Carbon emission data management method and device based on block chain

Abstract

The invention relates to the technical field of environment management, in particular to a carbon emission data management method and device based on a block chain, and the method comprises the following steps: screening missing information data based on the block chain, eliminating abnormal calculation efficiency, extracting user behavior analysis stability, adjusting authority mapping to generate a credible index, screening illegal nodes, and writing the illegal nodes into a tracking block. And comparing policy marking abnormity to obtain a compliance state. According to the method and the system, the data with missing identity labels and timestamps are screened out, the data validity ratio is improved, the timeliness and the credibility of written information are ensured, the uploading action is identified, the frequency and the operation interval are modified, the node behavior stability is described, the compliance degree is reflected, the access permission is dynamically adjusted according to the stability, and the hyper-permission operation records are screened out; high-frequency violation writing behaviors are detected and recorded, the real-time monitoring capability is enhanced, abnormity is dynamically marked, compliance evaluation and response of carbon emission data are achieved, and the credibility, precision and intelligent level of data management are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the field of environmental management technology, and in particular to a blockchain-based carbon emission data management method and device. Background Art

[0002] The field of environmental management technology includes various methods and means to control and optimize the environmental impacts of the use of natural resources. The core content of this technical field mainly involves the monitoring, assessment, control and information management of air, water, soil, energy use and waste disposal. With the advancement of global sustainable development goals, the field of environmental management continues to integrate information technology, and with the help of information collection, transmission, recording and management methods, it realizes the systematic control and supervision of key environmental factors such as carbon emissions, pollution sources, energy consumption, etc., and improves the scientific and intelligent level of overall ecological environmental governance.

[0003] The blockchain-based carbon emissions data management method, among others, achieves credible recording, storage, and sharing of carbon emissions-related data through the construction of a decentralized data structure and a multi-party consensus mechanism. This method primarily addresses technical issues such as the difficulty in ensuring data authenticity, the high risk of information tampering, and insufficient regulatory transparency that exist during the collection, transmission, and management of carbon emissions data. By leveraging the blockchain's distributed ledger structure to store raw carbon emissions data on-chain, establishing a data traceability path using timestamps as an index, and combining smart contracts to set emission standards and data verification logic, it enables compliance verification of data before recording and automatic on-chain triggering of the data upload process. The entire method explicitly relies on blockchain's consensus mechanism, hash function encryption technology, and smart contract scripting languages to standardize the management of the ownership, tracking, and sharing of carbon emissions data.

[0004] Existing technologies primarily rely on autonomous node reporting for carbon emissions data collection and on-chain upload. These technologies lack effective verification mechanisms for identity and time fields, resulting in data records with unknown sources or ambiguous time periods within the on-chain data, making it difficult to accurately locate regulatory traceability. Regarding data behavior analysis, operation frequency and behavioral trajectories are not quantified, making it impossible to identify frequent, unusual, or suspicious behavior patterns, reducing the ability to respond to malicious operations. Regarding permission control, current data access rights are often statically bound to nodes based on preset configurations, lacking a mechanism for dynamic behavior-based adjustments. This can easily lead to permission abuse or redundant access, compromising the confidentiality of sensitive data. Regarding violation detection, existing mechanisms often rely on regular audits and offline statistics, lacking the ability to identify and automatically record node violations in real time, leaving violations potentially hidden for a long time. Regarding policy matching, there is a lack of automated correlation and intelligent identification between carbon emissions data and policy provisions, making it difficult to promptly detect policy deviations. For example, when a node continuously exceeds its quota by uploading data within a short period of time, existing methods fail to issue timely alerts, resulting in data inflation and policy enforcement failures, seriously impacting the effectiveness and timeliness of carbon emissions regulation. Summary of the Invention

[0005] To address the technical issues in existing technologies, the present invention provides a blockchain-based carbon emissions data management method and device. The technical solution is as follows:

[0006] In one aspect, a blockchain-based carbon emissions data management method is provided, comprising:

[0007] S1: Based on the carbon emission data, user identity and submission records reported by the blockchain record node, filter the data entries with missing identity and timestamp fields, determine whether the data is within the write window period, eliminate the non-compliant data, and obtain the effective on-chain data ratio;

[0008] S2: Based on the effective on-chain data ratio, extract the user's upload actions, modification times, and call operations in the behavior record, filter out repeated behaviors with operation intervals lower than the set block determination period, analyze the integrity of data submission within a unit time, and obtain the blockchain behavior stability value;

[0009] S3: Based on the blockchain behavior stability value, extract the user node permissions and the data access level of the sensitive carbon emission segment in the blockchain, compare with the mapping rules in the permission configuration, filter access records with access levels greater than the current node level, adjust the access permission mapping relationship, and generate a trusted access permission index table;

[0010] S4: Call the node ID of the restricted record in the trusted access permission index table, extract the original carbon emission behavior chain record, filter the nodes with a continuous number of violations greater than the threshold, write the nodes into the violation tracking block, and obtain the list of illegal nodes on the chain.

[0011] As a further solution of the present invention, the effective on-chain data ratio includes the identity field occupancy rate, the timestamp field coverage rate, and the data write cycle compliance; the blockchain behavior stability value includes the operation interval consistency, the upload behavior frequency, and the behavior record continuity; the trusted access permission index table includes the access permission level identifier, the permission mapping update item, and the sensitive data access tag; the on-chain illegal node list includes the illegal node number, the excessive write frequency, and the illegal block index.

[0012] As a further solution of the present invention, the step of effective on-chain data ratio is specifically as follows:

[0013] S101: Based on the carbon emission data, user identity and submission records reported by the blockchain record node, extract the user identity and submission timestamp fields, check for empty and NULL values, remove invalid records, and perform summary statistics to obtain the complete data volume of the field;

[0014] S102: Call the submission timestamp field corresponding to each record in the complete data volume of the field, compare it with the start and end time of the write window set by the blockchain, eliminate records whose timestamps exceed the window period, count the number of remaining records and identify their proportion to all data entries in the blockchain, and obtain the effective on-chain data ratio.

[0015] As a further solution of the present invention, the steps of the blockchain behavior stability value are specifically as follows:

[0016] S201: Based on the effective on-chain data ratio, extract the user's uploaded carbon emission data, modification times, and call operations, filter out repeated behaviors that are less than a set period, compare the upload, modification, and call operation intervals, and filter out duplicate carbon emission data records;

[0017] S202: Based on the duplicate carbon emission data records, identifying the integrity of the carbon emission data submitted within a unit time, determining the difference between the data submitted within the unit time and the full data, and generating a carbon emission data integrity metric;

[0018] S203: Analyze the stability of data submission based on the carbon emission data integrity measurement and the blockchain behavior stability standard, and obtain the blockchain behavior stability value by comparing the stability of the carbon emission data submitted in real time with the set stability standard.

[0019] As a further solution of the present invention, the steps of indexing the trusted access rights table are specifically as follows:

[0020] S301: Extracting the user node authority value and the sensitive carbon emission segment access level value based on the blockchain behavior stability value, matching by node identifier, filtering records where the access level value is greater than the authority value, and obtaining the over-authority access segment quantity;

[0021] S302: extracting a permission configuration mapping based on the amount of super-authority access segments, identifying records with level mismatches, extracting node numbers and block numbers, and generating a permission mapping adjustment sequence;

[0022] S303: Call the node number and block number in the permission mapping adjustment sequence, replace the smart contract permission mapping field, identify the node number and access level value mapping, and generate a trusted access permission index table.

[0023] As a further solution of the present invention, the steps of listing the illegal nodes on the chain are specifically as follows:

[0024] S401: Calling the node ID of the restricted record in the trusted access permission index table, locating the original carbon emission behavior chain, extracting the written data block of the node, and identifying the position of the excess block by comparing the carbon emission value with the quota value in the data block to obtain the excess block sequence position information;

[0025] S402: Based on the position information of the exceeded block sequence, the data blocks are sorted by node ID, the cumulative number of consecutive exceeded blocks is identified, and the number is compared with the abnormal threshold. The nodes with the number of exceeded blocks exceeding the threshold are screened out, the carbon emission values of the associated blocks are extracted, and the data that does not meet the conditions is eliminated to generate a sequence of carbon emission values of consecutive abnormal nodes;

[0026] S403: Call the continuous abnormal node carbon emission value sequence, match the original chain node structure information, write all excessive nodes into the violation tracking block, collect node IDs, and obtain the list of illegal nodes on the chain.

[0027] As a further solution of the present invention, the super-displacement offset value of the block is calculated using the formula:

[0028] ;

[0029] in, Represents the block's overrun offset value, Representative The carbon emission value recorded in each node block, Representative The carbon emission quota value corresponding to each node block, Representative The weighted coefficient of the trusted authority level of each node, Representative The data timing position value of each node in the behavior chain, Representative Node and behavior chain The number of communications between associated nodes, Representative The average number of times a node communicates with its associated nodes in the current behavior chain, Represents the current behavior chain except The number of additional nodes beyond the nodes.

[0030] As a further embodiment of the present invention, the method further comprises step S5:

[0031] S5: Call the carbon emission block record of the corresponding node in the on-chain violation node list, identify the smart contract execution result and the corresponding policy number, determine the difference between the carbon emission data and the policy content, and mark it in the abnormal area to obtain the carbon emission chain compliance identification status;

[0032] The carbon emission chain compliance identification status includes policy matching deviation value, abnormal data identification number, and smart contract execution control item.

[0033] As a further solution of the present invention, the steps of identifying the carbon emission chain compliance status are specifically as follows:

[0034] S501: Call the carbon emission block record of the corresponding node in the on-chain illegal node list, extract the carbon emission block data, identify the smart contract execution result and the associated policy number, organize the correspondence between the carbon emission value, contract status and policy number in node order, and generate a node carbon emission data binding table;

[0035] S502: Based on the node carbon emission data binding table, compare the carbon emission data value with the carbon emission index value in the binding policy number, check whether the difference exceeds the deviation threshold, mark the abnormal record in the abnormal section field, and obtain the node carbon emission difference marking result;

[0036] S503: Call the node sequence of the abnormal segment marked in the node carbon emission difference marking result, summarize all abnormal marking statuses by node ID, collect management fields, and embed them into the on-chain node structure information to obtain the carbon emission chain compliance identification status.

[0037] On the other hand, an electric vehicle state monitoring device is provided, which is used to execute the above electric vehicle state monitoring method, and the device includes:

[0038] The data verification module records carbon emission data, user identities, and submission records reported by blockchain nodes. It filters data entries with empty identities and removes records with missing timestamp fields. It determines whether the timestamp is within the block write window period and removes entries that are not within the period range to obtain the effective on-chain data ratio.

[0039] The behavior stability module extracts upload operations, modification records, and call behaviors based on the effective on-chain data ratio, counts time intervals, identifies repeated operations below the block time, analyzes the frequency per unit time, calculates the continuity and timing rationality ratio, and establishes a block behavior stability structure;

[0040] The permission control module compares the user permission level with the sensitive data access level based on the stable structure of the block behavior, filters out the behavior records with permission levels lower than the access level, adjusts the mapping relationship value, writes the smart contract update field, and builds the on-chain permission index mapping;

[0041] The violation monitoring module locates the restricted node ID based on the on-chain permission index mapping, extracts the carbon emissions and writes them into the block, compares the emission values with the node carbon quota, identifies the continuous exceeding limit data, filters the nodes with the number of exceeding limits greater than the threshold, writes them into the tracking chain block, and generates the node violation data record;

[0042] The compliance marking module extracts the associated smart contract results and policy numbers based on the node violation data records, compares the emission data with the corresponding policy limit values, marks the difference block positions, and obtains the carbon emission chain compliance identification status.

[0043] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0044] By performing fine-grained processing on the on-chain recording, screening, and verification of carbon emissions data, data missing identity and timestamp fields is eliminated, ensuring the timeliness and source credibility of written data, improving the data validity ratio, and providing a foundation for subsequent behavioral analysis. By identifying upload actions, modification frequency, and operation intervals, repeated operations within short periods are filtered out to characterize the frequency stability of user data submission behavior, reflecting the degree of compliance of node behavior. Based on behavioral stability parameters, node access permissions are associated with the access level of sensitive data segments, enabling dynamic screening of over-privileged access records and on-chain adjustment of permission mapping relationships, effectively ensuring the rationality of data access and usage permissions. Continuous and frequent illegal write behavior is detected, establishing an on-chain violation tracking mechanism, strengthening real-time monitoring and early warning capabilities of node behavior, and improving the accuracy of carbon emissions data compliance screening. By identifying deviations between contract execution results and policy numbers, an anomaly annotation mechanism is established to dynamically assess the alignment of carbon emissions data with policies, enhancing the automated response capabilities of on-chain supervision. The overall logic embeds constraints and verification processes in multiple dimensions including data screening, behavior profiling, authority control, violation identification and policy matching, which greatly improves the credibility and compliance of carbon emission data in the generation, storage, access and evaluation stages, and enhances the accuracy and intelligence of data governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0046] Figure 2 This is a detailed flow chart of S1 of the present invention;

[0047] Figure 3 This is a detailed flow chart of S2 of the present invention;

[0048] Figure 4 This is a detailed flow chart of S3 of the present invention;

[0049] Figure 5 This is a detailed flow chart of S4 of the present invention;

[0050] Figure 6 This is a detailed flow chart of S5 of the present invention;

[0051] Figure 7 It is a flow chart of the device of the present invention. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0053] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0054] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0055] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0056] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0057] See also Figure 1 , an embodiment of the present invention provides a carbon emission data management method based on blockchain, the processing flow of which may include the following steps:

[0058] S1: Based on the carbon emission data, user identity and submission records reported by the blockchain record node, filter the data entries with missing identity and timestamp fields, determine whether the data is within the write window period, eliminate the non-compliant data, and obtain the effective on-chain data ratio;

[0059] S2: Based on the effective on-chain data ratio, extract the user's upload actions, modification times, and call operations in the behavior record, filter out repeated behaviors with operation intervals lower than the set block determination period, analyze the integrity of data submission within a unit time, and obtain the blockchain behavior stability value;

[0060] S3: Based on the blockchain behavior stability value, extract the user node permissions and the data access level of the sensitive carbon emission segment in the blockchain. Compare with the mapping rules in the permission configuration, filter the access records with access levels greater than the current node level, adjust the access permission mapping relationship, write into the smart contract update field, and generate a trusted access permission index table;

[0061] S4: Call the node ID of the restricted record in the trusted access permission index table, extract the original carbon emission behavior on-chain record, determine whether there are continuous write data blocks that exceed the quota, filter out nodes with a continuous number of exceeding the limit greater than the threshold, write the node into the violation tracking block, and obtain the list of illegal nodes on the chain;

[0062] S5: Call the carbon emission block record of the corresponding node in the on-chain violation node list, identify the smart contract execution result and the corresponding policy number, determine the difference between the carbon emission data and the policy content, and mark it in the abnormal area to obtain the carbon emission chain compliance identification status.

[0063] The effective on-chain data ratio includes the identity field occupancy rate, timestamp field coverage rate, and data write cycle compliance; the blockchain behavior stability value includes the consistency of operation intervals, upload behavior frequency, and behavior record continuity; the trusted access permission index table includes access permission level identification, permission mapping update items, and sensitive data access tags; the on-chain violation node list includes the violation node number, excessive write frequency, and violation block index; the carbon emission chain compliance identification status includes the policy matching deviation value, abnormal data identification number, and smart contract execution control item.

[0064] Specifically, if Figure 2 As shown, the steps for achieving an effective on-chain data ratio are:

[0065] S101: Based on the carbon emission data, user identity and submission records reported by the blockchain record node, extract the user identity and submission timestamp fields, check for empty and NULL values, remove invalid records, and perform summary statistics to obtain the complete data volume of the field;

[0066] The user ID and submission timestamp fields are extracted from the data records. During the actual execution process, the carbon emission data records for each node are automatically scanned and extracted. For each record, the user ID and timestamp fields are extracted. For example, if the data submitted by node A contains the user ID "12345" and the submission timestamp "05-08T12:00:00," the extracted fields are the user ID and time information. A check for null and NULL values is performed, primarily checking each extracted record for invalid or missing data, such as records without user ID information or with a blank timestamp field. Records detected as null or NULL are removed from the dataset to ensure data validity. Further statistics are performed to calculate the number of remaining valid records, obtain the amount of complete field data, and then count all valid records. Assuming that the original dataset contains 1000 records, 50 of which contain null or NULL values, the final count of valid records is 950. This data volume will serve as the basis for further processing in subsequent analysis.

[0067] S102: Call the commit timestamp field corresponding to each record in the complete data volume of the field, compare it with the start and end time of the write window set by the blockchain, remove records with timestamps outside the window period, count the number of remaining records and identify their proportion to all data entries in the blockchain to obtain the effective on-chain data ratio;

[0068] The commit timestamp field corresponding to each record in the complete data volume of the field is called and compared with the start and end time of the write window set by the blockchain. The write window time is a time period set by the blockchain, for example, the write window is from May 1 to May 7. For each record, its commit timestamp is first extracted. For example, if the commit timestamp of a record is "05-08T14:30:00", a comparison operation is performed to check whether the commit timestamp exceeds the set write window time range. In this example, May 8th exceeds the end time of May 7th, so this record will be deleted. For all records, time comparison is performed one by one. If the submission timestamp exceeds the window period, the invalid record will be automatically deleted. Assuming that there are 100 records in the valid records that exceed the write window time period, the timed-out records will eventually be deleted, and the number of remaining records will be counted. The number of remaining records will be counted and their proportion to all data entries in the blockchain will be calculated. For example, if there are 850 valid records remaining after comparison, and a total of 1,200 data are recorded on the blockchain, the effective on-chain data ratio is 850 / 1,200 ≈ 70.83%. The ratio represents the effective on-chain data ratio in the blockchain and serves as a data quality reference for subsequent operations.

[0069] Specifically, if Figure 3 As shown in the figure, the steps of blockchain behavior stability value are as follows:

[0070] S201: Based on the effective on-chain data ratio, extract the carbon emission data uploaded by users, the number of modifications and call operations, filter out repeated behaviors below the set period, compare the upload, modification and call operation intervals, and filter out duplicate carbon emission data records;

[0071] Extract user-uploaded carbon emissions data, modification times, and call operations. During this process, each record in the blockchain is analyzed to extract information such as the carbon emissions data, modification operations, and call operations contained in each record. For example, if a user uploaded data containing an original carbon emissions figure of 100 tons, then modified this data twice and submitted a call request, duplicate activity below a set interval needs to be screened. Duplicate activity refers to the repeated submission of the same or similar data within a very short period of time. This type of duplicate activity needs to be identified to ensure data authenticity. The screening process will check whether the same user has repeatedly uploaded, modified, or called up the same data within a set period (for example, a 24-hour period). If so, this is considered a duplicate and will be removed. The intervals between uploads, modifications, and calls are compared. For example, if a user uploads and modifies the same carbon emissions data multiple times within 10 minutes, and the interval between these operations is less than the set minimum valid interval (e.g., 30 minutes), this record will be considered a duplicate and screened. Duplicate carbon emissions data records identified will be removed or flagged for subsequent processing to ensure the accuracy and validity of the blockchain data.

[0072] S202: Based on the duplicate carbon emission data records, identify the integrity of the carbon emission data submitted within a unit time, determine the difference between the data submitted within the unit time and the full data, and generate a carbon emission data integrity metric;

[0073] To identify the completeness of carbon emissions data submitted within a unit timeframe, the carbon emissions data submitted in each record must first be time-stamped and the amount of data submitted within each timeframe calculated. Specifically, if a user submits five carbon emissions records within a unit timeframe (e.g., one day), the records are first counted to see if they cover all the data that should have been submitted within that timeframe. If carbon emissions data is missing for certain timeframes (e.g., a user's submissions from May 1st to May 2nd are missing records from 3:00 PM to 4:00 PM on May 1st), the incomplete data is identified, the difference between the data submitted within the unit timeframe and the full data set is determined, this difference is calculated, and a carbon emissions data completeness metric is generated. For example, if 10 data records should have been submitted within a unit timeframe, but only 8 were actually submitted, the data completeness metric is 80%. This metric is used to assess the completeness of the submitted data. If the completeness metric falls below the set standard, a warning is triggered or further data supplementation is required.

[0074] S203: Analyze the stability of data submission based on the carbon emission data integrity measurement and the blockchain behavior stability standard. Obtain the blockchain behavior stability value by comparing the stability of the carbon emission data submitted in real time with the set stability standard.

[0075] The stability of data submission is analyzed by comparing the stability of real-time carbon emission data with the set stability standard. The stability standard can be set based on the original data. Assume that the fluctuation range of carbon emission data submitted per unit time must not exceed a certain percentage. For example, if the stability standard is set to no more than 5% fluctuation of hourly carbon emission data, the hourly carbon emission data fluctuation will be calculated. If the data fluctuation exceeds the standard for a certain period of time, the data submission is considered unstable. By analyzing the stability of data submission, a behavioral stability value can be calculated. If the stability of real-time data is lower than the set standard, the behavioral stability value is negative or low. This value represents the stability of the blockchain data. If the stability value is low, it indicates that there are data anomalies or additional problems that require further analysis and processing.

[0076] Specifically, if Figure 4 As shown, the steps for the trusted access rights index table are as follows:

[0077] S301: Extract the user node authority value and the sensitive carbon emission segment access level value based on the blockchain behavior stability value, match them by node identifier, filter out records where the access level value is greater than the authority value, and obtain the over-authority access segment amount;

[0078] The status of each user node is evaluated based on the behavioral stability value recorded on the blockchain. This value uses an algorithm to analyze the user's raw behavioral data, such as access frequency, requested resource types, successful and failed transactions, etc., to ensure the stability and reliability of the node. By comparing the node's permission value and the access level value of the carbon emission segment, the node will be matched according to the node identifier. The permission value is a preset security value or assigned by the administrator, and the access level value indicates the level of carbon emission segment that the user can access. It is set by environmental policy or company regulations. A filtering operation is performed to filter out records with access level values greater than the permission value, that is, to find unauthorized access to higher-level carbon emission segments. For example, if a user node has a permission value of level 2 and a level value of 3 for accessing a sensitive carbon emission segment, the system will mark this behavior as excessive permission access and record the data for further processing. The obtained excessive permission access segment count is the number of segments accessed beyond the authorized range. Through this process, the occurrence of violations can be detected.

[0079] S302: Extract the permission configuration mapping based on the amount of over-authority access segments, identify records with level mismatches, extract node numbers and block numbers, and generate a permission mapping adjustment sequence;

[0080] Access data that exceeds the scope of permissions is aggregated to calculate the number of accesses to over-permissioned segments. This number can be used to assess the accuracy of permission configuration and the security of the application, and then determine whether the permission configuration needs to be adjusted. The current permission mapping data is extracted from the permission configuration file. The permission configuration file includes node numbers and their corresponding permission level information, as well as the mapping relationship between block numbers and access level values. This data is used to identify records with level mismatches. For example, a node is mistakenly assigned low permissions but can access higher-level blocks, resulting in a permission mismatch. After detecting this mismatch, the node number and the corresponding block number are extracted. When performing specific operations, the node number points to a specific user or device, while the block number identifies a specific segment in the blockchain. By incorporating this data into the adjustment strategy, a permission mapping adjustment sequence is generated. This sequence specifically indicates which nodes require permission upgrades or downgrades, and provides a data basis for subsequent permission corrections.

[0081] S303: Call the node number and block number in the permission mapping adjustment sequence, replace the smart contract permission mapping field, identify the node number and access level value mapping, and generate a trusted access permission index table;

[0082] The permission mapping is called to adjust the node and block numbers in the sequence. This sequence provides the data required for subsequent permission updates. Each node number represents a specific user or device, while the block number corresponds to a specific carbon emission zone or resource block. The combination of the two determines whether a node can access a specific carbon emission zone. By comparing this data, a replacement operation is performed on the smart contract permission mapping field. The smart contract stores access control information. After the adjustment, the access rights data between the node and the block are updated. For example, if a node's permissions are adjusted to access the level 5 carbon emission zone, the mapping field in the smart contract will be directly replaced to ensure that the node's new permissions are implemented. The mapping between node numbers and access level values will be identified, and the node's access capabilities will be reassessed to ensure that it can access the matching zone. A trusted access rights index table will be generated. This table records the access rights of all nodes and the corresponding block level values, ensuring that each node's access behavior is within legal and safe limits. For example, if a node's permissions are adjusted to access the level 3 zone, the index table will clearly list the access relationship between the node and the zone to ensure the accuracy of permissions and data integrity.

[0083] Specifically, if Figure 5 As shown, the steps for the on-chain illegal node list are as follows:

[0084] S401: Call the node ID of the restricted record in the trusted access permission index table, locate the original carbon emission behavior chain, extract the written data block of the node, compare the carbon emission value with the quota value in the data block, calculate the over-emission offset value of the block, identify the position of the over-limit block, and obtain the over-limit block sequence position information;

[0085] The original carbon emission behavior chain is located through the node ID. The positioning process mainly involves querying the carbon emission data block corresponding to the node ID from the database or blockchain, and obtaining all relevant information of the node, such as carbon emission value, quota value and its corresponding block location, etc. By comparing the carbon emission value and quota value in the data block, the over-limit block identification process is performed. First, the carbon emission value and quota value are extracted from each node data block. The specific steps are to obtain the carbon emission data stored in it by accessing the node's write data block. For example, if the node's carbon emission value is 150 tons and the quota value is 120 tons, then the node exceeds the quota limit and is determined to be an over-limit block. The data comparison results of each node are classified and the location information of all over-limit blocks is identified, and finally the over-limit block sequence location information is obtained, which is further used for subsequent screening and processing to ensure accurate identification and marking of all illegal nodes and their specific locations;

[0086] The block's overrun offset value is calculated using the formula:

[0087] ;

[0088] in, Represents the block's overrun offset value, Representative The carbon emission value recorded in each node block, Representative The carbon emission quota value corresponding to each node block, Representative The weighted coefficient of the trusted authority level of each node, Representative The data timing position value of each node in the behavior chain, Representative Node and behavior chain The number of communications between associated nodes, Representative The average number of times a node communicates with its associated nodes in the current behavior chain, Represents the current behavior chain except The number of additional nodes beyond the nodes;

[0089] The block's over-emission offset value indicates the extent to which a block node's carbon emissions exceed its allocated quota within a specific time period. It combines factors such as the node's data temporal position in the behavior chain, the node's trusted access level, and fluctuations in its communication behavior with other nodes, reflecting the node's high-risk emission anomalies in the overall carbon emission network. A larger value indicates that the node not only seriously exceeds its carbon limit, but also has a more influential position in the on-chain behavior, a higher level of authority, or more unstable communication behavior, thus requiring priority attention or regulatory intervention.

[0090] (No. Carbon emissions per node): obtained through monitoring node energy consumption data. Taking the Cardano network as an example, the average annual power consumption of each node is 199.45kWh;

[0091] (No. Carbon emission quota value of each node): determined according to the quota allocated in the carbon trading system. Assume that the annual carbon emission quota of each node is 180kgCO2e;

[0092] (No. The weighted coefficient of the trusted authority level of each node is calculated based on factors such as the node's original behavior, data integrity, and participation. Assume that the weighted coefficient of this node is 1.2;

[0093] (No. The data timing position value of the node in the behavior chain): indicates the position of the node in the behavior chain. The larger the value, the later it is. Assume that the node is at the 9th position;

[0094] (No. Node and The number of communications between related nodes): obtained through network communication log statistics, assuming that the number of communications with 5 nodes are 10, 12, 9, 11, and 13 respectively;

[0095] (No. The average number of times a node communicates with all related nodes): Calculate the average of the above communication times;

[0096] Dimensional unification and normalization:

[0097] Carbon emission value ( ) and quota values ( ): unit is kgCO2e;

[0098] Trusted authority level weighted coefficient ( ): dimensionless, normalized;

[0099] Data timing position value ( ): is an integer, indicating the position of the node in the behavior chain;

[0100] Number of communications ( ): is an integer, indicating the number of communications;

[0101] Substitute specific values into the calculation process:

[0102] Calculate the carbon emissions difference: ;

[0103] Calculate the weighting factors: ;

[0104] Calculate the average number of communications: ;

[0105] Calculate the deviation and the number of communications:

[0106] ;

[0107] Substitute the formula to calculate the superexclusion offset value: ;

[0108] No. The over-emission offset value of a node is approximately 11.67, which indicates the extent to which the node's carbon emissions exceed the quota. Factors such as its trusted authority level, position in the behavior chain, and communication activity with the node are taken into account. This value can be used to identify the location of the over-limit block and obtain the over-limit block sequence position information.

[0109] S402: Based on the position information of the exceeded block sequence, the data blocks are sorted by node ID, the cumulative number of consecutive exceeded blocks is identified, and the number is compared with the abnormal threshold. The nodes with the number of exceeded blocks exceeding the threshold are screened out, the carbon emission values of the associated blocks are extracted, and the data that does not meet the conditions is eliminated to generate a sequence of carbon emission values of consecutive abnormal nodes;

[0110] All data blocks are arranged in node ID order to create a clear arrangement structure, facilitating subsequent analysis and operations. During this sorting process, data blocks are uniquely identified by node ID. Assuming one node has ID A and another has ID B, the data blocks are allocated in an orderly manner. When operating on the sorted data blocks, by identifying the cumulative number of consecutive exceeding-limit blocks, abnormally frequent nodes can be effectively screened out. The operation accumulates and counts the exceeding-limit records for each node. For example, if node A has three exceeding-limit records, and each exceeding-limit value is greater than the set threshold, the cumulative number is 3. The cumulative number is compared with the preset abnormal threshold. If the cumulative number exceeds the threshold, it indicates that the node is a frequent exceeding-limit node. Next, the data is screened to remove nodes whose exceeding-limit number does not exceed the threshold. Finally, the data of nodes whose exceeding-limit number exceeds the threshold is retained. The data will form a continuous abnormal node carbon emission value sequence, which reflects the nodes that frequently exceed the limit within a certain period of time.

[0111] S403: Call the continuous abnormal node carbon emission value sequence, match the original chain node structure information, write all excessive nodes into the violation tracking block, collect node IDs, and obtain the list of illegal nodes on the chain;

[0112] By analyzing and calling the continuous abnormal node carbon emission value sequence, the original chain node structure information is matched. During the execution process, the structural information of the original chain node is compared with the data in the abnormal node carbon emission value sequence, with the aim of accurately finding those qualified over-limit nodes. According to the node ID, all identified over-limit node data are written into the violation tracking block, and the IDs of all violating nodes are collected to obtain a list of violating nodes on the chain. The key operations include data matching, writing, and information collection. The matching process is to retrieve the carbon emission data in the continuous abnormal node sequence and check whether the data matches the node ID in the original chain node structure information. If the match is successful, it is considered that the node has violated the rules and will be marked as a violating node and written into the tracking block. The IDs of all violating nodes will be collected into a list, which will serve as the basis for subsequent supervision and auditing to ensure that every violating node is clearly recorded and tracked.

[0113] Specifically, if Figure 6 As shown in the figure, the steps for identifying the compliance status of carbon emissions chain are as follows:

[0114] S501: Call the carbon emission block record of the corresponding node in the on-chain illegal node list, extract the carbon emission block data, identify the smart contract execution result and the associated policy number, organize the correspondence between carbon emission value, contract status and policy number in node order, and generate a node carbon emission data binding table;

[0115] The record contains the carbon emission behavior data of each node. Each node is matched with its corresponding carbon emission block record through its unique identifier. The carbon emission data of each node within a specified time period is extracted from the blockchain, including the carbon emissions generated by the node, the carbon emission segments accessed, and the status of its contract execution. The smart contract execution results associated with each block are identified and matched with the associated policy number to ensure that carbon emission behavior complies with current environmental policy standards. The execution results of the smart contract will provide feedback on the compliance of a node's behavior, such as whether the carbon emissions are within the prescribed range. The information is further organized by node sequence to establish a node carbon emission data binding table, which establishes a correspondence between carbon emissions, smart contract execution status, and policy numbers. For example, in an energy management platform, a node's smart contract executes an operation. Based on the contract execution results, the carbon emissions generated by the node are marked as compliant or non-compliant, providing a basis for subsequent inspection and analysis.

[0116] S502: Based on the node carbon emission data binding table, compare the carbon emission data value with the carbon emission index value in the binding policy number, check whether the difference exceeds the deviation threshold, mark the abnormal record in the abnormal section field, and obtain the node carbon emission difference marking result;

[0117] Based on this table, each node's carbon emissions data value is compared with the carbon emissions index value in the policy number to which it is bound. The carbon emissions index value, provided by environmental protection policies or industry standards, indicates the maximum carbon emissions tolerated by each node. Each node's carbon emissions value is then checked to see if it exceeds the deviation threshold set for that index value. The deviation threshold is a key parameter, defined or configured by the policy, representing the maximum acceptable deviation and often set to a specific percentage (e.g., 10%). For example, if a node's carbon emissions data is 100 tons of CO2, and the carbon emissions index value in the bound policy is 95 tons, the deviation must be determined to see if it exceeds a set threshold, such as 5%. If the threshold is exceeded, the node record is marked as an anomaly and stored in the anomaly section for further analysis and processing. If the deviation threshold is within the threshold, the record is marked as normal. The discrepancy check process can be performed using a preset deviation. When the calculated result exceeds the set threshold, it is considered an anomaly. The marked node carbon emissions discrepancy result is stored and used as input data for subsequent operations.

[0118] S503: Call the node sequence with the anomaly segment marked in the node carbon emission difference marking result, summarize all anomaly marking status by node ID, collect management fields, and embed them into the on-chain node structure information to obtain the carbon emission chain compliance identification status;

[0119] The status of all nodes marked as abnormal is aggregated based on their node ID. All abnormal statuses are aggregated into a centralized management field, recording information about all nodes marked abnormal after discrepancy checks. Each node's abnormal status includes specific information such as whether its carbon emissions exceed the standard and whether it complies with policy requirements. This information is then embedded with the node's structural information on the blockchain. This embedding process effectively integrates management information into each node's on-chain record, enabling real-time tracking of each node's carbon emissions compliance. For example, if a node with the ID "12345" exceeds the set policy deviation threshold and is therefore marked as abnormal, this information is synchronized with the node's on-chain data. This ensures that the node's compliance status is fully and dynamically reflected in the blockchain, resulting in a chain-based carbon emissions compliance identification status. Through blockchain, the compliance and traceability of each node's carbon emissions are ensured, making the entire carbon emissions monitoring system transparent, timely, and efficient.

[0120] like Figure 7 As shown, a carbon emission data management device based on blockchain includes:

[0121] The data verification module records carbon emission data, user identities, and submission records reported by blockchain nodes. It filters data entries with empty identities and removes records with missing timestamp fields. It determines whether the timestamp is within the block write window period and removes entries that are not within the period range to obtain the effective on-chain data ratio.

[0122] The behavior stability module extracts upload operations, modification records, and call behaviors based on the effective on-chain data ratio, counts time intervals, identifies repeated operations below the block time, analyzes the frequency per unit time, calculates the ratio of continuity and timing rationality, and establishes a block behavior stability structure;

[0123] The permission control module is based on the stable structure of block behavior, compares the user permission level with the sensitive data access level, filters out behavior records with permission levels lower than the access level, adjusts the mapping relationship value, writes the smart contract update field, and builds the on-chain permission index mapping;

[0124] The violation monitoring module locates the restricted node ID based on the on-chain permission index mapping, extracts carbon emissions and writes them into the block, compares the emission value with the node carbon quota, identifies the continuous exceeding data, filters the nodes with the number of exceeding limits greater than the threshold, writes them into the tracking chain block, and generates node violation data records;

[0125] The compliance labeling module extracts the associated smart contract results and policy numbers based on the node violation data records, compares the emission data with the corresponding policy limit values, marks the difference block positions, and obtains the carbon emission chain compliance identification status.

[0126] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A carbon emission data management method based on blockchain, characterized in that: The following steps are involved: S1: Based on the carbon emission data, user identity and submission records reported by the blockchain record node, filter the data entries with missing identity and timestamp fields, determine whether the data is within the write window period, eliminate the non-compliant data, and obtain the effective on-chain data ratio; S2: Based on the effective on-chain data ratio, extract the user's upload actions, modification times, and call operations in the behavior record, filter out repeated behaviors with operation intervals lower than the set block determination period, analyze the integrity of data submission within a unit time, and obtain the blockchain behavior stability value; S3: Based on the blockchain behavior stability value, extract the user node permissions and the data access level of the sensitive carbon emission segment in the blockchain, compare with the mapping rules in the permission configuration, filter access records with access levels greater than the current node level, adjust the access permission mapping relationship, and generate a trusted access permission index table; S4: Call the node ID of the restricted record in the trusted access permission index table, extract the original carbon emission behavior chain record, filter the nodes with a continuous number of violations greater than the threshold, write the nodes into the violation tracking block, and obtain the list of illegal nodes on the chain.

2. The blockchain-based carbon emission data management method according to claim 1 is characterized in that: The effective on-chain data ratio includes the identity field occupancy rate, the timestamp field coverage rate, and the data write cycle compliance; the blockchain behavior stability value includes the operation interval consistency, the upload behavior frequency, and the behavior record continuity; the trusted access permission index table includes the access permission level identifier, the permission mapping update item, and the sensitive data access tag; the on-chain illegal node list includes the illegal node number, the excessive write frequency, and the illegal block index.

3. The blockchain-based carbon emission data management method according to claim 1 is characterized in that: The steps for the effective on-chain data ratio are as follows: S101: Based on the carbon emission data, user identity and submission records reported by the blockchain record node, extract the user identity and submission timestamp fields, check for empty and NULL values, remove invalid records, and perform summary statistics to obtain the complete data volume of the field; S102: Call the submission timestamp field corresponding to each record in the complete data volume of the field, compare it with the start and end time of the write window set by the blockchain, eliminate records whose timestamps exceed the window period, count the number of remaining records and identify their proportion to all data entries in the blockchain, and obtain the effective on-chain data ratio.

4. The blockchain-based carbon emission data management method according to claim 3 is characterized in that: The steps of the blockchain behavior stability value are specifically as follows: S201: Based on the effective on-chain data ratio, extract the user's uploaded carbon emission data, modification times, and call operations, filter out repeated behaviors that are less than a set period, compare the upload, modification, and call operation intervals, and filter out duplicate carbon emission data records; S202: Based on the duplicate carbon emission data records, identifying the integrity of the carbon emission data submitted within a unit time, determining the difference between the data submitted within the unit time and the full data, and generating a carbon emission data integrity metric; S203: Analyze the stability of data submission based on the carbon emission data integrity measurement and the blockchain behavior stability standard, and obtain the blockchain behavior stability value by comparing the stability of the carbon emission data submitted in real time with the set stability standard.

5. The blockchain-based carbon emission data management method according to claim 4 is characterized in that: The steps of indexing the trusted access rights table are as follows: S301: Extracting the user node authority value and the sensitive carbon emission segment access level value based on the blockchain behavior stability value, matching by node identifier, filtering records where the access level value is greater than the authority value, and obtaining the over-authority access segment quantity; S302: extracting a permission configuration mapping based on the amount of super-authority access segments, identifying records with level mismatches, extracting node numbers and block numbers, and generating a permission mapping adjustment sequence; S303: Call the node number and block number in the permission mapping adjustment sequence, replace the smart contract permission mapping field, identify the node number and access level value mapping, and generate a trusted access permission index table.

6. The blockchain-based carbon emission data management method according to claim 5 is characterized in that: The steps for listing illegal nodes on the chain are as follows: S401: Calling the node ID of the restricted record in the trusted access permission index table, locating the original carbon emission behavior chain, extracting the written data block of the node, and identifying the position of the excess block by comparing the carbon emission value with the quota value in the data block to obtain the excess block sequence position information; S402: Based on the position information of the exceeded block sequence, the data blocks are sorted by node ID, the cumulative number of consecutive exceeded blocks is identified, and the number is compared with the abnormal threshold. The nodes with the number of exceeded blocks exceeding the threshold are screened out, the carbon emission values of the associated blocks are extracted, and the data that does not meet the conditions is eliminated to generate a sequence of carbon emission values of consecutive abnormal nodes; S403: Call the continuous abnormal node carbon emission value sequence, match the original chain node structure information, write all excessive nodes into the violation tracking block, collect node IDs, and obtain the list of illegal nodes on the chain.

7. The blockchain-based carbon emission data management method according to claim 6, characterized in that: The super-displacement offset value of the block is calculated using the formula: ； in, Represents the block's overrun offset value, Representative The carbon emission value recorded in each node block, Representative The carbon emission quota value corresponding to each node block, Representative The weighted coefficient of the trusted authority level of each node, Representative The data timing position value of each node in the behavior chain, Representative Node and behavior chain The number of communications between associated nodes, Representative The average number of times a node communicates with its associated nodes in the current behavior chain, Represents the current behavior chain except The number of additional nodes beyond the nodes.

8. The blockchain-based carbon emission data management method according to claim 1, characterized in that: The method further comprises step S5: S5: Call the carbon emission block record of the corresponding node in the on-chain violation node list, identify the smart contract execution result and the corresponding policy number, determine the difference between the carbon emission data and the policy content, and mark it in the abnormal area to obtain the carbon emission chain compliance identification status; The carbon emission chain compliance identification status includes policy matching deviation value, abnormal data identification number, and smart contract execution control item.

9. The blockchain-based carbon emission data management method according to claim 8, characterized in that: The specific steps for identifying the compliance status of carbon emissions chain are: S501: Call the carbon emission block record of the corresponding node in the on-chain illegal node list, extract the carbon emission block data, identify the smart contract execution result and the associated policy number, organize the correspondence between the carbon emission value, contract status and policy number in node order, and generate a node carbon emission data binding table; S502: Based on the node carbon emission data binding table, compare the carbon emission data value with the carbon emission index value in the binding policy number, check whether the difference exceeds the deviation threshold, mark the abnormal record in the abnormal section field, and obtain the node carbon emission difference marking result; S503: Call the node sequence of the abnormal segment marked in the node carbon emission difference marking result, summarize all abnormal marking statuses by node ID, collect management fields, and embed them into the on-chain node structure information to obtain the carbon emission chain compliance identification status.

10. A carbon emission data management device based on blockchain, characterized in that: The device is used to implement the blockchain-based carbon emission data management method according to any one of claims 1 to 9, and the device includes: The data verification module records carbon emission data, user identities, and submission records reported by blockchain nodes. It filters data entries with empty identities and removes records with missing timestamp fields. It determines whether the timestamp is within the block write window period and removes entries that are not within the period range to obtain the effective on-chain data ratio. The behavior stability module extracts upload operations, modification records, and call behaviors based on the effective on-chain data ratio, counts time intervals, identifies repeated operations below the block time, analyzes the frequency per unit time, calculates the continuity and timing rationality ratio, and establishes a block behavior stability structure; The permission control module compares the user permission level with the sensitive data access level based on the stable structure of the block behavior, filters out the behavior records with permission levels lower than the access level, adjusts the mapping relationship value, writes the smart contract update field, and builds the on-chain permission index mapping; The violation monitoring module locates the restricted node ID based on the on-chain permission index mapping, extracts the carbon emissions and writes them into the block, compares the emission values with the node carbon quota, identifies the continuous exceeding limit data, filters the nodes with the number of exceeding limits greater than the threshold, writes them into the tracking chain block, and generates the node violation data record; The compliance marking module extracts the associated smart contract results and policy numbers based on the node violation data records, compares the emission data with the corresponding policy limit values, marks the difference block positions, and obtains the carbon emission chain compliance identification status.

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