A blockchain-based carbon neutrality evidence storage system and method

Abstract

This invention discloses a blockchain-based carbon neutrality evidence storage system and method, belonging to the field of blockchain data processing technology. It addresses the technical problem of poor collaborative closed-loop supervision of dynamic updates of evidence storage for different carbon neutrality evidence storage entities in existing solutions. The system uses several preset indicator features to dynamically match and implement evidence update schemes for different carbon neutrality evidence storage entities. It monitors and analyzes the implementation effects of different evidence update schemes from both overall and local dimensions, and combines the analysis results from different dimensions to obtain a regulatory analysis set corresponding to different evidence update schemes. This regulatory analysis set is used to proactively optimize and manage the subsequent implementation of different evidence update schemes, achieving diversified proactive supervision and analysis in the later stages of evidence update scheme implementation. This, in turn, complements the dynamic matching and implementation of the evidence update scheme in the early stages, forming a collaborative closed-loop supervision of carbon neutrality evidence storage.

Description

Technical Field

[0001] This invention relates to the field of blockchain data processing technology, and specifically to a blockchain-based carbon neutrality evidence storage system and method. Background Technology

[0002] Evidence of carbon neutrality is a mechanism for preserving and verifying evidence of the process of achieving carbon neutrality.

[0003] Existing carbon neutrality certification schemes aim to ensure the authenticity, transparency, and traceability of carbon emission data through technological means, typically combining technologies such as blockchain, IoT, and third-party auditing. However, due to the unique nature of continuous monitoring of carbon neutrality effect data, the certification of continuously tracked data requires dynamic updates, leading to a contradiction between the immutability of blockchain data and the need for dynamic updates. Existing technologies only achieve this through a single, fixed, updatable NFT (such as ERC-3664) or state channel recording change history. This cannot conduct regulatory analysis on the update data scenarios corresponding to different carbon neutrality certification entities at different times and from different aspects, nor can it dynamically implement targeted certification dynamic update schemes based on the analysis results from different aspects. As a result, the collaborative certification closed-loop regulatory effect for dynamic updates of certifications corresponding to different carbon neutrality certification entities is not good. Summary of the Invention

[0004] The purpose of this invention is to provide a blockchain-based carbon neutrality evidence storage system and method to solve the technical problem of poor collaborative evidence storage closed-loop supervision effect in existing solutions for dynamic updates of evidence storage corresponding to different carbon neutrality evidence storage entities.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A blockchain-based carbon neutrality certificate system includes a carbon neutrality certificate matching and implementation module, which is used to dynamically match and implement certificate update schemes for different carbon neutrality certificate holders based on several preset indicator characteristics.

[0007] The carbon neutrality certificate monitoring and analysis module is used to monitor and analyze the implementation effects of different certificate renewal schemes from both overall and local perspectives. It combines the analysis results from different dimensions to obtain the monitoring analysis set corresponding to different certificate renewal schemes, and uses the monitoring analysis set to proactively provide optimization management prompts for the subsequent implementation of different certificate renewal schemes.

[0008] Preferably, indicator feature data corresponding to different carbon neutrality evidence holders are obtained according to a number of preset indicator features, and the number of indicator feature data corresponding to different carbon neutrality evidence holders are matched with the standard feature range associated with the corresponding indicator features.

[0009] If the indicator feature data does not belong to the standard feature range associated with the corresponding indicator feature, then the corresponding indicator feature is marked as the first indicator feature and associated with the carbon neutrality certificate holder.

[0010] Conversely, the corresponding indicator feature is marked as the second indicator feature and associated with the carbon neutrality certificate holder.

[0011] Preferably, all indicator characteristics associated with different carbon neutrality certificate holders are sequentially analyzed, and the first, second, or third certificate update schemes are implemented for the carbon neutrality certificate holders based on the analysis results.

[0012] Preferably, when monitoring and analyzing the implementation effects of different evidence preservation and update schemes from an overall perspective, the total number of all anomalies that occur after the dynamic implementation of different evidence preservation and update schemes is counted separately, and then expressed using a formula. Calculate the first abnormal change value YB1k corresponding to the dynamic implementation of different evidence preservation and update schemes; where k is 1, 2, and 3, representing the first, second, and third evidence preservation and update schemes, respectively; NYk is NY1, NY2, and NY3, representing the total number of all abnormalities that occur after the dynamic implementation of different evidence preservation and update schemes; NFk is NF1, NF2, and NF3, representing the total number of evidence preservation implementations after the dynamic implementation of different evidence preservation and update schemes; and Bk is B1, B2, and B3, representing the standard abnormal values ​​corresponding to different evidence preservation and dynamic update schemes.

[0013] And, through formula Calculate the second abnormal change value YB2k corresponding to the dynamic implementation of different evidence storage and update schemes; where YYk is YY1, YY2, and YY3, which are the abnormal impact coefficients corresponding to all abnormalities that occur after the dynamic implementation of different evidence storage and update schemes; Ck is C1, C2, and C3, which are the standard abnormal impact values ​​corresponding to different evidence storage and dynamic update schemes.

[0014] Preferably, the anomaly impact coefficients for all anomalies corresponding to different evidence preservation and update schemes are determined by the formula... The calculation yields the following formula: where i represents the different anomalies corresponding to the evidence update scheme, i = 1, 2, 3, ..., NY; NY is a positive integer; and αi is the anomaly weight corresponding to different anomalies.

[0015] Preferably, data analysis is performed on the first and second abnormal change values ​​obtained by dynamically processing different evidence preservation and update schemes;

[0016] If YB1k≤0 and YB2k≤0, then the corresponding evidence update scheme will be dynamically implemented with the overall normal label;

[0017] If YB1k≤0 or YB2k≤0, then the corresponding evidence update scheme will be dynamically implemented with the overall part of the normal label;

[0018] If YB1k > 0 and YB2k > 0, then the corresponding evidence update scheme will be dynamically associated with the overall abnormal label.

[0019] Preferably, when monitoring and analyzing the implementation effects of different evidence preservation and update schemes from a local perspective, different anomalies that occur after the dynamic implementation of the evidence preservation and update schemes are statistically analyzed in sequence, and then expressed using a formula. Calculate the local anomaly impact value JY of the same anomaly after the dynamic implementation of the evidence storage and update scheme; where n is the total number of the same anomaly after the dynamic implementation of the evidence storage and update scheme; and D is the standard value of the local anomaly impact of the corresponding anomaly.

[0020] The local anomaly impact values ​​corresponding to all anomalies that occur after the dynamic implementation of the evidence storage and update scheme are sorted and combined to obtain the corresponding local anomaly impact sequence.

[0021] Preferably, the local anomaly impact sequences obtained by dynamic post-processing of different evidence storage and update schemes are sequentially traversed and analyzed;

[0022] If all elements in the sequence affected by a local anomaly are less than or equal to 1, then the corresponding evidence update scheme will be dynamically labeled as locally completely normal.

[0023] If any element in the sequence affected by a local anomaly is greater than 1, and the total number of elements greater than 1 is less than or equal to M, then the corresponding evidence update scheme will be dynamically associated with a locally normal label; M is a positive integer.

[0024] If there are elements in the sequence affected by a local anomaly that are greater than 1, and the total number of elements greater than 1 is greater than M, then the corresponding evidence update scheme will be dynamically labeled with a local complete anomaly.

[0025] The tags obtained from the evidence preservation and update schemes, processed according to different dimensions, are sorted and combined to obtain the corresponding regulatory analysis set.

[0026] Preferably, when using the regulatory analysis set to proactively provide optimization management prompts for the subsequent implementation of different evidence storage and update schemes, the regulatory analysis set is traversed and analyzed.

[0027] If only the dynamic implementation of the overall normal label and the dynamic implementation of the partially normal label exist, then the subsequent implementation of the corresponding evidence storage and update scheme will continue.

[0028] If there are dynamically implemented overall normal labels and / or dynamically implemented partial normal labels, then the subsequent implementation of the relevant evidence storage update scheme will be subject to partial optimization management prompts.

[0029] If there are dynamically implemented overall abnormal labels and / or dynamically implemented partial complete abnormal labels, then the subsequent implementation of the relevant evidence storage and update scheme will be subject to overall optimization management prompts.

[0030] A blockchain-based method for carbon neutrality notarization includes:

[0031] Based on several preset indicator characteristics, dynamic matching and implementation of certificate renewal schemes are carried out for different carbon neutrality certificate holders.

[0032] The implementation effects of different evidence preservation and update schemes are monitored and analyzed from both overall and local perspectives. The analysis results from different perspectives are combined to obtain a regulatory analysis set corresponding to different evidence preservation and update schemes. The regulatory analysis set is used to proactively optimize and manage the subsequent implementation of different evidence preservation and update schemes.

[0033] Compared to existing solutions, the beneficial effects achieved by this invention are:

[0034] This invention uses several preset indicator features to dynamically match and implement the certificate renewal schemes of different carbon neutrality certificate holders. It realizes proactive multi-dimensional certificate renewal data analysis and matching in the early stage of certificate issuance. It can not only achieve adaptive matching of certificate renewal for different carbon neutrality certificate holders, but also provide reliable local matching data support for subsequent regulatory analysis of different certificate renewal schemes in different dimensions.

[0035] This invention monitors and analyzes the implementation effects of different evidence renewal schemes from both overall and local perspectives. It combines the analysis results from different dimensions to obtain a monitoring analysis set corresponding to each scheme. This monitoring analysis set is then used to proactively optimize and manage the subsequent implementation of different evidence renewal schemes. This enables diversified proactive monitoring and analysis in the later stages of the scheme's implementation, thus echoing the dynamic matching implementation in the early stages and forming a collaborative closed-loop monitoring system for carbon neutrality evidence. This improves the adaptability and reliability of dynamic updates for different carbon neutrality evidence holders. Attached Figure Description

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1 This is a block diagram of a blockchain-based carbon neutrality evidence storage system according to the present invention.

[0038] Figure 2 This is a flowchart of a blockchain-based carbon neutrality evidence storage method according to the present invention. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] Example 1: As Figure 1 As shown, the present invention is a blockchain-based carbon neutrality evidence storage system, including a carbon neutrality evidence storage matching implementation module and a carbon neutrality evidence storage regulatory analysis module.

[0041] The carbon neutrality certificate matching and implementation module is used to dynamically match and implement certificate update schemes for different carbon neutrality certificate holders based on several preset indicator characteristics; including:

[0042] Based on several preset indicator features, obtain indicator feature data corresponding to different carbon neutrality evidence holders, and match the several indicator feature data corresponding to different carbon neutrality evidence holders with the standard feature range associated with the corresponding indicator features.

[0043] The preset indicator features are determined according to the specific scenario. The scenarios in this embodiment of the invention include, but are not limited to, high-frequency low-sensitivity data scenarios, multi-party collaborative review scenarios, and long-cycle carbon sink tracking scenarios.

[0044] I. High-frequency, low-sensitivity data scenarios

[0045] Indicator characteristics: Update frequency: >50 times per day (e.g., real-time carbon emission data from IoT sensors);

[0046] Data complexity: Structured numerical values ​​(CO2 concentration, energy consumption);

[0047] Compliance requirements: Local standards;

[0048] Recommended solution: Optimistic Rollup + State Channel;

[0049] Off-chain batch processing of data, and periodic (e.g., hourly) submission of compressed proofs to the main chain;

[0050] II. Multi-party collaborative review scenario

[0051] Indicator characteristics:

[0052] Participants: Enterprises + Auditing Firms + Regulatory Agencies;

[0053] Compliance strength: Must simultaneously meet both EU ETS and Chinese carbon market rules;

[0054] Traceability depth: Full lifecycle version tracing;

[0055] Recommended solution:

[0056] ERC-7231 can update the NFT+IPFS version chain;

[0057] Each update requires a three-party signature, and metadata change records are stored in IPFS;

[0058] III. Long-term carbon sequestration tracking scenarios

[0059] Indicator characteristics:

[0060] Update frequency: Annual update (forestry carbon sequestration project growth monitoring);

[0061] Data complexity: Satellite imagery + manual verification report (GB-level unstructured data);

[0062] Recommended solution:

[0063] Arweave persistent blockchain + updatable NFT metadata pointers;

[0064] The original data is stored in Arweave; NFTs only store the content hash.

[0065] A new NFT version is created each year during the update, while pointers to the old version are retained.

[0066] The indicator features in the embodiments of the present invention can be added, deleted, or modified according to the indicator features corresponding to the above scenarios. Each indicator feature has a corresponding standard feature range set in advance. The specific values ​​of the standard feature ranges corresponding to different indicator features are not limited. They can be determined according to the existing industry standard requirements data or customized according to the application needs of the actual application scenario.

[0067] If the indicator feature data does not belong to the standard feature range associated with the corresponding indicator feature, then the corresponding indicator feature is marked as the first indicator feature and associated with the carbon neutrality certificate holder.

[0068] Conversely, the corresponding indicator feature is marked as the second indicator feature and associated with the carbon neutrality certificate holder.

[0069] The system sequentially analyzes all indicator features associated with different carbon neutrality evidence holders, counts all primary indicator features associated with each carbon neutrality evidence holder, counts the total number of primary indicator features belonging to different evidence renewal schemes, and assigns and implements the evidence renewal scheme corresponding to the largest total number of primary indicator features to each carbon neutrality evidence holder. The evidence renewal scheme includes the first evidence renewal scheme, the second evidence renewal scheme, and the third evidence renewal scheme.

[0070] The different evidence preservation and update schemes are specifically recommended schemes for different scenarios in the embodiments of this invention. The specific content and steps of the schemes will not be elaborated here.

[0071] Unlike existing technologies that rely solely on a single, fixed, updatable NFT (such as ERC-3664) or state channel to record change history, this approach cannot conduct regulatory analysis on different aspects of the update data scenarios corresponding to different carbon neutrality certificate holders, nor can it dynamically implement targeted dynamic update schemes for certificate storage based on the analysis results from different aspects.

[0072] In this embodiment of the invention, several preset indicator features are used to dynamically match and implement the certificate update schemes for different carbon neutrality certificate holders. This enables proactive multi-dimensional certificate update data analysis and matching in the early stage of certificate storage. It can achieve adaptive matching of certificate updates for different carbon neutrality certificate holders and provide reliable local matching data support for subsequent regulatory analysis of different certificate update schemes in different dimensions.

[0073] The carbon neutrality certificate of record (CERCR) regulatory analysis module is used to monitor and analyze the implementation effects of different CERCR schemes from both overall and specific perspectives. It combines the analysis results from different dimensions to obtain regulatory analysis sets corresponding to different CERCR schemes. These sets are then used to proactively provide optimization and management suggestions for the subsequent implementation of different CERCR schemes. This includes:

[0074] When monitoring and analyzing the implementation effects of different evidence preservation and update schemes from an overall perspective, the total number of anomalies that occur after the dynamic implementation of each scheme is statistically analyzed. Anomaly determination can be based on existing anomaly monitoring rules; specific rules and content are not limited, and the anomalies are determined using a formula. Calculate the first abnormal change value YB1k corresponding to the dynamic implementation of different evidence preservation and update schemes; where k is 1, 2, and 3, representing the first, second, and third evidence preservation and update schemes, respectively; NYk is NY1, NY2, and NY3, representing the total number of all abnormalities that occur after the dynamic implementation of different evidence preservation and update schemes; NFk is NF1, NF2, and NF3, representing the total number of evidence preservation implementations after the dynamic implementation of different evidence preservation and update schemes; Bk is B1, B2, and B3, representing the standard abnormal values ​​corresponding to different evidence preservation and update schemes. The specific values ​​are not limited and can be determined based on historical abnormal data of different evidence preservation and update schemes or customized according to the application requirements of actual application scenarios.

[0075] It should be noted that the first abnormal change value is used to calculate the total number of abnormalities and the total number of evidence storage and update schemes, so as to digitally represent the abnormal change status after the dynamic implementation of the evidence storage and update scheme.

[0076] And, through the formula Calculate the second abnormal change value YB2k corresponding to the dynamic implementation of different evidence storage and update schemes; where YYk is YY1, YY2, and YY3, which are the abnormal impact coefficients corresponding to all abnormalities that occur after the dynamic implementation of different evidence storage and update schemes; Ck is C1, C2, and C3, which are the standard abnormal impact values ​​corresponding to different evidence storage and dynamic update schemes. The specific values ​​are not limited and can be determined based on the historical abnormal data of different evidence storage and dynamic update schemes, or customized according to the application requirements of the actual application scenario.

[0077] It should be noted that the second abnormal change value is used to calculate the abnormal impact coefficient of the evidence storage and renewal scheme, so as to digitally represent the abnormal change status after the dynamic implementation of the evidence storage and renewal scheme.

[0078] Among them, the anomaly impact coefficients for all anomalies corresponding to different evidence preservation and update schemes are determined by the formula. The calculation yields the following formula: where i represents the different anomalies corresponding to the evidence update scheme, i = 1, 2, 3, ..., NY; NY is a positive integer representing the total number of anomalies; αi is the anomaly weight corresponding to different anomalies, all of which are positive integers with a value range of [1, 5]. It can be determined by professionals in this field based on their work experience and requirements. The larger the value, the more severe the impact of the corresponding anomaly.

[0079] It should be explained that the formula calculations involved in the embodiments of the present invention are all standardized before the calculation, such as removing the units and dimensioning the calculation data.

[0080] Furthermore, the formula calculation in the embodiments of the present invention is only a technical means, and it can also be achieved by existing technical means;

[0081] For example, the total number of anomalies in the evidence preservation update scheme and the total number of evidence preservation implementations are analyzed using a trained anomaly change identification model, and the value of the analysis output is set as the first anomaly change value.

[0082] Among them, the abnormal change identification model can be trained based on existing artificial intelligence models using sample training data;

[0083] Existing artificial intelligence models include, but are not limited to, BP neural network models or RBF neural network models;

[0084] The sample training data specifically consists of standard input data with attributes consistent with the total number of anomalies and the total number of evidence implementations, and standard output data representing anomaly changes.

[0085] In addition, existing artificial intelligence models can be trained using sample training data as a conventional technical solution. The specific implementation steps will not be elaborated here.

[0086] Data analysis was performed on the first and second abnormal change values ​​obtained from the dynamic processing of different evidence preservation and update schemes.

[0087] If YB1k≤0 and YB2k≤0, then the dynamic implementation of the relevant evidence update scheme is determined to be normal overall, and it is associated with the dynamic implementation overall normal tag.

[0088] If YB1k≤0 or YB2k≤0, the dynamic implementation of the relevant evidence update scheme is determined to be normal, and it is associated with the dynamic implementation overall normal tag.

[0089] If YB1k > 0 and YB2k > 0, then the dynamic implementation of the relevant evidence update scheme is determined to be abnormal, and it is associated with the dynamic implementation abnormality label.

[0090] In this embodiment of the invention, the first and second abnormal change values ​​obtained through processing are used to perform diversified processing and analysis of the overall dimensions after the dynamic implementation of different evidence storage and update schemes. Based on the analysis results, the overall dimension tags of different evidence storage and update schemes are dynamically associated. Compared with the existing technical solutions that only use a single technical means for data processing, analysis and prompting, this embodiment of the invention can effectively improve the diversity and reliability of overall dimension data supervision and analysis.

[0091] Furthermore, when monitoring and analyzing the implementation effects of different evidence preservation and update schemes from a local perspective, different anomalies that occur after the dynamic implementation of the evidence preservation and update schemes are statistically analyzed, and then processed using formulas. Calculate the local anomaly impact value JY of the same anomaly after the dynamic implementation of the evidence storage and update scheme; where n is the total number of the same anomaly after the dynamic implementation of the evidence storage and update scheme; D is the local anomaly impact standard value of the corresponding anomaly, and the specific value is not limited. It can be determined based on the historical anomaly data of different evidence storage and update schemes, or it can be customized according to the application requirements of the actual application scenario.

[0092] It should be noted that the local anomaly impact value is used to calculate the total number of occurrences of the same anomaly after the dynamic implementation of the evidence update scheme and the corresponding anomaly weight, so as to digitally represent the local anomaly impact.

[0093] The local anomaly impact values ​​corresponding to all anomalies that occur after the dynamic implementation of the evidence storage and update scheme are sorted and combined to obtain the corresponding local anomaly impact sequence.

[0094] The local anomaly impact sequences obtained from the dynamic post-processing of different evidence preservation and update schemes are sequentially analyzed.

[0095] If all elements in the local anomaly-affected sequence are less than or equal to 1, then the dynamic implementation of the relevant evidence update scheme is determined to be completely normal locally, and it is associated with the label of "dynamic implementation completely normal locally".

[0096] If there are elements greater than 1 in the local anomaly-affected sequence, and the total number of elements greater than 1 is less than or equal to M, then the dynamic implementation of the relevant evidence update scheme is determined to be normal in the local part, and it is associated with the dynamic implementation local part normal label; M is a positive integer, and the specific value can be half of the total number of all elements in the local anomaly-affected sequence, or half of the total number of all elements in the local anomaly-affected sequence can be rounded up using the rounding up function.

[0097] If there are elements in the sequence affected by a local anomaly that are greater than 1, and the total number of elements greater than 1 is greater than M, then the dynamic implementation of the evidence update scheme is determined to be a local complete anomaly, and it is associated with the dynamic implementation local complete anomaly label.

[0098] The tags obtained from different dimensions of the evidence preservation and update scheme are sorted and combined to obtain the corresponding regulatory analysis set.

[0099] It is worth noting that, unlike existing technical solutions that only use a single dimension to conduct regulatory analysis and provide guidance on the implementation effect of the evidence storage and update scheme, the reliability and diversity of the regulatory analysis are not good.

[0100] In this embodiment of the invention, by performing diverse processing and analysis on all abnormal data that appear after the dynamic implementation of different evidence storage and update schemes from both overall and local dimensions, and combining them, regulatory analysis sets obtained from different dimensions and processing methods are obtained, which can provide reliable regulatory analysis data support for the optimization management analysis of different evidence storage and update schemes.

[0101] When proactively optimizing and managing the implementation of different evidence storage and update schemes using the regulatory analysis set, the regulatory analysis set is traversed and analyzed.

[0102] If only the dynamic implementation of the overall normal label and the dynamic implementation of the partially normal label exist, then the subsequent implementation of the corresponding evidence storage and update scheme will continue.

[0103] If there are dynamically implemented overall normal labels and / or dynamically implemented partial normal labels, then the subsequent implementation of the relevant evidence storage update scheme will be subject to partial optimization management prompts.

[0104] If there are dynamic implementation of overall abnormal labels and / or dynamic implementation of partial complete abnormal labels, then the subsequent implementation of the relevant evidence storage and update scheme will be subject to overall optimization management prompts.

[0105] Among them, the local optimization management prompts can specifically suggest adding, deleting, or modifying several indicator features or implementation rules of the evidence storage and update scheme;

[0106] The overall optimization management prompts can specifically suggest adding, deleting, or modifying certain indicators and implementation rules of the evidence storage and update scheme; however, no specific optimization management content is limited.

[0107] In this embodiment of the invention, the implementation effects of different evidence renewal schemes are monitored and analyzed from both overall and local dimensions. The analysis results from different dimensions are combined to obtain a monitoring analysis set corresponding to different evidence renewal schemes. The monitoring analysis set is used to proactively optimize and manage the subsequent implementation of different evidence renewal schemes. This enables diversified proactive monitoring and analysis in the later stages of evidence renewal scheme implementation, which echoes the dynamic matching implementation in the early stages of evidence renewal schemes, forming a collaborative closed-loop monitoring of carbon neutrality evidence. This improves the adaptability and reliability of dynamic updates of evidence for different carbon neutrality evidence entities.

[0108] Example 2: As Figure 2 As shown, this invention is a blockchain-based method for carbon neutrality notarization, comprising:

[0109] Based on several preset indicator characteristics, dynamic matching and implementation of certificate renewal schemes are carried out for different carbon neutrality certificate holders.

[0110] The implementation effects of different evidence preservation and update schemes are monitored and analyzed from both overall and local perspectives. The analysis results from different perspectives are combined to obtain a regulatory analysis set corresponding to different evidence preservation and update schemes. The regulatory analysis set is used to proactively optimize and manage the subsequent implementation of different evidence preservation and update schemes.

[0111] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0112] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0113] Furthermore, the functional modules in the various embodiments of this invention can be integrated into one processing module, or each module can be physically stored separately, or two or more modules can be integrated into one module. The integrated module can be implemented in hardware or in a combination of hardware and software functional modules.

[0114] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the essential characteristics of the present invention.

[0115] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A blockchain-based carbon neutrality evidence storage system, characterized in that, It includes a carbon neutrality certificate matching and implementation module, which is used to dynamically match and implement certificate update schemes for different carbon neutrality certificate holders based on several preset indicator characteristics. The carbon neutrality certificate monitoring and analysis module is used to monitor and analyze the implementation effects of different certificate renewal schemes from both overall and local perspectives. It combines the analysis results from different dimensions to obtain the monitoring analysis set corresponding to different certificate renewal schemes. The monitoring analysis set is then used to proactively optimize and manage the subsequent implementation of different certificate renewal schemes. In the overall analysis of the implementation effects of different evidence preservation and update schemes, the total number of anomalies that occurred after the dynamic implementation of each scheme is statistically analyzed using a formula. Calculate the first abnormal change value YB1k corresponding to the dynamic implementation of different evidence preservation and update schemes; where k is 1, 2, and 3, representing the first, second, and third evidence preservation and update schemes, respectively; NYk is NY1, NY2, and NY3, representing the total number of all abnormalities that occur after the dynamic implementation of different evidence preservation and update schemes; NFk is NF1, NF2, and NF3, representing the total number of evidence preservation implementations after the dynamic implementation of different evidence preservation and update schemes; and Bk is B1, B2, and B3, representing the standard abnormal values ​​corresponding to different evidence preservation and dynamic update schemes. And, through formula Calculate the second abnormal change value YB2k corresponding to the dynamic implementation of different evidence storage and update schemes; where YYk is YY1, YY2, and YY3, which are the abnormal impact coefficients corresponding to all abnormalities that occur after the dynamic implementation of different evidence storage and update schemes; Ck is C1, C2, and C3, which are the standard abnormal impact values ​​corresponding to different evidence storage and dynamic update schemes. When monitoring and analyzing the implementation effects of different evidence preservation and renewal schemes from a local perspective, the different anomalies that occur after the dynamic implementation of the evidence preservation and renewal schemes are statistically analyzed, and then processed using a formula. Calculate the local anomaly impact value JY of the same anomaly after the dynamic implementation of the evidence storage and update scheme; where n is the total number of the same anomaly after the dynamic implementation of the evidence storage and update scheme; and D is the standard value of the local anomaly impact of the corresponding anomaly. The local anomaly impact values ​​corresponding to all anomalies that occur after the dynamic implementation of the evidence storage and update scheme are sorted and combined to obtain the corresponding local anomaly impact sequence. The system sequentially analyzes the local anomaly impact sequences obtained after the dynamic implementation of different carbon neutrality certificate renewal schemes. Based on the analysis results, it proactively provides optimization and management prompts for the subsequent implementation of different carbon neutrality certificate renewal schemes. This enables diversified proactive supervision and analysis in the later stages of the implementation of carbon neutrality certificate renewal schemes, which in turn corresponds to the dynamic matching implementation of carbon neutrality certificate renewal schemes in the early stages, forming a collaborative closed-loop supervision of carbon neutrality certificate issuance. This improves the adaptability and reliability of dynamic updates of carbon neutrality certificate issuance for different entities.

2. The blockchain-based carbon neutrality evidence storage system according to claim 1, characterized in that, Based on several preset indicator features, obtain indicator feature data corresponding to different carbon neutrality evidence holders, and match the several indicator feature data corresponding to different carbon neutrality evidence holders with the standard feature range associated with the corresponding indicator features. If the indicator feature data does not belong to the standard feature range associated with the corresponding indicator feature, then the corresponding indicator feature is marked as the first indicator feature and associated with the carbon neutrality certificate holder. Conversely, the corresponding indicator feature is marked as the second indicator feature and associated with the carbon neutrality certificate holder.

3. A blockchain-based carbon neutrality evidence storage system according to claim 2, characterized in that, The characteristics of all indicators associated with different carbon neutrality certificate holders are analyzed sequentially, and the first, second, or third certificate renewal schemes are implemented for the carbon neutrality certificate holders based on the analysis results.

4. A blockchain-based carbon neutrality evidence storage system according to claim 1, characterized in that, The abnormality impact coefficients for all anomalies corresponding to different evidence preservation and update schemes are calculated using the formula. The calculation yields the following formula: where i represents the different anomalies corresponding to the evidence update scheme, i = 1, 2, 3, ..., NY; NY is a positive integer; and αi is the anomaly weight corresponding to different anomalies.

5. A blockchain-based carbon neutrality evidence storage system according to claim 1, characterized in that, Data analysis was performed on the first and second abnormal change values ​​obtained from the dynamic processing of different evidence preservation and update schemes. If YB1k≤0 and YB2k≤0, then the corresponding evidence update scheme will be dynamically implemented with the overall normal label; If YB1k≤0 or YB2k≤0, then the corresponding evidence update scheme will be dynamically implemented with the overall part of the normal label; If YB1k > 0 and YB2k > 0, then the corresponding evidence update scheme will be dynamically associated with the overall abnormal label.

6. A blockchain-based carbon neutrality evidence storage system according to claim 5, characterized in that, The local anomaly impact sequences obtained from the dynamic post-processing of different evidence preservation and update schemes are sequentially analyzed. If all elements in the sequence affected by a local anomaly are less than or equal to 1, then the corresponding evidence update scheme will be dynamically labeled as locally completely normal. If any element in the sequence affected by a local anomaly is greater than 1, and the total number of elements greater than 1 is less than or equal to M, then the corresponding evidence update scheme will be dynamically associated with a locally normal label; M is a positive integer. If there are elements in the sequence affected by a local anomaly that are greater than 1, and the total number of elements greater than 1 is greater than M, then the corresponding evidence update scheme will be dynamically associated with a local complete anomaly label. The tags obtained from the evidence preservation and update schemes, processed according to different dimensions, are sorted and combined to obtain the corresponding regulatory analysis set.

7. A blockchain-based carbon neutrality evidence storage system according to claim 6, characterized in that, When proactively optimizing and managing the implementation of different evidence storage and update schemes using the regulatory analysis set, the regulatory analysis set is traversed and analyzed. If only the dynamic implementation of the overall normal label and the dynamic implementation of the partially normal label exist, then the subsequent implementation of the corresponding evidence storage and update scheme will continue. If there are dynamically implemented overall normal labels and / or dynamically implemented partial normal labels, then the subsequent implementation of the relevant evidence storage update scheme will be subject to partial optimization management prompts. If there are dynamically implemented overall abnormal labels and / or dynamically implemented partial complete abnormal labels, then the subsequent implementation of the relevant evidence storage and update scheme will be subject to overall optimization management prompts.

8. A blockchain-based carbon neutrality evidence storage method, employing a blockchain-based carbon neutrality evidence storage system as described in any one of claims 1-7, characterized in that, include: Based on several preset indicator characteristics, dynamic matching and implementation of certificate renewal schemes are carried out for different carbon neutrality certificate holders. The implementation effects of different evidence preservation and update schemes are monitored and analyzed from both overall and local perspectives. The analysis results from different perspectives are combined to obtain a regulatory analysis set corresponding to different evidence preservation and update schemes. The regulatory analysis set is used to proactively optimize and manage the subsequent implementation of different evidence preservation and update schemes.

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