Carbon data trusted circulation method and system based on block chain multi-sensor fusion

Abstract

The invention discloses a carbon data trusted circulation method based on block chain multi-sensor fusion. The method comprises the following steps that S1, equipment registers and authenticates in a block chain layer; s2, the data acquisition layer acquires data and signs; s3, verifying the received data by the fusion layer, and fusing after verification; s4, the block chain layer calculates a carbon sink scale according to the multi-node data; s5, the block header information responds to the edge node; s6, the application layer obtains data; the invention discloses a block chain multi-sensor fusion-based carbon data trusted circulation system. The system comprises a data acquisition layer, a fusion layer, a block chain layer and an application layer, according to the invention, on-chain device identity registration is carried out on the acquisition device, the detected carbon data is linked through a lightweight signature algorithm and a multi-sensor fusion algorithm, and final carbon data calculation is completed on the chain, so that manual processing of the data in the transmission and circulation process is reduced, and the risk of data tampering is reduced; the data circulation has credibility, and the authenticity transaction of the data can be promoted.

Description

Technical Field

[0001] The present invention belongs to the field of blockchain technology, and more specifically, it particularly relates to a carbon data trusted circulation method based on blockchain multi-sensor fusion. At the same time, the present invention also relates to a carbon data trusted circulation system based on blockchain multi-sensor fusion. Background Art

[0002] Carbon peak refers to the process in which the annual carbon dioxide emissions of a region or industry reach a historical high, then experience a plateau period and enter a period of continuous decline. It is the historical turning point when carbon dioxide emissions turn from increasing to decreasing, and it marks the decoupling of carbon emissions from economic development. The peak target includes the peak year and peak value.

[0003] Carbon neutrality refers to the fact that the direct and indirect carbon dioxide emissions from human activities in a certain area within a certain period of time (generally one year) are offset by the carbon dioxide absorbed through afforestation, etc., to achieve "net zero emissions" of carbon dioxide.

[0004] In promoting the marketization and development of data elements. As a new type of data asset for enterprises, carbon data faces many challenges in all stages of its life cycle, such as collection, transmission, processing and trading. There are many problems with traditional carbon data management methods, such as difficulty in ensuring data quality, opaque data exchange, low data credibility, unreliable data transmission and data islands. The use of blockchain technology can improve the reliability and credibility of key data of carbon assets such as carbon verification and carbon trading, which is of great significance to consolidating the data foundation of the carbon market and ensuring the smooth and effective operation of the carbon market.

[0005] Blockchain is decentralized and tamper-proof, and can store data securely. Once the data is uploaded to the chain, its credibility and authenticity can be guaranteed.

[0006] However, there are some problems with the existing technology: insufficient credibility of existing data, poor data quality, high risk of manual processing, concerns about data security and privacy, and difficulty in cross-regional data integration. Therefore, we propose a trusted circulation method and system for carbon data based on blockchain multi-sensor fusion. Summary of the invention

[0007] In view of the problems existing in the prior art, the purpose of the present invention is to provide a trusted circulation method and system for carbon data based on blockchain multi-sensor fusion. The identity of the collection equipment is registered on the chain through wireless sensor networking for different detection areas, and the detected carbon data is uploaded to the chain through a lightweight signature algorithm and a multi-sensor fusion algorithm, thereby reducing manual processing of data during transmission and circulation, reducing the risk of data tampering, making data circulation credible, and promoting authentic data transactions.

[0008] To achieve the above purpose, the present invention provides the following technical solution: a carbon data trusted circulation method based on blockchain multi-sensor fusion, comprising the following steps:

[0009] S1. Device registration and authentication at the blockchain layer: Register and authenticate each sensor device and edge node at the blockchain layer, issue public and private keys, and issue certificates;

[0010] S2, the data collection layer collects data and signs it: the sensor devices in the data collection layer collect data, then hash the data, and then use the private key to calculate the signature of the hashed data. The sensor sends the signature information and the original data information to the edge node;

[0011] S3, the fusion layer verifies the received data and then fuses it: after signature verification, the edge node in the fusion layer fuses the data collected by multiple sensors, pre-processes the collected data, and then extracts and classifies the collected data. The edge node uses the private key to calculate the signature of the classified data and sends the signature and related data to the blockchain layer;

[0012] S4. The blockchain layer calculates the carbon sink scale based on multi-node data: After verifying the signature of the edge node, the blockchain layer calculates the carbon emission equivalent of the transmitted data and the total carbon emissions in the current detection area through the smart contract, and calculates the carbon sink scale based on the carbon sink monitoring data. The calculation process and results are stored in the blockchain layer;

[0013] S5. Block header information response to edge node: The blockchain layer responds to the block header information to the edge node. The edge node, as a blockchain light node, only stores the block header information of the blockchain layer.

[0014] S6. Application layer obtains data: When conducting carbon trading and carbon data supervision, the upper-level applications in the application layer directly obtain carbon data and related calculation results from the blockchain layer to make the next decision.

[0015] Specifically, the registration and authentication of each sensor device and each edge node in S1 are respectively that the sensor device in the data acquisition layer is registered and authenticated in the blockchain layer, and each edge node in the fusion layer is registered and authenticated in the blockchain layer;

[0016] The public and private keys are respectively the private key sk and the public key pk.

[0017] Specifically, the sensor device is denoted as D = {D1, D2, ..., D n},D i ∈D; the edge node is denoted as T = {T1, T2, ..., T n},T i ∈T;

[0018] The public and private keys use the BLS short signature technology to ensure the security of data transmission collected by the sensor device. The bilinear mapping in the BLS short signature technology is e:G1×G2=G T , where G1, G2, G T is a multiplication group, the generator is P, and the hash function is selected The private key is sk and the public key is pk.

[0019] Specifically, the preprocessing in S3 includes denoising, cleaning, missing value compensation and normalization of the data;

[0020] The calculation formula for denoising is as follows:

[0021] For each data x i , use windows of different scales to perform median filtering, and then perform weighted fusion on the filtering results of different scales;

[0022]

[0023] Among them, y i represents the output data after filtering; S represents the number of scales, that is, the number of different scales used when performing the median filtering operation, and each scale corresponds to a filtering window of different sizes; w s Represents the weight of scale S, which is used to perform weighted averaging of filtering results of different scales; represents the result of median filtering the data at scale S. Specifically, It is all the data within the window range at scale S, that is, the neighborhood data at this scale; window s Represents the neighborhood window corresponding to the scale S. At different scales, the size of the neighborhood window is different.

[0024] Specifically, the calculation of the cleaning for outlier detection is as follows:

[0025] The Z-score of the data is obtained by calculating the difference between the data and the mean and dividing it by the standard deviation;

[0026]

[0027] Among them, x is the value of the data point, μ is the mean of the data set, σ is the standard deviation of the data set, and points with a value of Z greater than 3 or less than -3 are considered outliers and are removed;

[0028] The missing value compensation is used to fill the vacancies of outliers, and the missing values ​​are filled by the mean, and the mean is calculated as follows:

[0029]

[0030] Among them, x i ' is the value of non-missing data, and n is the number of non-missing data.

[0031] Specifically, the calculation of the normalization process is as follows:

[0032]

[0033] Among them, x i is the historical conversation record of input, x min is the minimum value in the data set, x max is the maximum value in the dataset, scaling the historical conversation records to the range of [0, 1].

[0034] Specifically, the sensor device in S2 is set to D i , sensor device D i Collect data data, sensor equipment D i Use the private key sk to calculate the signature Sign_D of the data i =h(data) sk , where h represents the hash algorithm, sensor device D i Send the signature information and the original data information s=data||Sign_D to the edge node i .

[0035] Specifically, the edge node in S3 integrates the monitoring data of multiple sensors, including CO2, CH4, N2O and other greenhouse gases, as well as soil organic matter content, ecosystem carbon storage and pH value, extracts and classifies the data, and forms n groups of detection data types G = {G1, G2, ..., G n},G i ∈G, the same type of data is recorded as C-data={C1-data,C2-data,…,C n -data},C i -data∈C-data; edge nodes use T i The private key sk calculates the signature Sign_T for s, G, C_data i =h(s,G,C_data) sk , and the signature and related data Msg = sign_T i ||G||C_data is sent to the blockchain layer.

[0036] Specifically, after the blockchain layer in S4 verifies the signature of the edge node, the parameter of the emission factor is set to Q = {Q1, Q2, ..., Q n},Q i ∈Q, calculate the carbon emission equivalent of the transmitted data through smart contracts And the total carbon emissions in the current detection area

[0037] A trusted carbon data circulation system based on blockchain multi-sensor fusion, including data collection layer, fusion layer, blockchain layer and application layer;

[0038] The data collection layer includes wireless sensors and remote sensing equipment registered in the blockchain network and deployed in the carbon data detection area, which are used to measure energy consumption, material consumption, travel activities, and other direct or indirect carbon emission activity data;

[0039] The fusion layer includes edge nodes, base stations and gateways. The fusion layer is used to fuse the data collected by multiple sensors, and then sign the data after pre-processing, and then send the signature and related data to the blockchain layer;

[0040] The blockchain layer is used to store the measured data;

[0041] The application layer includes data authenticity verification applications, carbon rights pricing applications, carbon rights trading applications and carbon data supervision applications. When the application layer uses carbon data, it verifies the credibility and source authenticity of the data based on blockchain.

[0042] Technical effects and advantages of the present invention:

[0043] The data collection layer of the present invention is used to measure energy consumption, material consumption, travel activities, and other direct or indirect carbon emission activity data; the fusion layer calculates carbon emissions and elimination data by fusing multi-sensor data based on the set emission factors; and the blockchain layer is used to store the measured data on the chain through the core network; when the application layer uses carbon data, the credibility and source authenticity of the data are verified based on the blockchain;

[0044] Compared with the traditional centralized, multi-manual carbon data system, the carbon data trusted circulation method and system based on blockchain multi-sensor fusion has the advantages of full data traceability and supervision. The data endorsed by the blockchain has a higher trust value and can effectively reduce the trust cost in the carbon trading process.

[0045] The present invention registers the device identity of the collection equipment on the chain through wireless sensor networking for different detection areas. The detected carbon data is signed and fused through a lightweight signature algorithm and a multi-sensor fusion algorithm, and the final carbon data is completed on the chain to reduce data transmission on the chain, reduce manual processing of data during transmission and circulation, and reduce the risk of data tampering; the data circulation based on this system is credible and can promote authentic data transactions.

[0046] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the steps provided by the present invention;

[0048] Figure 2 It is a schematic diagram of the system structure provided by the present invention. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] like Figure 1 As shown, the carbon data trusted circulation method based on blockchain multi-sensor fusion provided by the embodiment of the present invention includes the following steps:

[0051] S1. Device registration and authentication at the blockchain layer: Register and authenticate each sensor device and edge node at the blockchain layer, issue public and private keys, and issue certificates;

[0052] S2, the data collection layer collects data and signs it: the sensor devices in the data collection layer collect data, then hash the data, and then use the private key to calculate the signature of the hashed data. The sensor sends the signature information and the original data information to the edge node;

[0053] S3, the fusion layer verifies the received data and then fuses it: after signature verification, the edge node in the fusion layer fuses the data collected by multiple sensors, pre-processes the collected data, and then extracts and classifies the collected data. The edge node uses the private key to calculate the signature of the classified data and sends the signature and related data to the blockchain layer;

[0054] S4. The blockchain layer calculates the carbon sink scale based on multi-node data: After verifying the signature of the edge node, the blockchain layer calculates the carbon emission equivalent of the transmitted data and the total carbon emissions in the current detection area through the smart contract, and calculates the carbon sink scale based on the carbon sink monitoring data. The calculation process and results are stored in the blockchain layer;

[0055] S5. Block header information response to edge node: The blockchain layer responds to the block header information to the edge node. The edge node, as a blockchain light node, only stores the block header information of the blockchain layer.

[0056] S6. Application layer obtains data: When conducting carbon trading and carbon data supervision, the upper-level applications in the application layer directly obtain carbon data and related calculation results from the blockchain layer to make the next decision.

[0057] In this embodiment, preferably, the registration and authentication of each sensor device and each edge node in S1 are respectively that the sensor device in the data acquisition layer is registered and authenticated in the blockchain layer, and each edge node in the fusion layer is registered and authenticated in the blockchain layer;

[0058] The public and private keys are respectively private key sk and public key pk;

[0059] It should be noted that registration and authentication of each sensor device and each edge node are performed on the blockchain layer, which facilitates the identification of each sensor device and each edge node and improves security.

[0060] In this embodiment, preferably, the sensor device is recorded as D={D1, D2, ..., D n},D i ∈D;

[0061] The edge nodes are denoted as T = {T1, T2, ..., T n},T i ∈T;

[0062] The public and private keys use the BLS short signature technology to ensure the security of data transmission collected by the sensor device. The bilinear mapping in the BLS short signature technology is e:G1×G2=G T , where G1, G2, G T is a multiplication group, the generator is P, and the hash function is selected The private key is sk and the public key is pk;

[0063] It should be noted that by setting up sensor devices, edge nodes and blockchain layers, it is convenient to achieve accurate transmission and processing of data, and the BLS short signature technology is used to ensure the security of data transmission collected by sensor devices.

[0064] In this embodiment, preferably, the preprocessing in S3 includes denoising, cleaning, missing value compensation and normalization of the data;

[0065] The calculation formula for denoising is as follows:

[0066] For each data x i , use windows of different scales to perform median filtering, and then perform weighted fusion on the filtering results of different scales;

[0067]

[0068] Among them, y i represents the output data after filtering; S represents the number of scales, that is, the number of different scales used when performing the median filtering operation, and each scale corresponds to a filtering window of different sizes; w s Represents the weight of scale S, which is used to perform weighted averaging of filtering results of different scales; represents the result of median filtering the data at scale S. Specifically, It is all the data within the window range at scale S, that is, the neighborhood data at this scale; window s Represents the neighborhood window corresponding to the scale S. At different scales, the size of the neighborhood window is different;

[0069] It should be noted that the data collected by the sensor device is filtered through denoising processing to improve the accuracy of the data and facilitate subsequent data calculation and processing.

[0070] In this embodiment, preferably, the calculation of the cleaning outlier detection is as follows:

[0071] The Z-score of the data is obtained by calculating the difference between the data and the mean and dividing it by the standard deviation;

[0072]

[0073] Among them, x is the value of the data point, μ is the mean of the data set, σ is the standard deviation of the data set, and points with a value of Z greater than 3 or less than -3 are considered outliers and are removed;

[0074] The missing value compensation is used to fill the vacancies of outliers, and the missing values ​​are filled by the mean, and the mean is calculated as follows:

[0075]

[0076] Among them, x i ' is the value of non-missing data, n is the number of non-missing data;

[0077] It should be noted that in order to detect outliers in the data and eliminate them, the accuracy of the data is improved, and the elimination of outliers and compensation for missing values ​​are performed to improve the integrity of the data.

[0078] In this embodiment, preferably, the calculation of the normalization process is as follows:

[0079]

[0080] Among them, x i is the historical conversation record of input, xmin is the minimum value in the data set, x max is the maximum value in the data set, scaling the historical conversation records to the range of [0, 1];

[0081] It should be noted that by processing the data through normalization, the data can be mapped, which is convenient for reducing the subsequent calculation pressure and complexity of the data.

[0082] In this embodiment, preferably, the sensor device in S2 is set to D i , sensor device D i Collect data data, sensor equipment D i Use the private key sk to calculate the signature Sign_D of the data i =h(data) sk , where h represents the hash algorithm, sensor device D i Send the signature information and the original data information s=data||Sign_D to the edge node i ;

[0083] It should be noted that the data in the carbon data detection area is collected through sensor equipment, and the data is calculated and signed to facilitate secure transmission of the data.

[0084] In this embodiment, preferably, the edge node in S3 integrates the monitoring data of multiple sensors, including CO2, CH4, N2O and other greenhouse gases, as well as soil organic matter content, ecosystem carbon storage and pH value, and extracts and classifies the data to form n groups of data types G = {G1, G2, ..., G n},G i ∈G;

[0085] The same type of data is recorded as C-data = {C1-data, C2-data, ..., C n -data},C i -data∈C-data; edge nodes use T i The private key sk calculates the signature Sign_T for s, G, C_data i =h(s,G,C_data) sk , and the signature and related data Msg = sign_T i ||G||C_data is sent to the blockchain layer;

[0086] It should be noted that the monitored data is classified and processed, and the edge node fuses the data from multiple sensors to facilitate the subsequent feature extraction and classification of the data, and the data is signed again to achieve the transmission of the data to the blockchain layer.

[0087] In this embodiment, preferably, after the blockchain layer in S4 verifies the signature of the edge node, the parameter of the emission factor is set to Q = {Q1, Q2, ..., Q n},Q i ∈Q, calculate the carbon emission equivalent of the transmitted data through smart contracts And the total carbon emissions in the current detection area

[0088] It should be noted that the blockchain layer calculates the data based on the emission factors and smart contracts to obtain carbon emission equivalents and total carbon emissions, which facilitates the acquisition of carbon data and related calculation results for the next decision.

[0089] refer to Figure 2 , a carbon data trusted circulation system based on blockchain multi-sensor fusion, including data collection layer, fusion layer, blockchain layer and application layer;

[0090] The data collection layer includes wireless sensors and remote sensing equipment registered in the blockchain network and deployed in the carbon data detection area, which are used to measure energy consumption, material consumption, travel activities, and other direct or indirect carbon emission activity data;

[0091] The fusion layer includes edge nodes, base stations and gateways. The fusion layer is used to fuse the data collected by multiple sensors, and then sign the data after pre-processing, and then send the signature and related data to the blockchain layer;

[0092] The blockchain layer is used to store the measured data;

[0093] The application layer includes data authenticity verification applications, carbon rights pricing applications, carbon rights trading applications, and carbon data supervision applications. When the application layer uses carbon data, it verifies the credibility and source authenticity of the data based on the blockchain;

[0094] It should be noted that for different detection areas, the collection equipment is registered on the chain through wireless sensor networking, and the detected carbon data is uploaded to the chain through a lightweight signature algorithm and a multi-sensor fusion algorithm, reducing manual processing of data during transmission and circulation, and reducing the risk of data tampering; the data flow based on this system is credible and can promote authentic data transactions.

[0095] The specific operation process of this application:

[0096] Step 1: Register and authenticate the device in the blockchain layer: Register and authenticate each sensor device and each edge node in the blockchain layer, register and authenticate the sensor devices in the data collection layer in the blockchain layer, register and authenticate each edge node in the fusion layer in the blockchain layer, issue public and private keys, and issue certificates;

[0097] Step 2: The data collection layer collects data and signs it: The sensor devices in the data collection layer collect data, then hash the data, and then use the private key to calculate the signature of the hashed data. The sensor sends the signature information and the original data information to the edge node;

[0098] The sensor device is denoted as D = {D1, D2, ..., D n},D i ∈D; the edge node is denoted as T = {T1, T2, ..., T n},T i ∈T;

[0099] The public and private keys use the BLS short signature technology to ensure the security of data transmission collected by the sensor device. The bilinear mapping in the BLS short signature technology is e:G1×G2=G T , where G1, G2, G T is a multiplication group, the generator is P, and the hash function is selected The private key is sk and the public key is pk;

[0100] The sensor device is set to D i , sensor device D i Collect data data, sensor equipment D i Use the private key sk to calculate the signature Sign_D of the data i =h(data) sk , where h represents the hash algorithm, sensor device D i Send the signature information and the original data information s=data||Sign_D to the edge node i ;

[0101] Step 3: The fusion layer verifies the received data and then fuses it: After signature verification, the edge node in the fusion layer fuses the data collected by multiple sensors, pre-processes the collected data, and then extracts and classifies the collected data. The edge node uses the private key to calculate the signature of the classified data and sends the signature and related data to the blockchain layer;

[0102] The edge node integrates the monitoring data of multiple sensors, including CO2, CH4, N2O and other greenhouse gases, as well as soil organic matter content, ecosystem carbon storage and pH value, extracts and classifies the data, and forms n groups of detection data types G = {G1, G2, ..., G n},G i ∈G, the same type of data is recorded as C-data={C1-data,C2-data,…,C n -data},C i -data∈C-data; edge nodes use T i The private key sk calculates the signature Sign_T for s, G, C_data i =h(s,G,C_data) sk , and the signature and related data Msg = sign_T i ||G||C_data is sent to the blockchain layer;

[0103] Step 4: The blockchain layer calculates the carbon sink scale based on multi-node data: After verifying the signature of the edge node, the blockchain layer calculates the carbon emission equivalent of the transmitted data and the total carbon emissions in the current detection area through the smart contract, and calculates the carbon sink scale based on the carbon sink monitoring data. The calculation process and results are stored in the blockchain layer;

[0104] After the blockchain layer verifies the signature of the edge node, it sets the emission factor parameter to Q = {Q1, Q2, …, Q n},Q i ∈Q, calculate the carbon emission equivalent of the transmitted data through smart contracts And the total carbon emissions in the current detection area

[0105] Step 5: Block header information is responded to the edge node: The blockchain layer responds to the block header information to the edge node. The edge node, as a blockchain light node, only stores the block header information of the blockchain layer.

[0106] Step 6: Application layer obtains data: When conducting carbon trading and carbon data supervision, the upper-layer applications in the application layer directly obtain carbon data and related calculation results from the blockchain layer to make the next decision;

[0107] Through neural networks, potential correlations and trends of carbon data and related calculations can be analyzed to achieve decision-making calculation analysis:

[0108] Propagation from input layer to hidden layer:

[0109] Among them, a k is the input of the hidden layer neuron, w this the weight from the input layer to the hidden layer, x i is the output of the input layer neuron, b k is the bias of the hidden layer neurons, n is the number of input layer neurons;

[0110] Propagation from hidden layer to output layer:

[0111] Among them, y i is the output of the output layer neuron, w hj is the weight from the hidden layer to the output layer, σ is the activation function, m is the number of neurons in the hidden layer, and b j is the bias of the output layer;

[0112] Through the output of the output layer, the potential correlation and trend of carbon data and related calculations are obtained.

[0113] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A trusted carbon data circulation method based on blockchain multi-sensor fusion, characterized in that: The steps include: S1. Device registration and authentication at the blockchain layer: Register and authenticate each sensor device and edge node at the blockchain layer, issue public and private keys, and issue certificates; S2, the data collection layer collects data and signs it: the sensor devices in the data collection layer collect data, then hash the data, and then use the private key to calculate the signature of the hashed data. The sensor sends the signature information and the original data information to the edge node; S3, the fusion layer verifies the received data and then fuses it: after signature verification, the edge node in the fusion layer fuses the data collected by multiple sensors, pre-processes the collected data, and then extracts and classifies the collected data. The edge node uses the private key to calculate the signature of the classified data and sends the signature and related data to the blockchain layer; S4. The blockchain layer calculates the carbon sink scale based on multi-node data: After verifying the signature of the edge node, the blockchain layer calculates the carbon emission equivalent of the transmitted data and the total carbon emissions in the current detection area through the smart contract, and calculates the carbon sink scale based on the carbon sink monitoring data. The calculation process and results are stored in the blockchain layer; S5. Block header information response to edge node: The blockchain layer responds to the block header information to the edge node. The edge node, as a blockchain light node, only stores the block header information of the blockchain layer. S6. Application layer obtains data: When conducting carbon trading and carbon data supervision, the upper-level applications in the application layer directly obtain carbon data and related calculation results from the blockchain layer to make the next decision.

2. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 1 is characterized by: The registration and authentication of each sensor device and each edge node in S1 are respectively the registration and authentication of the sensor device in the data acquisition layer in the blockchain layer, and the registration and authentication of each edge node in the fusion layer in the blockchain layer; The public and private keys are respectively the private key sk and the public key pk.

3. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 2 is characterized by: The sensor device is denoted as D = {D1, D2, ..., D n },D i ∈D; the edge node is denoted as T = {T1, T2, ..., T n },T i ∈T; The public and private keys use the BLS short signature technology to ensure the security of data transmission collected by the sensor device. The bilinear mapping in the BLS short signature technology is e:G1×G2=G T , where G1, G2, G T is a multiplication group, the generator is P, and the hash function is selected The private key is sk and the public key is pk.

4. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 1 is characterized by: The preprocessing in S3 includes denoising, cleaning, missing value compensation and normalization of the data; The calculation formula for denoising is as follows: For each data x i , use windows of different scales to perform median filtering, and then perform weighted fusion on the filtering results of different scales; Among them, y i represents the output data after filtering; S represents the number of scales, that is, the number of different scales used when performing the median filtering operation, and each scale corresponds to a filtering window of different sizes; w s Represents the weight of scale S, which is used to perform weighted averaging of filtering results of different scales; represents the result of median filtering the data at scale S. Specifically, It is all the data within the window range at scale S, that is, the neighborhood data at this scale; window s Represents the neighborhood window corresponding to the scale S. At different scales, the size of the neighborhood window is different.

5. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 3 is characterized by: The calculation of the cleaning pair outlier detection is as follows: The Z-score of the data is obtained by calculating the difference between the data and the mean and dividing it by the standard deviation; Among them, x is the value of the data point, μ is the mean of the data set, σ is the standard deviation of the data set, and points with a value of Z greater than 3 or less than -3 are considered outliers and are removed; The missing value compensation is used to fill the vacancies of outliers, and the missing values ​​are filled by the mean, and the mean is calculated as follows: Among them, x i ' is the value of non-missing data, and n is the number of non-missing data.

6. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 4 is characterized by: The calculation of the normalization process is as follows: Among them, x i is the historical conversation record of input, x min is the minimum value in the data set, x max is the maximum value in the dataset, scaling the historical conversation records to the range of [0, 1].

7. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 2 is characterized by: The sensor device in S2 is set to D i , sensor device D i Collect data data, sensor equipment D i Use the private key sk to calculate the signature Sign_D of the data i =h(data) sk , where h represents the hash algorithm, sensor device D i Send the signature information and the original data information s=data||Sign_D to the edge node i .

8. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 2 is characterized by: The edge node in S3 integrates the monitoring data of multiple sensors, including CO2, CH4, N2O and other greenhouse gases, as well as soil organic matter content, ecosystem carbon storage and pH value, and extracts and classifies the data to form n groups of detection data types G = {G1, G2, ..., G n },G i ∈G, the same type of data is recorded as C-data={C1-data,C2-data,…,C n -data},C i -data∈C-data; edge nodes use T i The private key sk calculates the signature Sign_T for s, G, C_data i =h(s,G,C_data) sk , and the signature and related data Msg = sign_T i ||G||C_data is sent to the blockchain layer.

9. The carbon data trusted circulation method based on blockchain multi-sensor fusion according to claim 1 is characterized by: After the blockchain layer in S4 verifies the signature of the edge node, the parameter of the emission factor is set to Q = {Q1, Q2, ..., Q n },Q i ∈Q, calculate the carbon emission equivalent of the transmitted data through smart contracts And the total carbon emissions in the current detection area 10. The carbon data trusted circulation system based on blockchain multi-sensor fusion is characterized by: The system is used to implement the method described in any one of claims 1 to 9, including a data collection layer, a fusion layer, a blockchain layer and an application layer; The data collection layer includes wireless sensors and remote sensing equipment registered in the blockchain network and deployed in the carbon data detection area, which are used to measure energy consumption, material consumption, travel activities, and other direct or indirect carbon emission activity data; The fusion layer includes edge nodes, base stations and gateways. The fusion layer is used to fuse the data collected by multiple sensors, and then sign the data after pre-processing, and then send the signature and related data to the blockchain layer; The blockchain layer is used to store the measured data; The application layer includes data authenticity verification applications, carbon rights pricing applications, carbon rights trading applications and carbon data supervision applications. When the application layer uses carbon data, it verifies the credibility and source authenticity of the data based on blockchain.

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