A blockchain-based method and system for recording and monitoring carbon emission data

Abstract

The application discloses a kind of recording and supervision method and system of carbon emission data based on blockchain, including collecting carbon emission data, verifying the rationality of carbon emission data, upload to the reference opinion generated by the supervision committee that is randomly formed by the node with the most successful verification frequency and the highest credit value, update the credit value of each verification node, and construct the consensus mechanism of carbon emission data rationality verification.Record and check the carbon emission data opinions submitted by each verification node, form aggregated opinions and final reference opinions, and construct a blockchain supervision mechanism based on node credit value.Packaging node records the carbon emission data verified in this round and the credit value change of related nodes, and publicizes the participating nodes, to construct a three-level supervision packaging mechanism based on credit value.The credibility of data processing is increased.The system is extremely flexible and adaptable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain carbon asset management, in particular to a recording and supervision method and system for carbon emission data based on blockchain. BACKGROUND

[0002] Blockchain technology is a decentralized distributed ledger technology maintained and recorded by multiple nodes. Blockchain links transaction data in the form of blocks to form an unalterable chain, thereby realizing secure recording and verification of transactions. It has the characteristics of decentralization, unalterability and high security.

[0003] Machine learning technology is a machine learning method based on artificial neural networks, which simulates and learns the internal characteristics of complex data through multiple neuron layers. Machine learning connects neuron layers together to build a multi-layer neural network, thereby realizing efficient representation of data and learning of complex patterns. It has strong model representation ability, adaptability and high parallelization.

[0004] Climate problems have become a major concern of society. SUMMARY

[0005] In view of the above problems, the present application is proposed.

[0006] Therefore, the technical problem solved by the present application is that the carbon emissions generated by highway transportation are difficult to collect due to their large quantity, so the carbon emission data generated by vehicle transportation is uploaded by users or enterprises, which may generate a large amount of false and incorrect data, causing difficulties in carbon emission data management and statistics. The existing carbon emission data management scheme based on blockchain technology lacks supervision of participating nodes, and if a node is attacked from the inside, false data or malicious incorrect results are difficult to be discovered and intervened from the outside.

[0007] To solve the above technical problems, the present application provides the following technical scheme: a recording and supervision method for carbon emission data based on blockchain, comprising:

[0008] Collecting carbon emission data, verifying the rationality of the carbon emission data, uploading to a supervision committee randomly composed of nodes with the most verification success frequency and the highest credit value to generate reference opinions, updating the credit value of each verification node, and constructing a consensus mechanism for carbon emission data rationality verification;

[0009] The supervision committee node records and checks the carbon emission data opinions submitted by each verification node to form aggregated opinions and final reference opinions, and constructs a blockchain supervision mechanism based on node credit value;

[0010] The packaging node records the carbon emission data passed by the verification in this round and the change of the credibility value of the related node, and publicizes the participating node, thereby constructing a three-level supervision packaging mechanism based on the credibility value.

[0011] As a preferred scheme of the blockchain-based carbon emission data recording and supervision method, the platform parameters include task information and resource information.

[0012] As a preferred scheme of the blockchain-based carbon emission data recording and supervision method, the verification of the rationality of the carbon emission data includes constructing a linear regression model for the verification of the rationality of the carbon emission data according to the single-transport driving distance, the transport vehicle load, the driver driving habit, and the fuel type used by the vehicle to form a reasonable range of the carbon emission amount.

[0013] The consensus mechanism for the verification of the rationality of the carbon emission data includes that a plurality of independent nodes form a system, and each node verifies the rationality of the carbon emission data by using the linear regression model.

[0014] The node N information code is represented as:

[0015]

[0016] wherein, represents the credibility value of the node N in the i-1th round; V C respectively represent the verification times of the node N on the node M, and the correct times of the node N verifying the rationality of the carbon emission data; N , respectively represent the total times of the node participating in the packaging, and the total times of the node successfully packaging; C S C respectively represent the supervision times of the node N on the node M, and the total times of the node N successfully supervising.

[0017] As a preferred scheme of the blockchain-based carbon emission data recording and supervision method, the consensus mechanism for the verification of the rationality of the carbon emission data further includes the selection and grouping of the participating nodes for the verification of the carbon emission data: the i-th round is selected from the nodes that do not participate in the verification, supervision, and packaging in the last round, and the nodes with the top 50% credibility values form a candidate node pool N B ; when i=1, a non-repeating random sequence with a length of half of the total number of nodes is randomly generated, the nodes ranked in the top 5% of the sequence form the candidate node pool for the data packaging in this round, the nodes ranked in the top 5% to 15% form the supervision candidate node pool for the verification of the rationality of the carbon emission data in this round, the nodes ranked in the top 5% to 15% form the verification node candidate pool for the verification of the rationality of the carbon emission data in this round, and the credibility value of the node not entering the candidate node pool will be increased by R C ​As compensation;

[0018] Forming a random sequence N with length n and no repetition with parent block as seed n ={N1,N2,…,N n}, the nodes belonging to the sequence in the candidate node pool will be selected to participate in the rationality verification of the carbon emission data D i in the i th round;

[0019] Grouping the selected n nodes, and requiring each node group to meet the following conditions:

[0020]

[0021] Among them, represents the voting proportion of the selected node n in verifying the rationality of carbon emission data.

[0022] As a preferred scheme of the blockchain-based carbon emission data recording and supervision method, wherein: the blockchain supervision mechanism based on the node reputation value comprises that the supervision committee is randomly composed of nodes with the highest verification success frequency and the highest reputation value;

[0023] The supervision committee accepts reports from other nodes and receives evidence from the reporting nodes, and decides whether the report is successful after reaching an agreement in the supervision committee; after supervision and accepting the report, the supervision committee node updates the reputation value of each participating node according to the final opinion formed by the supervision committee;

[0024] Supervision committee node selection and update mechanism: introduce intersection degree S NM represents the degree of association of node N to node M;

[0025] The information code of node N is represented as:

[0026]

[0027] The information code of node M is represented as:

[0028]

[0029] Then the calculation formula of S NM is:

[0030]

[0031] The candidate supervision node pool N s is composed of nodes in the candidate node pool N B that do not participate in the verification of the carbon emission data D i and have a verification success rate in the top 50%;

[0032] Based on the intersection degree S of nodes NM The supervision node selection function F(C, R, S) of the node reputation value R, wherein C represents the set of node information codes of the candidate supervision node pool, R represents the set of node reputation values of the candidate supervision node pool; S represents the intersection degree matrix of the candidate supervision node pool; through the selection function F(C, R, S), a number of supervision nodes with high reputation values and low intersection degrees between nodes are selected from the candidate supervision node pool N s to form a supervision committee, and the selection function is represented as:

[0033] F(C, S, R) = ∑R + ∑S ij

[0034] After every T rounds of supervision, the supervision committee is updated and iterated through the selection function F(C, R, S).

[0035] As a preferred scheme of the blockchain-based carbon emission data recording and supervision method, wherein: the verification node supervision mechanism includes, after the verification node verifies the carbon emission data D i in the i th round, the supervision committee node in the supervision committee records the opinions of the selected verification nodes on the reasonableness of D i ; after all the committee nodes record, compare their own records with the records of other committee nodes, if more than 50% of the committee nodes are consistent, the record is taken as the final aggregated opinion msg i , and the reference opinion view i is formed by msg i ; if all the records in the supervision committee do not reach 50% consistency, the current supervision committee is dissolved and re-elected until the final aggregated opinion msg i is formed.

[0036] If a node reports the data on the chain, the supervision committee will accept the report and check the node's evidence; if more than half of the committee nodes think that the report is true, the node's report is successful, the reported data is discarded, and all nodes participating in uploading the data are punished; otherwise, the node's report fails, and the committee does not perform any operation.

[0037] As a preferred scheme of the blockchain-based carbon emission data recording and supervision method, wherein: the reputation value and information code update rule of the supervision node includes that the reputation value of the known supervision node is updated to R i-1 after the i-1 th round of supervision, and the reputation value of the supervision node is updated to R i after the i th round of supervision. i-1+ΔR, where ΔR is the change in reputation value of the monitoring node in the i-th round; if the monitoring node provides a correct record in the i-th round of monitoring or provides a correct opinion during the handling of a report, then the formula for calculating ΔR is:

[0038]

[0039] in, μ depends on the frequency of successful supervision by regulatory nodes The value of μ increases first and then decreases until the frequency of successful monitoring by the monitoring node reaches 1 / 2.

[0040] If a regulatory node provides an erroneous record in the i-th round of supervision, experiences committee dissolution, or fails to reach a consensus during the handling of a complaint, and assuming the i-th round of regulatory committee consists of γ nodes, then the formula for calculating ΔR is:

[0041]

[0042] After the i-th round of on-chain processing, the information code of the regulatory node will be updated; the total number of regulatory actions taken by the committee node in the formation of the aggregated opinion message will be recorded. Increase by 1, after one committee dissolution, the total number of times the committee node has been supervised. Add 1, accept one report, and the total number of times the committee node supervises. Add 1; committee nodes form aggregated opinion messages. i If a correct record is provided during the process, the number of successful monitoring cycles for that node is S. C Increase by 1; if a consensus is reached during the process of accepting reports at a node, then the number of successful regulatory actions S at that node will increase by 1. C Increase by 1.

[0043] As a preferred embodiment of the blockchain-based carbon emission data recording and monitoring method described in this invention, a three-tiered supervision and packaging mechanism based on reputation values ​​includes:

[0044] Based on reputation value, the packing node is selected from the aforementioned candidate node pool for the i-th round; if i = 1, the packing node is selected from the selected packing candidate node pool; a non-repeating random sequence N of length z is formed using the parent block as the seed. z ={N1,N2,…,N z}, in the random sequence N z Select the frequency of successful supervision from the z nodes. Largest node z As a packaging node for this round of carbon emission data on the blockchain; if there is a high frequency of successful regulatory oversight. If there are identical nodes, the node with the highest reputation value will be selected as the packaging node for uploading carbon emission data to the blockchain in this round.

[0045] Let Node be the node after the (i-1)th round of chaining. z Reputation value Node z Data D needs to be... i In the i-th round, the verification node verifies the data D. i The aggregate opinion formed by the rationality verification msg i The changes in the reputation values ​​of the verification and oversight nodes in round i are packaged together and uploaded to the blockchain, and are subject to inspection by the nodes participating in round i of verification and oversight; if Node z If all records are correct, then consider Node z Packaging successful; if there are node pairs... z The record was disputed, and upon inspection, Node z If the record is indeed incorrect, then Node will be considered... z Packaging failed;

[0046] Node in round i z Reputation value ΔR is the node in the i-th round. z The change in reputation value; if the Node of the i-th round of packaging z If the packaging is successful, the calculation formula is as follows:

[0047]

[0048] If Node z The i-th round of packaging node z If the packaging fails, then Node.js will... z Reputation value halved:

[0049]

[0050] A blockchain-based system for recording and monitoring carbon emission data employing any of the methods described in this invention, wherein:

[0051] The collection module collects carbon emission data, verifies the rationality of the carbon emission data, uploads it to the regulatory committee, which is randomly composed of nodes with the highest verification frequency and the highest reputation value, generates reference opinions, updates the reputation value of each verification node, and constructs a consensus mechanism for verifying the rationality of carbon emission data.

[0052] The regulatory module involves regulatory committee nodes recording and verifying the carbon emission data opinions submitted by each verification node, forming aggregated opinions and final reference opinions, and constructing a blockchain regulatory mechanism based on node reputation values.

[0053] A packaging module records the carbon emission data passed by verification in this round and the change of the reputation value of the related node, and publicizes the participating nodes, and constructs a three-level supervision packaging mechanism based on the reputation value.

[0054] A verification module constructs a linear regression model for verifying the rationality of carbon emission data according to the single transportation driving distance, the transportation vehicle load, the driver driving habit and the fuel type used by the vehicle.

[0055] A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method in any one of the embodiments.

[0056] A computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the method in any one of the embodiments.

[0057] The method for recording and supervising carbon emission data based on a blockchain provided by the application increases the credibility of data processing by using blockchain technology, all data verification and recording operations are tamper-proof and completely transparent, scientifically evaluates whether the data conforms to the actual situation by verifying the rationality of carbon emission data through a linear regression model, and strengthens the self-discipline of the industry through the supervision and packaging mechanism between nodes. BRIEF DESCRIPTION OF DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can be obtained by those skilled in the art without creative labor.

[0059] Figure 1 A whole flow chart of the method for recording and supervising carbon emission data based on a blockchain provided by the first embodiment of the application;

[0060] Figure 2 A node selection and inclusion relationship diagram of the verification, supervision and packaging three links of the method for recording and supervising carbon emission data based on a blockchain provided by the first embodiment of the application;

[0061] Figure 3A flowchart of a consensus mechanism of carbon emission data rationality based on a linear regression model in a blockchain-based carbon emission data recording and supervision method according to the first embodiment of the present application is provided.

[0062] Figure 4 A flowchart of forming aggregated opinions and reference opinions in a committee supervision mechanism based on reputation values in a blockchain-based carbon emission data recording and supervision method according to the first embodiment of the present application is provided.

[0063] Figure 5 A flowchart of handling reports by a supervision committee in a committee supervision mechanism in a blockchain-based carbon emission data recording and supervision method according to the first embodiment of the present application is provided.

[0064] Figure 6 A flowchart of a three-level supervision packaging mechanism based on reputation values in a blockchain-based carbon emission data recording and supervision method according to the first embodiment of the present application is provided.

[0065] Figure 7 A verification model graph in a blockchain-based carbon emission data recording and supervision method according to the second embodiment of the present application is provided. DETAILED DESCRIPTION

[0066] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0067] Embodiment 1

[0068] Reference Figure 1 For an embodiment of the present application, a blockchain-based carbon emission data recording and supervision method is provided, comprising:

[0069] S1: Collect carbon emission data, verify the rationality of the carbon emission data, upload to a supervision committee randomly formed by nodes with the most verification success frequency and the highest reputation value to generate reference opinions, update the reputation value of each verification node, and construct a consensus mechanism for carbon emission data rationality verification.

[0070] The verification of the rationality of the carbon emission data includes constructing a linear regression model for carbon emission data rationality verification according to the reasonable range of carbon emission amount constituted by single transportation driving distance, transportation vehicle load, driver driving habit and fuel type used by the vehicle;

[0071] The consensus mechanism for verifying the reasonableness of the carbon emission data comprises a system composed of multiple independent nodes, each of which uses a linear regression model to verify the reasonableness of the carbon emission data;

[0072] The information code of the node N is represented as:

[0073]

[0074] wherein, represents the reputation value of the node N in the i-1th round; V C respectively represent the verification times of the node N on the node M and the correct times of the node N verifying the reasonableness of the carbon emission data; N , respectively represent the total times of the node participating in the packaging and the total times of the node successfully packaging; C S C respectively represent the supervision times of the node N on the node M and the total times of the node N successfully supervising.

[0075] Selection and grouping of the participating nodes for verifying the carbon emission data: it is assumed that the top 50% of the nodes in the reputation value are selected from the nodes that do not participate in the verification, supervision and packaging in the i-1th round to form a candidate node pool N B ; when i = 1, a random sequence with a length of half of the total number of nodes is randomly generated, and the nodes ranked in the top 5% of the sequence form the candidate node pool for the data packaging in the i-1th round, the nodes ranked in the top 5% to 15% of the sequence form the supervision candidate node pool for the reasonableness verification of the carbon emission data in the i-1th round, and the nodes ranked in the top 5% to 15% of the sequence form the verification node candidate pool for the reasonableness verification of the carbon emission data in the i-1th round. The reputation value of the node not entering the candidate node pool will be increased by R C as compensation;

[0076] A random sequence N n ={N1,N2,…,N n} with a length of n and without repetition is formed by taking the parent block as a seed, and then the nodes belonging to the sequence in the candidate node pool will be selected to participate in the reasonableness verification of the carbon emission data D i in the i-1th round;

[0077] The selected n nodes are grouped, and each node group is required to satisfy the following conditions:

[0078]

[0079] wherein, represents the voting proportion of the selected node n in verifying the reasonableness of the carbon emission data.

[0080] ​In detail, in the consensus mechanism for the reasonableness of carbon emission data based on the linear regression model, the mechanism for determining the reasonableness of carbon emission data is as follows: In the i-th round, the verification node uses the linear regression model to verify the carbon emission data D uploaded by the user. i The reasonableness of the carbon emission data D will be assessed, and the judgment will be uploaded to the regulatory committee. If a node group believes that the carbon emission data D is reasonable... i If a reasonable percentage of the votes cast by a node group exceeds half of the total votes cast by that node group, then that node group considers D to be the winner. i That's reasonable; otherwise, the node group would consider D to be... i This is unreasonable. If more than half of the node groups believe that D... i If it is reasonable, then D i After the i-th round of verification, and with carbon emission data D... i Reference opinions view i =1; otherwise, consider D to be 1. i Unreasonable, and makes the view... i =0, discard carbon emission data D i .

[0081] In detail, in the consensus mechanism for the reasonableness of carbon emission data based on a linear regression model, the update of the verification node reputation value is as follows: Let the reputation value of node n after the (i-1)th round of on-chain processing be... When the verification node n is paired with D i Evaluation opinions and reference opinions i When they are the same, the reputation value of node n in the i-th round ΔR represents the change in reputation value of node n in the i-th round, and its calculation formula is as follows:

[0082]

[0083] When the verification node n is paired with D i Evaluation opinions and reference opinions i At the same time, node reputation value The deduction and the carbon emission data D i The difficulty of judging reasonableness is related to the number of verification nodes that make incorrect judgments; the more verification nodes that make incorrect judgments, the more reasonable D is. i The greater the difficulty in judgment, the better. Let θ be the number of nodes that make incorrect judgments in this round of verification. Then, the reputation value of the nodes that make incorrect judgments will decrease by the original value.

[0084]

[0085] As a preferred embodiment of the carbon emission data recording and monitoring method based on linear regression verification and reputation consensus using blockchain technology described in this invention, the reputation-based committee monitoring mechanism includes: a mechanism for selecting and updating the monitoring node committee, a monitoring mechanism for verification nodes, and rules for updating the reputation value and information code of the monitoring nodes. (Nodes with more successful verifications are upgraded for monitoring, and those with more monitoring are packaged for upgrade; monitoring nodes record the opinions of verification nodes, and also incorporate majority rule comparisons.)

[0086] Based on the reasonable range of carbon emissions determined by factors such as single-trip driving distance, vehicle load, driver driving habits, and the type of fuel used, a linear regression model is constructed to verify the reasonableness of carbon emission data.

[0087] The data uploaded by users includes the driving distance of a single transport trip (L), the load of the vehicle in this transport (B), the driver's driving habits (DH), and the type of fuel used by the vehicle (F). T The linear regression verification model for the carbon emissions C of a single transport trip by this vehicle is as follows:

[0088] C=0.0024×L+0.1317×B+26.2994×DH-128.6085×F T +272.4340

[0089] S2: The regulatory committee node records and verifies the carbon emission data opinions submitted by each verification node, forms aggregated opinions and final reference opinions, and constructs a blockchain regulatory mechanism based on node reputation value.

[0090] The regulatory committee is randomly composed of nodes with the highest verification success rate and the highest reputation score;

[0091] The regulatory committee accepts reports from other nodes and receives evidence from the reporting nodes. After reaching a consensus within the regulatory committee, it decides whether the report is successful. After supervision and acceptance of reports, the regulatory committee nodes update the reputation value of each participating node by comparing the final opinion formed by the regulatory committee.

[0092] Selection and updating mechanism for regulatory committee nodes: Introducing the intersection degree S NM This indicates the degree of association between node N and node M;

[0093] The information code of node N is represented as follows:

[0094]

[0095] The information code of node M is represented as follows:

[0096]

[0097] Then S NM The calculation formula is:

[0098]

[0099] candidate supervisor node pool N s from the candidate node pool N B that did not participate in the verification of the current round of carbon emission data D i and whose verification success rate is in the top 50% of nodes;

[0100] Based on the intersection degree S NM of the nodes and the node reputation value R, a supervisor node selection function F(C, R, S) is used, where C represents a set of node information codes of the candidate supervisor node pool, R represents a set of node reputation values of the candidate supervisor node pool, and S represents an intersection degree matrix of the candidate supervisor node pool. Through the selection function F(C, R, S), a number of supervisor nodes with high reputation values and low intersection degrees among nodes are selected from the candidate supervisor node pool N s to form a supervisor committee, and the selection function is represented as:

[0101] F(C, S, R) = ΣR + ΣS ij

[0102] Every T rounds of supervision, the supervisor committee is updated and iterated through the selection function F(C, R, S).

[0103] The verification node supervision mechanism includes, at the i-th round, after the verification node verifies the carbon emission data D i , the supervisor committee node records the opinions of each selected verification node on the reasonableness of D i . After all the committee nodes record, compare their records with the records of other committee nodes, if more than 50% of the committee nodes are consistent, the record is the final aggregated opinion msg i , and the msg i forms a reference opinion view i . If all the records in the supervisor committee do not reach 50% consistency, the current supervisor committee is dissolved and re-elected until the final aggregated opinion msg i is formed.

[0104] If a node reports the data on the chain, the supervisor committee will accept the report and check the node's evidence. If more than half of the committee nodes believe that the report is true, the node's report is successful, the reported data is discarded, and all nodes involved in uploading the data are punished. Otherwise, the node's report fails, and the committee does not take any action.

[0105] The reputation value and information code update rules of the supervisor node include that the reputation value of the known supervisor node is updated to Ri-1 , then the reputation value of the regulatory node is updated as R i after the i-th round of supervision i-1 , ΔR is the change of the reputation value of the regulatory node node in the i-th round of supervision; if the regulatory node gives a correct record in the i-th round of supervision or gives a correct opinion in the process of accepting the report, the calculation formula of ΔR is:

[0106]

[0107] , wherein μ increases with the frequency of successful supervision of the regulatory node , and decreases after increasing, and μ reaches the maximum value when the frequency of successful supervision of the regulatory node reaches ;

[0108] If the regulatory node gives an incorrect record in the i-th round of supervision or experiences committee dissolution or fails to give a consistent opinion in the process of accepting the report, and the i-th round of supervision committee is composed of γ nodes, the calculation formula of ΔR is:

[0109]

[0110] After the i-th round of chain ends, the information code of the regulatory node will be updated; the total number of supervision of the committee node participating in the formation of the aggregated opinion msg increases by 1 , experiences committee dissolution, and the total number of supervision of the committee node increases by 1 , accepts a report, and the total number of supervision of the committee node increases by 1 ; the committee node gives a correct record in the process of forming the aggregated opinion msg i , and the number of successful supervision S C of the node increases by 1; in the process of accepting the report of the node, a consistent opinion is given, and the number of successful supervision S C of the node increases by 1.

[0111] In detail, in the committee supervision mechanism based on the reputation value, the supervision mechanism of the verification node is: in the i-th round, after the verification node verifies the carbon emission data D i , the supervision committee node records the opinions of the verification node on the reasonableness of D i . After all the committee nodes record, compare their records with the records of other committee nodes, if more than 1 / 2 of the committee nodes record are consistent, the record is taken as the final aggregated opinion msg i , and the msg i forms a reference opinion View i; if all records in the regulatory committee do not reach a consensus rate of 1 / 2, the current regulatory committee is dissolved and re-elected until the final aggregated opinion msg is formed i . The form is msg i = 10X111, msg i [n] takes 1, 0, X respectively represent the nth node participating in the verification of the evaluation opinion of carbon emission data D i is reasonable, unreasonable or missing.

[0112] In particular, if a node reports the data on the chain, the regulatory committee will accept the report and check the evidence of the node. If more than half of the committee nodes think that the report is true, the node report is successful, the reported data is discarded, and all nodes participating in uploading the data are punished; otherwise, the node report fails, and the committee does not perform any operation.

[0113] S3: The packaging node records the carbon emission data passed by the verification in this round and the change of the reputation value of the related node, and publicizes the participating nodes, and constructs a three-level supervision packaging mechanism based on the reputation value.

[0114] Based on the selection of the reputation value of the packaging node, the i-th round of packaging node is selected from the above candidate node pool; if i = 1, the packaging node is selected from the selected packaging candidate node pool; the parent block is taken as the seed to form a random sequence N z = {N1, N2, …, N z} with a length of z and no repetition, among the z nodes belonging to the random sequence N z , the node Node z with the maximum regulatory success frequency is selected as the packaging node of the carbon emission data in this round; if there are nodes with the same regulatory success frequency , the node with the highest reputation value is selected as the packaging node of the carbon emission data in this round;

[0115] Let the reputation value of the node Node z after the i-1th round of chain is Node z needs to package the data D i , the aggregated opinion msg i formed by the data D i of the i-th round of verification node, and the change of the reputation value of the verification and regulatory node in the i-th round are packaged on the chain, and the node participating in the i-th round of verification and regulation is checked; if the records of Node z are correct, Node z is considered to be packaged successfully; if a node disagrees with the records of Node z , after checking, Nodez If the record is indeed incorrect, then Node will be considered... z Packaging failed;

[0116] Node in round i z Reputation value ΔR is the node in the i-th round. z The change in reputation value; if the Node of the i-th round of packaging z If the packaging is successful, the calculation formula is as follows:

[0117]

[0118] If Node z The i-th round of packaging node z If the packaging fails, then Node.js will... z Reputation value halved:

[0119]

[0120] In detail, in the three-level supervised packaging mechanism based on reputation value, the selection of packaging nodes based on reputation value is as follows: the packaging node in the i-th round is selected from the aforementioned candidate node pool; if i = 1, the packaging node is selected from the selected packaging candidate node pool. A non-repeating random sequence N of length z is formed using the parent block as the seed. z ={N1,N2,…,N z}, in the random sequence N Z Select the frequency of successful supervision from the z nodes. Largest node z As a packaging node for this round of carbon emission data on the blockchain; if there is a high frequency of successful regulatory oversight. If the nodes are the same, the node with the highest reputation value will be selected as the packaging node for uploading carbon emission data to the blockchain in this round.

[0121] Based on the reasonable range of carbon emissions determined by factors such as single-trip driving distance, vehicle load, driver driving habits, and the type of fuel used, a linear regression model is constructed to verify the reasonableness of carbon emission data.

[0122] The data uploaded by users includes the driving distance of a single transport trip (L), the load of the vehicle in this transport (B), the driver's driving habits (DH), and the type of fuel used by the vehicle (F). T The linear regression verification model for the carbon emissions C of a single transport trip by this vehicle is as follows:

[0123] C=0.0024×L+0.1317×B+26.2994×DH-128.6085×F T +272.4340

[0124] In another aspect, the embodiment also provides a blockchain-based carbon emission data recording and supervision system, which comprises:

[0125] A collection module collects carbon emission data, verifies the rationality of the carbon emission data, uploads the carbon emission data to a supervision committee formed by randomly selected nodes with the highest verification success frequency and the highest credit value, generates a reference opinion, updates the credit value of each verification node, and constructs a consensus mechanism for verifying the rationality of carbon emission data.

[0126] A supervision module records and checks the carbon emission data opinions submitted by each verification node to form aggregated opinions and final reference opinions, and constructs a blockchain supervision mechanism based on the credit value of the nodes.

[0127] A packaging module records the carbon emission data passing the verification in the current round and the change in the credit value of the related nodes, and publicizes the participating nodes, thereby constructing a three-level supervision packaging mechanism based on the credit value.

[0128] A verification module constructs a linear regression model for verifying the rationality of carbon emission data according to the single transportation driving distance, the transportation vehicle load, the driver's driving habits, and the fuel type used by the vehicle to constitute a reasonable range of carbon emissions.

[0129] If the above functions are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various program code storage media.

[0130] The logic and / or steps represented in flow diagrams or otherwise described herein, for example, can be considered as a sequence of executable instructions for implementing logic functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this specification, a "computer-readable medium" can be any means that can contain, store, communicate, propagate or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium.

[0131] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can also be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for example via an optical scanner, then compiled, interpreted, or otherwise processed, and stored in a computer memory in a form that is then employable by a computer.

[0132] It should be understood that aspects of the application can be implemented in hardware, software, firmware or combinations thereof. In the above embodiments, the various steps or methods can be implemented in software or firmware that is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any of the following technologies, or combinations thereof, can be used with the necessary logic gates and circuitry for implementing logic functions on data signals: discrete logic circuitry having logic gates for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gates, programmable gate arrays (PGA), field programmable gate arrays (FPGA), and so forth.

[0133] Example 2

[0134] The data uploaded by the enterprise contains the driving distance L of each transport, the vehicle load B of the transport, the driving habit DH of the driver and the fuel type F used by the vehicle of different drivers in different time periods T .

[0135] After recognizing that the enterprise uploads data, a random sequence N of length n and no repetition is formed with the parent block as the seed n = {N1, N2,..., N n} Select nodes belonging to the sequence from the nodes ranked in the top 50% of reputation value as verification nodes.

[0136] Each node is assigned a node information code, such as the information code of node N. in, This represents the reputation value of node N in the (i-1)th round; V C P represents the number of times node N verifies the data from node M, and the number of times node N correctly verifies the reasonableness of the carbon emission data, respectively. N P C These represent the total number of times a node participated in packaging and the total number of times a node successfully packaged, respectively. S C These represent the number of times node N monitors node M and the total number of times node N successfully monitors it, respectively.

[0137] The selected n verification nodes are grouped into verification node groups, and each node group must meet the following conditions:

[0138]

[0139] in, This indicates the voting weight of a certain verification node n in verifying the rationality of carbon emission data.

[0140] Each verification node will use a linear regression model to verify the data uploaded by the enterprises one by one. Let C be the carbon emissions of a single transport vehicle during a transport trip. The linear regression verification model for C is:

[0141] C=0.0024×L+0.1317×B+26.2994×DH-128.6085×F T +272.4340

[0142] The verification node uses a linear regression model to verify the carbon emission data D uploaded by the user. i The reasonableness of the carbon emission data D will be assessed, and the judgment will be uploaded to the regulatory committee. If a node group believes that the carbon emission data D is reasonable... i If a reasonable percentage of the votes cast by a node group exceeds half of the total votes cast by that node group, then that node group considers D to be the winner. i It is reasonable; otherwise, the node group considers D to be reasonable. i This is unreasonable. If more than half of the node groups believe that D... i If it is reasonable, then D i After the i-th round of verification, and with carbon emission data D... i Reference opinions view i =1; otherwise, consider D to be 1. i Unreasonable, and makes the view... i =0, discard carbon emission data Di .

[0143] Let the reputation value of node n after the end of the i-1th round of chaining be When the evaluation opinion of the verification node n on D i is the same as the reference opinion view i , the reputation value of node n in the i th round is ΔR is the reputation value change of node n in the i th round, and its calculation formula is:

[0144]

[0145] When the evaluation opinion of the verification node n on D i is different from the reference opinion view i , the reputation value of node n is reduced The deduction of the reputation value of node n is related to the difficulty of the rationality judgment of the carbon emission data D i This round, the more the verification nodes that make mistakes, the greater the difficulty of judging D i . Let θ be the number of nodes that make mistakes in this round, and the reputation value of the nodes that make mistakes is reduced by

[0146]

[0147] The nodes participating in the verification cannot participate in the supervision and packaging. Any two nodes have an intersection degree S NM Table

[0148] The association degree of node N to node M is shown, and the information code of node N is represented as

[0149]

[0150] The information code of node M is represented as The calculation formula of S NM is:

[0151]

[0152] The candidate supervision node pool N s is composed of nodes in the candidate node pool N B that do not participate in the verification of the carbon emission data D i this round and have a verification success rate in the top 50%;

[0153] Based on the intersection degree S NMA supervision node selection function F(C, R, S) of the node reputation value R, wherein C represents a set of node information codes of the candidate supervision node pool, R represents a set of node reputation values of the candidate supervision node pool, and S represents an intersection degree matrix of the candidate supervision node pool; through the selection function F(C, R, S), a number of supervision nodes with high reputation values and low intersection degrees among nodes are selected from the candidate supervision node pool N s to form a supervision committee, and the selection function is represented as:

[0154] F(C, S, R) = ΣR + ΣS ij

[0155] In the i-th round, the verification nodes verify the carbon emission data D i After verification, the supervision committee nodes in the supervision committee record the reasonableness opinions of the verification nodes on D i All the committee nodes record, and after the completion of the record, the record of the own node is compared with the records of other committee nodes, if more than 1 / 2 of the committee nodes are consistent, the record is taken as the final aggregated opinion msg i , and the reference opinion View i is formed by msg i ; if all the records in the supervision committee do not reach the 1 / 2 consistency rate, the current supervision committee is dissolved and re-election is performed until the final aggregated opinion msg i is formed. The msg i is like 10X111, and msg i [n] takes 1, 0, and X to respectively represent that the evaluation opinion of the n-th node participating in verification on the carbon emission data D i is reasonable, unreasonable, or missing.

[0156] There will be nodes to report errors or fake data to the supervision committee. If a node reports the data on the chain, the supervision committee will accept the report and check the evidence of the node, if more than half of the committee nodes think that the report is true, the node report is successful, the reported data is discarded, and all nodes participating in uploading the data are punished; otherwise, the node report fails, and the committee does not perform any operation.

[0157] It is known that the reputation value of the supervision node is updated to R i-1 after the i-1-th round of supervision, and the reputation value of the supervision node is updated to R i after the i-th round of supervision, R i-1 + ΔR, and ΔR is the reputation change amount of the i-th supervision node Node. If the supervision node gives a correct record in the i-th round of supervision or gives a correct opinion in the process of accepting the report, the calculation formula of ΔR is:

[0158]

[0159] in, μ depends on the frequency of successful supervision by regulatory nodes First increase, then decrease, until the frequency of successful monitoring by the monitoring node reaches... When μ reaches its maximum value.

[0160] If a regulatory node provides an erroneous record in the i-th round of supervision, experiences committee dissolution, or fails to reach a consensus during the handling of a complaint, and assuming the i-th round of regulatory committee consists of γ nodes, then the formula for calculating ΔR is:

[0161]

[0162] After the i-th round of on-chain processing concludes, the information code of the regulatory node will be updated. Committee nodes participate in a single aggregation of opinions (msg). i The total number of times a committee is formed, dissolved, or receives a complaint is recorded. Add 1; committee nodes form aggregated opinion messages. i If a correct record is provided during the process or a consistent opinion is given during the handling of a report at a node, then the number of successful regulatory actions S at that node is counted. C Increase by 1.

[0163] Verification nodes and supervisory nodes cannot participate in the packaging process. If i = 1, the packaging node is selected from the pool of selected packaging candidate nodes. A non-repeating random sequence N of length z is formed using the parent block as the seed. z ={N1,N2,…,N z}, in the random sequence N Z Select the frequency of successful supervision from the z nodes. Largest node z As a packaging node for this round of carbon emission data on the blockchain; if there is a high frequency of successful regulatory oversight. If the nodes are the same, select the node with the highest reputation value. z As a packaging node for uploading carbon emission data to the blockchain in this round.

[0164] Node z Data D needs to be... i In the i-th round, the verification node verifies the data D. i The aggregate opinion formed by the rationality verification msg i The changes in the reputation values ​​of the verification and oversight nodes in round i are packaged together and uploaded to the blockchain, and are subject to inspection by the nodes participating in round i of verification and oversight. If Node z If all records are correct, then consider Node z Packaging successful; if there are node pairs... z The record was disputed, and upon inspection, Node zIf the record of Node z is indeed wrong, Node z is regarded as a new node.

[0165] Suppose that the record of Node z is indeed wrong, Node z is regarded as a new node. Suppose that the record of Node z is indeed wrong, Node z is regarded as a new node. Suppose that the record of Node z is indeed wrong, Node z is regarded as a new node. z is regarded as a new node. z is regarded as a new node.

[0166]

[0167] is regarded as a new node. z is regarded as a new node. z is regarded as a new node.

[0168]

[0169] First, in the data collection stage, the starting position and ending position of each trip are automatically recorded by the GPS device of the vehicle, the total driving distance is calculated, the load data is automatically recorded before and after each transportation by the built-in load sensor of the vehicle, the acceleration, braking frequency, average speed and other data are recorded by the driving data recording device of the vehicle to reflect the driving habits of the driver, the driver inputs the type of fuel used (such as diesel, gasoline, electricity, etc.) through the system interface before starting transportation, and the carbon dioxide emission of the vehicle is directly measured by the tail gas analyzer. All collected data will be automatically transmitted to the relevant database, then a linear regression equation is established by using the method of machine learning, the relationship between the driving distance, load rate (full load or empty load), driver driving habit (smooth driving and frequent sudden acceleration or sudden braking), and carbon emission coefficient of different fuel types and carbon emission is obtained, thereby a verification model is constructed, as shown in Figure 7 .

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for recording and supervising carbon emission data based on blockchain, characterized in that, Comprise: Collecting carbon emission data, verifying the rationality of carbon emission data, uploading to the reference opinion generated by the supervision committee randomly composed of the nodes with the most verification success frequency and the highest credit value, updating the credit value of each verification node, and constructing a consensus mechanism for verifying the rationality of carbon emission data; The supervision committee node records and checks the carbon emission data opinions submitted by each verification node, forms the aggregated opinion and the final reference opinion, and constructs a blockchain supervision mechanism based on the credit value of the node; The packaging node records the carbon emission data verified in this round and the credit value change of the related node, and publicizes the participating nodes, and constructs a three-level supervision packaging mechanism based on the credit value; According to the reasonable range of carbon emission constituted by single transportation driving distance, transportation vehicle load, driver driving habit and fuel type used by the vehicle, a linear regression model for verifying the rationality of carbon emission data is constructed; The three-level supervision packaging mechanism based on the credit value includes, The selection of the packaging node based on the credit value, the i th round of packaging node is selected from the candidate node pool; If i = 1, the packing node is selected from the pool of selected packing candidate nodes; a non-repeating random sequence N of length z is formed using the parent block as the seed. z ={N1,N2,…,N z }, in the random sequence N z Select the frequency of successful supervision from the z nodes. Largest node z As a packaging node for this round of carbon emission data on the blockchain; if there is a high frequency of successful regulatory oversight. If there are identical nodes, the node with the highest reputation value will be selected as the packaging node for uploading carbon emission data to the blockchain in this round. The reputation value of the node Node z after the end of the i-1th round of chaining is The reputation value of the node Node z needs to be packaged and chained together with the data D i , the aggregated opinion msg i formed by the verification nodes in the i th round of verification on the rationality of the data D i , and the change amount of the reputation value of the verification and supervision nodes in the i th round, and is subject to the inspection of the nodes participating in the i th round of verification and supervision; if the records of the node Node z are correct, the packaging of the node Node z is considered successful; if any node disagrees with the records of the node Node z , and after inspection, it is found that the records of the node Node z are indeed incorrect, the packaging of the node Node z is considered failed. The i-th round node z Reputation value ΔR is the reputation value change amount of the i-th round node Node z ​ If the i-th round of packing node Node z The calculation formula is: If Node z The i-th round of packing nodes Node z If the packing fails, Node z The reputation value is halved: 2.The blockchain-based recording and monitoring of carbon emission data method of claim 1, wherein: The consensus mechanism for verifying the rationality of carbon emission data includes that a plurality of independent nodes form a system, and each node verifies the rationality of carbon emission data using a linear regression model; The information code of node N is represented as: wherein, represents the reputation value of node N in the i-1th round; V C respectively represent the number of verifications of node N on node M, and the number of correct times of node N verifying the reasonableness of carbon emission data; N , P C respectively represent the total number of times of node participating in packaging, and the total number of times of node packaging successfully; S C respectively represent the number of times of node N supervising node M, and the total number of times of node N successfully supervising. 3.The blockchain-based recording and regulation of carbon emission data method of claim 2, wherein: The consensus mechanism of the carbon emission data rationality verification further comprises selection and grouping of the participating nodes of the carbon emission data verification: set the i-th round to select the nodes with the top 50% of reputation values from the nodes that did not participate in the verification, supervision and packaging in the last round to form a candidate node pool N B When i = 1, a random sequence of non-repeating random numbers is randomly generated, with a length of half of the total number of nodes. The nodes ranked in the top 5% of the sequence form the candidate node pool for data packaging in the round. The nodes ranked in the top 5% to 15% form the supervision candidate node pool for data rationality verification in the round. The nodes ranked in the top 5% to 15% form the verification node candidate pool for carbon emission data rationality verification in the round. The reputation value of the nodes not entering the candidate node pool will increase R C as compensation; Forming a random sequence N of length n without repetition with the parent block as seed n = {N1, N2, …, N n}, then the nodes belonging to the sequence in the candidate node pool will be selected to participate in the reasonableness verification of the carbon emission data D i of the i th round; Group the selected n nodes, and require each node group to meet the following conditions: wherein, represents the voting proportion of the selected node n in verifying the reasonableness of the carbon emission data. 4.The method of claim 3, wherein: The blockchain supervision mechanism based on the credit value of the node includes that the supervision committee accepts the reports of other nodes and receives the evidence of the reporting node, and decides whether the report is successful after reaching an agreement in the supervision committee; After supervision and acceptance of the report, the supervision committee node compares the opinions finally formed by the supervision committee to update the credit value of each node participating in the supervision; Selection and update mechanism of regulatory committee nodes: introduce intersection degree S NM Denotes the degree of association of node N to node M; The information code of node N is represented as: The information code of node M is represented as: Then S The calculation formula is: NM The calculation formula is: pool of candidate supervisory nodes N s by the pool of candidate nodes N B did not participate in the verification of the current round of carbon emission data D i the previous round was verified and the success rate of verification consists of the top 50% of nodes; Based on the intersection degree S of nodes NM The supervision node selection function F(C, R, S) of the node reputation value R, wherein C represents the set of node information codes of the candidate supervision node pool, R represents the set of node reputation values of the candidate supervision node pool; S represents the intersection degree matrix of the candidate supervision node pool; through the selection function F(C, R, S), a number of supervision nodes with high reputation values and low intersection degrees between nodes are selected from the candidate supervision node pool N s to form a supervision committee, and the selection function is represented as: F(C,R,S) = ∑R + ∑S ij Every T rounds of supervision, the supervision committee is updated and iterated by the selection function F(C, R, S). 5.The blockchain-based recording and regulation of carbon emission data method of claim 4, wherein: The verification node supervision mechanism includes, in the i th round, the verification node verifying the carbon emission data D i After verification, the rationality of the selected verification node to D i is recorded by the supervision committee node; after all the committee nodes record, the record is compared with the records of other committee nodes, if more than 50% of the committee nodes are consistent, the record is the final aggregated opinion msg i , and the reference opinion view i is formed by msg i ; if all the records in the supervision committee do not reach 50% consistency, the current supervision committee is dissolved and re-election is carried out until the final aggregated opinion msg i is formed; If a node reports the data on the chain, the supervision committee will accept the report and check the node's evidence; if more than half of the committee nodes think that the report is true, the node's report is successful, the reported data is discarded, and all nodes participating in uploading the data are punished; otherwise, the node's report fails, and the committee does not take any action. 6.The method of recording and supervising carbon emission data based on blockchain according to claim 5, wherein: The reputation value of the supervision node and the information code updating rule comprises that the reputation value of the known supervision node is updated to R after the supervision in the i-1th round i-1 Then, the reputation value of the supervision node is updated to R after the supervision in the ith round i i-1 + ΔR, and ΔR is the reputation value change amount of the supervision node node in the ith round. If the supervision node gives the correct record in the ith round or gives the correct opinion in the accepted report process, the calculation formula of ΔR is:​ in, μ depends on the frequency of successful supervision by regulatory nodes The value of μ increases first and then decreases until the frequency of successful monitoring by the monitoring node reaches 1 / 2. If the supervision node gives a wrong record in the i th round of supervision or the committee is dissolved or fails to give a consistent opinion in the process of accepting the report, and the i th round of supervision committee is composed of γ nodes, then the calculation formula of ΔR is: After the end of the i-th round of chaining, the information code of the supervisor node will be updated; the supervisor node participates in the formation of one aggregated opinion msg, and the total number of supervision of the supervisor node increases by 1, experiences one committee dissolution, and the total number of supervision of the supervisor node increases by 1, accepts one report, and the total number of supervision of the supervisor node increases by 1; the supervisor node gives correct records in the process of forming the aggregated opinion msg i , and the successful supervision number S of the node increases by 1 C ; the supervisor node gives consistent opinions in the process of accepting node reports, and the successful supervision number S of the node increases by 1 C .

7. A blockchain-based carbon emission data recording and supervision system using the method of any one of claims 1-6, characterized in that: The collection module collects carbon emission data, verifies the rationality of carbon emission data, uploads to the reference opinion generated by the supervision committee randomly composed of the nodes with the most verification success frequency and the highest credit value, updates the credit value of each verification node, and constructs a consensus mechanism for verifying the rationality of carbon emission data; The supervision module, the supervision committee node records and checks the carbon emission data opinions submitted by each verification node, forms the aggregated opinion and the final reference opinion, and constructs a blockchain supervision mechanism based on the credit value of the node; The packaging module records the carbon emission data passed by verification and the change of the reputation value of the related node in this round, and publicizes the participating node, and constructs a three-level supervision packaging mechanism based on the reputation value; The verification module constructs a linear regression model for verifying the reasonableness of carbon emission data according to the reasonable range of carbon emission constituted by single transportation driving distance, transportation vehicle load, driver driving habit and fuel type used by the vehicle.

8. A computer device comprising: A memory and a processor; The memory stores a computer program, and the processor executes the computer program to implement the steps of the recording and supervision method of the carbon emission data based on the blockchain in any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by the processor to implement the steps of the recording and supervision method of the carbon emission data based on the blockchain in any one of claims 1 to 6.

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