Carbon data evidence storage method and system in combination with block chain and cryptographic algorithm

Abstract

The invention discloses a carbon data evidence storage method and system combined with a block chain and a national secret algorithm, and belongs to the technical field of carbon data credible evidence storage, and the technical scheme is characterized in that the method comprises the steps: obtaining original carbon data and traceability data, and obtaining a business logic feature set and a data variation degree through a preset business rule and carbon flow traceability filtering; based on the business logic feature set and the data variation degree, a verification instruction is obtained through a mode recognition confidence model; based on the verification instruction and the original carbon data, obtaining a verification group list through a quantum consensus model; according to the method, through the synergistic effect of the mode recognition confidence model and the quantum consensus model, the problems that in carbon data management, the credibility of a data source is insufficient, the verification process is prone to human intervention, and the tamper-proofing capacity of an evidence storage result is weak are solved; and the source credibility, the process fairness and the result reliability of carbon data management are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of carbon data trusted evidence storage technology, and more specifically to a carbon data evidence storage method and system that combines blockchain and national cryptographic algorithms. Background Technology

[0002] Currently, in the field of carbon data management, traditional carbon data management methods mostly rely on a combination of self-reporting by enterprises and periodic audits by third-party institutions. However, in practical applications, many limitations have gradually been exposed: the automation and transparency of data collection at the source are insufficient; errors or even human tampering are easily introduced during the generation, transmission, and aggregation of data, and it is difficult to accurately trace and quantitatively assess data anomalies; existing technologies are unable to conduct multi-dimensional and intelligent credibility analysis of massive amounts of carbon data, lack in-depth mining of the business logic and industry relevance behind the data, and cannot effectively identify complex and hidden anomaly patterns; the method of storing verification results is still mainly centralized storage, which poses a risk of data being tampered with or lost by a single node, and the audit traceability chain is weak. Therefore, existing technologies have shortcomings. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention aims to provide a carbon data notarization method and system that combines blockchain and national cryptographic algorithms. It achieves accurate quantitative assessment of data credibility through the collaborative processing of carbon flow tracing filtering and preset business rules; realizes intelligent multi-dimensional verification decision-making through a pattern recognition confidence model integrating industry knowledge graphs; achieves anti-collusion random node assignment through a quantum walk-based consensus mechanism; and constructs a secure and reliable notarization chain through national cryptographic algorithms and blockchain notarization contracts. This completes a closed-loop technology from data collection, credibility assessment, intelligent decision-making, consensus verification to secure notarization, solving the problems of insufficient data source credibility, questionable fairness in the verification process, and weak tamper-proofing of notarization results in existing carbon data management. It improves the source credibility, process fairness, result reliability, and ability to identify complex anomaly patterns in the carbon data notarization system.

[0004] To achieve the above objectives, the present invention provides the following technical solution: This invention provides a carbon data notarization method combining blockchain and national cryptographic algorithms, comprising: Obtain raw carbon data and source data; Based on the original carbon data and source tracing data, a set of business logic features and data variability are obtained through preset business rules and carbon stream source tracing filtering. Based on the business logic feature set and data variability, verification instructions are obtained through a pattern recognition confidence model. The pattern recognition confidence model includes a feature engineering layer, a graph convolutional network aggregation layer, a multidimensional confidence generation layer, and a comprehensive decision layer. Based on the verification instructions and the original carbon data, a list of verification groups is obtained through a quantum consensus model. Based on the aforementioned verification group list, on-chain evidence records are obtained through national cryptographic algorithms and blockchain evidence storage contracts.

[0005] As a further improvement of the present invention, the step of obtaining a business logic feature set and data variability based on the original carbon data and source tracing data through preset business rules and carbon stream source tracing filtering includes: Based on the original carbon data and the source data, a first state vector is obtained through carbon flow source filtering. Based on the original carbon data and source tracing data, a second state vector and a business logic feature set are obtained through preset business rules; Based on the first state vector and the second state vector, the data variability is obtained by calculating the residual.

[0006] As a further improvement of the present invention, the step of obtaining verification instructions based on the business logic feature set and data variability through a pattern recognition confidence model includes: Based on the business logic feature set and data variability, an initial feature vector is obtained through the feature engineering layer; Obtain an industry knowledge graph, and based on the industry knowledge graph and the initial feature vector, obtain a graph embedding vector through a graph convolutional network aggregation layer; Based on the initial feature vector and graph embedding vector, a confidence vector is obtained through a multi-dimensional confidence generation layer; Verification instructions are obtained through the comprehensive decision-making layer based on the confidence vector.

[0007] As a further improvement of the present invention, the step of obtaining the initial feature vector through a feature engineering layer based on the business logic feature set and data variability includes: Based on the business logic feature set and data variability, a normalized feature sequence is obtained through data standardization processing; Based on the normalized feature sequence, a composite feature group is obtained through feature cross-combination; Based on the composite feature set, an initial feature vector of standard dimensions is obtained through dimension alignment.

[0008] As a further improvement of the present invention, the step of obtaining a graph embedding vector through a graph convolutional network aggregation layer based on the industry knowledge graph and the initial feature vector includes: Based on the industry knowledge graph and initial feature vectors, the target enterprise node is determined through node localization. Based on the target enterprise node, the set of neighboring nodes of the target enterprise node is obtained through neighborhood sampling; Based on the set of neighboring nodes and the initial feature vector, a graph embedding vector is obtained through a graph convolutional neural network.

[0009] As a further improvement of the present invention, the step of obtaining a confidence vector through a multi-dimensional confidence generation layer based on the initial feature vector and the graph embedding vector includes: Based on the initial feature vector and the graph embedding vector, a fused feature vector is obtained by vector concatenation; Based on the fused feature vector, a basic confidence score is obtained through a fully connected neural network; A multidimensional confidence vector is obtained based on the basic confidence score through a normalized exponential function.

[0010] As a further improvement of the present invention, the step of obtaining the verification group list based on the verification instructions and the original carbon data through a quantum consensus model includes: Based on the verification command, the verification node and its status are obtained through the node management service. Based on the verification instructions and the original carbon data, task attributes are obtained through task parsing; Based on the verification node status and task attributes, a verification group list is obtained through a quantum consensus model.

[0011] As a further improvement of the present invention, the step of obtaining the verification group list based on the verification node status and task attributes through a quantum consensus model includes: An initial quantum graph is obtained based on the state of the verification node using a quantum state initialization algorithm; Based on the aforementioned task attributes, the quantum potential energy configuration is obtained through potential energy optimization. Based on the initial quantum graph structure and quantum potential energy configuration, a list of verification groups is obtained through a quantum consensus model.

[0012] As a further improvement of the present invention, the step of obtaining on-chain evidence records based on the verification group list through national cryptographic algorithms and blockchain evidence storage contracts includes: Based on the verification group list, raw carbon data, and preset business rules, the group signature is obtained through the national cryptographic SM2 threshold signature algorithm and the national cryptographic SM3 hash algorithm; Based on the group signature, an on-chain evidence record is obtained through a blockchain evidence storage contract.

[0013] This invention provides a carbon data storage system combining blockchain and Chinese cryptographic algorithms. The carbon data storage system combining blockchain and Chinese cryptographic algorithms includes: Data tracing module: acquires raw carbon data and tracing data; based on the raw carbon data and tracing data, obtains business logic feature set and data variability through preset business rules and carbon stream tracing filtering; Confidence Decision Module: Based on the business logic feature set and data variability, verification instructions are obtained through a pattern recognition confidence model. The pattern recognition confidence model includes a feature engineering layer, a graph convolutional network aggregation layer, a multi-dimensional confidence generation layer, and a comprehensive decision layer. Quantum consensus module: Based on the verification instructions and raw carbon data, a list of verification groups is obtained through a quantum consensus model; National Cryptographic Evidence Preservation Module: Based on the verification group list, on-chain evidence preservation records are obtained through national cryptographic algorithms and blockchain evidence preservation contracts.

[0014] This invention, based on raw carbon data and source data, achieves quantitative assessment of data quality and anomaly severity through preset business rules and carbon stream source filtering. It obtains differentiated verification instructions based on business logic feature sets and data variability using a pattern recognition confidence model. Then, based on the verification instructions and raw carbon data, it completes anti-collusion random assignment of verification nodes through a quantum consensus model. Finally, based on the verification group list, it securely stores verification evidence using national cryptographic algorithms and blockchain notarization contracts. This achieves closed-loop management of carbon data from collection and assessment, intelligent decision-making, consensus verification to trusted notarization, solving the problems of insufficient data source credibility, susceptibility to human intervention in the verification process, and weak anti-tampering capabilities of notarization results in traditional carbon data management. It improves the automation level, verification fairness, system security, and anomaly identification accuracy of carbon data management. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating the steps of the carbon data notarization method combining blockchain and national cryptographic algorithms of the present invention. Figure 2 A flowchart illustrating the steps involved in obtaining verification instructions using a pattern recognition confidence model; Figure 3 A flowchart illustrating the steps involved in obtaining a list of verification groups using the quantum consensus model; Figure 4 This is a schematic diagram of the carbon data storage system that combines blockchain and national cryptographic algorithms according to the present invention. Detailed Implementation

[0016] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof.

[0017] The term "and / or" in the following text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0018] like Figure 1 As shown, this invention provides a carbon data notarization method combining blockchain and national cryptographic algorithms, comprising: Obtain raw carbon data and source data; Based on raw carbon data and source tracing data, business logic feature sets and data variability are obtained through preset business rules and carbon stream source tracing filtering. Based on the business logic feature set and data variability, verification instructions are obtained through a pattern recognition confidence model. The pattern recognition confidence model includes a feature engineering layer, a graph convolutional network aggregation layer, a multi-dimensional confidence generation layer, and a comprehensive decision layer. Based on the verification instructions and raw carbon data, a list of verification groups is obtained through a quantum consensus model. Based on the verification group list, on-chain evidence records are obtained through national cryptographic algorithms and blockchain evidence storage contracts.

[0019] The raw carbon data consists of time-series data reflecting core quantitative information about a company's carbon emissions and carbon reductions during its production and operation activities. This data is acquired in real-time through company data interfaces, energy consumption ledger statistics, and testing by third-party testing institutions. It includes company identification, carbon production data, carbon energy consumption data, basic company information, and company registration information. Basic company information includes industry classification codes and operating scale parameters. The traceability data records the source and flow of raw carbon data from generation, collection, transmission to processing. This data is acquired through data acquisition terminal logs, transmission link node tracking, and data processing system operation records. It includes upstream data source identification, company industry classification codes, company operating scale parameters, and core carbon emission data. The system includes verification node registration information, industry knowledge graph, and verification node professional capability tags; preset business rules are an executable rule base built on national carbon accounting standards, obtained through natural language processing technology, used to guide carbon data accounting and verification; carbon flow tracing filtering is an algorithm model that integrates data tracing credentials and business rules and dynamically perceives and processes carbon data relationships, used to filter noise interference and correct data deviations during data transmission; business logic feature set is a combination of indicators reflecting the correlation between carbon emissions and production and operation, including carbon emissions per unit product and energy consumption carbon emission intensity; data variability is an indicator used to quantify the degree to which the original carbon data and tracing data deviate from the expected state during collection, transmission, and processing; The pattern recognition confidence model is a multi-dimensional evaluation system based on deep learning, integrating industry knowledge graphs and multi-dimensional features. It comprehensively judges data credibility and includes a feature engineering layer, a graph convolutional network aggregation layer, a multi-dimensional confidence generation layer, and a comprehensive decision layer. The feature engineering layer is a data preprocessing module used to standardize and combine input features; the graph convolutional network aggregation layer is a graph-based feature extraction module used to extract related enterprise information from the industry knowledge graph; the multi-dimensional confidence generation layer is a neural network inference module used to obtain multi-dimensional confidence scores; and the comprehensive decision layer is a policy execution module used to... Verification instructions are obtained based on multi-dimensional confidence scores; these instructions, derived from a pattern recognition confidence model, are used to instruct the initiation of the carbon data verification process; the quantum consensus model is a random assignment algorithm based on quantum walks, used to generate unpredictable and task-matched verification group allocation schemes; the verification group list is a set of selected verification node identifiers; the national cryptographic algorithm is a cryptographic algorithm approved by the State Cryptography Administration, used to implement data signing and encryption; the blockchain evidence storage contract is a smart contract deployed on the blockchain, used to achieve automated evidence storage and verification; and the on-chain evidence storage record is an immutable verification evidence stored on the blockchain.

[0020] This embodiment achieves dynamic perception and noise filtering of data relationships through carbon flow tracing filtering, multi-dimensional credibility assessment and verification decision-making through pattern recognition confidence model, anti-collusion random assignment and professional matching through quantum consensus model, and high-security and trustworthy evidence storage through national cryptographic algorithms and blockchain evidence storage contracts. It realizes closed-loop management of the entire process from data collection, credibility assessment, consensus verification to on-chain evidence storage, improving the source credibility, process fairness, result reliability and system anti-attack capability of the carbon data evidence storage system.

[0021] Furthermore, this embodiment provides a step for obtaining a business logic feature set and data variability based on raw carbon data and source tracing data, through preset business rules and carbon stream source tracing filtering, including: Based on the raw carbon data and source tracing data, the first state vector is obtained through carbon flow source tracing filtering; Based on raw carbon data and source tracing data, a second state vector and a set of business logic features are obtained through preset business rules. The data variability is obtained by calculating the residuals based on the first and second state vectors.

[0022] The carbon flow source tracing filtering is based on the Kalman filter algorithm. It estimates the state vector of carbon data through state prediction and update steps. The state transition equation and observation model are constructed based on the dynamic characteristics of carbon flow. The first state vector is the state estimation result obtained by initializing, predicting and updating the state of the original carbon data and the source data through carbon flow source tracing filtering. It includes the carbon emission estimate, the upstream data source hash value and the initial data variability. The second state vector is the state output obtained by logically verifying the original carbon data and the source data based on preset business rules. It includes the theoretical carbon emission value, the upstream data source standard hash value and the data variability threshold. The business logic feature set is a combination of indicators reflecting the correlation between carbon emissions and production and operation, including carbon emissions per unit product and energy consumption carbon emission intensity. The residual calculation is a data processing method that quantifies the degree of data anomaly by comparing the difference between the first state vector and the second state vector.

[0023] Specifically, firstly, carbon flow source tracing filtering is performed on the raw carbon data and source tracing data to obtain the first state vector: Initial carbon flow source tracing filtering is performed on the enterprises to which the raw carbon data belongs, resulting in an initial state vector. This initial state vector includes the initial carbon emission estimate, the initial hash value of the upstream data source in the source tracing data, and the initial data variability reference value. Then, based on the historical carbon data and source tracing data of each enterprise, the carbon emission value and state covariance at the current moment are predicted using a time series prediction algorithm. The actual raw carbon data and source tracing data are then processed through carbon flow source tracing filtering to calculate the initial residual between the actual and predicted values. The observation noise matrix is ​​dynamically adjusted based on the model complexity and the number of data sources according to the source tracing data, resulting in an updated state vector and covariance matrix. These are then integrated to obtain the first state vector. ,in This is an estimated value for carbon emissions. The hash value of the upstream data source. The initial data variability is used; then, based on a preset business rule base, the original carbon data is substituted into the carbon accounting formula in the preset business rule base to calculate the theoretical carbon emission value. Combined with the standard hash value of the upstream data source in the preset business rule base and preset data variability threshold The second state vector is obtained. Simultaneously, carbon production and carbon energy consumption data are obtained from the original carbon data and correlated with the calculated carbon emission estimates. By filtering out the correlation indicators that conform to the business rules, a business logic feature set is obtained. Then, based on the first state vector and the second state vector, the difference is calculated one by one for the elements of the same dimension in the first state vector and the second state vector. The difference of each dimension is transformed to the [0,1] interval by the Min-Max normalization method, and then the final data variability is obtained by weighted summation.

[0024] This embodiment achieves dynamic tracking and noise filtering of the actual state of carbon data through carbon stream tracing filtering, benchmark judgment of carbon data compliance and extraction of associated features through preset business rules, quantification of the deviation between the actual and expected states of data through residual calculation, and accurate assessment of the credibility of carbon data sources through multi-dimensional data processing collaboration. It solves the problems of traditional methods being unable to quantify the degree of data anomaly and non-standard business feature extraction, improves the pertinence of business logic feature sets and the accuracy of data variability, and provides high-quality input for the verification decision of subsequent pattern recognition confidence models.

[0025] Furthermore, this embodiment provides a step for obtaining verification instructions based on a business logic feature set and data variability through a pattern recognition confidence model, including: Based on the business logic feature set and data variability, an initial feature vector is obtained through a feature engineering layer; Obtain an industry knowledge graph, and based on the industry knowledge graph and initial feature vectors, obtain graph embedding vectors through a graph convolutional network aggregation layer; Based on the initial feature vector and graph embedding vector, a confidence vector is obtained through a multi-dimensional confidence generation layer; Verification instructions are obtained through a comprehensive decision-making layer based on the confidence vector.

[0026] The pattern recognition confidence model is trained using historical carbon data and multi-source reliable data. The historical carbon data comes from publicly available carbon emission datasets, historically reported data from enterprises, and third-party audit data to ensure the representativeness and reliability of the training data. Cross-validation and outlier filtering methods are used during the training process to improve the robustness of the model. The initial feature vector is a standardized feature representation obtained by standardizing, cross-combining, and dimensional alignment of the business logic feature set and data variability. The industry knowledge graph is a graph structure database including enterprise nodes, product nodes, and their relationships, constructed using publicly available data from industry regulatory authorities and industry chain survey data. The graph embedding vector is a vectorized representation obtained by aggregating the features of the target enterprise and its neighboring nodes in the industry knowledge graph using a graph convolutional network. The confidence vector is a multi-dimensional confidence score set obtained by fusing the initial feature vector and the graph embedding vector using a neural network.

[0027] Specifically, such as Figure 2 As shown, firstly based on the business logic feature set and data variability By using the feature engineering layer of the pattern recognition confidence model, a normalized feature sequence is obtained through the Z-score normalization method. By using the feature cross-combination method, a composite feature group containing interaction terms is obtained. Principal component analysis is used to align dimensions and obtain initial feature vectors of standard dimensions. Then based on the initial feature vector Industry knowledge graph By using a graph convolutional network aggregation layer based on a pattern recognition confidence model, target enterprise nodes are located in the industry knowledge graph through a node matching algorithm. The first-order and second-order neighbor sets of each target enterprise node are obtained through a random walk sampling strategy. Graph embedding vectors are obtained by performing feature propagation and aggregation operations through a graph convolutional neural network. Then, based on the initial feature vector And graph embedding vector By using a multi-dimensional confidence generation layer in a pattern recognition confidence model, a vector concatenation operation is performed to obtain a fused feature vector. The basic confidence score is obtained by performing a nonlinear transformation through a fully connected neural network. The multidimensional confidence vector is obtained by normalization using the softmax function. The multidimensional confidence vector includes the time series smoothness score. Logical consistency score Deviation score from peers Ultimately based on multidimensional confidence vectors The comprehensive decision-making layer of the pattern recognition confidence model uses a preset threshold vector. The verification instruction is received, which includes the range of data to be verified and the urgency level. The urgency level is a quantified value of the verification task priority, including a preset threshold vector. Including time series smoothness scoring threshold Logical consistency scoring threshold And industry deviation score threshold The preset threshold vector is set based on statistical analysis of historical carbon data and dynamically adjusted through an online learning mechanism: the system regularly evaluates the accuracy of verification instructions and feedback results, and optimizes threshold parameters through reinforcement learning to adapt to changes in data distribution and business needs; threshold verification is conducted through backtesting and simulation environments to ensure its effectiveness in different scenarios; the urgency levels include: global urgency, local urgency, targeted importance, and routine sampling; global urgency requires immediate execution and emergency mobilization of the system's maximum resource quota; local urgency requires high-priority execution, i.e., initiation within a short period of time, usually targeting specific enterprises or related links, and its priority is lower than global urgency; targeted importance is a standard priority, arranged and completed within a predetermined period, with resources allocated according to standard quotas, and targeted verification is performed for clearly defined abnormal indicators, and its priority is lower than local urgency; routine sampling is a background or batch processing priority, executed when the system load is low, with resource consumption controlled to the minimum level, mainly used for continuous monitoring and compliance sampling, and its priority is lower than targeted importance; The verification instructions include: if the time series smoothness score is greater than the time series smoothness score threshold, the logical consistency score is greater than the logical consistency score threshold, and the peer deviation score is greater than the peer deviation score threshold, then the overall data credibility is high. The verification instruction is: perform routine sampling, with a priority of P3, and the range of data to be verified is a random sample not exceeding [a certain threshold]. The enterprise's original carbon data and traceability data; if any of the three confidence scores is less than or equal to its threshold, a key verification is triggered. The key verification includes time series anomaly analysis, logical contradiction investigation, and peer benchmarking review. The instructions are subdivided according to the specific anomaly item as follows: If only the time series smoothness score is less than or equal to the time series smoothness score threshold, the verification instruction is: execute time series anomaly analysis, priority is P2, and the range of data to be verified is the corresponding enterprise's continuous... The verification instruction is as follows: Perform a full-volume time-series data analysis for each period. If only the logical consistency score is less than or equal to the logical consistency score threshold, the verification instruction is: Perform a logical contradiction investigation, with a priority of P2. The data to be verified includes the accounting unit that triggered the logical alarm and related traceability data. If only the peer deviation score is less than or equal to the peer deviation score threshold, the verification instruction is: Perform a peer benchmarking review, with a priority of P2. The data to be verified includes randomly selected data from the corresponding company and its peer companies. The system requires verification of the company's original carbon data and traceability data. If any two of the three confidence scores are less than or equal to their thresholds, an emergency verification is triggered. Emergency verification includes investigations into suspected data tampering, suspected systemic concealment, and suspected violations of accounting methods. Based on the abnormal combination, the instructions are subdivided as follows: If only the peer deviation score is greater than the peer deviation score threshold, the verification instruction is: execute a suspected data tampering investigation, with priority P1, and the scope of data to be verified is expanded to the company's direct upstream data source companies and all of their original carbon data and traceability data; if only the logical consistency score is greater than the logical consistency score threshold, the verification instruction is: execute a suspected systemic concealment investigation, with priority P1, and the scope of data to be verified is expanded to the company's main... The system requires full access to raw carbon data and traceability data from competing companies and the company itself. If only the time-series smoothness score is greater than the time-series smoothness score threshold, the verification instruction is: execute an investigation into suspected violations of the accounting method, with a priority of P1. The scope of data to be verified is expanded to include full access to raw carbon data and traceability data from all affiliated companies within the group to which the company belongs. If the time-series smoothness score, logical consistency score, and industry deviation score are all less than or equal to the corresponding thresholds, it indicates a very high risk of a complete collapse in data credibility, triggering an emergency verification. The verification instruction is: execute a comprehensive emergency verification and systemic risk assessment, with a priority of P0. The scope of data to be verified includes full access to data from all affiliated companies, and the cross-chain verification and regulatory early warning process is immediately initiated. In this embodiment, the priority of the verification instructions from high to low is P0, P1, P2, and P3, where P0 takes precedence over P1, P1 takes precedence over P2, and P2 takes precedence over P3. This priority order is used to clarify the order of verification instructions in system resource scheduling and task execution sequence.

[0028] For example, this embodiment assumes a business logic feature set of a certain enterprise. Carbon emissions per unit product Energy consumption and carbon emission intensity are The data variability is The initial feature vector is obtained after processing through the feature engineering layer. In industry knowledge graphs The target enterprise node is obtained through mid-location. and its neighbor node set Graph embedding vectors are obtained by aggregation through graph convolutional networks. The confidence vector is calculated through a multi-dimensional confidence generation layer. = ,in Scoring for time series smoothness Scoring for logical consistency To score the deviation from industry peers, the preset threshold vector in the comprehensive decision-making layer is: ,in The threshold for time series smoothness scoring. The threshold for logical consistency scoring. For the industry deviation scoring threshold, this embodiment, for example, initializes the threshold based on statistical analysis of historical carbon data. The initial value of the preset threshold vector can be taken as the time-series smoothness scoring threshold. Logical consistency scoring threshold Industry deviation score threshold Furthermore, it is dynamically adjusted through online learning mechanisms combined with verification feedback. Due to the time-series smoothness scoring... Greater than the time series smoothness score threshold And logical consistency score Greater than the logical consistency score threshold And industry deviation score The deviation score is greater than the industry average. Therefore, the verification instruction is: Perform routine sampling, with a priority of P3, and the range of data to be verified is a random sample of no more than [number missing]. The original carbon data and traceability data of the enterprise are included. The initial value of the preset threshold vector in this embodiment is merely an example; those skilled in the art can set it according to actual circumstances, and this embodiment does not impose any limitations on it.

[0029] This embodiment achieves standardization and enhancement of multi-source features through a feature engineering layer, integrates industry-related information through a graph convolutional network aggregation layer, accurately quantifies multi-dimensional credibility through a multi-dimensional confidence generation layer, and intelligently executes verification strategies through a comprehensive decision-making layer. It realizes deep integration of multi-source carbon data information and identification of complex anomaly patterns, solves the problem of one-sided evaluation by traditional single indicators, and improves the scientificity and pertinence of verification instructions, as well as the accuracy of anomaly pattern identification and the scientificity of verification decisions.

[0030] Furthermore, this embodiment provides a step for obtaining an initial feature vector through a feature engineering layer based on a business logic feature set and data variability, including: Based on the business logic feature set and data variability, a normalized feature sequence is obtained through data standardization processing; Composite feature groups are obtained by combining normalized feature sequences through feature crossover. Based on the composite feature set, the initial feature vector of standard dimensions is obtained through dimension alignment.

[0031] Among them, data standardization is a data preprocessing method that uses the Z-score method to transform features of different dimensions to the same dimension, thereby eliminating differences in dimensions between features; normalized feature sequence is a feature sequence in which each feature value conforms to a standard normal distribution after data standardization, including the standardization results corresponding to the business logic feature set and the data variability; feature cross combination is a feature engineering method that performs product and ratio operations on single features with correlation in the normalized feature sequence to obtain new features, thereby mining implicit relationships between features; composite feature group is a feature set obtained through feature cross combination that includes the original standardized features and the newly generated composite features; and dimension alignment is a data processing method that uses principal component analysis to normalize feature dimensions to a preset standard dimension, thereby ensuring consistency of feature dimensions.

[0032] Specifically, firstly, based on the business logic feature set and data variability The normalized value of each feature is calculated using the Z-score normalization method, resulting in a normalized feature sequence. Then, based on the normalized feature sequence Interactive features are obtained by performing feature product operations through a feature cross-combiner, and ratio features are obtained by performing feature ratio operations. The original normalized feature sequences are then combined. The generated interaction features and ratio features yield a composite feature set. Ultimately based on composite feature groups Principal component analysis is performed using dimension alignment to extract key feature components, and the feature dimensions are then normalized to a predefined standard dimension. This yields the initial feature vectors of standard dimensions. .

[0033] This embodiment, based on business logic feature sets and data variability, achieves unified representation of multi-source heterogeneous features through multi-level processing of data standardization, feature cross-combination, and dimension alignment. This improves the completeness of feature expression and the standardization of model input, solves the model calculation deviation problem caused by differences in dimensions and dimensional chaos of traditional features, and improves the accuracy of subsequent graph convolutional network aggregation and confidence evaluation, providing high-quality feature input for pattern recognition confidence models.

[0034] Furthermore, this embodiment provides a step for obtaining an industry knowledge graph, and obtaining graph embedding vectors based on the industry knowledge graph and initial feature vectors through a graph convolutional network aggregation layer, including: Target enterprise nodes are determined through node localization based on industry knowledge graphs and initial feature vectors. The set of neighboring nodes of the target enterprise node is obtained through neighborhood sampling. Based on the set of neighboring nodes and the initial feature vector, a graph embedding vector is obtained through a graph convolutional neural network.

[0035] The system comprises the following components: node localization, a search and localization method that matches corresponding graph nodes in the industry knowledge graph based on enterprise identifiers to establish the association between enterprise data and graph nodes; target enterprise nodes, which are graph nodes in the industry knowledge graph corresponding to the enterprise to which the original carbon data being processed belongs; neighborhood sampling, a graph traversal method that obtains the associated neighbor nodes from the target enterprise node to construct the local graph structure; the neighbor node set, obtained through neighborhood sampling, consisting of the first-order and second-order neighbor nodes of the target enterprise node; and a graph convolutional neural network, a deep learning model for feature extraction based on graph structure data, used to aggregate target nodes. Based on the feature vector output by the feature engineering layer, the graph convolutional network aggregation layer uses a two-layer graph convolutional structure to extract features, including the feature information of the graph and its neighboring nodes. The input dimension of the first graph convolutional structure is the same as the dimension of the initial feature vector, and the output dimension is 128. The output dimension of the second graph convolutional structure is 64. The activation function is ReLU, resulting in a 64-dimensional graph embedding vector. During training, the learning rate of the graph convolutional neural network is set to 0.001, with 200 iterations. The Adam optimizer is used, employing a contrastive loss function combined with a dropout rate of 0.3 and L2 regularization to enhance generalization ability. The number of neurons, learning rate, and training parameters of the graph convolutional network in this embodiment are merely examples. Those skilled in the art can set them according to actual conditions, and this embodiment does not impose any limitations.

[0036] Specifically, the initial feature vector is first obtained. Its corresponding corporate logo In industry knowledge graphs The corresponding target enterprise node is obtained through an identifier matching algorithm. Then, with the target enterprise node Starting from the target enterprise node, a random walk sampling algorithm is used to obtain the node with respect to the target enterprise. The set of neighbor nodes is obtained by combining directly connected first-order neighbor nodes and indirectly connected second-order neighbor nodes. Ultimately based on the set of neighbor nodes and initial feature vector Feature propagation and aggregation operations are performed using a graph convolutional neural network. The graph convolution formula is used for inter-layer feature transfer, and after two layers of graph convolution, the graph embedding vector is obtained. .

[0037] This embodiment, based on an industry knowledge graph and initial feature vectors, achieves deep integration of target enterprise carbon data and industry background information through the collaborative processing of node localization, neighborhood sampling, and graph convolutional neural networks. It solves the problem that traditional assessments only focus on data from a single enterprise and cannot identify collaborative fraud, improves the ability to identify hidden and correlated data anomalies, and provides industry-dimensional feature support for multi-dimensional confidence assessment.

[0038] Furthermore, this embodiment provides a step for obtaining a confidence vector based on an initial feature vector and a graph embedding vector through a multi-dimensional confidence generation layer, including: A fused feature vector is obtained by concatenating the initial feature vector and the graph embedding vector. The basic confidence score is obtained based on the fused feature vector through a fully connected neural network; A multidimensional confidence vector is obtained by normalizing the base confidence score using an exponential function.

[0039] Vector concatenation is a data fusion method that connects and combines multiple feature vectors along a specified dimension to integrate feature information from different sources. The fused feature vector is a comprehensive feature representation obtained by concatenating the initial feature vector and the graph embedding vector. The fully connected neural network contains three hidden layers with 256, 128, and 64 neurons respectively, all using ReLU activation functions. The output layer has three neurons corresponding to the three confidence scores. During training, the learning rate is set to 0.0005, with 150 iterations, using the AdamW optimizer and mean squared error as the loss function. He initialization and a step-decay learning rate strategy are employed to stabilize the training process. The fully connected neural network is a deep learning model where each layer of neurons is connected to the next fully connected layer for nonlinear transformation of features. The base confidence score is the original confidence score calculated by the neural network. The normalized exponential function is a mathematical function that converts a vector into a probability distribution, used for normalizing the confidence score. In this embodiment, the number of neurons, learning rate, and training parameters of the fully connected neural network are examples; those skilled in the art can set them according to actual conditions, and this embodiment does not impose any limitations.

[0040] Specifically, firstly, based on the initial feature vector And graph embedding vector By concatenating the feature dimensions through vector concatenation, a fused feature vector is obtained. Then based on the fused feature vector The basic confidence score is obtained by calculating using a three-layer fully connected neural network and performing a nonlinear transformation using the ReLU activation function. Ultimately based on the baseline confidence score The multidimensional confidence vector is obtained by calculating the normalized exponential function and the softmax function. Each dimension represents a confidence score for a different aspect, including the time series smoothness score. Logical consistency score Deviation score from peers .

[0041] This embodiment, based on initial feature vectors and graph embedding vectors, achieves credibility assessment of carbon data in both its own characteristics and industry-related characteristics through cascaded processing of vector concatenation, fully connected neural networks, and normalized exponential functions. This solves the problem that traditional single-dimensional assessment cannot fully reflect data credibility, improves the comprehensiveness and accuracy of confidence assessment, and provides a quantitative basis for generating verification instructions for the comprehensive decision-making level.

[0042] Furthermore, this embodiment provides a step for obtaining a list of verification groups based on verification instructions and raw carbon data using a quantum consensus model, including: Based on the verification command, the verification node and its status are obtained through the node management service. Based on the verification instructions and raw carbon data, task attributes are obtained through task parsing. Based on the verification node status and task attributes, a list of verification groups is obtained through a quantum consensus model.

[0043] The system comprises the following components: Node Management Service, a system service for maintaining and managing the real-time status of all registered verification nodes, obtains the verification node status by calling the node registry and status monitoring interface; Verification Nodes are servers or containers deployed with Trusted Execution Environment (TEE) technology, used to independently execute verification algorithms in a protected, isolated environment; Verification Node Status is a dynamic set of the statuses of all currently available verification nodes, including node identifier, professional capability tag, and current resource utilization; Task Parsing is a process of obtaining key features of the verification task based on the range of data to be verified in the verification instruction and the enterprise attribute information in the original carbon data, used to determine the required verification algorithm type and resource constraints, and the verification task is obtained according to the verification instruction; Task Attributes are structured data objects that fully describe the technical characteristics of each verification task, including the target enterprise industry, verification algorithm type, urgency, and resource requirements; and the Quantum Consensus Model is a random assignment algorithm based on the quantum walk principle, used to generate unpredictable verification group allocation schemes that match the requirements of the verification task.

[0044] Specifically, firstly, the target enterprise identifier is determined based on the range of data to be verified in the verification instruction. Then, based on the corresponding enterprise basic information in the original carbon data, the industry classification code and data characteristic pattern of the target enterprise are obtained, along with the urgency level in the verification instruction. Next, based on the complete verification instruction, the node management service calls the node registry query interface to obtain the attribute information of all active verification nodes. The status monitoring interface collects the current resource utilization and response performance indicators of each verification node in real time, integrating them to obtain the verification node status, including node identifier, professional capability tag, and current resource utilization. Then, based on the target enterprise's industry classification code, data characteristic pattern, and urgency level in the verification instruction, the task parser calculates the verification algorithm type and resource requirements required for the verification task, obtaining complete task attributes. Finally, based on the verification node status and task attributes, a multi-dimensional optimized node matching and anti-predictive random selection process is executed through a quantum consensus model to obtain a list of verification groups that meet the professional requirements of the verification task, are load-balanced, and possess unpredictability.

[0045] For example, this embodiment assumes that the received verification instruction is to perform a logical contradiction check, with a priority of P2, and the scope of the data to be verified is explicitly specified as the full amount of raw carbon data of enterprise B in the current period, with an urgency level of P2; based on the registration information of enterprise B in the raw carbon data, its industry classification is determined to be industry D, and the data feature pattern is high-frequency sampling; the verification node identifiers obtained through the node management service are N201, N202, and N203, and the corresponding verification node statuses are: {N201: ["Perform logical contradiction check", "High-frequency data processing"], resource utilization rate} }, {N202:["Anomaly Detection"], Resource Utilization }、{N203:["Execution logic contradiction investigation","Source analysis"], resource utilization rate }; The task attributes obtained through task parsing are {Industry: "Industry D", Verification Algorithm Type: ["Execution Logic Conflict Investigation"], Urgency: P2, Resource Requirements:} The verification group list is [N201, N203] obtained after optimization calculation based on the verification node status and task attributes using the quantum consensus model.

[0046] This embodiment achieves unified monitoring and scheduling of verification node resources through node management services, transforms abstract verification instructions into computable task feature vectors through task parsing, realizes anti-collusion random assignment and multi-objective optimization selection through quantum consensus model, and ensures system data consistency through complete parameter transmission and state maintenance. It realizes the professional matching, resource balance and unpredictability of the selection process of verification groups, and eliminates the possibility of collusion and human manipulation in the verification process from the mechanism, which significantly improves the fairness, security and resource utilization efficiency of carbon data verification system.

[0047] Furthermore, this embodiment provides a step for obtaining a verification group list based on the verification node status and task attributes using a quantum consensus model, including: The initial quantum graph is obtained based on the state of the verification nodes using a quantum state initialization algorithm; Quantum potential energy configuration is obtained through potential energy optimization based on task attributes; Based on the initial quantum graph structure and quantum potential energy configuration, a list of verification groups is obtained through a quantum consensus model.

[0048] The quantum state initialization algorithm is a computational method that maps the state of verification nodes to a graph structure with a quantum superposition state distribution, used to construct an initial quantum graph capable of quantum walk evolution. The initial quantum graph is a graph model with verification nodes as vertices, professional similarity between verification nodes as edges, and each verification node is assigned a probability amplitude distribution of the initial quantum state. Potential energy optimization is a computational process that dynamically adjusts the potential energy value on each edge of the initial quantum graph according to the task attributes, used to construct a task-adaptive quantum potential energy landscape to guide the quantum walk path. The quantum potential energy configuration is the set of potential energy values ​​on all edges of the initial quantum graph, the value of which is jointly determined by the professional similarity between the verification node and its corresponding verification task, and the resource utilization rate of the verification node.

[0049] Specifically, such as Figure 3 As shown, firstly, based on the professional competence labels of all verification nodes in the verification node state, the professional similarity between any two verification nodes is calculated using a quantum state initialization algorithm. Edge connections are then established based on all professional similarities and a preset professional similarity threshold; that is, an edge connection is established only when the professional similarity is greater than the preset threshold. Each verification node is mapped to a quantum node in the quantum graph, and an initial quantum state is assigned to each verification node using the uniform superposition principle. , to obtain Each node and Initial quantum graph of the edge Then, based on the verification algorithm type requirements in the task attributes and the professional competence tags in the verification node status, the professional matching score between each verification node and the verification task is calculated using the potential energy optimization algorithm. Combined with the current resource utilization rate of the verification node and network topology information, the score for each edge is calculated. Potential energy value ,in These are the indices corresponding to the two different verification nodes that establish the edge connection. The lower the professional matching degree or the higher the node load, the greater the potential energy value of the corresponding edge, thus obtaining the complete quantum potential energy configuration. Finally, based on the initial quantum graph structure and quantum potential energy configuration, a quantum consensus model is used to project and measure the system's quantum state using continuous-time quantum walk evolution. A weighted random selection algorithm is then employed based on the measured probability distribution to determine the final selected combination of verification nodes, thus obtaining a list of verification groups. In the quantum consensus model of this embodiment, each verification node is mapped to a qubit, whose ground state... This indicates that the verification node was not selected, and the excited state... This indicates that a verification node has been selected, and the overall system state is the tensor product of all verification node qubits. The quantum circuit includes an initialization phase, an evolution phase, and a measurement phase. During the initialization phase, all qubits are in a uniform superposition state. , This indicates an initial equal probability selection. This represents the total number of verification nodes. This serves as the index number of the verification node in the initial quantum graph; the evolution phase employs a continuous-time quantum walk model, with evolution operators... Hamiltonian From the initial quantum diagram The adjacency matrix A and the quantum potential energy-based configuration Constructed diagonal potential matrix Build together For evolution time, The starting node number is used to define the edges in the potential energy matrix. Individual potential energy values ​​in the matrix are dynamically configured based on task attributes and the status of verification nodes. The measurement phase occurs during the evolution time. Then, the quantum state is projected and measured, and the list of verification groups is obtained by weighted random sampling algorithm based on the probability distribution.

[0050] This embodiment, based on complete verification node states and task attributes, first constructs a quantum graph structure that accurately reflects the capability relationships of verification nodes through quantum state initialization. Then, it achieves precise matching between task requirements and node capabilities through potential energy optimization. On this basis, it realizes node selection with both randomness and optimization characteristics through quantum walk evolution and subsequent quantum state measurement. The evolution process of quantum walks relies on fundamental properties such as quantum superposition and interference. Its inherent randomness stems from the no-cloning principle and measurement collapse behavior of quantum systems, belonging to true randomness at the physical level. This randomness is fundamentally different from traditional cryptographic pseudo-random numbers generated by deterministic algorithms and are essentially predictable. Thus, at the mechanism level, it significantly improves the anti-collusion capability, professional matching degree, and overall intelligence level of the verification group assignment process.

[0051] Furthermore, this embodiment provides a step for obtaining on-chain evidence records based on a verification group list using national cryptographic algorithms and a blockchain evidence storage contract, including: Based on the verification group list, raw carbon data and preset business rules, the group signature is obtained through the national cryptographic SM2 threshold signature algorithm and the national cryptographic SM3 hash algorithm; On-chain evidence records are obtained through a blockchain evidence storage contract based on group signatures.

[0052] Among them, the SM2 threshold signature algorithm is a threshold signature scheme based on elliptic curve cryptography approved by the State Cryptography Administration. It is used to share signature authority among multiple verification nodes in the verification group list. Through a distributed key generation and signature collaboration process, a valid signature can only be generated if a preset threshold number of verification nodes agree. The SM3 hash algorithm is a cryptographic hash algorithm approved by the State Cryptography Administration. It is used to perform hash operations on the original carbon data and preset business rules to generate a fixed-length data digest. A one-way hash function ensures data integrity and tamper-proof. The group signature is a digital signature jointly obtained by multiple verification nodes in the verification group list based on the SM2 threshold signature algorithm and the SM3 hash algorithm, indicating the consistent recognition of the verification result.

[0053] Specifically, firstly, based on the identifier of each verification node in the verification group list, the public key certificate and private key fragment of the corresponding verification node are obtained through the node management service. At the same time, based on the original carbon data and preset business rules, the data hash value is calculated using the national cryptographic SM3 hash algorithm. Then based on the data hash value The private key of each verification node is fragmented, and a distributed signature protocol is executed using the SM2 threshold signature algorithm, a national cryptographic standard. This protocol includes key negotiation, partial signature generation, and signature aggregation steps to obtain a complete group signature. Then based on group signature Data hash value The verification metadata is obtained through the input interface of the blockchain evidence storage contract. The verification metadata includes the verification task identifier and the verification node list. Finally, the evidence storage transaction is submitted to the blockchain network by calling the evidence storage method of the blockchain evidence storage contract. After consensus verification, an immutable on-chain evidence storage record is obtained. .

[0054] For example, this embodiment assumes a verification group list. The original carbon data is The preset business rules are The data hash value is calculated using the national cryptographic SM3 hash algorithm. Based on the nodes in the verification group list, distributed signatures are performed using the national cryptographic SM2 threshold signature algorithm, and finally the group signature is obtained by aggregation. Subsequently, the notarization transaction is recorded on the blockchain through a blockchain notarization contract, generating an on-chain notarization record. .

[0055] This embodiment uses the national cryptographic SM3 hash algorithm to reliably ensure data integrity, uses the national cryptographic SM2 threshold signature algorithm to achieve non-repudiation and resistance to single points of failure through multi-verification node collaborative signature, uses a blockchain evidence storage contract to automate and make the evidence storage process transparent, and achieves high-security evidence storage and tamper-proof traceability of carbon data verification results through a complete technical closed loop, thereby improving the anti-counterfeiting capability, audit credibility and system robustness of the carbon data evidence storage system.

[0056] Furthermore, such as Figure 4 As shown, this application provides a carbon data storage system combining blockchain and national cryptographic algorithms. The system includes a data traceability module, a confidence decision module, a quantum consensus module, and a national cryptographic storage module. Data traceability module: acquires raw carbon data and traceability data; based on the raw carbon data and traceability data, obtains business logic feature set and data variability through preset business rules and carbon stream traceability filtering; Confidence Decision Module: Based on the business logic feature set and data variability, verification instructions are obtained through a pattern recognition confidence model. The pattern recognition confidence model includes a feature engineering layer, a graph convolutional network aggregation layer, a multi-dimensional confidence generation layer, and a comprehensive decision layer. Quantum consensus module: Based on verification instructions and raw carbon data, a list of verification groups is obtained through a quantum consensus model; National Cryptographic Evidence Preservation Module: Based on the verification group list, on-chain evidence preservation records are obtained through national cryptographic algorithms and blockchain evidence preservation contracts.

[0057] The data tracing module, confidence decision module, quantum consensus module, and national cryptographic evidence storage module are all located on the server. The server receives data transmitted from the acquisition devices and performs further analysis. The acquisition devices include enterprise energy metering terminals, production process data collectors, data interfaces of third-party monitoring equipment, and blockchain light node gateways. The data tracing module is used to realize the whole-process trusted processing of carbon data from acquisition to feature extraction. It obtains a feature set reflecting data quality and business logic through carbon stream tracing filtering and business rule execution. The confidence decision module performs multi-dimensional credibility assessment based on the business logic feature set and data variability, through pattern recognition. The confidence model generates differentiated verification instructions; the quantum consensus module dynamically constructs anti-collusion verification groups based on the verification instructions and raw carbon data, and achieves random, fair, and professionally matched assignment of verification nodes through the quantum consensus model; the national cryptographic evidence storage module is used to cryptographically securely store the verification results of the verification groups, and achieves tamper-proof storage of verification evidence through national cryptographic algorithms and blockchain evidence storage contracts. When the blockchain evidence storage contract detects data anomalies or inconsistent verification results, the carbon data evidence storage system automatically triggers a re-verification process, sends a re-evaluation instruction to the confidence decision module, and updates the verification group list, forming a closed-loop control. The modules collaborate through standardized data interfaces and event-driven mechanisms. The output of the data traceability module serves as the input of the confidence decision module. The verification instructions generated by the confidence decision module trigger the node assignment process of the quantum consensus module. The verification group list output by the quantum consensus module is handed over to the national cryptographic evidence storage module for final evidence storage. This forms a complete closed loop from trusted data processing, intelligent decision-making, consensus assignment to secure evidence storage, and together constructs a carbon data evidence storage system with high reliability, anti-collusion, and traceability.

[0058] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0060] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for storing carbon data using blockchain and national cryptographic algorithms, characterized in that, include: Obtain raw carbon data and source data; Based on the original carbon data and source tracing data, a set of business logic features and data variability are obtained through preset business rules and carbon stream source tracing filtering. Based on the business logic feature set and data variability, verification instructions are obtained through a pattern recognition confidence model. The pattern recognition confidence model includes a feature engineering layer, a graph convolutional network aggregation layer, a multidimensional confidence generation layer, and a comprehensive decision layer. Based on the verification instructions and the original carbon data, a list of verification groups is obtained through a quantum consensus model. Based on the aforementioned verification group list, on-chain evidence records are obtained through national cryptographic algorithms and blockchain evidence storage contracts.

2. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 1, characterized in that, The process of obtaining a business logic feature set and data variability based on the original carbon data and source tracing data through preset business rules and carbon stream source tracing filtering includes: Based on the original carbon data and the source data, a first state vector is obtained through carbon flow source filtering. Based on the original carbon data and source tracing data, a second state vector and a business logic feature set are obtained through preset business rules; Based on the first state vector and the second state vector, the data variability is obtained by calculating the residual.

3. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 1, characterized in that, The verification instruction obtained based on the business logic feature set and data variability through a pattern recognition confidence model includes: Based on the business logic feature set and data variability, an initial feature vector is obtained through the feature engineering layer; Obtain an industry knowledge graph, and based on the industry knowledge graph and the initial feature vector, obtain a graph embedding vector through a graph convolutional network aggregation layer; Based on the initial feature vector and graph embedding vector, a confidence vector is obtained through a multi-dimensional confidence generation layer; Verification instructions are obtained through the comprehensive decision-making layer based on the confidence vector.

4. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 3, characterized in that, The process of obtaining an initial feature vector through a feature engineering layer based on the business logic feature set and data variability includes: Based on the business logic feature set and data variability, a normalized feature sequence is obtained through data standardization processing; Based on the normalized feature sequence, a composite feature group is obtained through feature cross-combination; Based on the composite feature set, an initial feature vector of standard dimensions is obtained through dimension alignment.

5. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 3, characterized in that, The process of obtaining graph embedding vectors through a graph convolutional network aggregation layer based on the industry knowledge graph and initial feature vectors includes: Based on the industry knowledge graph and initial feature vectors, the target enterprise node is determined through node localization. Based on the target enterprise node, the set of neighboring nodes of the target enterprise node is obtained through neighborhood sampling; Based on the set of neighboring nodes and the initial feature vector, a graph embedding vector is obtained through a graph convolutional neural network.

6. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 3, characterized in that, The process of obtaining a confidence vector through a multi-dimensional confidence generation layer based on the initial feature vector and graph embedding vector includes: Based on the initial feature vector and the graph embedding vector, a fused feature vector is obtained by vector concatenation; Based on the fused feature vector, a basic confidence score is obtained through a fully connected neural network; A multidimensional confidence vector is obtained based on the basic confidence score through a normalized exponential function.

7. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 1, characterized in that, The step of obtaining the verification group list based on the verification instructions and the original carbon data through a quantum consensus model includes: Based on the verification command, the verification node and its status are obtained through the node management service. Based on the verification instructions and the original carbon data, task attributes are obtained through task parsing; Based on the verification node status and task attributes, a verification group list is obtained through a quantum consensus model.

8. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 7, characterized in that, The verification group list, obtained through a quantum consensus model based on the verification node status and task attributes, includes: An initial quantum graph is obtained based on the state of the verification node using a quantum state initialization algorithm; Based on the aforementioned task attributes, the quantum potential energy configuration is obtained through potential energy optimization. Based on the initial quantum graph structure and quantum potential energy configuration, a list of verification groups is obtained through a quantum consensus model.

9. The carbon data storage method combining blockchain and national cryptographic algorithms according to claim 1, characterized in that, The on-chain evidence records obtained based on the verification group list through national cryptographic algorithms and blockchain evidence storage contracts include: Based on the verification group list, raw carbon data, and preset business rules, the group signature is obtained through the national cryptographic SM2 threshold signature algorithm and the national cryptographic SM3 hash algorithm; Based on the group signature, an on-chain evidence record is obtained through a blockchain evidence storage contract.

10. A carbon data storage system combining blockchain and national cryptographic algorithms, used to implement the carbon data storage method combining blockchain and national cryptographic algorithms as described in any one of claims 1-9, characterized in that, The carbon data storage system combining blockchain and national cryptographic algorithms includes: The data tracing module acquires raw carbon data and tracing data; based on the raw carbon data and tracing data, it obtains a business logic feature set and data variability through preset business rules and carbon stream tracing filtering. The confidence decision module obtains verification instructions based on the business logic feature set and data variability through a pattern recognition confidence model. The pattern recognition confidence model includes a feature engineering layer, a graph convolutional network aggregation layer, a multi-dimensional confidence generation layer, and a comprehensive decision layer. The quantum consensus module, based on the verification instructions and the original carbon data, obtains a list of verification groups through a quantum consensus model. The national cryptographic evidence storage module obtains on-chain evidence records based on the verification group list using national cryptographic algorithms and blockchain evidence storage contracts.

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