A dual-carbon intelligent data management method and system

Abstract

The present application relates to the field of data analysis technology and discloses a dual-carbon intelligent data management method and system, including: collecting dual-carbon data of multiple target groups; storing the dual-carbon data of each target group in corresponding data nodes according to a preset time window, and screening out key data nodes by voting between data nodes; uploading the corresponding dual-carbon data to the corresponding chain nodes of the first blockchain for storage according to the key data nodes of each target group, and establishing associations between the chain nodes of the first blockchain by analyzing the collaborative relationship between the target groups; dividing the first blockchain into different areas, and verifying the data upload process of each area of ​​the first blockchain by the second blockchain to store and manage the dual-carbon data of multiple target groups; the present application can improve the security and correctness of the dual-carbon data storage process.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a dual-carbon intelligent data management method and system. Background Art

[0002] At present, all industries are facing the need to manage dual-carbon data such as carbon emissions data and energy consumption data. The data types of dual-carbon data are diverse, the time span is long, and it involves multiple subjects. The existing data management methods have problems with low data storage efficiency and the inability to guarantee data security when processing dual-carbon data.

[0003] The existing technology has the following problems: it is impossible to classify, store and manage dual-carbon data according to data type and data time, and the data storage and analysis process cannot meet the real-time needs of data; the direct data upload and storage method is adopted, the upload process is complicated, and it cannot be guaranteed that the status of the uploaded data nodes can meet the data upload security requirements; the single data encryption method is difficult to ensure the security of data in multi-subject collaboration, and the association relationship between the target groups is ignored and the data is directly analyzed, resulting in low efficiency of the data analysis process; the single data storage process verification method has low efficiency of the data verification process and cannot guarantee the integrity and correctness of the data storage; in order to solve at least one of the above problems, the present invention proposes a dual-carbon intelligent data management method and system. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the main purpose of the present invention is to provide a dual-carbon intelligent data management method and system that can effectively solve the problems in the background technology. The specific technical solutions of the present invention are as follows:

[0005] A dual-carbon intelligent data management method, comprising:

[0006] Collect dual carbon data of multiple target groups;

[0007] The carbon-diverse data of each target group is stored in the corresponding data nodes according to the preset time window. Key data nodes are selected through voting among the data nodes, and data transmission is carried out between the data nodes.

[0008] According to the key data nodes of each target group, the corresponding dual-carbon data will be uploaded to the corresponding chain nodes of the first blockchain for storage. By analyzing the collaborative relationship between the target groups, the association will be established between the chain nodes of the first blockchain;

[0009] The first blockchain is divided into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups.

[0010] Specifically, the carbon and carbon data for each target group are stored in corresponding data nodes according to a preset time window, and key data nodes are screened out through voting among the data nodes, including:

[0011] For the dual-carbon data of each target group, by analyzing the data type of the dual-carbon data, data of different data types are stored in corresponding data nodes according to the preset time window;

[0012] The data nodes are grouped, key data nodes are screened out from each group of data nodes by voting, and the key data nodes are updated according to a preset time window.

[0013] Specifically, the data nodes are grouped, key data nodes are selected from each group of data nodes by voting, and the key data nodes are updated according to a preset time window, including:

[0014] According to the distance between data nodes and the similarity of corresponding data types, the data nodes are grouped to obtain multiple groups of data nodes;

[0015] In each group of data nodes, each data node votes among itself, and the data node with the most votes is selected as the first key data node;

[0016] When the first key data node fails, the data node with the second highest number of votes is selected from the highest to the lowest number of votes as the second key data node;

[0017] According to a preset time window, a voting screening is performed among the data nodes, and the first key data node or the second key data node is updated.

[0018] Specifically, according to the key data nodes of each target group, the corresponding dual-carbon data are uploaded to the corresponding chain nodes of the first blockchain for storage, and by analyzing the collaborative relationship between the target groups, associations are established between the chain nodes of the first blockchain, including:

[0019] According to the key data nodes of each target group, the carbon-dual data of the target group is encrypted in layers, and the encrypted carbon-dual data is uploaded to the corresponding chain node of the first blockchain for storage;

[0020] The degree of association is calculated by analyzing the collaborative relationship between the target groups, and the degree of association is used as the weight of the association edge to create an association edge between corresponding chain nodes in the first blockchain. The association edge is updated by updating the association edge weight or removing the association edge.

[0021] Specifically, the step of performing layered encryption on the carbon-dual data of the target group according to the key data nodes of each target group and uploading the encrypted carbon-dual data to the corresponding chain node of the first blockchain for storage includes:

[0022] Encrypting the dual-carbon data using a preset first encryption method to obtain first encrypted data;

[0023] Encrypting the first encrypted data using a preset second encryption method to obtain second encrypted data;

[0024] According to the key data nodes of each target group, the second encrypted data of the target group is uploaded to the corresponding chain nodes of the first blockchain for storage.

[0025] Specifically, the method includes calculating the association degree by analyzing the collaborative relationship between the target groups, creating an association edge between corresponding chain nodes in the first blockchain using the association degree as the weight of the association edge, and updating the association edge by updating the association edge weight or removing the association edge, including:

[0026] Analyze the collaborative relationship between target groups through the preset association weight analysis model and calculate the association degree between target groups;

[0027] Filtering out the target groups whose correlation degree is greater than a preset correlation degree threshold as the first target group set;

[0028] In the first target group set, establishing association edges between corresponding target groups according to the association degrees between the target groups, and using the association degrees as weights of the association edges;

[0029] Analyze the collaborative relationships between target groups through the preset collaborative relationship analysis model and predict collaborative changes;

[0030] According to the collaboration change, the associated edge weight is updated or the associated edge is removed, and the associated edge is updated.

[0031] Specifically, the first blockchain is divided into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups, including:

[0032] Dividing the first blockchain into different regions based on the connection status of the associated edges between the chain nodes in the first blockchain;

[0033] In each region, the second blockchain verifies the data upload process of each chain node in the first blockchain to store and manage the dual-carbon data of multiple target groups.

[0034] Specifically, the first blockchain is divided into different areas according to the connection status of the associated edges between the chain nodes in the first blockchain, including:

[0035] Extracting chain node association features based on the association edge connections between chain nodes in the first blockchain;

[0036] Based on the chain node association characteristics, the first blockchain is divided into multiple connected subgraphs, each connected subgraph is regarded as a region, and multiple regions are obtained.

[0037] Specifically, within each region, the second blockchain verifies the data upload process of each chain node in the first blockchain to store and manage the dual-carbon data of multiple target groups, including:

[0038] In each region, a corresponding set of supervisory nodes is allocated in the second blockchain based on the connection status of the chain nodes;

[0039] For each chain node in the regulatory node set, the regional verification result is obtained by analyzing the correctness of the chain node data upload process in the corresponding region;

[0040] According to the regional verification results, the chain nodes with abnormal data upload processes in the corresponding region are screened out to obtain abnormal chain nodes;

[0041] The data stored in the abnormal chain nodes are verified and updated to store and manage the dual-carbon data of multiple target groups.

[0042] A dual-carbon intelligent data management system, used to implement the dual-carbon intelligent data management method, comprising:

[0043] The dual-carbon data collection module collects dual-carbon data of multiple target groups;

[0044] The data node screening module stores the carbon-dioxide data of each target group in the corresponding data nodes according to the preset time window, and selects key data nodes through voting among the data nodes, wherein data transmission is carried out between the data nodes;

[0045] The dual-carbon data upload module uploads the corresponding dual-carbon data to the corresponding chain nodes of the first blockchain for storage based on the key data nodes of each target group. By analyzing the collaborative relationship between the target groups, it establishes associations between the chain nodes of the first blockchain;

[0046] The dual-carbon data verification module divides the first blockchain into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups.

[0047] Compared with the prior art, this application has the following beneficial effects:

[0048] This application stores dual-carbon data in different data nodes according to data type and time window, votes among data nodes to screen key data nodes, performs double-layer encryption on the dual-carbon data through key data nodes, and uploads it to the chain node corresponding to the first blockchain for storage. In combination with the collaborative relationship between target groups, the associated edges between chain nodes are established, the first blockchain is divided into multiple connected subgraph areas, and the data upload process of each area is verified by the second blockchain; through data classification storage and key node screening, invalid data transmission can be reduced and the data upload storage speed can be improved. Combined with layered encryption processing and target group collaborative relationship analysis, the security and correctness of the data storage process can be improved. The data storage process of the first blockchain is verified by the second blockchain in the dual-chain structure, thereby improving the integrity and verification efficiency of the data storage process. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 This is a workflow diagram of a dual-carbon intelligent data management method in Example 1 of the present invention;

[0050] Figure 2 Schematic diagram of the first key data node screening process in Example 1 of the present invention;

[0051] Figure 3 This is a schematic diagram of the first blockchain construction process in Example 1 of the present invention;

[0052] Figure 4 This is a schematic diagram of the second blockchain verifying the first blockchain in Example 1 of the present invention;

[0053] Figure 5 This is a structural diagram of a dual-carbon intelligent data management system in Example 2 of the present invention. DETAILED DESCRIPTION

[0054] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0057] Example 1:

[0058] This embodiment provides a dual-carbon intelligent data management method, such as Figure 1 As shown, the dual-carbon intelligent data management method includes:

[0059] S101. Collecting carbon and carbon data of multiple target groups;

[0060] S102: For each target group, the carbon and carbon data are stored in corresponding data nodes according to a preset time window, and key data nodes are selected through voting among the data nodes, wherein data transmission is performed between the data nodes;

[0061] S103. Upload the corresponding dual-carbon data to the corresponding chain nodes of the first blockchain for storage based on the key data nodes of each target group. Establish associations between the chain nodes of the first blockchain by analyzing the collaborative relationships between the target groups.

[0062] S104: Divide the first blockchain into different areas, and use the second blockchain to verify the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups.

[0063] Dual-carbon data is a full-dimensional, multi-level data set. The data types are diverse, the time span is long, and the subjects involved are wide. It is relatively complex and specifically includes carbon emission data, energy data, environmental data, etc. When storing and managing dual-carbon data, it is necessary to verify the data integrity and correctness during the storage process of dual-carbon data.

[0064] In this embodiment, first, data collection equipment is used to collect dual-carbon data of multiple target groups, and the collected data is pre-processed by cleaning, filtering, and standardization to obtain pre-processed dual-carbon data. Then, for the dual-carbon data of each target group, the data is stored in the corresponding data node according to a preset time window. Key data nodes that can reflect the characteristics of the dual-carbon data of the target group are screened out through voting among the data nodes. The key data nodes summarize the dual-carbon data of the target group and upload them to the corresponding chain nodes of the first blockchain for storage. At the same time, the collaborative relationship between the target groups is analyzed, and association edges are constructed between the corresponding chain nodes of the first blockchain based on the collaborative relationship. By analyzing the association relationship between the chain nodes, the first blockchain is divided into different areas. The data upload process of each area of ​​the first blockchain is verified in real time using the second blockchain. Abnormal chain nodes with abnormal data upload processes in the first blockchain are screened out, and the data of the abnormal chain nodes are corrected to ensure the security and accuracy of the dual-carbon data and meet the requirements for data authenticity in the formulation of corresponding dual-carbon governance measures and the supervision process.

[0065] In this embodiment, for each target group, various data collection equipment and sensors deployed in the target group's work area are used to collect data such as energy consumption, raw material usage, product output, and waste gas emissions during the production process of the target group. The collected raw data are pre-processed by cleaning, filtering, and standardization to obtain pre-processed dual-carbon data. By collecting dual-carbon data from different target groups, data support is provided for data analysis and management, reflecting the carbon emissions and carbon absorption of the target group.

[0066] Specifically, the dual-carbon data of each target group is divided according to a preset time window, and the data in each time window is stored in one or more corresponding data nodes. Data transmission and communication can be carried out between the data nodes of each target group. The data nodes use a voting mechanism and combine the importance, integrity, real-time performance and other indicators of the data in the data nodes to screen out the most representative key data nodes. The dual-carbon data of the target group are summarized in the key data nodes and uploaded to the corresponding blockchain nodes for storage; by classifying and storing data through time windows, data in different time periods can be managed to improve data retrieval and processing efficiency. By voting between data nodes to screen out key data nodes, the security and stability of the data upload and storage process can be improved.

[0067] Preferably, according to the key data nodes of each target group, the corresponding dual-carbon data are uploaded to the corresponding chain nodes of the first blockchain for storage, and by analyzing the collaborative relationship between the target groups, an association is established between the chain nodes of the first blockchain; there will be collaborative relationships between the target groups in terms of business, region, industry, etc., and according to the impact of the collaborative relationship on the carbon emission data, an association is established between the corresponding chain nodes in the first blockchain to obtain a chain node network; by using the blockchain to store dual-carbon data, the data is guaranteed to be tamper-proof and traceable, and the credibility and security of the data storage process are improved. By establishing associations between chain nodes, the dual-carbon data between target groups can be collaboratively analyzed, which promotes cross-group collaborative emission reduction and optimal resource allocation, and can formulate more efficient emission reduction plans.

[0068] Specifically, the first blockchain is divided into different areas according to the association relationship between chain nodes, and the data upload process of each area of ​​the first blockchain is verified through the second blockchain to store and manage the dual-carbon data of multiple target groups; the first blockchain is divided into different areas according to the regional division rules, and each area is responsible for managing the dual-carbon data within the corresponding range, constructing a dual-chain structure blockchain, and using the second blockchain as the verification chain to perform real-time verification on the data upload process of each area of ​​the first blockchain, and verify the accuracy and completeness of the dual-carbon data storage process; by dividing the first blockchain into areas, data of different types and ranges can be classified and managed, improving data retrieval and processing efficiency, and using the second blockchain to verify the data upload process in the first blockchain can ensure the accuracy and completeness of the data uploaded to the first blockchain, thereby improving the quality of dual-carbon data.

[0069] This application stores dual-carbon data in different data nodes according to data type and time window, votes among data nodes to screen key data nodes, performs double-layer encryption on the dual-carbon data through key data nodes, and uploads it to the chain node corresponding to the first blockchain for storage. In combination with the collaborative relationship between target groups, the associated edges between chain nodes are established, the first blockchain is divided into multiple connected subgraph areas, and the data upload process of each area is verified by the second blockchain; through data classification storage and key node screening, invalid data transmission can be reduced and the data upload storage speed can be improved. Combined with layered encryption processing and target group collaborative relationship analysis, the security and correctness of the data storage process can be improved. The data storage process of the first blockchain is verified by the second blockchain in the dual-chain structure, thereby improving the integrity and verification efficiency of the data storage process.

[0070] Furthermore, the carbon and carbon data for each target group are stored in corresponding data nodes according to a preset time window, and key data nodes are screened out through voting among the data nodes, including:

[0071] S201: For each target group's carbon and carbon data, by analyzing the data type of the carbon and carbon data, store data of different data types in corresponding data nodes according to a preset time window;

[0072] S202: Group the data nodes, select key data nodes from each group of data nodes by voting, and update the key data nodes according to a preset time window.

[0073] This embodiment analyzes the data type of the dual-carbon data of each target group, stores data of different data types in different data nodes within a preset time window, classifies and stores the dual-carbon data to improve data storage efficiency, and screens key data nodes from the data node group through grouping and node voting among data nodes, and ensures the timeliness of key data nodes through regular updates; through data classification storage and key data node screening and updating, the efficiency and orderliness of the dual-carbon data management process are improved, and the efficiency of data storage management is improved.

[0074] In this embodiment, for the dual-carbon data of each target group, the data type of the dual-carbon data is analyzed, and data of different data types are stored in corresponding data nodes according to preset time windows. Different types of dual-carbon data differ in data characteristics, change frequency, and importance. The data type of the dual-carbon data is analyzed, and different types of dual-carbon data are stored in corresponding data nodes according to preset time windows. The dual-carbon data collected from each target group are analyzed to determine the corresponding data type, which specifically includes energy consumption data, carbon emission data, carbon sink data, equipment operation data, etc.; according to the data change frequency of different data types, a corresponding time window is set for each data type. For energy consumption data, which is continuously consumed during the production process, an hourly time window is set; for carbon sink data, since vegetation growth and carbon absorption processes are relatively slow, a monthly time window is set; according to the corresponding time window, each type of data is uploaded to the corresponding data node; through classified uploading and updating according to time windows, corresponding data storage management is performed for different types of dual-carbon data, avoiding the waste of resources caused by using a unified storage method for all data, making the data storage process more in line with actual needs.

[0075] Specifically, after data is uploaded to the corresponding data nodes, the data nodes are grouped. Key data nodes are selected from each group through voting, and these key data nodes are updated according to a preset time window. By selecting key data nodes from each group through voting, representative and important data nodes can be selected from a large number of data nodes. At the same time, key data nodes are updated according to the preset time window to ensure that they always have the latest data information. By grouping and selecting key data nodes, data can be centrally managed and scheduled, improving the efficiency and flexibility of data management.

[0076] Furthermore, the data nodes are grouped, key data nodes are selected from each group of data nodes by voting, and the key data nodes are updated according to a preset time window, including:

[0077] S301, grouping the data nodes according to the distances between the data nodes and the similarities of the corresponding data types to obtain multiple groups of data nodes;

[0078] S302: In each group of data nodes, each data node votes among itself, and the data node with the most votes is selected as the first key data node;

[0079] S303: When the first key data node fails, select the data node with the second highest number of votes as the second key data node according to the number of votes received.

[0080] S304: Perform a voting screening among the data nodes according to a preset time window, and update the first key data node or the second key data node.

[0081] In this embodiment, data nodes are grouped according to the distance between data nodes and the similarity of corresponding data types to obtain multiple groups of data nodes. The distance between data nodes and the similarity of corresponding data types reflect the correlation between data. In combination with the node distance and the similarity of data types, data nodes with similar characteristics and associations can be grouped together. The node distance is quantified by calculating the physical distance between data nodes. For the data types stored in the data nodes, the type characteristics of the data stored in the data nodes are extracted, including electricity, coal, natural gas, etc.; the similarity between the data types corresponding to different data nodes is calculated by calculating the cosine similarity between the type characteristics; in combination with the node distance and the similarity of data types, the data nodes are clustered using a clustering algorithm to obtain multiple groups of data nodes; by grouping data nodes with similar characteristics, a corresponding management method can be formulated for each group of data, thereby improving the pertinence and effectiveness of data management.

[0082] like Figure 2As shown, in each group of data nodes, the data nodes are evaluated based on factors such as the quality and importance of the data stored in the data nodes, and voting is conducted among each data node. The data node with the most votes is selected as the first key data node; Figure 2 Data nodes B, D, and E all vote for data node A, and the highest number of votes for data node A is 3. At this time, data node A serves as the first key data node; voting rules are set. Based on indicators such as data completeness, accuracy, timeliness, and importance to dual-carbon analysis, in each group of data nodes, each data node evaluates and scores other data nodes in the group according to the voting rules. After the scoring is completed, each data node votes on the data node based on the scoring results and votes for the data node with the highest score; based on the scoring results of each data node, the number of votes for each data node is counted, and the data node with the most votes is regarded as the first key data node of the group. When the number of votes is the same, the first key data node is determined by comparing the earlier collection time of the data nodes or the priority of the data source; through mutual voting between data nodes in the group, the first key data node that best represents the data characteristics of the group can be screened out from a group of data nodes, avoiding the processing of all data nodes in the group, reducing the workload of data processing, and improving data utilization efficiency.

[0083] Specifically, when the first key data node fails, the data node with the second highest number of votes is selected from high to low according to the number of votes as the second key data node; when the first key data node has a storage device failure, data transmission error, data anomaly, etc., the data node fails and data transmission cannot be performed. At this time, the data node with the second highest number of votes is selected from high to low according to the number of votes as the second key data node. When the data node with the second highest number of votes also fails, the data node with the third highest number of votes is selected, and so on, until a valid data node is found as the second key data node; when the first key data node fails, quickly finding a replacement data node can ensure the continuity of data processing and analysis work, avoid data loss due to the failure of key data nodes, improve the system's emergency processing capabilities, and enhance the security and reliability of data management.

[0084] Preferably, according to a preset time window, a voting screening is performed between the data nodes, and the first key data node or the second key data node is updated; the time window is set according to the change frequency of the dual-carbon data of the target group and the data management requirements. In this embodiment, the time window is set to 24 hours, and a data node vote is performed within the data node group every 24 hours. Based on the latest number of votes, a new key data node is determined, and the first key data node or the second key data node is updated; by regularly updating the key data nodes, the key data can be dynamically updated, and the most representative key data nodes are always selected for the transmission of dual-carbon data, thereby ensuring the effectiveness and representativeness of the key data nodes and improving the adaptability and flexibility of the data management system.

[0085] Furthermore, according to the key data nodes of each target group, the corresponding dual-carbon data are uploaded to the corresponding chain nodes of the first blockchain for storage, and by analyzing the collaborative relationship between the target groups, associations are established between the chain nodes of the first blockchain, including:

[0086] S401. Perform layered encryption on the carbon-dioxide data of each target group based on the key data nodes of each target group, and upload the encrypted carbon-dioxide data to the corresponding chain node of the first blockchain for storage;

[0087] S402: Calculate the degree of association by analyzing the collaborative relationship between the target groups, use the degree of association as the weight of the association edge, create an association edge between the corresponding chain nodes in the first blockchain, and update the association edge by updating the association edge weight or removing the association edge.

[0088] This embodiment performs layered encryption based on the dual-carbon data in the key data nodes of each target group by analyzing the sensitivity and importance of the data. Through the blockchain client, the key data nodes upload the encrypted data to the corresponding chain nodes of the first blockchain, and after verification by the consensus mechanism, the encrypted data is stored in the blockchain. Based on the collaborative relationship data between the target groups, the correlation value between the target groups is calculated using a correlation calculation model. The correlation calculation model is specifically a neural network model. The neural network model is trained using a large amount of historical collaborative relationship data to obtain a pre-trained neural network model. The collaborative relationship data between the target groups is input into the pre-trained neural network model to calculate the correlation value between the target groups. Based on the correlation value, correlation edges are created between the corresponding chain nodes in the first blockchain. Based on the change in the correlation value, the weight of the correlation edge is updated or removed, and the correlation between the blockchain nodes is updated. Through layered encryption and blockchain node correlation edge construction, the security of dual-carbon data during transmission, storage, and use can be improved, data leakage and tampering can be prevented, and the strict data security requirements of the dual-carbon field can be met. Based on the collaborative relationship between the target groups, the association between chain nodes is established, the management of dual-carbon data is strengthened, the timeliness and accuracy of the data are guaranteed, and the efficiency and flexibility of data management are improved.

[0089] In this embodiment, the dual-carbon data of the target group is subjected to layered encryption processing according to the key data nodes of each target group, and the encrypted dual-carbon data is uploaded to the chain node corresponding to the first blockchain for storage; the layered encryption processing encrypts the dual-carbon data through multiple encryption layers, and different encryption layers correspond to different data security requirements and access rights, thereby improving the security of data during transmission and storage, and uploading the encrypted data to the chain node of the first blockchain for storage. By utilizing the decentralized and tamper-proof characteristics of the blockchain, the security of the dual-carbon data can be further guaranteed to prevent the data from being maliciously tampered with or leaked.

[0090] Specifically, the correlation degree is calculated by analyzing the collaborative relationship between the target groups, and the correlation degree is used as the weight of the correlation edge to create an association edge between the corresponding chain nodes in the first blockchain, and the association edge is updated by updating the association edge weight or removing the association edge; establishing chain node association based on the correlation degree can reflect the collaborative relationship between the target groups, and combining the collaborative relationship between the groups to verify the correlation and data correctness of the dual-carbon data, which can improve the accuracy of the data.

[0091] Furthermore, the step of performing layered encryption on the carbon-dual data of the target group according to the key data nodes of each target group and uploading the encrypted carbon-dual data to the corresponding chain node of the first blockchain for storage includes:

[0092] S501, encrypting the dual-carbon data using a preset first encryption method to obtain first encrypted data;

[0093] S502: Encrypt the first encrypted data using a preset second encryption method to obtain second encrypted data;

[0094] S503. According to the key data nodes of each target group, the second encrypted data of the target group is uploaded to the corresponding chain nodes of the first blockchain for storage.

[0095] In this embodiment, first, the dual-carbon data is encrypted by a preset first encryption method to obtain first encrypted data; the first encryption method selects an algorithm with high encryption efficiency and low computing resource consumption to quickly convert the original dual-carbon data into ciphertext form, preliminarily ensuring the security of the data during transmission and storage, and preventing the data from being stolen and interpreted; the first encryption method in this embodiment selects the AES algorithm in the symmetric encryption algorithm, sets the corresponding encryption key, encrypts the dual-carbon data by the AES algorithm, and converts the dual-carbon data into first encrypted data; the first encryption method can quickly encrypt the dual-carbon data, meet the real-time requirements of the data encryption process, and does not affect the timeliness of data upload.

[0096] Specifically, the first encrypted data is encrypted by a preset second encryption method to obtain second encrypted data; on the basis of the first encrypted data, the second encryption method is used to perform secondary encryption to further enhance the security of the data, and the second encryption method selects an algorithm with higher security and greater encryption strength; the second encryption method of this embodiment selects the RSA algorithm in the asymmetric encryption algorithm, and according to the generated public key and private key, the first encrypted data is re-encrypted by the RSA algorithm to obtain the second encrypted data. Double encryption can improve the security of the data transmission process, effectively prevent network attacks and data theft, and ensure the confidentiality and security of data during storage and transmission.

[0097] Preferably, according to the key data nodes of each target group, the second encrypted data of the target group is uploaded to the chain node corresponding to the first blockchain for storage; the encrypted data of each target group is aggregated into the key data node of each target group, and the second encrypted data that has been doubly encrypted is uploaded to the chain node corresponding to the first blockchain for storage through the key data node; combining blockchain with double encryption, the security of the dual-carbon data storage process is improved, and the use of key data nodes for data upload can ensure the integrity of the data upload process.

[0098] Furthermore, the step of calculating the degree of association by analyzing the collaborative relationship between the target groups, creating an associated edge between corresponding chain nodes in the first blockchain using the degree of association as the weight of the associated edge, and updating the associated edge by updating the associated edge weight or removing the associated edge includes:

[0099] S601: Analyze the collaborative relationship between target groups using a preset association weight analysis model to calculate the association degree between target groups;

[0100] S602: Filter out the target groups whose correlation is greater than a preset correlation threshold as a first target group set;

[0101] S603: In the first target group set, establishing association edges between corresponding target groups according to the association degrees between the target groups, and using the association degrees as weights of the association edges;

[0102] S604: Analyze the collaborative relationships between target groups using a preset collaborative relationship analysis model to predict collaborative changes.

[0103] S605: Update the associated edge weight or remove the associated edge according to the collaboration change, and update the associated edge.

[0104] In this embodiment, first, the collaborative relationship between the target groups is analyzed through a preset association weight analysis model to calculate the correlation between the target groups; indicators that affect the closeness of the collaborative relationship between the target groups are determined, including material supply frequency, number of joint R&D projects, etc., and the collaborative relationship between the target groups is analyzed based on the set collaborative relationship elements. The association weight analysis model of this embodiment includes but is not limited to a weighted model, which sets a corresponding weight for each collaborative relationship element. The correlation between each two target groups is calculated through the weighted model to obtain the correlation data between the target groups; by calculating the correlation, the target group collaborative relationship can be quantified to provide data support for constructing association edges.

[0105] Specifically, based on the calculated correlation between the target groups, target groups with a correlation greater than a preset correlation threshold are screened out as the first target group set; based on the dual-carbon data storage accuracy requirements and data distribution, a correlation threshold is set. In this embodiment, the correlation threshold is set to 0.7, all target groups are traversed, and the correlation between each target group and other target groups is compared with the set correlation threshold. Target groups with a correlation greater than the correlation threshold are screened out to form the first target group set; through correlation screening, target groups with close collaborative relationships can be quickly screened out, so that data collaborative management and analysis are concentrated between target groups with strong correlations, avoiding the waste of resources by analyzing all correlations.

[0106] like Figure 3 As shown, in the first target group set, according to the correlation between the target groups, the correlation is used as the weight of the correlation edge to establish correlation edges between the corresponding target groups; in the first blockchain, according to the chain nodes corresponding to each target group in the first target group set, the correlation between the target groups is used, and the correlation value is used as the weight of the correlation edge to establish correlation edges between the corresponding chain nodes; by establishing correlation edges and weights in the blockchain, the collaborative relationship between the target groups can be reflected, the connection and closeness between the target groups can be combined in data management and analysis, and multiple target groups can be combined for corresponding management when storing and verifying data.

[0107] Specifically, the collaborative relationship between the target groups is analyzed through a preset collaborative relationship analysis model to predict collaborative changes. The collaborative relationship analysis model includes models such as decision trees and neural networks. The collaborative relationship analysis model in this embodiment selects a neural network model, and uses a large amount of historical data to train the neural network model to obtain a pre-trained neural network model. The collaborative relationship data of the current target group is input into the pre-trained neural network model, and the model outputs the predicted collaborative changes between the target groups, specifically including changes in correlation, changes in collaborative relationships, etc. By predicting changes in collaborative relationships in advance, the association relationships between blockchain nodes can be updated in a timely manner to ensure that the association edges in the blockchain can reflect changes in actual collaborative relationships in a timely manner, thereby planning data collaborative management methods in advance.

[0108] Preferably, according to the collaboration changes, the associated edge weights are updated or the associated edges are removed, and the associated edges are updated so that the associated edges in the blockchain are always consistent with the actual collaboration relationship of the target groups; the content and degree of changes in the collaboration relationship between each target group are analyzed according to the collaboration changes, and when the collaboration change is a change in the correlation degree, the weight of the corresponding associated edge is updated according to the changed correlation degree value; when the collaboration change indicates that the cooperative relationship between the target groups has terminated, the associated edges between the corresponding chain nodes are deleted in the blockchain; by timely updating the associated edges, the problem of inconsistent data associations caused by changes in the collaborative relationship can be avoided, ensuring that the collaborative relationship data between the chain nodes in the blockchain is consistent with the actual collaborative relationship, and improving data quality.

[0109] Furthermore, the first blockchain is divided into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups, including:

[0110] S701. Divide the first blockchain into different regions based on the connection status of the associated edges between the chain nodes in the first blockchain;

[0111] S702. In each region, the second blockchain verifies the data upload process of each chain node in the first blockchain to store and manage the dual-carbon data of multiple target groups.

[0112] like Figure 4 As shown, first, the associated edge connections between the chain nodes in the first blockchain are analyzed, and the first blockchain is divided into different areas. Each area contains a group of chain nodes with data association. Then, a dual-chain structure blockchain is constructed, and a second blockchain architecture is built, including deploying nodes, setting up a consensus mechanism and corresponding verification rules; the chain nodes of the second blockchain are responsible for verifying the data upload process of the chain nodes in each area of ​​the first blockchain, screening out abnormal chain nodes and updating the data in the abnormal chain nodes; by partitioning the first blockchain and combining the second blockchain to verify the data upload process of each area in the first blockchain, the data quality in each area can be guaranteed.

[0113] In this embodiment, the first blockchain is divided into different areas according to the associated edge connections between chain nodes in the first blockchain; the areas are divided according to the associated edge connections, and chain nodes with close collaborative relationships and strong data correlations are divided into the same area. After the regional division, the data is classified and stored according to the collaborative relationships and data correlations of the target groups. When performing data management and verification, the corresponding area can be directly located, which reduces the data search range and improves data verification efficiency.

[0114] Specifically, in each region, the data upload process of each chain node in the first blockchain is verified through the second blockchain; by building a blockchain with a dual-chain structure, the second blockchain is used as an independent verification chain to verify the data upload process of the chain nodes in each region of the first blockchain. By checking the correctness and completeness of the data upload process, the correctness and security of the data upload storage process are guaranteed, thereby ensuring the quality and credibility of the dual-carbon data.

[0115] Furthermore, the first blockchain is divided into different areas according to the connection status of the associated edges between the chain nodes in the first blockchain, including:

[0116] S801. Extracting node association features based on the association edge connections between the chain nodes in the first blockchain;

[0117] S802: Based on the chain node association characteristics, divide the first blockchain into multiple connected subgraphs, each connected subgraph as a region, to obtain multiple regions.

[0118] In this embodiment, first, based on the connection status of the associated edges between the chain nodes in the first blockchain, the chain node association features are extracted. The chain node association features specifically include features such as the tightness of the connection between the chain nodes, the interaction frequency, and the position information in the entire blockchain network; by traversing all chain nodes in the first blockchain, the associated edge data between each chain node and other chain nodes are obtained, and the chain node association features are obtained by calculating the node degree, the sum of the associated edge weights, the clustering coefficient, etc. of the chain nodes. The extracted chain node association features are combined to obtain a chain node association feature data set; by calculating the chain node association features, the abstract association relationship between the chain nodes is quantified, providing data support for blockchain partitioning.

[0119] Specifically, based on the calculated chain node association characteristics, the first blockchain is divided into multiple connected subgraphs, each connected subgraph is regarded as a region, and multiple regions are obtained; the chain node association characteristics are analyzed by a graph segmentation algorithm, and the first blockchain is partitioned. The graph segmentation algorithm in this embodiment specifically selects the K-Cut algorithm, and the calculated chain node association characteristics and the graph structure composed of the first blockchain chain nodes and associated edges are input into the K-Cut algorithm. The algorithm divides the blockchain network according to the association strength and feature similarity between the chain nodes, and divides it into multiple connected subgraphs, and regards each connected subgraph as a region; by dividing the blockchain into regions, dual-carbon data can be stored and managed according to regions. The chain nodes in each region have a close association relationship, and corresponding data collaboration strategies and data verification mechanisms can be formulated for each region to improve data collaboration efficiency and effectiveness.

[0120] Furthermore, within each region, the second blockchain verifies the data upload process of each chain node in the first blockchain to store and manage the dual-carbon data of multiple target groups, including:

[0121] S901. Allocate a corresponding set of supervisory nodes in the second blockchain based on the connection status of the chain nodes in each region;

[0122] S902. For each chain node in the supervisory node set, obtain a regional verification result by analyzing the correctness of the chain node data upload process in the corresponding region;

[0123] S903. Filter out chain nodes with abnormal data upload processes in the corresponding region based on the region verification result to obtain abnormal chain nodes;

[0124] S904: Verify and update the data stored in the abnormal chain node to store and manage the dual-carbon data of multiple target groups.

[0125] In this embodiment, a corresponding set of supervisory nodes is allocated in the second blockchain based on the connection status of the chain nodes in each region of the first blockchain; for each region of the first blockchain, the connection relationship between the chain nodes in the region is analyzed, including information such as the number of associated edges between nodes, associated edge weights, and node degrees, and a supervisory node allocation strategy is set based on the connection status of the chain nodes in the region and the node resources of the second blockchain; this embodiment allocates supervisory nodes based on connection density, allocating one group of supervisory nodes to densely connected sub-regions within the region and another group of supervisory nodes to sparsely connected sub-regions; this enables the supervisory nodes to cover all chain nodes in the region, thereby improving verification efficiency; in the second blockchain, corresponding supervisory nodes are allocated to each region from the available node resources; supervisory nodes are allocated based on the connection status of the chain nodes in the region, so that the supervision work meets the data verification needs of each region, and focuses on key data nodes and areas with frequent data interactions, thereby improving the pertinence and effectiveness of data verification work.

[0126] Specifically, for each chain node in the second blockchain regulatory node set, the regional verification result is obtained by analyzing the correctness of the data upload process of the chain node in the corresponding first blockchain area; each chain node in the second blockchain regulatory node set analyzes the data upload process of the chain node in the corresponding area of ​​the first blockchain, and verifies the data by checking whether the sender's digital signature is in the authorization list, whether the hash value in the data transmission process is consistent, whether the data format is correct, and other factors to obtain a verification result; each regulatory node set summarizes the verification results to obtain a node verification report, and the nodes in the regulatory node set integrate the verification reports of each node through a consensus mechanism to finally obtain the regional verification result of the data upload process of the corresponding area; by verifying the data upload process of each area, the security and accuracy of the data upload and storage process can be improved.

[0127] Preferably, according to the regional verification results, the chain nodes with abnormal data upload processes in the corresponding area are screened out to obtain abnormal chain nodes, and the data stored in the abnormal chain nodes are verified and updated; by analyzing the regional verification results, the chain nodes with problems in the data upload process are extracted as abnormal chain nodes, and the error information of the abnormal chain nodes is analyzed, including the error type, error data record, etc., the abnormal data in the abnormal chain nodes are re-uploaded, and the newly uploaded data is verified by the supervision node of the second blockchain. When the data verification passes, the new data is updated to the abnormal chain node of the first blockchain to replace the erroneous data; when the data verification fails, the data is re-uploaded until the data verification passes and the update is completed; by timely screening of abnormal chain nodes, the spread and use of erroneous data in the first blockchain is prevented, and the security and reliability of other normal data in the blockchain are protected; by verifying and updating the abnormal data, the accuracy and reliability of the dual-carbon data stored in the first blockchain are ensured, and the availability and accuracy of the dual-carbon data are improved.

[0128] Example 2:

[0129] In this embodiment, if Figure 5 , provides a dual-carbon intelligent data management system for implementing the dual-carbon intelligent data management method, comprising:

[0130] The dual-carbon data collection module collects dual-carbon data of multiple target groups;

[0131] The data node screening module stores the carbon-dioxide data of each target group in the corresponding data nodes according to the preset time window, and selects key data nodes through voting among the data nodes, wherein data transmission is carried out between the data nodes;

[0132] The dual-carbon data upload module uploads the corresponding dual-carbon data to the corresponding chain nodes of the first blockchain for storage based on the key data nodes of each target group. By analyzing the collaborative relationship between the target groups, it establishes associations between the chain nodes of the first blockchain;

[0133] The dual-carbon data verification module divides the first blockchain into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups.

[0134] In this embodiment, the dual-carbon data acquisition module collects the dual-carbon data of the target groups through data acquisition equipment, sensors and other equipment set in multiple target groups. The dual-carbon data specifically includes energy consumption, carbon emissions, carbon sink data and other data. The collected data is pre-processed such as data cleaning to realize the collection of dual-carbon data of multiple target groups, providing an accurate data basis for the analysis and management of dual-carbon data; the data node screening module stores the dual-carbon data of each target group in the corresponding data node according to the preset time window. The data nodes vote with each other according to the preset voting rules, and the data node with the most votes is screened from each group of data nodes as the key data node. When the key data node fails, the replacement node is selected from the highest to the lowest number of votes; by screening the key data nodes, the most representative and valuable data nodes can be selected, and the key data nodes are used to upload the dual-carbon data to the blockchain, which can improve the accuracy of the data upload process and improve the quality of the data stored in the blockchain.

[0135] Specifically, the dual-carbon data upload module performs layered encryption processing on the dual-carbon data according to the key data nodes of each target group to improve the security of the data, uploads the encrypted data to the chain node corresponding to the first blockchain for storage, analyzes the collaborative relationship between the target groups through the preset association weight analysis model, calculates the association degree, and establishes association edges between the chain nodes with the association degree as the weight; the security of the dual-carbon data upload and storage process can be enhanced by encrypting the data, building the association relationship of the chain nodes, and verifying the correctness and integrity of the dual-carbon data in combination with the association between the chain nodes, which can improve the efficiency of data verification; the dual-carbon data verification module is based on the association edges of the chain nodes in the first blockchain. The first blockchain is divided into different areas according to the connection status. In each area, the corresponding set of supervisory nodes is matched in the second blockchain according to the connection status of the chain nodes. The correctness of the data upload process of the corresponding regional chain nodes of the first blockchain is verified by the supervisory nodes, abnormal chain nodes are screened out, and the data of the abnormal chain nodes are verified and updated; the data upload and storage process in the first blockchain is verified by the second blockchain, which can ensure the quality of the data in the first blockchain, prevent false and erroneous data storage, and improve the stability and reliability of the blockchain system. At the same time, through regional division and supervision node allocation, the data verification efficiency is improved, and the authenticity and integrity of the dual-carbon data are ensured.

[0136] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. A dual-carbon intelligent data management method, characterized in that: include: Collect dual carbon data of multiple target groups; For the dual-carbon data of each target group, by analyzing the data type of the dual-carbon data, data of different data types are stored in corresponding data nodes according to the preset time window, wherein data transmission is carried out between data nodes; The data nodes are grouped, and key data nodes that can reflect the dual-carbon data characteristics of the target group are screened out through voting in each group of data nodes, and the key data nodes are updated according to a preset time window; According to the key data nodes of each target group, the corresponding dual-carbon data will be uploaded to the corresponding chain nodes of the first blockchain for storage. By analyzing the collaborative relationship between the target groups, the association will be established between the chain nodes of the first blockchain; The first blockchain is divided into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups.

2. A dual-carbon intelligent data management method according to claim 1, characterized in that: The data nodes are grouped, key data nodes are selected from each group of data nodes by voting, and the key data nodes are updated according to a preset time window, including: According to the distance between data nodes and the similarity of corresponding data types, the data nodes are grouped to obtain multiple groups of data nodes; In each group of data nodes, each data node votes among itself, and the data node with the most votes is selected as the first key data node; When the first key data node fails, the data node with the second highest number of votes is selected from the highest to the lowest number of votes as the second key data node; According to a preset time window, a voting screening is performed among the data nodes, and the first key data node or the second key data node is updated.

3. A dual-carbon intelligent data management method according to claim 1, characterized in that: According to the key data nodes of each target group, the corresponding dual-carbon data are uploaded to the corresponding chain nodes of the first blockchain for storage, and by analyzing the collaborative relationship between the target groups, an association is established between the chain nodes of the first blockchain, including: According to the key data nodes of each target group, the carbon-dual data of the target group is encrypted in layers, and the encrypted carbon-dual data is uploaded to the corresponding chain node of the first blockchain for storage; The degree of association is calculated by analyzing the collaborative relationship between the target groups, and the degree of association is used as the weight of the association edge to create an association edge between corresponding chain nodes in the first blockchain. The association edge is updated by updating the association edge weight or removing the association edge.

4. A dual-carbon intelligent data management method according to claim 3, characterized in that: The method of performing layered encryption processing on the carbon-dual data of the target group according to the key data nodes of each target group and uploading the encrypted carbon-dual data to the chain node corresponding to the first blockchain for storage includes: Encrypting the dual-carbon data using a preset first encryption method to obtain first encrypted data; Encrypting the first encrypted data using a preset second encryption method to obtain second encrypted data; According to the key data nodes of each target group, the second encrypted data of the target group is uploaded to the corresponding chain nodes of the first blockchain for storage.

5. A dual-carbon intelligent data management method according to claim 3, characterized in that: The step of calculating the degree of association by analyzing the collaborative relationship between the target groups, creating an associated edge between corresponding chain nodes in the first blockchain using the degree of association as the weight of the associated edge, and updating the associated edge by updating the associated edge weight or removing the associated edge includes: Analyze the collaborative relationship between target groups through the preset association weight analysis model and calculate the association degree between target groups; Filtering out the target groups whose correlation degree is greater than a preset correlation degree threshold as the first target group set; In the first target group set, establishing association edges between corresponding target groups according to the association degrees between the target groups, and using the association degrees as weights of the association edges; Analyze the collaborative relationships between target groups through the preset collaborative relationship analysis model and predict collaborative changes; According to the collaboration change, the associated edge weight is updated or the associated edge is removed, and the associated edge is updated.

6. A dual-carbon intelligent data management method according to claim 3, characterized in that: The first blockchain is divided into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups, including: Dividing the first blockchain into different regions based on the connection status of the associated edges between the chain nodes in the first blockchain; In each region, the second blockchain verifies the data upload process of each chain node in the first blockchain to store and manage the dual-carbon data of multiple target groups.

7. A dual-carbon intelligent data management method according to claim 6, characterized in that: The first blockchain is divided into different areas according to the connection status of the associated edges between the chain nodes in the first blockchain, including: Extracting chain node association features based on the association edge connections between chain nodes in the first blockchain; Based on the chain node association characteristics, the first blockchain is divided into multiple connected subgraphs, each connected subgraph is regarded as a region, and multiple regions are obtained.

8. A dual-carbon intelligent data management method according to claim 6, characterized in that: In each region, the second blockchain verifies the data upload process of each chain node in the first blockchain to store and manage the dual-carbon data of multiple target groups, including: In each region, a corresponding set of supervisory nodes is allocated in the second blockchain based on the connection status of the chain nodes; For each chain node in the regulatory node set, the regional verification result is obtained by analyzing the correctness of the chain node data upload process in the corresponding region; According to the regional verification results, the chain nodes with abnormal data upload processes in the corresponding region are screened out to obtain abnormal chain nodes; The data stored in the abnormal chain nodes are verified and updated to store and manage the dual-carbon data of multiple target groups.

9. A dual-carbon intelligent data management system, characterized in that: A method for implementing a dual-carbon intelligent data management method as claimed in any one of claims 1 to 8, comprising: The dual-carbon data collection module collects dual-carbon data of multiple target groups; The data node screening module analyzes the data type of the dual-carbon data of each target group and stores data of different data types in corresponding data nodes according to a preset time window, wherein data transmission is performed between the data nodes; the data nodes are grouped, and key data nodes that can reflect the dual-carbon data characteristics of the target group are screened by voting in each group of data nodes, and the key data nodes are updated according to the preset time window; The dual-carbon data upload module uploads the corresponding dual-carbon data to the corresponding chain nodes of the first blockchain for storage based on the key data nodes of each target group. By analyzing the collaborative relationship between the target groups, it establishes associations between the chain nodes of the first blockchain; The dual-carbon data verification module divides the first blockchain into different areas, and the second blockchain verifies the data upload process of each area of ​​the first blockchain to store and manage the dual-carbon data of multiple target groups.

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