing flow of manufacturing process data extracts key parameters such as the air compressor load rate, boiler thermal efficiency, and cooling system circulation volume through the equipment operation parameter parsing engine. The dynamic emission factor model constructs a correlation matrix of process improvement and energy consumption changes based on parameter time-series data, and uses a logistic regression algorithm trained with historical data to verify the impact trend of process adjustment on carbon emission intensity. The output layer of the model generates emission factor correction values including version numbers and confidence intervals, which are updated to the on-chain accounting rule library through smart contracts, triggering subsequent audit processes to verify the compliance of the correction logic.

[0040] The blockchain distributed ledger synchronizes data digests and verification records across multiple nodes through a consensus mechanism. The ledger structure design adopts a sharding storage strategy, writing supplier production data, manufacturing process model parameters, and logistics transportation records into independent data channels respectively. The block generation interval for each channel sets different frequencies according to data types. The production data channel adopts a block generation strategy at the second level, and the model parameter update channel adopts a minute-level batch confirmation mechanism to balance the requirements of data real-time and system throughput. The on-chain records after writing are opened to regulatory nodes through cross-chain query interfaces, supporting the traceability verification of carbon emission data throughout the life cycle.

[0041] Step S103, input the on-chain record into the smart contract execution engine, and make an audit decision based on the preset carbon emission compliance rules. When it is detected that the verification identifier corresponding to the energy consumption deviates from the industry benchmark threshold, trigger the data correction instruction generation process, and perform multi-party signature confirmation through the blockchain consensus node; The smart contract execution engine loads the preset carbon emission compliance rule library. The rule library adopts a hierarchical logical structure, including industry emission standards, regional regulatory policies, and enterprise-defined constraint conditions. The audit decision module performs pattern recognition on the verification identifiers of the on-chain records based on the matching priority of the rule library, and uses a sliding window mechanism to analyze the time-series deviation characteristics of the energy consumption. When the continuous deviation times exceed the preset threshold, trigger the anomaly event classifier to generate the metadata of the data correction instruction.

[0042] The data correction instruction generation process calls the transaction construction interface of the blockchain network, and encapsulates the correction target data field, benchmark threshold version number, and deviation magnitude into a structured transaction request. The consensus node verifies the legality of the transaction based on the preset audit permission level, and performs a distributed signature operation on the hash digest of the correction instruction using a threshold signature mechanism. The signature process introduces a time constraint protocol, requiring at least two nodes with different regulatory permissions to complete collaborative signatures within a preset time window. The signature shards generate a complete composite digital signature through an aggregation algorithm.

[0043] The corrected instruction that passes the signature verification updates the on-chain data record through the smart contract state machine. The update operations include incrementing the version number, writing the correction timestamp, and saving the snapshot of the previous data state. The updated data hash value is anchored to the new block header through the Merkle tree structure. The block header synchronously records the certificate identifiers and timestamp sequences of the signature participating nodes, forming a traceable correction operation chain. After the audit log generation module captures the state change event, it generates log entries including the comparison of data before and after correction, the list of signature nodes, and the trigger rules, and writes them to the immutable storage layer of the blockchain for subsequent regulatory audit calls.

[0044] Step S104, generate a supply chain optimization instruction according to the result of the audit decision, and feedback the supply chain optimization instruction to the management-side execution device, where the supply chain optimization instruction includes supplier evaluation parameters and transportation path planning strategies dynamically adjusted based on the anomaly detection result, forming a low-carbon supply chain management closed-loop system that runs through data collection, verification, and decision-making.

[0045] The supply chain optimization instruction generation module dynamically adjusts the weight coefficients of the supplier evaluation parameters by using the weighted priority algorithm based on the anomaly event distribution characteristics in the audit decision result. The evaluation parameter set integrates historical performance records, carbon emission deviation rates, and rectification response timeliness indicators, and calculates the grading adjustment range of the supplier credit rating through the rule engine. The transportation path planning strategy calls the digital twin system to model the multimodal transportation network, combines the real-time traffic flow data and the regional carbon emission intensity layer, and generates an optimization plan set including the carbon emission simulation values of alternative paths.

[0046] The management-side execution device receives the optimization instruction through the cross-chain communication protocol of the blockchain network. The instruction data packet is encapsulated as a structured transaction including the execution time window, target parameter values, and constraint conditions. The supplier evaluation parameter update task is written to the supplier management database through the enterprise resource planning system interface, triggering the automatic adjustment of the supplier production scheduling and energy procurement strategies. The transportation path optimization instruction is distributed to the path planning engine of the logistics scheduling platform, forming a feedback control loop with the real-time navigation data of the vehicle terminal, and dynamically correcting the driving route of the transportation vehicle.

[0047] The closed-loop management system continuously obtains the supplier equipment status, logistics track, and recycling progress information through the data collection layer, and inputs them into the blockchain network to execute a new round of data standardization and trustification processing procedures. The verification layer performs secondary verification on the processed data based on the updated dynamic emission factor model. The decision-making layer triggers the iterative generation of optimization instructions based on the real-time verification results. The state change events of the data in each link are synchronized to the audit log module through the event bus, forming a traceability chain covering the entire cycle of instruction execution, and realizing the self-adaptive optimization of the carbon data management system.

[0048] In step S101, the edge computing gateway is deployed to collect the energy consumption of the supplier's production equipment in real time, and the industrial Internet of Things protocol is used to synchronously obtain multi-source heterogeneous data. The edge computing gateway integrates the device-level data preprocessing function to preliminarily clean and convert the format of the original energy consumption data, and eliminate the noise data caused by sensor sampling errors or communication interference. The logistics transportation data collects the real-time operating condition parameters of the transportation vehicle through the vehicle-mounted terminal, including the engine speed, load change, and driving trajectory coordinates, and transmits them to the cloud data center through a lightweight communication protocol. In the recycling and processing link, radio frequency identification devices and material weighing systems are deployed to obtain the recovery rate data of each metal component during the product disassembly process, forming a multi-modal data acquisition network covering the entire life cycle of the supply chain.

[0049] In step S102, the blockchain network extracts the spatio-temporal features of the multi-source heterogeneous data through the data standardization module, and generates a structured data summary including geographical coordinates and timestamps. The supplier production data calls the regional carbon emission factors in the industry benchmark database, and uses the dynamic threshold comparison algorithm to perform data deviation verification. The verification results are written into the blockchain distributed ledger in the form of encrypted identifiers. The manufacturing process data constructs a dynamic emission factor model based on the device thermal efficiency and load rate through the bidirectional mapping of the device operation parameters and process improvement records. This model verifies the correlation between the process parameter adjustment and the energy consumption change through the on-chain verification mechanism, and generates a carbon emission accounting rule set with version identifiers. The logistics transportation data generates a theoretical optimal carbon emission benchmark value through the path planning algorithm, and forms a cross-verification mechanism with the actual operating condition data collected by the vehicle-mounted sensors.

[0050] In step S103, the smart contract execution engine loads the preset carbon emission compliance rule library to perform pattern recognition and anomaly detection on the multi-dimensional data recorded on the chain. When the verification identifier deviates from the industry benchmark threshold, a data correction process based on multi-party collaboration is triggered: the consensus nodes jointly verify the correction instructions based on the elliptic curve digital signature algorithm, and the verified correction data generates a new hash feature value and updates the on-chain state. The audit decision module synchronously analyzes the version iteration records of the dynamic emission factor model in the manufacturing process, evaluates the influence weight of the process improvement measures on the carbon emission accounting rules, and generates an audit report covering the upstream and downstream nodes of the supply chain.

[0051] In step S104, based on the distribution characteristics of abnormal events in the audit report, the supply chain optimization instruction dynamically adjusts the weight coefficients of supplier evaluation parameters using machine learning algorithms. The logistics path planning strategy integrates the spatial topological data of the multimodal transport carbon emission tracking map and combines the alternative path simulation results generated by the digital twin system to generate an optimization plan that takes into account both transportation timeliness and carbon emission reduction goals. After the management terminal executes the device receiving optimization instruction, it distributes the parameter adjustment instruction to the supplier production control system and the logistics scheduling platform through the cross-chain communication protocol, forming a closed-loop control system from data collection, on-chain verification to execution feedback. The real-time operation status data of each link is continuously input into the blockchain network, driving the periodic iterative update of the dynamic emission factor model and the compliance rule base, and realizing the adaptive optimization of the supply chain carbon management system.

[0052] Specifically, for the method for trusted management of supply chain carbon data based on blockchain, the step S102 further includes: Calling the raw material procurement database of a third-party certification agency through the blockchain oracle interface, extracting the hash digest of the original procurement voucher, and generating a hash feature value including a timestamp; Inputting the hash feature value and the bill of materials data in the life cycle assessment report into the zero-knowledge proof verification module to verify the relevance and consistency of the procurement voucher and the carbon emission inventory; Generating a blockchain data block including a trusted identifier according to the verification result, and the trusted identifier is used to update the integrity assessment status of supplier production data.

[0053] The blockchain oracle interface establishes a communication link with the third-party raw material procurement database through a preset authentication protocol, and uses asymmetric encryption technology to protect the transmission channel end-to-end. The original procurement voucher is parsed by the data parsing module to extract key fields, including the supplier code, material batch, and procurement quantity, generates a unique hash digest through a collision-resistant hash function, and embeds the timestamp information provided by the trusted time source into the digest encoding sequence to form an immutable hash feature value. This feature value is transmitted to the zero-knowledge proof verification module through the cross-chain routing protocol as an input parameter for the verification process.

[0054] The zero-knowledge proof verification module constructs a verification relationship based on the preset circuit logic and logically maps the hash feature value and the bill of materials data in the life cycle assessment report. The verification process simulates the relevance constraint conditions of the procurement voucher and the carbon emission inventory by constructing an arithmetic circuit, and generates a verifiable zero-knowledge proof result without revealing the original data. When there is a logical conflict between the raw material carbon footprint data marked in the bill of materials and the procurement record corresponding to the hash feature value, the verification module outputs a verification report including an exception identifier, triggering an on-chain data correction process.

[0055] The trusted identity generation module writes metadata including a verification status code into the blockchain data block according to the zero-knowledge proof verification result. The encoding rule of the trusted identity integrates the supplier identity information, the verification timestamp, and the associated data block hash value, and updates the integrity assessment status of the supplier production data through a smart contract. The integrity assessment status is recorded on the chain node in the form of a dynamic weight value, which serves as a key input parameter for the supplier credit rating in the subsequent audit decision-making process. The blockchain network broadcasts the updated assessment status to all nodes through the synchronization mechanism of the distributed ledger, supporting all parties involved in the supply chain to retrieve the verification records in real time.

[0056] Specifically, in the method for trusted management of supply chain carbon data based on blockchain according to the present invention, the step S102 includes Access the waste disposal blockchain of the regulatory platform through a cross-chain relay node to obtain the digitally signed electronic consignment note data; Extract the time-series consumption data of the chemical inventory management system at the production end, and calculate the dynamic deviation rate between the time-series consumption data and the disposal volume of the electronic consignment note; When the dynamic deviation rate exceeds the preset threshold, trigger the smart contract to lock the on-chain status of the carbon emission calculation result of the corresponding production batch.

[0057] The cross-chain relay node establishes a communication connection with the waste disposal blockchain of the government regulatory platform through a preset cross-chain protocol, and uses a distributed identity authentication mechanism to verify the legitimacy of the regulatory chain node. After the electronic consignment note data is parsed by the digital signature verification module, the structured fields including the waste type, disposal volume, and timestamp are extracted, and the format conversion and semantic alignment between heterogeneous blockchains are realized through the data cache layer of the relay node. The electronic consignment note data of the regulatory chain is synchronized to the verification node of the enterprise production chain in a light node mode, forming a cross-chain data verifiable reference relationship.

[0058] The chemical inventory management system at the production end collects time-series consumption data through the industrial Internet of Things interface, including the raw material input volume, waste generation rate, and storage container status parameters. The time-series data processing module performs a sliding window statistic on the consumption data, calculates the average consumption rate within a unit time window, and normalizes it with the disposal volume recorded in the electronic consignment note. The dynamic deviation rate calculation engine generates a quantitative index reflecting the matching degree between consumption and disposal volume based on a preset sliding window step size and weight allocation strategy. The time series similarity algorithm is introduced in the index calculation process to eliminate the influence of equipment start-stop fluctuations on data comparison.

[0059] When the smart contract detects that the dynamic deviation rate exceeds the preset threshold, it calls the carbon emission calculation contract state machine stored on the chain and performs a read-only locking operation on the carbon emission data corresponding to the abnormal production batch. The locked state is updated to all nodes through the blockchain consensus mechanism, restricting the subsequent modification permissions of the batch of data, and at the same time generating an audit trail record including the locking reason and timestamp. After the state locking is triggered, the off-chain collaborative verification module starts a multi-source data review process, integrates the production equipment operation logs and waste transportation trajectory data, and generates a supplementary verification report for subsequent manual review decisions. The unlocking of the locked state requires the joint signature verification of the review results by multiple consensus nodes to reactivate the state update function of the smart contract.

[0060] Specifically, for the blockchain-based trusted management method for supply chain carbon data described in the present invention, the step S102 includes: Based on the starting or ending coordinates of the transportation task and the vehicle load parameters, call the path planning algorithm to generate the benchmark carbon emission value of the theoretically optimal transportation path; Obtain the real-time working condition data of the actual transportation trajectory through in-vehicle sensors, and calculate the incremental difference between the actual carbon emissions and the benchmark carbon emission value; When the incremental difference exceeds the path deviation threshold preset in the smart contract, create an abnormal transportation event record in the blockchain ledger and trigger a suspension execution instruction for the on-chain freight settlement contract.

[0061] The path planning engine generates the topological structure of the theoretically optimal transportation path based on the starting and ending coordinate geofence parameters of the transportation task and combines the non-linear relationship model of the vehicle load on energy consumption. The path planning algorithm adopts a multi-objective optimization strategy, comprehensively considers the road network topological data and historical transportation energy consumption characteristics, constructs a decision-making model with carbon emission minimization as the core constraint condition, and outputs the benchmark carbon emission value including the path node sequence and the expected energy consumption intensity. During the generation of the benchmark carbon emission value, the regional carbon emission factor weight parameter is embedded to achieve the dynamic adaptation of the transportation mode and geographical characteristics.

[0062] The in-vehicle sensors collect the engine speed, fuel consumption rate, and GPS positioning data in real time, and the edge computing node performs noise filtering and time alignment processing on the original working condition data. The real-time working condition data analysis module inputs the cleaned data stream into the carbon emission calculation model, combines the load change curve and the instantaneous vehicle speed parameters, and generates a real-time carbon emission time series indexed by timestamp. The incremental difference calculation engine, based on the sliding window mechanism, dynamically matches the discrete points of the benchmark carbon emission value and the actual value, and uses a normalization algorithm to eliminate the interference of path segmentation errors on the deviation calculation, generating a quantitative index reflecting the deviation degree of carbon emissions during the transportation process.

[0063] When the intelligent contract detects that the incremental difference exceeds the path deviation threshold, it calls the transaction generation interface of the blockchain ledger to create a record of abnormal transportation events. The event record includes the transportation task identifier, the deviation magnitude, and the trigger timestamp, and is anchored to the immutable storage layer of the blockchain through the Merkle tree structure. The state machine of the freight settlement contract on the chain synchronously receives the abnormal event trigger signal, executes the conditional judgment logic in the contract code, and sets the settlement instruction queue to the paused state. During the pause, the blockchain network starts a multi-party verification process, integrates historical path optimization data and vehicle maintenance records, and generates a review report of abnormal events for subsequent manual intervention or automated decision callback.

[0064] Specifically, the method for trusted management of supply chain carbon data based on blockchain described in the present invention further includes: parsing the energy consumption log data of port handling equipment by an edge computing node, and extracting the speed and cargo volume parameters in the vessel AIS trajectory data; Synchronously collecting the GPS trajectory data and load change parameters of road transport vehicles, the freight formation information and energy consumption monitoring data of railway transport; Based on the speed and cargo volume parameters, constructing a carbon emission calculation model for the sea transport segment, integrating the real-time load, path optimization parameters of road transport, and the energy consumption coefficient of railway transport, and generating a carbon emission integration model covering multimodal transport; Extracting the carbon emission intensity characteristics of sea, road, and railway transport, and associating and mapping them with the transport identifier through a preset coding rule; Generating a carbon emission feature vector including the vessel identifier, the road transport batch code, and the railway freight waybill number, and realizing the binding of carbon footprint identifiers across transport modes.

[0065] Cross-chain hash binding the carbon emission feature vector with the blockchain data of the road, railway, and sea transport segments, and generating a carbon footprint tracking map covering multiple transport modes of road, railway, and sea through an intelligent contract.

[0066] The edge computing node accesses the energy consumption monitoring system of the port handling equipment through a preset industrial communication protocol, and parses the correlation data between power consumption and operation time in the equipment operation log. The vessel automatic identification system (AIS) trajectory data extracts the speed, cargo volume, and navigation status parameters through a data cleaning module, and uses the time series alignment algorithm to convert the discrete AIS message data into a continuous navigation feature curve. The parsed vessel energy consumption data and the handling equipment operation log are matched through a spatio-temporal correlation model to generate the input parameters for calculating the carbon emissions of the sea transport segment including the port operation stage and the sea transport stage.

[0067] The GPS trajectory data of road transportation vehicles is uploaded through in-vehicle terminals in an event-driven mode. The edge computing node constructs a load-path association matrix based on the real-time weight change curve collected by the load sensor. The freight formation information of railway transportation is obtained through the open interface of the railway dispatching system, and the traction energy consumption monitoring data of the train and the formation topology relationship of the carriages are integrated to generate an energy consumption feature set of railway sections based on the traction force distribution model. The synchronous acquisition of multi-source heterogeneous data is realized by using a distributed message queue, and a cross-transport mode data association index is established through data timestamps and task identifiers.

[0068] The carbon emission calculation model for the sea transportation section is based on the ship navigation resistance formula and the non-linear relationship between cargo volume and speed, and constructs a carbon emission intensity curve per unit of cargo turnover. The road transportation model introduces the influence coefficient of real-time load on vehicle rolling resistance, combines the road slope and traffic flow parameters output by the path planning engine, and dynamically corrects the carbon emission calculation logic. The railway transportation model generates a carbon emission allocation rule based on the power distribution ratio of freight formation and the traction energy consumption characteristics. The multi-modal carbon emission fusion model integrates the characteristics of each transportation section through a weight allocation strategy, and uses a sliding window mechanism to dynamically adjust the contribution weights of the road, railway and sea transportation models.

[0069] The carbon emission feature vector generation module uses the ship's IMO number, road transportation batch code, and railway freight waybill number as the primary key identifiers, and embeds the carbon emission intensity values, timestamps, and geographical fence coordinates of each transportation section. The data structure design of the feature vector supports the cross-chain query interface specification, and realizes the data semantic interoperability of different blockchain networks through a standardized coding format. The cross-chain hash binding protocol decomposes the feature vector into road, railway and sea transportation sub-vectors, and performs binding operations with the blockchain transaction hash values of the corresponding transportation chains respectively to generate an indivisible combined hash digest.

[0070] The intelligent contract calls the multi-modal transportation tracking graph generation algorithm based on the combined hash digest. The graph nodes represent the carbon emission data units at each stage of the transportation task, and the edge relationship represents the carbon footprint connection logic when the transportation mode is switched. A time window constraint mechanism is introduced during the graph generation process to dynamically aggregate multi-transportation section data within a preset period to form a traceable spatio-temporal evolution structure of the carbon footprint. The blockchain network conducts consensus verification on the integrity of the graph data through cross-chain verification nodes, and the verified graph metadata is written into the distributed ledger to support supply chain participants to query the carbon footprint topology relationship of the entire link according to their permissions.

[0071] Specifically, for the blockchain-based trusted management method of supply chain carbon data described in the present invention, the step S103 includes: Based on the metal element content parameters marked in the product design bill of materials, calculate the theoretical recovery threshold according to the metallurgical recovery rate formula; The actual extraction volume reported by the recycler is compared with the theoretical recycling threshold value by inputting it into the blockchain smart contract for deviation comparison; When it is detected that the actual withdrawal volume exceeds the theoretical threshold and lasts for more than the preset period, the supplier performance deduction rule module in the smart contract is activated, and an alarm instruction including an abnormal batch identification is sent to the blockchain node with regulatory authority.

[0072] The product design bill of materials extracts metal element content parameters, including element type, mass percentage and material batch identification, through the structured data analysis module. The metallurgical recovery rate formula is based on the metal smelting process characteristics and recycling production line equipment parameters. It constructs a calculation model including smelting efficiency, slag loss rate and purity correction factor, and outputs the theoretical recovery threshold. The feature weight coefficient trained by historical recycling data is introduced in the process of generating the theoretical threshold to dynamically adapt to the differences in the physicochemical properties of different metal components.

[0073] The blockchain smart contract deploys a deviation comparison logic module, receives the actual extraction volume data stream reported by the recycler, and converts the unstructured report into a numerical sequence of unified measurement units through the data standardization interface. The deviation comparison engine uses a sliding window mechanism to calculate the dynamic deviation rate between the actual extraction volume and the theoretical threshold. The window step size setting is synchronized with the recycling operation cycle to eliminate the interference of short-term fluctuations within the batch on abnormal judgment. The comparison results are written into the on-chain state database in the form of a timestamp index, supporting cross-batch data backtracking analysis.

[0074] When it is detected that the actual extraction volume exceeds the theoretical threshold continuously and the duration reaches the preset period, the smart contract calls the supplier performance deduction rule engine. Based on the preset deduction gradient table and the cumulative frequency of abnormal events, the rule engine generates a dynamically adjusted performance deduction value and updates it to the supplier credit rating chain data set. The alarm instruction generation module simultaneously creates a structured alarm message including abnormal batch code, overscalar level and time interval, and transmits it to the supervision node through the permission filtering channel of the blockchain node. After receiving the alarm, the supervision node activates the off-chain review process, retrieves the temperature and humidity sensor time series data of the corresponding batch and the disassembly operation video for evidence, and generates a composite verification report and writes it into the distributed file storage system.

[0075] Specifically, the blockchain-based supply chain carbon data trusted management method of the present invention further includes: collecting monitoring data of warehouse temperature and humidity sensors within a preset time window to generate an environmental parameter time series sequence bound to the product recycling batch code; The energy consumption anomaly detection model trained based on the environmental parameter time series data of historical recycling data is used to identify the energy consumption fluctuation characteristics during non-operating periods; The energy consumption fluctuation characteristics are associated and matched with the timestamp of the recycling processing data to generate a credibility score that is written into the verification parameter library of the blockchain smart contract.

[0076] The warehouse temperature and humidity sensors establish a communication connection with the edge computing nodes through the Low-Power Wide-Area Network (LPWAN) protocol and collect environmental parameters at a fixed sampling interval within a preset time window. The sensor node deployment strategy is based on the thermodynamic characteristics of the recycling operation area, and adopts a spatial grid sampling method to cover the material storage area and the disassembly operation area, generating a multi-dimensional monitoring data stream bound to the recycling batch code. The time series generation module performs timestamp alignment and outlier removal on the original data, constructs a feature vector including the mean, extreme values, and change rate of temperature and humidity, and transmits it to the blockchain data preprocessing layer through an encrypted channel.

[0077] The energy consumption anomaly detection model trained based on historical recycling data uses time series segmentation technology to define the non-operation period as a time window when the equipment is shut down and the personnel activities are static. After the input layer of the model receives the time series of environmental parameters, it extracts the implicit correlation features between temperature and humidity fluctuations and basic energy consumption through the Long Short-Term Memory (LSTM) network, and identifies the characteristic patterns of abnormal energy consumption events, such as the abnormal startup of the air conditioning system or the unauthorized operation of the ventilation equipment during the non-operation period. The output layer of the model generates a detection report including the type of anomaly, confidence level, and start time, and converts it into a structured data format recognizable by the blockchain through a feature encoder.

[0078] The correlation matching module for fluctuation features and recycling process data uses the Dynamic Time Warping (DTW) algorithm to compare the timestamps of abnormal energy consumption events with the time intervals of disassembly operation records for similarity. A sliding window constraint mechanism is introduced during the matching process to eliminate the timing misalignment problems caused by equipment start-stop delays or sensor clock deviations. The credibility score generation engine calculates the score value using a multi-dimensional weighted strategy based on the type weight, duration, and historical occurrence frequency of abnormal events, and writes the score result into the on-chain database through the verification parameter update interface of the smart contract. The blockchain network broadcasts the change status of the verification parameter library to the entire chain through a cross-node synchronization mechanism, supporting subsequent audit processes to call the score data to perform supplier credit assessment and carbon emission accounting correction.

[0079] Specifically, for the blockchain-based trusted management method of supply chain carbon data described in the present invention, step S103 includes: Set at least two consensus nodes with auditing functions in the blockchain network, and configure the carbon emission auditing permission identifier in the node certificate; When the data correction instruction involves the update of the industry benchmark threshold, extract the corrected data features to generate a hash value to be signed; Call the elliptic curve digital signature algorithm through the consensus nodes to jointly sign and verify the hash value, and anchor the signature result to the blockchain transaction block header.

[0080] The blockchain network deploys consensus nodes with auditing functions through a preset node access mechanism. The node certificates embed carbon emission auditing permission identifiers in the X.509 standard format, and the permission identifiers include verifiable auditing scope and operation type constraint parameters. The geographical distribution strategy of the consensus nodes is designed based on the regulatory levels of supply chain participants, forming a decentralized auditing network topology structure. The communication links between nodes are established using a national cryptography algorithm encrypted transmission protocol.

[0081] When the data correction instruction triggers the update of the industry benchmark threshold, the modified data feature extraction module performs semantic parsing on the changed carbon emission factors, equipment parameters, or path optimization rules, generating a structured data packet including the version number and change summary. The to-be-signed hash value generation engine performs a summary operation on the structured data packet based on a quantum-resistant hash algorithm. The previous block hash is introduced as a random salt value during the operation process, generating a unique hash summary with chain association characteristics. The hash summary is pushed to the signature task queue of the auditing node through an event-driven interface.

[0082] The consensus node calls the key management module of the elliptic curve digital signature algorithm. After verifying the legality of the auditing operation based on the permission identifier, it performs a distributed signature operation on the hash summary using a threshold signature mechanism. The joint signature verification process synchronizes the signature shards of each node through a preset multi-party cooperation protocol, aggregating to generate a complete composite digital signature. The signature result is written into the transaction metadata field through a blockchain transaction constructor, and the transaction hash is anchored to the block header of the new block based on the Merkle tree structure. The historical signature summary of the previous auditing operation is synchronously recorded in the block header, forming an audit traceability chain data structure.

[0083] The transaction block after anchoring is broadcast to all network nodes after being verified by the consensus algorithm. The audit traceability chain data and the carbon emission accounting records of the main chain form a two-way reference relationship. The intelligent contract state machine updates the audit log based on the signature verification status in the block header. The log entries include auditing node identifiers, signature timestamps, and associated data version numbers, supporting subsequent regulatory parties to trace the full life cycle operation records of threshold changes through a cross-chain query interface.

[0084] Specifically, the blockchain-based supply chain carbon data trusted management method described in the present invention further includes: calculating the percentage deviation rate of the carbon emissions of each node from the industry benchmark value based on the real-time carbon emission data of each link in the supply chain; Performing spatial clustering analysis on the percentage deviation rate of the carbon emissions of each node from the industry benchmark value through a geographic information system to identify regional centralized abnormal distribution characteristics; Writing production capacity adjustment suggestions including regional identifiers into the intelligent contract execution queue, and the intelligent contract execution queue triggers the update of supplier evaluation parameters according to the first-in-first-out rule.

[0085] The real-time carbon emission data of each link in the supply chain is normalized through a distributed data middleware, integrating multi-source heterogeneous data streams from production equipment sensors, logistics vehicle terminals, and recycling and processing systems. The percentage deviation rate calculation engine, based on the dynamic update mechanism of the industry benchmark database, extracts the benchmark values matching the node's geographical location, production scale, and process type, and uses a sliding window statistical model to calculate the carbon emission deviation rate within a continuous time window. The equipment operation status correction factor is introduced into the deviation rate calculation process to eliminate the influence of temporary shutdowns or load fluctuations on the statistical results.

[0086] The Geographic Information System (GIS) performs spatial encoding conversion on the deviation rate data set, maps the node's geographical location information to longitude and latitude coordinate grids, and identifies abnormal regions with spatial aggregation characteristics through density clustering algorithms. The clustering analysis module generates a heat map reflecting the regional carbon emission deviation distribution based on preset neighborhood radius and minimum sample number thresholds, and marks the grid cells with heat values exceeding the critical threshold as abnormal clusters. The feature extraction engine for abnormal regions combines the supply chain topology relationship, analyzes the production process relevance and logistics path overlap degree of the nodes within the cluster, and generates a diagnostic report including regional coding, abnormal type, and influence scope.

[0087] The production capacity adjustment suggestion generation module defines adjustment strategies according to the abnormal types in the diagnostic report. For example, it gives suggestions on optimizing the production rhythm or upgrading energy equipment in high deviation rate regions. The suggestion data is encapsulated into a standardized instruction format, appended with regional identifiers and priority weights, and written into the execution queue through the intelligent contract interface. The queue management engine adopts a first-in-first-out scheduling strategy, triggers the supplier evaluation parameter update task according to the instruction priority and timestamp sorting. The blockchain consensus mechanism is called during the parameter update process to verify the compliance of the instructions, and the updated evaluation parameters are synchronized to the supplier production control system and the supervision platform through the cross-chain protocol, forming a closed-loop management mechanism for regional carbon emission anomalies.

[0088] In the second aspect, for the blockchain-based supply chain carbon data trusted management method of the present invention, the construction of the dynamic emission factor model includes: Collect the thermal efficiency values of production equipment, energy consumption coefficients of transportation vehicles, and material conversion rates of recycling and processing through the industrial Internet of Things interface to generate a standardized parameter data set; Input the standardized parameter data set into the genetic algorithm optimization engine, and perform chromosome crossover and mutation iterations under the emission constraint conditions that meet the GHG Protocol Scope 1-3; Conduct compliance voting verification on the generated candidate emission factor set through blockchain consensus nodes, and select the parameter combinations with a voting passing rate exceeding the preset threshold to update the dynamic emission factor model coefficients.

[0089] The industrial Internet of Things interface establishes a data channel with the production equipment control system through the OPC UA protocol, and real-time collects the thermal efficiency value, equipment load rate, and start-stop status parameters. The energy consumption coefficient of the transport vehicle extracts the engine operating condition data stream through the in-vehicle CAN bus parsing module, and combines the load sensor and GPS positioning information to generate the dynamic energy consumption characteristics per unit of transportation volume. The material conversion rate in the recycling process collects the metal component content data through the weighing system and the spectral analyzer, and forms a standardized parameter sequence after removing the noise through the data cleaning module. The multi-source heterogeneous data is integrated into a structured data set including device identifiers, transportation task codes, and recycling batch numbers through timestamp alignment and spatial grid coding.

[0090] The genetic algorithm optimization engine encodes the standardized parameters into a chromosome gene sequence, and each gene locus corresponds to the optimization weight of a specific parameter type (such as thermal efficiency, energy consumption coefficient). The fitness function design integrates the composite constraint conditions of GHG Protocol Scope 1-3. The Scope 1 constraint focuses on the matching degree between the direct emission parameters and the equipment operating state. The Scope 2 constraint introduces the regional power grid carbon emission factor correction rule. The Scope 3 constraint integrates the indirect emission correlation model of the transportation route and the recycling process. The chromosome crossover and mutation operations adopt the two-point crossover and adaptive mutation rate strategy, and iteratively generate a candidate emission factor set within the solution space that meets the emission constraint boundary. Each round of iteration eliminates the invalid solutions that deviate from the constraint threshold by more than 20%.

[0091] The blockchain consensus nodes perform compliance voting verification on the candidate emission factor set, and the voting weight distribution strategy sets different permissions according to the node type (such as regulatory nodes, enterprise nodes). The candidate factor set is distributed to each node through a smart contract. The node performs compliance verification based on the local verification rule library (such as industry emission standards, regional regulatory policies), and generates a voting record including the verification summary. The voting result aggregation module counts the candidate parameter combinations with a passing rate exceeding the preset threshold (such as 75%), and triggers the model coefficient update instruction. The updated emission factor coefficient is synchronized to the carbon emission calculation nodes in all links of the supply chain through the cross-chain protocol, and the version number, effective time, and associated voting summary are recorded in the blockchain distributed ledger, forming a traceable model iteration history chain.

[0092] The specific implementation manner of the present invention is as follows: In the production link of the supplier in Region A, the edge computing gateway deployed on the stamping equipment collects the power consumption data at a sampling frequency of once per second, and transmits it to the data processing node through the industrial Internet of Things protocol. The logistics vehicle in Region B is equipped with a GPS positioning terminal, records the transportation coordinates once per minute, and combines the in-vehicle sensors to obtain the load parameter in real time. The data is uploaded to the cloud through the MQTT protocol. The standardized processing module calls the power emission factor of 0.532 kgCO 2 / kWh in Region A and the road transportation emission factor of 0.82 kgCO2 / km, convert the original energy consumption and transportation mileage into a unified carbon emission equivalent value. The anomaly detection model optimizes the feature weights through the genetic algorithm. When the carbon emission deviation between the energy consumption of the stamping process and the transportation mileage of the same batch exceeds 20%, the historical data retrieval process of the load sensor is triggered for verification.

[0093] In the metadata generation stage, the SHA-256 algorithm is used to perform a hash operation on the device timestamp and the transportation trajectory coordinates to generate a 128-bit data identifier, and a structured tag including the supplier code and the logistics batch is constructed. The blockchain sharding chain writes the data in Area A into the Hyperledger Fabric framework according to the supplier's registered location, and the data in Area B is stored in the FISCO BCOS framework. The block generation interval is set to 5 seconds, and the transaction confirmation time is controlled within 3 seconds. The smart contract engine calls the on-chain verification code every 15 minutes to compare the real-time carbon emission value with the industry benchmark parameters. When it is detected that the carbon emission of the sea container transportation exceeds the benchmark value by 15%, the early warning mechanism is triggered to generate an alarm event including the container number.

[0094] After the edge computing node responds to the early warning, it retrieves the ship AIS trajectory data for cross-verification with the hash value stored in the blockchain, and preferentially uses the net weight of the goods recorded by the load sensor to correct the carbon emission record in the transportation link. When the daily data volume of the new supplier reaches the 10GB threshold, the system automatically creates an independent sharding chain based on the Quorum framework, and synchronizes the Merkle Root hash values of each sharding chain through the cross-chain relay protocol. The dynamic monitoring module synchronizes and updates the benchmark parameters from the regulatory database every quarter. When the carbon emission increment caused by the change of the logistics path exceeds the threshold set by the smart contract, the digital twin system calls the Monte Carlo algorithm to generate three alternative path plans, and the screening conditions include the transportation time deviation ≤ 48 hours and the carbon emission reduction ≥ 25%.

[0095] In the recycling process, the warehouse temperature and humidity sensors collect environmental data at a frequency of once every 5 minutes, and generate a time series sequence bound to the recycling batch code. The machine learning model trained based on historical recycling data identifies the energy consumption fluctuation characteristics during non-operation periods, associates the fluctuation amplitude with the timestamp, and generates a credibility score, which is written into the smart contract verification parameter library. When it is detected that the metal recycling amount exceeds the theoretical threshold and continues to exceed the limit for 24 hours, the smart contract activates the supplier performance deduction rule and sends an encrypted alarm instruction to the regulatory node.

[0096] Configure at least two consensus nodes with auditing functions in the blockchain network. The node certificates include carbon emission auditing permission identifiers. When the industry benchmark threshold needs to be updated, extract the corrected data features to generate a hash value to be signed, and perform joint verification through the elliptic curve digital signature algorithm. The signature result is anchored to the transaction block header. The geographic information system conducts spatial clustering analysis on the carbon emission deviation rates of all links in the supply chain. After identifying centralized anomalies in Region A, generate production capacity adjustment suggestions and write them into the intelligent contract execution queue, and trigger the update of supplier evaluation parameters according to the first-in, first-out rule.

[0097] Through the technical integration of multi-source data collection, dynamic model verification, and blockchain collaborative verification, the above implementation method realizes the trustworthy control of the full-link carbon footprint from raw material procurement to waste recycling, and solves the core problems of data islands, verification lag, and inefficient auditing in the existing methods. Example 1: In the production process of a chip supplier in Region A, the edge computing gateway deployed on the lithography machine group collects power consumption data at a sampling frequency of twice per second and transmits it to the blockchain node in real time through the OPC UA protocol. The raw material database of silicon wafers purchased by the supplier is connected through the blockchain oracle interface, and the purchase order is subjected to SHA-256 hash operation to generate a 128-bit feature value. The zero-knowledge proof verification module conducts relevance verification on the feature value and the LCA report provided by the certification agency in Region B. When it is detected that the deviation between the silicon material purity data and the power consumption exceeds 15%, block data with a red trust mark is generated. The intelligent contract engine calls the semiconductor industry benchmark parameter of 0.532 kgCO 2 / kWh in Region A to dynamically correct the hourly energy consumption data. Abnormal data with a correction amplitude exceeding 20% triggers the multi-signature process of the auditing node.

[0098] Example 2: Thermocouple sensors are deployed on the coal-fired boilers of a chemical plant in Region C to collect steam pressure and coal calorific value data once per minute. Combining with the blockchain of electronic waybills provided by the supervision platform, obtain the hazardous waste disposal volume data through the cross-chain relay node. When it is detected that there is a 4.3% deviation between the monthly coal consumption of 120 tons and the actual combustion volume of 115 tons recorded in the disposal waybill of the boiler, the intelligent contract automatically locks the carbon emission calculation result of this batch. The dynamic emission factor model generates a range 1 emission coefficient correction value according to the boiler thermal efficiency of 78% and the equipment load rate parameter, and writes the corrected carbon emission data into a specific channel of the Fabric shard chain, and compresses the block generation time interval to 3 seconds.

[0099] Example 3: In the transportation task of auto parts from Region D to Region E, the path planning engine generates a theoretically optimal path based on parameters such as a load of 28 tons and a transportation distance of 850 kilometers, and sets the benchmark carbon emission value to 2.1 tons of CO 2The actual driving path deviation mileage collected by the in-vehicle OBD terminal reaches 37 kilometers. After the smart contract detects that the carbon emission increment exceeds the 12% threshold, it triggers a freight settlement freeze instruction. The edge computing node synchronously analyzes the ship AIS data of the port in Region F, extracts the parameters of the cargo capacity of 650 TEU and the sailing speed of 14 knots, constructs the carbon emission feature vector of the sea transportation section, and generates a multimodal transportation tracking map after cross-chain binding with the land transportation data. The map update frequency is set to once every 30 minutes.

[0100] Example 4: Temperature and humidity sensors are deployed in the battery recycling factory in Region F, and the environmental parameters of the disassembly workshop are collected every 10 minutes to generate a time-series data sequence bound to the recycling batch BN2024-07. When it is detected that the actual cobalt recovery amount of the ternary lithium battery reaches 92 kg, exceeding the theoretical threshold of 85 kg marked in the design BOM file and continuously exceeding the limit for 8 hours, the smart contract activates the supplier KPI deduction rule. The energy consumption anomaly detection model analyzes the energy consumption fluctuation characteristics of the air conditioning system during the non-operating period from 1:00 to 5:00 in the early morning, generates a credibility score of 72 points and writes it into the verification parameter library, and triggers a chain-based alarm instruction to be synchronized to the regulatory node in Region G. After the geographic information system identifies 3 suppliers with more than 15% carbon emission deviation concentrated in Region H, it generates a production capacity compression suggestion and writes it into the smart contract queue.

[0101] The technical features of the present invention are explained as follows: Edge computing gateway: Deployed on the Internet of Things terminal of the supplier's production equipment, it is used to collect data such as energy consumption in real time, and has local data processing capabilities to reduce cloud transmission latency. For example, in the chip production process, the power consumption data of the lithography machine group is collected at a sampling frequency of twice per second and transmitted to the blockchain node through an industrial protocol (such as OPC UA).

[0102] Blockchain network: A distributed ledger system adopting a sharded chain architecture, which stores data separately according to the region where the supplier belongs (for example, the Hyperledger Fabric framework is used in Region A, and the FISCO BCOS framework is used in Region B), and synchronizes the block header hash value through the cross-chain relay protocol to ensure the global consistency of multi-region data.

[0103] Data standardization module: A processing unit that converts multi-source heterogeneous data (such as power consumption, transportation mileage, recovery rate) into a unified carbon emission equivalent value. Call the industry benchmark database (such as the emission factor of 0.532 kgCO 2 / kWh in the semiconductor industry in Region A) for unit conversion, and generate a structured data summary including timestamps and geographical coordinates.

[0104] Dynamic Emission Factor Model: An algorithm model that dynamically adjusts the carbon emission calculation logic based on equipment operation parameters (such as boiler thermal efficiency of 78% and equipment load rate). By verifying the logical relevance between process improvement records and energy consumption data, it generates on-chain records with verification identifiers to support the dynamic correction of carbon emission accounting.

[0105] Blockchain Oracle Interface: Middleware that connects to external data sources (such as the raw material procurement database of a third-party certification agency), performs SHA-256 hashing on the original procurement vouchers to generate 128-bit eigenvalues for cross-chain data interaction and verification.

[0106] Zero-Knowledge Proof Verification Module: A functional module that uses cryptographic algorithms to verify the relevance between procurement vouchers and carbon emission inventories. For example, comparing the hash features of silicon wafer procurement with the bill of materials data in the LCA report to verify data consistency without revealing the original data.

[0107] Cross-Chain Relay Node: A communication node that enables data interaction between different blockchain networks (such as the waste disposal chain of the regulatory platform and the enterprise production chain). It verifies the electronic waybill data through digital signatures, synchronizes the hazardous waste disposal volume information to the enterprise chain, and triggers the locking operation of smart contracts for abnormal data.

[0108] Path Planning Algorithm: An algorithm module that generates the theoretically optimal path and benchmark carbon emission values based on transportation task parameters (load of 28 tons and distance of 850 kilometers). Combining the actual trajectory data collected by on-vehicle sensors, it calculates the carbon emission increment difference, and when the deviation exceeds 12%, it triggers the recording of on-chain abnormal events.

[0109] Metallurgical Recovery Rate Formula: A mathematical model that calculates the theoretical recovery threshold based on the metal element content (such as 85 kg of cobalt element) in the product design bill of materials, combined with metallurgical process parameters. When the actual extraction amount exceeds the threshold and continues to exceed the limit for 8 hours, it triggers the supplier performance deduction rule.

[0110] Genetic Algorithm Optimization Engine: A calculation module that simulates the biological evolution mechanism to optimize carbon emission factors. It encodes parameters such as the thermal efficiency of production equipment (such as 82%) and the energy consumption coefficient of transportation vehicles (such as 0.35 L / km) as chromosome genes, iteratively generates a candidate factor set under the GHGProtocol range 1-3 constraints, and selects the optimal parameter combination through voting by blockchain consensus nodes.

[0111] Geographic Information System Spatial Clustering Analysis: A technical means for regional analysis of the carbon emission deviation rates of each link in the supply chain. For example, after identifying that 3 suppliers in region S have a deviation of more than 15%, it generates production capacity adjustment suggestions and writes them into the smart contract execution queue, triggering the update of evaluation parameters according to the first-in, first-out rule.

[0112] Credibility Score: A verification metric generated based on environmental sensor data (such as warehouse temperature and humidity) and machine learning models. By analyzing the characteristics of energy consumption fluctuations during non-operating periods (such as abnormal operation of the air conditioning system from 1:00 to 5:00 in the morning), a score ranging from 0 to 100 is generated and written into the smart contract parameter library. A score below 70 triggers the data review process.

[0113] Multi-Party Signature Confirmation Process: A joint verification mechanism for data correction instructions by audit nodes (at least two) in the blockchain network. The elliptic curve digital signature algorithm (such as SM2 / SM9) is used to sign the corrected hash value, and the signature result is anchored to the block header to ensure the legality and traceability of data correction.

[0114] Explanation of GHG Protocol Scope 1 - 3: The GHG Protocol divides carbon emissions into three accounting scopes: Scope 1 covers greenhouse gas emissions from production facilities directly controlled by the enterprise (such as CO 2 emissions from factory coal-fired boilers); Scope 2 involves indirect emissions related to purchased energy (such as carbon emissions from power plants corresponding to production electricity consumption); Scope 3 includes other indirect emissions generated by activities in the upstream and downstream of the supply chain (such as carbon emissions in raw material transportation, product use, and waste treatment). In the present invention, constructing a dynamic emission factor model needs to simultaneously meet the composite constraint conditions of Scope 1 (direct production emission verification), Scope 2 (carbon footprint accounting of energy consumption), and Scope 3 (emission tracking of logistics transportation and recycling), and generate a candidate set of carbon emission factors covering the entire supply chain through a genetic algorithm optimization engine to make the accounting system meet the requirements of international carbon footprint standards.

[0115] The present invention constructs a data collection and trustworthiness processing system covering all links of the supply chain to solve the problem of integrating multi-source heterogeneous data. In the production link of suppliers, edge computing gateways are deployed to collect energy consumption in real time, and combined with blockchain oracles to call the raw material procurement data of third-party certification agencies. Through hash digest extraction and zero-knowledge proof verification, the relevance between procurement vouchers and carbon emission lists is ensured. Manufacturing process data is logically verified through a dynamic emission factor model for equipment operation parameters and process improvement records to generate standardized on-chain records. Logistics transportation data uses a path planning algorithm to generate a theoretical optimal path benchmark value, and calculates the incremental difference in combination with real-time vehicle sensor working conditions data. When the deviation exceeds the threshold, an abnormal event record is triggered. In the recycling link, the theoretical threshold is calculated based on the metallurgical recovery rate formula, and the actual extraction amount is compared through a smart contract to dynamically adjust the supplier performance parameters.

[0116] The blockchain sharding chain technology realizes the isolated storage and cross-chain synchronization verification of multi-regional data. The supplier data in Region A and Region B are written into different blockchain frameworks respectively, and the block header hash values are synchronized through the cross-chain relay protocol to ensure global data consistency. The smart contract engine periodically calls the on-chain verification code, compares the real-time data with the industry benchmark, and triggers an early warning, after which the edge nodes perform off-chain cross-verification. In the multi-signature confirmation process, the audit nodes use the elliptic curve digital signature algorithm to jointly verify the corrected hash value, and the signature result is anchored to the block header to enhance the credibility of data correction.

[0117] The dynamic monitoring mechanism optimizes the weights of the anomaly detection model through the genetic algorithm, combines the spatial clustering analysis of the geographic information system, and identifies the regional centralized carbon emission deviation. When the logistics path changes, the digital twin system generates multiple alternative plans, and the screening conditions take into account both the transportation timeliness and the carbon emission reduction target. The temperature and humidity sensor data in the recycling link cooperate with the machine learning model to identify the energy consumption fluctuation characteristics during non-operation periods, and generate a credibility score to be written into the smart contract parameter library. The above technical solutions form a "collection-verification-decision" closed loop, realizing the full-link dynamic supervision and accurate audit of the supply chain carbon footprint.

Claims

1. A blockchain-based supply chain carbon data trust management method, characterized in that: include: Step S101, obtaining data from all links of the supply chain, including supplier production data, manufacturing process data, logistics and transportation data, and recycling data, wherein the supplier production data collects energy consumption in real time through an edge computing gateway deployed on the supplier's production equipment, the manufacturing process data includes energy equipment sensor data and process improvement records in the factory, the logistics and transportation data obtains real-time operating parameters of the transport vehicle through the vehicle terminal, and the recycling data includes the recovery rate of product disassembly materials; Step S102, inputting the data of all links of the supply chain into the blockchain network for trust processing, generating a data summary including spatiotemporal characteristics through the data standardization module, and writing the data summary into the blockchain distributed ledger, wherein the following processing is performed on the supplier production data: calling the regional carbon emission factor in the industry benchmark database to perform data deviation verification, and generating a chain record including a verification mark; constructing a dynamic emission factor model for the manufacturing process data combined with the equipment operation parameters, and verifying the logical correlation between the process improvement record and the energy consumption through the dynamic emission factor model; Step S103, inputting the on-chain records into the smart contract execution engine, and making audit decisions based on the preset carbon emission compliance rules, wherein when it is detected that the verification mark corresponding to the energy consumption deviates from the industry benchmark threshold, the data correction instruction generation process is triggered, and multi-party signature confirmation is performed through the blockchain consensus node; Step S104, generating supply chain optimization instructions based on the results of the audit decision, and feeding back the supply chain optimization instructions to the management-end execution device, wherein the supply chain optimization instructions include supplier evaluation parameters and transportation route planning strategies dynamically adjusted based on the anomaly detection results, forming a low-carbon supply chain management closed-loop system throughout data collection, verification, and decision-making.

2. The blockchain-based supply chain carbon data trusted management method according to claim 1 is characterized in that: The step S102 further includes: The raw material procurement database of a third-party certification agency is called through the blockchain oracle interface to extract the hash summary of the original procurement voucher and generate a hash feature value including a timestamp; Input the hash characteristic value and the bill of materials data in the life cycle assessment report into the zero-knowledge proof verification module to verify the consistency of the correlation between the procurement voucher and the carbon emission inventory; A blockchain data block including a trusted identifier is generated based on the verification result, and the trusted identifier is used to update the supplier production data integrity assessment status.

3. The blockchain-based supply chain carbon data trusted management method according to claim 1 is characterized in that: The step S102 includes Access the waste disposal blockchain of the regulatory platform through the cross-chain relay node to obtain the digitally signed electronic receipt data; Extract the time series consumption data of the production-side chemical inventory management system, and calculate the dynamic deviation rate between the time series consumption data and the electronic form disposal quantity; When the dynamic deviation rate exceeds the preset threshold, the smart contract is triggered to lock the on-chain status of the carbon emission calculation results of the corresponding production batch.

4. The blockchain-based supply chain carbon data trusted management method according to claim 1 is characterized in that: The step S102 includes: Based on the starting or ending coordinates of the transport task and the vehicle load parameters, the path planning algorithm is called to generate the benchmark carbon emission value of the theoretical optimal transport path; Obtaining real-time operating data of the actual transport trajectory through on-board sensors, and calculating the incremental difference between the actual carbon emissions and the benchmark carbon emissions value; When the incremental difference exceeds the path deviation threshold preset in the smart contract, an abnormal transportation event record is created in the blockchain ledger, and a suspension instruction of the on-chain freight settlement contract is triggered.

5. The blockchain-based supply chain carbon data trusted management method according to claim 4 is characterized in that: Also includes: Analyze the energy consumption log data of port loading and unloading equipment through edge computing nodes, and extract the speed and cargo capacity parameters from the ship's AIS trajectory data; Simultaneously collect GPS trajectory data and load change parameters of road transport vehicles, freight marshaling information and energy consumption monitoring data of railway transport; A carbon emission calculation model for the sea transport segment is built based on speed and cargo volume parameters, integrating the real-time load and route optimization parameters of road transport and the energy consumption coefficient of rail transport to generate a carbon emission fusion model covering multimodal transport. Extract the carbon emission intensity characteristics of sea, road and rail transport, and associate and map them with transport identification through preset coding rules; Generate a carbon emission feature vector including the vessel identifier, road transport batch code and railway freight bill number; The carbon emission characteristic vector is cross-chain hashed and bound to the blockchain data of road, rail and sea sections, and a carbon footprint tracking map covering multiple transport modes such as road, rail and sea is generated through smart contracts.

6. The blockchain-based supply chain carbon data trusted management method according to claim 1 is characterized in that: The step S103 includes: According to the metal element content parameters marked in the product design bill of materials, the theoretical recycling threshold is calculated based on the metallurgical recovery rate formula; The actual extraction volume reported by the recycler is compared with the theoretical recycling threshold value by inputting it into the blockchain smart contract for deviation comparison; When it is detected that the actual withdrawal volume exceeds the theoretical threshold and lasts for more than the preset period, the supplier performance deduction rule module in the smart contract is activated, and an alarm instruction including an abnormal batch identification is sent to the blockchain node with regulatory authority.

7. The blockchain-based supply chain carbon data trusted management method according to claim 6 is characterized in that: Also includes: Collect monitoring data from warehouse temperature and humidity sensors within a preset time window to generate a time series of environmental parameters bound to the product recycling batch code; The energy consumption anomaly detection model trained based on the environmental parameter time series data of historical recycling data is used to identify the energy consumption fluctuation characteristics during non-operating periods; The energy consumption fluctuation characteristics are associated and matched with the timestamp of the recycling processing data to generate a credibility score that is written into the verification parameter library of the blockchain smart contract.

8. The blockchain-based supply chain carbon data trusted management method according to claim 1 is characterized in that: The step S103 includes: Set up at least two consensus nodes with audit functions in the blockchain network, and configure the node certificates to include carbon emission audit authority identification; When the data correction instruction involves the update of the industry benchmark threshold, the corrected data features are extracted to generate a hash value to be signed; The consensus node calls the elliptic curve digital signature algorithm to perform joint signature verification on the hash value, and anchors the signature result to the blockchain transaction block header.

9. The blockchain-based supply chain carbon data trusted management method according to claim 1 is characterized in that: Also includes: Based on the real-time carbon emission data of each link in the supply chain, calculate the percentage deviation rate of carbon emissions at each node from the industry benchmark value; Through the geographic information system, the spatial cluster analysis of the percentage deviation rate of carbon emissions of each node and the industry benchmark value is carried out to identify the regional concentrated abnormal distribution characteristics; The capacity adjustment suggestion including the regional identification is written into the smart contract execution queue, and the smart contract execution queue uses the first-in-first-out rule to trigger the update of the supplier evaluation parameters.

10. The blockchain-based supply chain carbon data trusted management method according to any one of claims 1 to 9, characterized in that: The construction of the dynamic emission factor model includes: Collect the thermal efficiency value of production equipment, the energy consumption coefficient of transportation vehicles and the conversion rate of recycled materials through the industrial Internet of Things interface to generate a standardized parameter data set; Inputting the standardized parameter data set into a genetic algorithm optimization engine to perform chromosome crossover mutation iterations under emission constraints satisfying GHG Protocol scopes 1-3; The generated candidate emission factor set is verified for compliance through voting by blockchain consensus nodes, and parameter combinations with voting pass rates exceeding the preset threshold are selected to update the dynamic emission factor model coefficients.

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