CN-122022842-A - Block chain-based industry chain green electricity consumption footprint tracking method and system

CN122022842ACN 122022842 ACN122022842 ACN 122022842ACN-122022842-A

Abstract

The invention discloses a block chain-based industrial chain green electricity consumption footprint tracking method and system, and relates to the technical field of intelligent power grids and energy information. The method comprises the steps of carrying out feature vector comparison by adopting hash deterministic verification and feature auxiliary analysis, associating a blockchain anchor point hash value through a unique order number, combining a traceability link embedded by a steganography algorithm to finish hash verification and feature matching degree analysis of traceability certificates, comprehensively judging footprint validity based on the hash comparison result, distinguishing three types of invalid footprints, introducing a time sequence attention mechanism and a group decision algorithm to realize invalid footprint grading attribution and valid footprint compliance grading, and realizing invalid footprint problem link positioning and restoration scheme output or valid footprint full-chain quantized data acquisition by using a blockchain distributed consensus mechanism. The method guarantees the authenticity, traceability and self-adaptive optimization capability of the system of the green electricity consumption footprint through dynamic hierarchical verification, full chain Ha Xifu cover and non-tamperable evidence storage.

Inventors

YANG TA
DAI YUYANG
ZHONG MEIFANG
ZHANG FAN
FU XIAO

Assignees

广东省国际工程咨询有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (9)

1. The industrial chain green electricity consumption footprint tracking method based on the block chain is characterized by comprising the following steps of: Step one, verifying a to-be-verified green electricity consumption credential data set, namely acquiring N to-be-verified green electricity consumption credential data sets, carrying out deterministic verification on the N to-be-verified green electricity consumption credential data sets and a blockchain distributed consensus anchor point through a hash algorithm, and terminating a verification process and outputting a first consistency verification result if the hash values are inconsistent; Based on the green electricity consumption tracing certificate, acquiring a standard identification anchor point hash value prestored in a blockchain, finishing hash deterministic verification, extracting actual identification characteristics of a third anti-counterfeiting tracing area in the tracing certificate for auxiliary analysis, and outputting a second consistency verification result; Step three, judging the effectiveness of the footprint, namely, comprehensively analyzing based on the first consistency verification result and the second consistency verification result to determine the effectiveness of the green electricity consumption footprint to be verified, wherein the effectiveness result comprises an invalid footprint and an effective footprint, and the invalid footprint is further divided into characteristic mismatch type invalidity, anchor point deletion type invalidity and evidence tampering type invalidity; And step four, full chain tracking, namely, based on the effectiveness result and the footprint tracing area, performing full chain tracking to acquire the complete quantitative data of the effective footprint and the compliance utility evaluation report, or positioning invalid footprint problem links and nodes and outputting a hierarchical repairing scheme.
2. The method of claim 1, wherein in the first step, before the verifying the consistency of the green electricity consumption certificate data set to be verified, the method further comprises: Acquiring a basic identification image of a green electricity consumption certificate to be verified, and calculating an image hash value to serve as a temporary anchor point to be verified through an SHA-256 algorithm; And calling a first green electricity data characteristic extraction model, extracting characteristics of a basic identification image of the to-be-verified certificate, and identifying a green electricity type, a consumption scene and a core characteristic identification for positioning a corresponding anchor point hash value in the blockchain.
3. The method of claim 2, wherein the generating of the first green electricity data feature extraction model comprises: Basic identification images of different types of green electricity consumption certificates and corresponding standard feature libraries thereof are obtained, key features are marked, a feature sample set is formed and divided into a training set and a testing set; constructing a convolutional neural network model, and training by taking a training set as input and standard features as output to obtain a preliminary model; and (3) performing accuracy test by using the test set, outputting a model meeting preset accuracy as a first green electricity data feature extraction model, and outputting 512-dimensional high-level semantic feature vectors.
4. The method of claim 1, wherein the logic for generating the second green electricity data feature comparison model comprises: Acquiring a pre-stored green electricity consumption data credential set, selecting a credential with a hash value consistent with an anchor point on a chain, performing core region segmentation and feature extraction to obtain a 512-dimensional semantic vector corresponding to a core data segment, and establishing association with an anchor point feature abstract; forming a sample pair of semantic vector to be verified, anchor point semantic vector and difference degree label, and splitting the sample pair into a training set and a testing set; And constructing a lightweight neural network model, training by taking a training set as input and a difference degree label as output, obtaining a model meeting the analysis precision requirement as a second green data characteristic comparison model, calculating the matching degree by adopting a cosine similarity algorithm, and presetting a similarity threshold value to carry out difference judgment.
5. The method of claim 1, wherein the logic for obtaining the standard identification anchor hash value and performing the hierarchical verification in step two comprises: Scanning a unique order number area in the green electricity consumption traceability certificate, obtaining a unique number, and calling a standard identification anchor point hash value corresponding to the blockchain and a characteristic abstract thereof; And if the two types of the feature vectors are inconsistent, extracting 512-dimensional high-level semantic feature vectors corresponding to the actual identification features, matching the 512-dimensional high-level semantic feature vectors with standard identification anchor point feature abstract vectors prestored in a block chain, calling a second green data feature comparison model to calculate the overall matching degree and the subset matching degree, and outputting feature deviation details.
6. The method of claim 1, wherein in the third step, performing the comprehensive analysis based on the first and second consistency verification results comprises: Extracting Ha Xibi pairs of core conclusions, wherein the characteristic analysis details are used for assisting in positioning the problem; If the first verification result Ha Xibi is inconsistent with the second verification result, judging that the characteristic is not matched with the invalid footprint; If the first verification result is inconsistent and the second verification result is consistent, judging that the characteristics are not matched with the invalid footprint; If the first verification result is consistent and the second verification result is inconsistent, judging that the voucher is a tamper-type invalid footprint; and if the first verification result and the second verification result are consistent and a complete anchor point link exists, judging that the first verification result and the second verification result are valid footprints.
7. The method according to claim 1, wherein in the fourth step, when the footprint result is an invalid footprint, the method includes: scanning and analyzing a third anti-counterfeiting traceability area, extracting a green electricity footprint traceability link through a steganography algorithm, and jumping to a block chain distributed traceability interface; Combining the hash comparison result, the abnormal type and the comprehensive risk value positioning problem, tracing the uploading node through a timestamp, locking the responsibility main body, and outputting the repairing action according to the repairing priority; The problem node treatment state and the repair action execution result are uplink, and the node credit rating parameter is updated; when the footprint result is an effective footprint, comprising: acquiring a segmented tracing code and a basic tracing image of a footprint tracing area, and analyzing the segmented tracing code to acquire a data splicing rule; Generating a traceable data body according to the combination of the splicing rules, and calculating the SHA-256 hash value of the traceable data body and comparing and confirming the anchor point on the chain; And analyzing the traceable data body to obtain full-chain quantized data, retrieving node signature and timestamp information, integrating characteristic analysis deviation causes, compliance grading and utility value sequencing results, and generating a traceable report with priority labels.
8. The method of claim 1, further comprising constructing an ineffective footprint risk scoring matrix based on a group decision algorithm consensus with the blockchain, comprising: configuring distributed scoring nodes of a block chain, wherein each node scores the abnormal type in three dimensions of repair effect, occurrence probability and severity through an improved Delphi method, and a node consensus scoring vector is generated; Verifying through a practical Bayesian fault-tolerant mechanism, and writing a distributed account book when node scores exceeding a preset consensus proportion threshold are consistent; and constructing a green electricity footprint anomaly scoring matrix, calculating a comprehensive risk value by combining the industrial chain link point disposal rate and the historical occurrence frequency, and determining the repair priority.
9. The system for tracking the green electricity consumption footprint of the industrial chain based on the blockchain is used for realizing the method for tracking the green electricity consumption footprint of the industrial chain based on the blockchain as claimed in claim 1, and is characterized by comprising a green electricity data characteristic extraction module, a data automatic uploading and anchoring module, a footprint quantization module and a blockchain distributed storage module; The green electricity data feature extraction module is used for extracting the basic identification image features of the green electricity consumption certificate to be verified through the first green electricity data feature extraction model, and identifying the green electricity type, the consumption scene and the core feature identification; The data automatic uploading and anchoring module is used for acquiring green electricity consumption data and credential images of each link through intelligent acquisition equipment, generating a unique SHA-256 hash value as a digital fingerprint, and synchronously uploading the digital fingerprint to a blockchain node to realize data uploading, namely anchoring; The footprint quantization module is used for acquiring green electricity consumption quantization information of each link through the sensing equipment and the intelligent terminal, binding each part of quantization information with a hash anchor point of a corresponding link, and forming a one-to-one correspondence relationship between data and anchor points; And the block chain distributed storage module is used for storing the green electricity consumption total information and the corresponding hash anchor points, packaging and linking the data-hash anchor point pairs according to the time stamp sequence, and ensuring the synchronization and consistency of the hash values of the anchor points of all nodes through a consensus mechanism.

Description

Block chain-based industry chain green electricity consumption footprint tracking method and system Technical Field The invention belongs to the technical field of intelligent power grids and energy information, and particularly relates to an industrial chain green electricity consumption footprint tracking method and system based on a block chain. Background With the acceleration of global energy structures toward green low-carbon transformation, green power consumption has become a core path for enterprises to achieve carbon neutralization targets and fulfill environmental social responsibilities. Under the background, the source, transmission and final use of green electricity consumption are reliably and accurately traced in a full chain, and the system becomes a key infrastructure for supporting green electricity certificate transaction, carbon footprint accounting and green metal development. Currently, the industry mainly relies on the data non-tamperable characteristic of a blockchain technology to construct a traceability evidence-storing system, and introduces an artificial intelligence technology to improve the automation level of bill identification and data processing, so as to realize the green electricity footprint visualization from a power generation end to a power utilization end. However, when the prior art scheme constructs a trusted traceability system, the inherent contradiction that the root of trust and the processing efficiency are difficult to be compatible is faced. On the one hand, if the block chain certification is simply relied on, the tamper-proof of the uplink data can be ensured, but the authenticity of the source data and the consistency of the physical certificates under the chain and the records on the chain are lack of effective automatic verification means, and the dependence on manual verification leads to low efficiency and easy error. On the other hand, if the data authenticity judgment and the information verification are mainly carried out by relying on an artificial intelligent model, the inherent black box characteristic of the model, the dependence on training data and the risk of resistance attack are caused, so that the final reliability of the judgment result is doubtful, and the application requirements of green electricity consumption in high trust requirement scenes such as compliance audit, carbon market transaction and the like are difficult to meet. Therefore, the industry is urgent to explore a new technology integration path, which aims to break through the two-dimensional dilemma of trust and efficiency. Based on the above, the invention provides a block chain-based industry chain green electricity consumption footprint tracking method and system. Disclosure of Invention In order to overcome the drawbacks and disadvantages of the prior art, a first object of the present invention is to provide a method for tracking the green electricity consumption footprint of an industrial chain based on a blockchain, and a second object of the present invention is to provide a system for tracking the green electricity consumption footprint of an industrial chain based on a blockchain. The first object of the invention adopts the following technical scheme: the industrial chain green electricity consumption footprint tracking method based on the block chain comprises the following steps: Step one, verifying a to-be-verified green electricity consumption credential data set, namely acquiring N to-be-verified green electricity consumption credential data sets, carrying out deterministic verification on the N to-be-verified green electricity consumption credential data sets and a blockchain distributed consensus anchor point through a hash algorithm, and terminating a verification process and outputting a first consistency verification result if the hash values are inconsistent; Based on the green electricity consumption tracing certificate, acquiring a standard identification anchor point hash value prestored in a blockchain, finishing hash deterministic verification, extracting actual identification characteristics of a third anti-counterfeiting tracing area in the tracing certificate for auxiliary analysis, and outputting a second consistency verification result; Step three, judging the effectiveness of the footprint, namely, comprehensively analyzing based on the first consistency verification result and the second consistency verification result to determine the effectiveness of the green electricity consumption footprint to be verified, wherein the effectiveness result comprises an invalid footprint and an effective footprint, and the invalid footprint is further divided into characteristic mismatch type invalidity, anchor point deletion type invalidity and evidence tampering type invalidity; And step four, full chain tracking, namely, based on the effectiveness result and the footprint tracing area, performing full chain tracking to acquire the complete quanti