CN-121998668-A - Tracing method, device, medium and equipment for abnormal carbon emission

CN121998668ACN 121998668 ACN121998668 ACN 121998668ACN-121998668-A

Abstract

The application discloses a tracing method, a tracing device, a tracing medium and tracing equipment for abnormal carbon emission, which belong to the field of carbon emission, and positioning target link data from the blockchain storage, and executing signature verification and hash consistency verification to ensure the authenticity and consistency of the data. And if the target link data passes the verification, verifying the containing relation of the Merkle tree root hash of each link in a hierarchical upward way so as to confirm the integrity of the full-chain data. If any level verification fails, the feature data of the level is combined with an anomaly detection model based on an isolated forest algorithm to locate the anomaly data node. And then, according to signature information of the abnormal data node and a preset tracking key, a group signature verification and private key deduction flow is utilized to trace back to a responsible party, so that a tracing process of carbon emission abnormality is completed, and the problem that the prior art cannot accurately and efficiently trace back the carbon emission abnormality is effectively solved.

Inventors

CHEN FENG
HE DONG
ZHOU SHENG
ZHAO XINZHE
ZHANG CHENGXIN
YU MINMING

Assignees

国网浙江省电力有限公司信息通信分公司

Dates

Publication Date: 20260508
Application Date: 20260409

Claims (10)

1. The tracing method for the abnormal carbon emission is characterized by comprising the following steps: acquiring a traceability request of a renewable energy full-chain carbon emission data chain, wherein the traceability request comprises a data identifier to be verified; Positioning target link data from stored evidence storage data of a preset blockchain according to the data identification to be verified, and executing signature verification and hash consistency verification on the target link data, wherein the evidence storage data is formed by signing self carbon emission data by each level participant and uplink after Merkle tree processing; If the target link data passes verification, verifying the inclusion relation of the Merkle tree root hash of each link layer by taking the Merkle tree root hash corresponding to the target link data as a starting point; If any level fails to verify, detecting abnormal data nodes based on feature data of the level failing to verify and combining a preset abnormal detection model obtained by training based on an isolated forest algorithm; And obtaining a tracing result of the carbon emission abnormality through a group signature verification method and a preset private key deduction flow based on the signature information of the abnormal data node and a preset tracing key.
2. The method for tracing a carbon emission anomaly according to claim 1, further comprising, after obtaining the tracing result of the carbon emission anomaly: The tracing result comprises basic tracing information, a data layer verification result, a hierarchy up verification result, an abnormal detection result and an identity tracing result, wherein the abnormal detection result comprises abnormal data node hash, tampering position and tampering behavior characteristics; Based on the identity tracing result, associating the abnormal data node attribution record through the decentralization identity of the participant, and combining the signature submitting record of the participant on the data corresponding to the abnormal data node to confirm that the participant is the responsible party of the abnormal data node; Transmitting the basic traceability information, the data layer verification result, the hierarchy up verification result and the abnormality detection result to a responsible party so that the responsible party corrects the abnormal data and outputs corrected data; And performing signature verification, hash consistency verification and hierarchical Merkle tree root hash inclusion relation verification on the corrected data again.
3. The method for tracing abnormal carbon emission according to claim 1, wherein the step of locating target link data from stored certification data of a preset blockchain and performing signature verification and hash consistency verification on the target link data is specifically as follows: Screening a certificate storage record matched with a target link from certificate storage data stored in a preset blockchain according to a data identifier to be verified, and extracting a target link data signature packet in the certificate storage record, wherein the data signature packet comprises original carbon emission data, a participant private key signature value, a time stamp and a participant center-removing identity identifier; the public key corresponding to the participant de-centralized identity is called from a blockchain, and a challenge value is calculated after the original carbon emission data and the timestamp are spliced according to a Schnorr signature verification rule; verifying whether the private key signature value meets a preset discrete logarithm equation or not according to the challenge value, and if yes, confirming that the signature verification is passed; Based on a target link Merkle tree verification path in the certificate record, calculating a hash value of original carbon emission data by adopting a SHA-256 hash algorithm, and carrying out layer-by-layer aggregation on the hash value of the original carbon emission data and node hashes in the verification path to obtain a top hash value, and if the top hash value is consistent with a target link Merkle tree root hash, confirming that hash consistency verification passes.
4. The method for tracing abnormal carbon emission according to claim 1, wherein the step-by-step verification of the inclusion relationship of Merkle root hashes of each link is performed by taking Merkle root hashes corresponding to the target link data as a starting point, specifically comprises: Splicing the Merkle tree root hash corresponding to the target link data with the target link level identification in the data identification to be verified, and calculating to obtain the leaf node hash to be verified of the Merkle tree of the last level; extracting the Merkle tree root hash of the last level and the corresponding Merkle tree verification path from the evidence storage data stored in the blockchain; Calculating parent node hashes layer by layer according to preset rules by using leaf node hashes to be verified and neighboring node hashes in a Merkle tree verification path until top-level aggregation hashes are obtained; if the top layer aggregation hash is consistent with the Merkle tree root hash of the last layer, confirming that the containing relation of the layer is established, taking the last layer as a new target link, and repeatedly executing the step of verifying the containing relation of the layer.
5. The method for tracing carbon emission anomaly according to claim 1, wherein if any one of the levels fails to verify, based on feature data of the level that fails to verify, an anomaly data node is detected by combining a preset anomaly detection model trained based on an isolated forest algorithm, specifically: Extracting feature data of a verification failure level, wherein the feature data comprises a participant entity type, a region identifier, a generating capacity fluctuation value and a correlation degree of the verification failure level and a Merkle tree root hash of the previous level; Calculating the carbon emission factor deviation degree of the verification failure level; inputting the characteristic data and the carbon emission factor deviation degree into a preset anomaly detection model which is obtained based on isolated forest algorithm training, so that the anomaly detection model calculates anomaly scores; if the anomaly score is greater than a preset dynamic threshold, marking the characteristic data as suspected anomaly data; And based on the suspected abnormal data, performing recursive subtree hash comparison on the Merkle tree of the verification failure level and the Merkle tree of the last level, comparing the subtree hash values from the root node level by level, positioning leaf nodes corresponding to hash differences, and confirming the leaf nodes as abnormal data nodes.
6. The tracing method of abnormal carbon emission according to claim 1, wherein the tracing result of abnormal carbon emission is obtained by a group signature verification method and a preset private key derivation process based on signature information of the abnormal data node and a preset tracing key, specifically: Analyzing the signature information corresponding to the abnormal data node to obtain a promise value, a response value and a challenge value in the signature information; According to a preset tracking key, combining the promise value, the response value and the challenge value, and adopting a group signature verification method to verify the validity of the signature information to obtain a signature verification result; If the signature validity verification is passed, according to a preset private key derivation flow, deriving the private key characteristics of the participants corresponding to the abnormal data nodes based on the association relation between the preset tracking key and the signature information; Inquiring the mapping relation between the private key characteristics stored in the blockchain and the identities of the participants according to the private key characteristics, and acquiring corresponding identity information of the participants; and integrating the abnormal data node, the signature verification result and the identity information of the participators to obtain a tracing result of abnormal carbon emission.
7. The utility model provides a carbon emission anomaly's traceability device which characterized in that includes: the acquisition module is used for acquiring a traceability request of a renewable energy full-chain carbon emission data chain, wherein the traceability request comprises a data identifier to be verified; The first verification module is used for positioning target link data from stored evidence storage data stored in a preset blockchain according to the data identification to be verified, and executing signature verification and hash consistency verification on the target link data, wherein the evidence storage data is formed by signing self carbon emission data by each level participant and processing a Merkle tree and then uploading the self carbon emission data; the second verification module is used for verifying the inclusion relation of Merkle tree root hash of each link in a layer-by-layer upward way by taking Merkle tree root hash corresponding to the target link data as a starting point if the target link data passes verification; The detection module is used for detecting abnormal data nodes based on characteristic data of a hierarchy with verification failure and combining a preset abnormal detection model obtained by training based on an isolated forest algorithm if any hierarchy with verification failure; And the tracing module is used for obtaining a tracing result of the carbon emission abnormality through a group signature verification method and a preset private key deduction process based on the signature information of the abnormal data node and a preset tracing key.
8. The device for tracing a carbon emission anomaly according to claim 7, further comprising, after the tracing result of the carbon emission anomaly is obtained: The tracing result comprises basic tracing information, a data layer verification result, a hierarchy up verification result, an abnormal detection result and an identity tracing result, wherein the abnormal detection result comprises abnormal data node hash, tampering position and tampering behavior characteristics; Based on the identity tracing result, associating the abnormal data node attribution record through the decentralization identity of the participant, and combining the signature submitting record of the participant on the data corresponding to the abnormal data node to confirm that the participant is the responsible party of the abnormal data node; Transmitting the basic traceability information, the data layer verification result, the hierarchy up verification result and the abnormality detection result to a responsible party so that the responsible party corrects the abnormal data and outputs corrected data; And performing signature verification, hash consistency verification and hierarchical Merkle tree root hash inclusion relation verification on the corrected data again.
9. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a device in which the computer readable storage medium is located to perform the method for tracing the carbon emission anomaly as claimed in any one of claims 1 to 6.
10. A terminal device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the method of tracing a carbon emission anomaly as claimed in any one of claims 1 to 6 when the computer program is executed.

Description

Tracing method, device, medium and equipment for abnormal carbon emission Technical Field The invention relates to the field of carbon emission, in particular to a method, a device, a medium and equipment for tracing abnormal carbon emission. Background As global energy structures are transformed to low carbonization, renewable energy installations have shown explosive growth. Meanwhile, a carbon market mechanism is also rapidly developed, and under the background, accurate traceability of renewable energy carbon emission attributes becomes a key foundation for realizing effective linkage of green electricity trading and an electricity carbon market. However, currently, renewable energy is managed by different subjects from production, transmission to consumption and other multi-link data, there is a risk of data tampering, and each link data lacks effective association. When the cross-level tracing is performed, data anomalies are difficult to quickly locate, responsibility is clear, and the tracing logic closed loop of the full-link carbon emission attribute is difficult to realize. In addition, the existing carbon emission attribute traceability technology mainly surrounds the data acquisition, evidence storage and verification expansion of multiparty participation, but has the following problems: The centralized platform management data is that the existing carbon emission traceability system based on the centralized platform is used for centralized management of data of all parties by a single mechanism (such as a power grid company), and data inquiry and verification are realized through authority allocation. The mode has the problems of monopoly, easy tampering, difficult cross-domain cooperation and the like, and the identity management depends on a central server, so that the security and anonymity are hard to balance. Single-level data uplink the basic blockchain tracing scheme adopts single-level data uplink and realizes the certification through the distributed characteristic of the blockchain. However, the scheme lacks a hierarchical data association mechanism, the data of each link exists in isolation, and full link level verification from production to consumption cannot be realized. The anomaly detection only depends on simple threshold judgment, the identity tracing depends on public identity information, and privacy protection and supervision requirements are difficult to be considered. The data format is disordered, a standardized hierarchical acquisition mechanism is not established, the data format of each link is disordered, core items are missing, and a single-level evidence storage mode is adopted, so that hierarchical anchoring cannot be realized, the relevance of all-link data is broken, and the complete chain from production to consumption cannot be verified. The anomaly detection and the identity tracing are insufficient, namely the anomaly detection only depends on simple threshold judgment, the real-time analysis capability is lacking, and the identity tracing is not combined with the group signature and private key derivation technology, so that the positioning of the anomaly data is lagged, the responsible main body is difficult to trace anonymously, and the privacy cannot be ensured and the supervision efficiency is influenced. And due to the lack of data change history tracing, the association and re-verification flow on the data chain is not designed to be modified, so that the change history cannot be traced after abnormal data is modified, and the consistency of the whole link is difficult to recover. These deficiencies result in the inability of the prior art to accurately and efficiently trace the carbon emission anomaly. Disclosure of Invention The invention provides a tracing method, a tracing device, a tracing medium and tracing equipment for abnormal carbon emission, which are used for solving the problem that the prior art cannot accurately and efficiently trace the abnormal carbon emission point. In a first aspect, the present application provides a method for tracing carbon emission anomalies, including: acquiring a traceability request of a renewable energy full-chain carbon emission data chain, wherein the traceability request comprises a data identifier to be verified; Positioning target link data from stored evidence storage data of a preset blockchain according to the data identification to be verified, and executing signature verification and hash consistency verification on the target link data, wherein the evidence storage data is formed by signing self carbon emission data by each level participant and uplink after Merkle tree processing; If the target link data passes verification, verifying the inclusion relation of the Merkle tree root hash of each link layer by taking the Merkle tree root hash corresponding to the target link data as a starting point; If any level fails to verify, detecting abnormal data nodes based on feature data of the level fail