Carbon data verification method, device and computer equipment transmitted across a blockchain

Abstract

The application relates to a carbon data verification method, device and computer equipment for cross-blockchain transmission. The method comprises the following steps: obtaining carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain; extracting a plurality of source semantic data from the carbon emission data, and mapping each source semantic data into target semantic data in the target blockchain; converting the plurality of source semantic data into a source semantic hash tree, and detecting a first target summary value of the source semantic hash tree; converting the plurality of target semantic data into a target semantic hash tree, and detecting a second target summary value of the target semantic hash tree; and verifying the transmission consistency of the carbon emission data between the source blockchain and the target blockchain based on the first target summary value and the second target summary value. The method can accurately verify the carbon data for cross-blockchain transmission.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchains, and in particular to a carbon data verification method and device for cross-blockchain transmission and a computer device. BACKGROUND

[0002] Carbon emission data management is gradually shifting from traditional centralized storage mode to distributed and trusted storage system based on blockchain. Currently, power companies, carbon trading platforms and green certification agencies have generally built their own independent blockchain systems. The interaction and right confirmation of carbon data between different platforms are increasingly in demand. Therefore, there is an urgent need for a means of consistent verification of carbon data transmitted across blockchains.

[0003] In traditional technology, the means of consistent verification of carbon data transmitted across blockchains generally uses mechanisms such as field hash comparison, anchor structure synchronization or relay node consensus. For example, the first field hash value of each carbon emission data in the source blockchain is detected, as well as the second field hash value of the same carbon emission data in the target blockchain. The first field hash value is compared with the second field hash value. When the first field hash value is consistent with the second field hash value, it means that the carbon data is consistent in the source blockchain and when transmitted to the target blockchain.

[0004] However, the current carbon data verification method for cross-blockchain transmission still has the problem of inaccuracy. SUMMARY

[0005] Therefore, it is necessary to provide an accurate carbon data verification method, device, computer device, computer readable storage medium and computer program product for cross-blockchain transmission in view of the above technical problems.

[0006] In a first aspect, the present application provides a carbon data verification method for cross-blockchain transmission, the method comprising:

[0007] obtaining carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain;

[0008] extracting a plurality of source semantic data from the carbon emission data, and mapping each source semantic data to target semantic data in the target blockchain;

[0009] converting the plurality of source semantic data into a source semantic hash tree, and detecting a first target digest value of the source semantic hash tree;

[0010] converting the plurality of target semantic data into a target semantic hash tree, and detecting a second target digest value of the target semantic hash tree;

[0011] The transmission consistency of the carbon emission data between the source blockchain and the target blockchain is verified based on the first target summary value and the second target summary value.

[0012] In one of the embodiments, the plurality of source semantic data is converted into a source semantic hash tree, including:

[0013] For each source semantic data, the importance quantitative value of the source semantic data and the semantic structure level corresponding to the source semantic data are detected;

[0014] Based on the importance quantitative value and the semantic structure level, the source semantic data is converted into semantic information corresponding to the leaf node in the source semantic hash tree.

[0015] In one of the embodiments, the first target summary value of the source semantic hash tree is detected, including:

[0016] The sub-summary value of the semantic information corresponding to each leaf node in the source semantic hash tree is detected;

[0017] Based on each sub-summary value, the root summary value corresponding to the root node of the source semantic hash tree is detected;

[0018] The root summary value is determined as the first target summary value of the source semantic hash tree.

[0019] In one of the embodiments, before the plurality of source semantic data is converted into the source semantic hash tree, the method further includes:

[0020] The plurality of source semantic data is subjected to semantic standardization processing;

[0021] The semantic standardization processing of the plurality of source semantic data includes:

[0022] The field name and the field value of each of the plurality of source semantic data are obtained;

[0023] The field name is mapped to a target field name of the same format, and the field value is mapped to a target field value of the same format.

[0024] In one of the embodiments, the source semantic hash tree includes a plurality of first leaf nodes, and the target semantic hash tree includes a plurality of second leaf nodes, the first leaf nodes and the second leaf nodes corresponding one by one;

[0025] The transmission consistency of the carbon emission data between the source blockchain and the target blockchain is verified based on the first target summary value and the second target summary value, including:

[0026] In the case that the first target summary value and the second target summary value are inconsistent, the first sub-summary value corresponding to the semantic information of each first leaf node and the second sub-summary value corresponding to the semantic information of each second leaf node are obtained;

[0027] For any leaf node combination, query the target leaf node combination different from the first sub summary value and the second sub summary value, wherein the leaf node combination includes any first target leaf node in the plurality of first leaf nodes and a second target leaf node matched with the first target leaf node in the plurality of second leaf nodes;

[0028] Obtain the first target node identifier corresponding to the first target leaf node and the second target node identifier corresponding to the second target leaf node in the target leaf node combination, and combine the first target node identifier, the second target node identifier, the source semantic data corresponding to the first target node identifier, and the target semantic data corresponding to the second target node identifier to obtain the verification failure information of the carbon emission data.

[0029] In one of the embodiments, each source semantic data is respectively mapped to target semantic data in the target blockchain, including:

[0030] Obtain the semantic conversion relationship between the source blockchain and the target blockchain, wherein the semantic conversion relationship includes at least one of unit conversion factor conversion relationship, carbon factor standard version conversion relationship, and field name conversion relationship;

[0031] For each source semantic data, map the source semantic data to target semantic data in the target blockchain according to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship.

[0032] In one of the embodiments, after verifying the consistency of the transmission of the carbon emission data between the source blockchain and the target blockchain based on the first target summary value and the second target summary value, the method further includes:

[0033] When the first target summary value and the second target summary value are consistent, generate carbon data consistency proof information between the source blockchain and the target blockchain, and load the carbon data consistency proof information into the contract agreement between the source blockchain and the target blockchain.

[0034] In a second aspect, the application also provides a carbon data verification device for cross-blockchain transmission, the device comprising:

[0035] An initial data acquisition module is configured to acquire carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain;

[0036] A semantic data acquisition module is configured to extract a plurality of source semantic data from the carbon emission data, and map each source semantic data to target semantic data in the target blockchain;

[0037] The first abstract generation module is configured to convert the plurality of pieces of source semantic data into a source semantic hash tree, and detect a first target abstract value of the source semantic hash tree.

[0038] The second abstract generation module is configured to convert the plurality of pieces of target semantic data into a target semantic hash tree, and detect a second target abstract value of the target semantic hash tree.

[0039] The verification module is configured to verify the transmission consistency of the carbon emission data between the source blockchain and the target blockchain based on the first target abstract value and the second target abstract value.

[0040] In a third aspect, the present application further provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

[0041] obtaining carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain;

[0042] extracting a plurality of pieces of source semantic data from the carbon emission data, and mapping each piece of source semantic data to target semantic data in the target blockchain;

[0043] converting the plurality of pieces of source semantic data into a source semantic hash tree, and detecting a first target abstract value of the source semantic hash tree;

[0044] converting the plurality of pieces of target semantic data into a target semantic hash tree, and detecting a second target abstract value of the target semantic hash tree;

[0045] verifying the transmission consistency of the carbon emission data between the source blockchain and the target blockchain based on the first target abstract value and the second target abstract value.

[0046] In a fourth aspect, the present application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the following steps:

[0047] obtaining carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain;

[0048] extracting a plurality of pieces of source semantic data from the carbon emission data, and mapping each piece of source semantic data to target semantic data in the target blockchain;

[0049] converting the plurality of pieces of source semantic data into a source semantic hash tree, and detecting a first target abstract value of the source semantic hash tree;

[0050] converting the plurality of pieces of target semantic data into a target semantic hash tree, and detecting a second target abstract value of the target semantic hash tree;

[0051] Verify the transmission consistency of the carbon emission data between the source blockchain and the target blockchain based on the first target digest value and the second target digest value.

[0052] In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the following steps:

[0053] Obtain carbon emission data to be verified in the source blockchain, wherein the carbon emission data is data to be transmitted to the target blockchain;

[0054] Extract a plurality of source semantic data from the carbon emission data, and map each source semantic data to target semantic data in the target blockchain respectively;

[0055] Convert the plurality of source semantic data into a source semantic hash tree, and detect a first target digest value of the source semantic hash tree;

[0056] Convert the plurality of target semantic data into a target semantic hash tree, and detect a second target digest value of the target semantic hash tree;

[0057] Verify the transmission consistency of the carbon emission data between the source blockchain and the target blockchain based on the first target digest value and the second target digest value.

[0058] The above cross-blockchain transmission carbon data verification method, device, computer equipment, computer readable storage medium and computer program product, the traditional method is easy to ignore the semantic information carried in the carbon emission data, and cannot judge whether the data under the same structure expresses the same meaning, therefore, the present application proposes a more accurate carbon data verification method, in the whole process, instead of relying on the way of comparing the hash of the field value, but introducing the semantic data of the carbon emission data, and mapping each source semantic data to target semantic data in the target blockchain, then converting the source semantic data into a source semantic hash tree, and converting the plurality of target semantic data into a target semantic hash tree, to detect the digest value in the source semantic hash tree and the target semantic hash tree, to ensure that the data in different blockchains even if the structure is similar, the semantics can also be judged to be completely consistent, and the accuracy of verifying the transmission consistency of the carbon emission data between the source blockchain and the target blockchain is improved. BRIEF DESCRIPTION OF DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can obtain other related drawings without creative labor.

[0060] Figure 1 An application environment diagram of the carbon data verification method across blockchains in an embodiment;

[0061] Figure 2 A flowchart of the carbon data verification method across blockchains in an embodiment;

[0062] Figure 3 A flowchart of the carbon data verification method across blockchains in another embodiment;

[0063] Figure 4 A flowchart of the carbon data verification method across blockchains in yet another embodiment;

[0064] Figure 5 A flowchart of the carbon data verification method across blockchains in still another embodiment;

[0065] Figure 6 A structural block diagram of the carbon data verification device across blockchains in an embodiment;

[0066] Figure 7 An internal structure diagram of the computer device in an embodiment. DETAILED DESCRIPTION

[0067] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are used to explain the present application and do not limit the present application.

[0068] Carbon emission data management is gradually shifting from traditional centralized storage mode to distributed trusted notarization system based on blockchain. Currently, power enterprises, carbon trading platforms and green certification agencies have generally constructed their own independent blockchain systems. The interaction and right confirmation of carbon data between different platforms are increasingly growing. Therefore, there is an urgent need for a means of consistent verification of carbon data across different blockchains.

[0069] In traditional technologies, the means of consistent verification of carbon data across different blockchains generally adopts mechanisms such as field hash comparison, anchor structure synchronization or relay node consensus. For example, the first field hash value of each carbon emission data in the source blockchain is detected, as well as the second field hash value of the same carbon emission data in the target blockchain. Then, the first field hash value is compared with the second field hash value. When the first field hash value is consistent with the second field hash value, it means that the carbon data is consistent in the source blockchain and the target blockchain.

[0070] However, this way ignores the semantic information carried by carbon emission data, and cannot determine whether the data under the same structure expresses the same meaning, resulting in many risks and defects in actual business. This problem is reflected in the following aspects:

[0071] 1. The current cross-blockchain technology cannot identify the difference in field meaning, for example, the "carbon emission" field has the same name in two platforms, but one is based on electricity consumption and the other is based on power generation, and the actual meaning is completely different.

[0072] 2. Different platforms may use different carbon factor sources, unit conversion standards, time periods, and statistical coverage, making it difficult to ensure semantic consistency even if the field structure is consistent.

[0073] 3. The current data cross-blockchain verification mechanism lacks the ability to model the "carbon data context", and cannot identify and confirm the logic model behind the carbon emission indicators, which can easily cause misreading and misuse, affecting the reliable transmission and application of data.

[0074] Therefore, to solve the above problems, the present application provides a carbon data verification method for cross-blockchain transmission. In the whole process, instead of relying on the way of comparing the hash values of field values, the semantic data of carbon emission data is introduced, and each source semantic data is mapped into target semantic data in the target blockchain. Then, the source semantic data is converted into a source semantic hash tree, and multiple target semantic data is converted into a target semantic hash tree, to detect the digest values in the source semantic hash tree and the target semantic hash tree, and ensure that the data in different blockchains is consistent in structure. Even if the semantics are completely consistent, it improves the accuracy of verifying the consistency of carbon emission data transmission between the source blockchain and the target blockchain.

[0075] The cross-blockchain transmission carbon data verification method provided by the embodiments of the present application can be applied in the application environment as shown in Figure 1 The first terminal 101 of the first institution is deployed with a source blockchain 102, the second terminal 103 of the second institution is deployed with a target blockchain 104, the source blockchain 102 and the target blockchain 104 communicate through a relay server 106, and the first terminal 101 and the second terminal 103 both communicate with the relay server 106. The data storage system can store the data required to be processed by the relay server 106. The data storage system can be integrated on the relay server 106, or placed on the cloud or other network servers.

[0076] The staff triggers a carbon data verification control on a carbon data verification interface of the first terminal 101. The first terminal 101 generates a carbon data verification request in response to the triggering request of the carbon data verification control, and sends the carbon data verification request to the relay server 106. The relay server 106 obtains the carbon emission data to be verified in the source blockchain 102, wherein the carbon emission data is data to be transmitted to the target blockchain 104, extracts a plurality of source semantic data from the carbon emission data, and maps each source semantic data to target semantic data in the target blockchain 104. The plurality of source semantic data is converted into a source semantic hash tree, and a first target digest value of the source semantic hash tree is detected. The plurality of target semantic data is converted into a target semantic hash tree, and a second target digest value of the target semantic hash tree is detected. Based on the first target digest value and the second target digest value, the transmission consistency of the carbon emission data between the source blockchain 102 and the target blockchain 104 is verified. Further, the relay server 106 can push the verification result of the transmission consistency of the carbon emission data between the source blockchain 102 and the target blockchain 104 to the second terminal 103, which is displayed to the staff by the second terminal 103, so that the staff determines that the carbon emission data is consistent in the source blockchain and the target blockchain. At this time, the carbon emission data can be transmitted from the source blockchain 102 to the target blockchain 104.

[0077] The first terminal 101 and the second terminal 103 can be, but are not limited to, various personal computers, notebook computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things device can be a smart speaker, a smart television, a smart air conditioner, a smart vehicle device, a projection device, etc. The portable wearable device can be a smart watch, a smart bracelet, a head-mounted device, etc. The head-mounted device can be a virtual reality (VR) device, an augmented reality (AR) device, smart glasses, etc. The relay server 106 can be a standalone physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0078] In an exemplary embodiment, as shown in Figure 2 , a carbon data verification method for cross-blockchain transmission is provided. The method is applied to the relay server 106 in Figure 1 for example. Wherein:

[0079] S100, obtaining carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain.

[0080] The blockchain is an integrated application of distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm and other technologies, and has the characteristics of decentralization, non-tamperability and traceability.

[0081] Specifically, carbon emission data management gradually shifts from the traditional centralized storage mode to a distributed and trustworthy storage system based on blockchain. Currently, power enterprises, carbon trading platforms and green certification agencies have generally built their own independent blockchain systems. The interaction and right confirmation of carbon data among different agencies are increasingly growing. Therefore, when carbon data needs to be transmitted from one agency to another target agency, the carbon data is transmitted from the source blockchain corresponding to the one agency to the target blockchain corresponding to the target agency. During this transmission process, the transmission consistency of the carbon emission data between the source blockchain and the target blockchain needs to be verified. At this time, the carbon emission data to be verified in the source blockchain is first obtained. In practical applications, the carbon emission data can be the carbon emission of electricity consumption and green electricity consumption record.

[0082] S200, extracting a plurality of source semantic data from the carbon emission data, and mapping each source semantic data to target semantic data in the target blockchain.

[0083] The semantic data is data that gives a clear meaning to the data, so that it can be understood and processed by a computer, and focuses on the meaning of the data rather than the format.

[0084] Specifically, a plurality of source semantic data with semantic attributes are automatically extracted from the carbon emission data. The source semantic data includes but is not limited to the following fields:

[0085] indicator_name (indicator name);

[0086] value (value);

[0087] unit (unit, such as );

[0088] emission_factor_source (carbon factor source);

[0089] region (applicable region);

[0090] period (statistical period);

[0091] calculation_method (calculation method).

[0092] The above fields are source semantic data, which can provide basic units for the construction of a semantic hash tree. Further, the plurality of source semantic data can be sorted according to a predetermined hierarchical structure of each source semantic data, and a semantic structure can be formed, and then the semantic structure is converted into a hash tree.

[0093] Further, each source semantic data can also be mapped into target semantic data in the target blockchain respectively, so that the transmission consistency of the carbon emission data between the source blockchain and the target blockchain can be verified based on the source semantic data and the target semantic data of the source semantic data.

[0094] S300, converting the plurality of source semantic data into a source semantic hash tree, and detecting a first target digest value of the source semantic hash tree.

[0095] The hash tree is a data structure that maps keys to storage locations through a hash function, used for fast data access, and its characteristics include efficient lookup, handling hash collisions, key uniqueness, dynamic expansion, and space-time trade-off. The working principle is to convert the key into an array index through a hash function to locate the data. Application scenarios include database indexing, caching systems, dictionary implementation, and uniqueness checking. The digest value is a fixed-length string generated by the hash function, used to uniquely identify the original data and verify its integrity.

[0096] The semantic hash tree is a data structure with semantic information, used to hash the key semantic fields of carbon data, supporting cross-system consistency verification.

[0097] Specifically, after the extraction of the plurality of source semantic data is completed, the plurality of source semantic data is converted into a source semantic hash tree, and a first target digest value of the source semantic hash tree is detected, which can be detected by a hash encryption algorithm, such as SHA (Secure Hash Algorithm, secure hash algorithm) -256 or the national standard SM3 algorithm.

[0098] S400, converting the plurality of target semantic data into a target semantic hash tree, and detecting a second target digest value of the target semantic hash tree.

[0099] Specifically, similarly, the plurality of target semantic data can also be converted into a target semantic hash tree, and the first target digest value of the source semantic hash tree can be detected by a hash encryption algorithm, such as the hash encryption algorithm can be SHA-256 or the national standard SM3 algorithm.

[0100] In one embodiment, when the plurality of target semantic data is converted into a target semantic hash tree, the plurality of target semantic data can be converted into a target semantic hash tree in the same predefined order as the source semantic hash tree, so that different blockchains use consistent field ordering when building semantic hash trees.

[0101] S500, based on the first target digest value and the second target digest value, verifying the transmission consistency of the carbon emission data between the source blockchain and the target blockchain.

[0102] wherein the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is verified based on whether the first target abstract value is consistent with the second target abstract value, if consistent, the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is verified, if inconsistent, the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is not verified. In the case that the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is verified, the carbon emission data can be transmitted from the source blockchain to the target blockchain.

[0103] It needs to be explained that the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is verified by the first target abstract value and the second target abstract value, which actually verifies the semantics and structure of the carbon emission data in the source blockchain and the target blockchain. The semantics are verified because the target abstract value is the semantic information of each leaf node in the semantic hash tree after abstract value conversion, and is recursively generated, therefore, the semantics of the carbon emission data in the source blockchain and the target blockchain can be verified by the first target abstract value and the second target abstract value, and because the semantic information of each leaf node in the semantic hash tree corresponds to which source semantic data is determined by the structure of the carbon emission data, therefore, the structure of the carbon emission data in the source blockchain and the target blockchain can also be verified by the first target abstract value and the second target abstract value.

[0104] In an exemplary embodiment, the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is verified based on the first target abstract value and the second target abstract value, which supports implementation through cross-blockchain relay nodes, smart contracts or gateway service interfaces.

[0105] In the above-mentioned cross-blockchain transmission carbon data verification method, the traditional method easily ignores the semantic information carried in the carbon emission data, and cannot judge whether the data with the same structure expresses the same meaning, therefore, the present application proposes a more accurate carbon data verification method, in the whole process, instead of relying on the way of comparing the hash values of the field values, the semantic data of the carbon emission data is introduced, and each source semantic data is mapped into the target semantic data in the target blockchain, then the source semantic data is converted into a source semantic hash tree, and multiple target semantic data is converted into a target semantic hash tree, to detect the abstract values in the source semantic hash tree and the target semantic hash tree, to ensure that the semantics of the data in different blockchains are completely consistent even if the structures are similar, and to improve the accuracy of verifying the consistency of the carbon emission data transmission between the source blockchain and the target blockchain.

[0106] In an exemplary embodiment, converting the plurality of source semantic data into a source semantic hash tree comprises:

[0107] For each source semantic data, the importance quantitative value of the source semantic data and the semantic structure level corresponding to the source semantic data are detected; and the source semantic data is converted into semantic information corresponding to a leaf node in the source semantic hash tree based on the importance quantitative value and the semantic structure level.

[0108] The leaf nodes of the hash tree are the bottom layer nodes of the tree and directly store hash values of data blocks. These data blocks can be files, data records, or other forms of data.

[0109] Specifically, after the extraction of the source semantic data of the carbon emission data is completed, the source semantic data is organized as semantic information corresponding to a leaf node of the source semantic hash tree in a predefined order, that is, one leaf node corresponds to one source semantic data, and the source semantic data is taken as the semantic information corresponding to the leaf node. The predefined order can be defined by a semantic rule agreement signed between the institutions corresponding to the source blockchain and the target blockchain, so as to guarantee the symmetry and verifiability of the hash tree structure between the source blockchain and the target blockchain.

[0110] Further, in the present application, the source semantic data is not randomly converted into semantic information corresponding to any leaf node in the source semantic hash tree, but the importance quantitative value of the source semantic data and the semantic structure level corresponding to the source semantic data are detected, and then the predefined order is obtained according to the importance quantitative value and the semantic structure level, and then the multiple source semantic data are sorted based on the predefined order, and then the sorted source semantic data is converted into semantic information corresponding to a specific leaf node in the source semantic hash tree according to the sorting result. Further, when the importance quantitative value of a certain source semantic data is high, the predefined order of the source semantic data is also in the front, and when the semantic structure level of a certain source semantic data is in the front, the predefined order of the source semantic data is also in the front.

[0111] For example, the sorting of the source semantic data according to the predefined order can be:

[0112] indicator_name (indicator name);

[0113] unit (unit);

[0114] value (value);

[0115] emission_factor_source (carbon factor source);

[0116] calculation_method (calculation method);

[0117] region (region);

[0118] period (statistical period).

[0119] The sorted source semantic data is further converted into semantic information corresponding to specific leaf nodes in the source semantic hash tree, such as converting the indicator name of the first level into the first leaf node of the first level excluding the root node, converting the unit of the second level into the second leaf node of the first level excluding the root node, converting the value of the third level into the first leaf node of the second level excluding the root node, and so on.

[0120] In the above embodiment, by detecting the importance quantitative value of the source semantic data and the semantic structure level corresponding to the source semantic data, the source semantic data can be accurately converted into semantic information corresponding to the leaf nodes in the source semantic hash tree, and in the subsequent construction process of the target semantic hash tree, the same predefined order can be used for construction, so that the field structure of the two is consistent.

[0121] In an exemplary embodiment, a first target summary value of the source semantic hash tree is detected, including:

[0122] The sub-summary value of the semantic information corresponding to each leaf node in the source semantic hash tree is detected, the root summary value corresponding to the root node of the source semantic hash tree is detected based on each sub-summary value, and the root summary value is determined as the first target summary value of the source semantic hash tree.

[0123] The leaf node of the hash tree is the bottom layer node of the tree, which directly stores the hash value of the data block; the root node is the only node at the top of the tree, which stores the hash value summary of the whole tree. The change of the leaf node data will affect the hash value layer by layer, finally changing the root node value, ensuring the data integrity traceability.

[0124] Specifically, since the efficiency of sequentially comparing multiple source semantic data with target semantic data is low, the present application can first compare the multiple source semantic data with the target semantic data as a whole. That is, after the source semantic hash tree is constructed, a data that can uniquely identify the source semantic hash tree needs to be found.

[0125] Therefore, the present application detects the sub-summary value of the semantic information corresponding to each leaf node in the source semantic hash tree, detects the root summary value corresponding to the root node of the source semantic hash tree based on each sub-summary value, and determines the root summary value as the first target summary value of the source semantic hash tree, so as to take the first target summary value as the unique identifier of the source semantic hash tree.

[0126] In the process of detecting the root summary value of the source semantic hash tree based on each sub-summary value, it is usually started from the bottom layer, the sub-summary values of the two adjacent leaf nodes are spliced and hashed again to generate the summary value of the parent node, and the splicing and hashing calculation of the hash values of the adjacent nodes of the last layer are continued, until the root summary value of the unique root node is finally generated, and the root summary value is taken as the first target summary value of the source semantic hash tree. And in the case that the leaf nodes of a layer are odd, the last leaf node of the layer is to calculate the sub-summary value of the parent node by copying its own sub-summary value and pairing it.

[0127] For example, after detecting the sub-summary value of the semantic information corresponding to each leaf node in the source semantic hash tree, the sub-summary value H1~H6 of the semantic information corresponding to each leaf node 1~6 is as follows:

[0128] =Hash("indicator_name:electricity carbon emission");

[0129] =Hash("indicator_name:electricity carbon emission"); ");

[0130] =Hash("region:xx province");

[0131] =Hash("period:20xx year Qx quarter");

[0132] =Hash("factor_source:enterprise factor V1.3");

[0133] =Hash("method:electricity consumption x factor");

[0134] The above sub-summary values are sorted according to the hierarchical structure of each leaf node in the source semantic hash tree, and the first target summary value of the source semantic hash tree is calculated.

[0135] In the above embodiment, through the sub-summary value corresponding to each leaf node, the root summary value corresponding to the root node of the source semantic hash tree can be accurately detected layer by layer, and the root summary value is determined as the first target summary value of the source semantic hash tree, and then the first target summary value is accurately taken as the unique identification value of the source semantic hash tree.

[0136] In an exemplary embodiment, before converting the plurality of source semantic data into a source semantic hash tree, the method further comprises:

[0137] The plurality of pieces of source semantic data are subjected to semantic standardization processing. The semantic standardization processing of the plurality of pieces of source semantic data includes: obtaining respective field names and field values of the plurality of pieces of source semantic data; mapping the field names into target field names of a same format, and mapping the field values into target field values of a same format.

[0138] The semantic standardization refers to a process of unifying and standardizing data semantics, and ensuring consistent understanding of data meanings by different systems or users.

[0139] Specifically, before converting the plurality of pieces of source semantic data into the source semantic hash tree, the plurality of pieces of source semantic data need to be subjected to semantic standardization processing to ensure semantic consistency of the semantic data.

[0140] Further, in the process of the semantic standardization processing of the plurality of pieces of source semantic data, the semantic standardization processing of the plurality of pieces of source semantic data can be performed by calling a carbon data semantic dictionary and a field mapping table.

[0141] Specifically, the carbon data semantic dictionary and the field mapping table can be:

[0142] The carbon data semantic dictionary includes common index terms (such as CO2 emission, carbon intensity, and power generation carbon factor) and unified semantic identifiers (such as carbon_emission_value and emission_intensity, which represent the same meaning of carbon emission carbon_emission_value and carbon concentration), supports rules such as Chinese and English synonyms, regional nouns, and unit conversion, and the like;

[0143] The field mapping table can be a structured configuration file (such as JSON / XML), which maps “source institution field names” to “unified field names”, for example: {“CO2_emission”: “carbon_emission_value”, “elec_used”: “electricity_consumed”}, which means that the field name CO2_emission of carbon dioxide emission is mapped to the field name carbon_emission_value of carbon emission, and the field name elec_used of power usage is mapped to the field name electricity_consumed of power consumption.

[0144] The semantic standardization processing process is: reading the field name and field value of each source semantic data, mapping and replacing the field name based on the field mapping table to map the field name to the target field name of the same format, and performing unit conversion (such as kWh→MWh) and numerical standardization (precision retention) on the field value based on the carbon data semantic dictionary, and text normalization (such as unifying simplified and traditional Chinese, and capitalizing), so that the field value is mapped to the target field value of the same format, and finally a data segment with unified semantic expression is formed for subsequent hash tree construction.

[0145] In the above embodiment, through the carbon data semantic dictionary and the field mapping table, the field name can be accurately mapped to the target field name of the same format, and the field value can be mapped to the target field value of the same format, and then the semantic standardization processing is performed on the plurality of source semantic data. Further, by performing semantic standardization processing on the plurality of source semantic data, the plurality of source semantic data can be converted into a standard format, so as to improve the efficiency of converting the plurality of source semantic data into the source semantic hash tree.

[0146] In one exemplary embodiment, the source semantic hash tree includes a plurality of first leaf nodes, and the target semantic hash tree includes a plurality of second leaf nodes, and the first leaf nodes correspond one-to-one to the second leaf nodes; as shown in Figure 3 S500 further includes:

[0147] S520, in the case where the first target summary value and the second target summary value are inconsistent, obtaining a first sub-summary value corresponding to the semantic information of each first leaf node, and a second sub-summary value corresponding to the semantic information of each second leaf node.

[0148] S540, for any leaf node combination, querying a target leaf node combination with different first sub-summary values and second sub-summary values, wherein the leaf node combination includes any first target leaf node in the plurality of first leaf nodes and a second target leaf node matching the first target leaf node in the plurality of second leaf nodes.

[0149] S560, obtaining a first target node identifier corresponding to the first target leaf node and a second target node identifier corresponding to the second target leaf node in the target leaf node combination, and combining the first target node identifier, the second target node identifier, source semantic data corresponding to the first target node identifier, and target semantic data corresponding to the second target node identifier to obtain verification failure information of the carbon emission data.

[0150] Specifically, in the case where the first target summary value and the second target summary value are inconsistent, it is considered that the transmission consistency of the carbon emission data between the source blockchain and the target blockchain fails to pass the verification.

[0151] At this time, the specific verification failure reason can be further analyzed, that is, the first sub-digest value corresponding to the semantic information of each first leaf node and the second sub-digest value corresponding to the semantic information of each second leaf node can be obtained, and it is queried which leaf node combination has inconsistent sub-digest values.

[0152] The leaf node combination includes any first target leaf node in the plurality of first leaf nodes and a second target leaf node matched with the first target leaf node in the plurality of second leaf nodes.

[0153] The target leaf node combination with different first sub-digest values and second sub-digest values can be regarded as a target leaf node combination that causes the verification to fail. At this time, the first target node identifier corresponding to the first target leaf node and the second target node identifier corresponding to the second target leaf node in the target leaf node combination are obtained, and the first target node identifier, the second target node identifier, the source semantic data corresponding to the first target node identifier, and the target semantic data corresponding to the second target node identifier are combined to obtain the verification failure information of the carbon emission data, wherein the source semantic data and the target semantic data each include a field name and a field value. In practical applications, the verification failure information can be a difference report, and the difference report can be output to a worker for the worker to correct errors according to the difference report.

[0154] For example, if the target leaf node combination represents that the carbon factor sources are inconsistent; if the target leaf node combination represents different units, it indicates that the unit conversion is not unified.

[0155] In one embodiment, querying the target leaf node combination with different first sub-digest values and second sub-digest values means that the first leaf node combination with different first sub-digest values and second sub-digest values is queried from the next level node of the root node in the semantic hash tree.

[0156] In the above embodiment, by querying the target leaf node combination with different first sub-digest values and second sub-digest values, the transmission consistency of the carbon emission data between the source blockchain and the target blockchain can be verified in a deeper level to find the detailed reason for failing the verification.

[0157] In an exemplary embodiment, mapping each source semantic data to target semantic data in the target blockchain includes:

[0158] The semantic conversion relationship between the source blockchain and the target blockchain is obtained, wherein the semantic conversion relationship includes at least one of a unit conversion factor conversion relationship, a carbon factor standard version conversion relationship, and a field name conversion relationship; and for each source semantic data, the source semantic data is mapped into target semantic data in the target blockchain according to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship.

[0159] The semantic conversion relationship is an equivalent conversion relationship of field naming, units, algorithms, models, etc. between different blockchains.

[0160] Specifically, the semantic conversion relationship transmitted by the target blockchain is received, wherein the semantic conversion relationship is a semantic conversion relationship between the source blockchain and the target blockchain, such as a unit conversion factor conversion relationship, a carbon factor standard version conversion relationship, and a field name conversion relationship between the source blockchain and the target blockchain.

[0161] According to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship, each piece of source semantic data can be mapped into target semantic data in the target blockchain.

[0162] More specifically, the semantic conversion relationship includes:

[0163] 1. Carbon factor standard version conversion relationship: such as xx power grid version in 2022, xx power grid version V1.3, each version corresponds to a set of default factor values and algorithms, which can be identified and automatically called by version number. When converting source semantic data between the field name of the source blockchain and the target blockchain, the carbon factor values and algorithms corresponding to a version number can be converted into carbon factor values and algorithms of another version.

[0164] 2. Unit conversion factor conversion relationship: such as 1 kilowatt hour kWh = 0.001 megawatt hour MWh, or = and so on. According to the unit declared in the field, the conversion ratio is automatically applied to the numerical value conversion between the source blockchain and the target blockchain, wherein t is the English symbol of the mass unit "ton".

[0165] 3. Field name conversion relationship: define the corresponding relationship between the field name of the source blockchain and the field name of the target blockchain, which can be represented as a set of mapping pairs, such as {"emission_factor":"carbon_factor"}, that is, the field name of the emission factor emission_factor is mapped to the carbon emission factor carbon_factor.

[0166] Therefore, in the case that the semantic conversion relationship includes the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship, the source semantic data can be equivalently converted according to the configuration of the above-mentioned semantic conversion relationship, and the conversion process includes the following steps:

[0167] 1. According to the field name conversion relationship, the field name of the original blockchain is uniformly replaced by the standard field name of the target blockchain.

[0168] 2. For the unit field (such as kilowatt-hour kWh and megawatt-hour MWh), the unit field is converted according to the unit conversion factor conversion relationship, and the value of the value field is updated.

[0169] 3. If the carbon factor standard version of the source blockchain and the target blockchain is different, the carbon emission value is recalculated according to the carbon factor standard version conversion relationship, and the carbon factor source emission_factor_source and the value field are updated.

[0170] In addition, for the period field, it can also be reconstructed according to a unified time expression method (such as converting the quarter “Q4” to “xxxx year-x1 month-x2 month”), which improves the consistency of semantic analysis.

[0171] At this time, the target semantic data in multiple target blockchains can be collected to form a standard semantic structure of the target blockchain, and after the standardization of the multiple target semantic data in the standard semantic structure, the standard semantic structure participates in the generation of the subsequent target semantic hash tree.

[0172] In the above embodiment, according to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship, the source semantic data can be accurately mapped to the target semantic data in the target blockchain, and since the conversion rules are pre-set, the conversion process is also more efficient.

[0173] In one exemplary embodiment, after verifying the consistency of the carbon emission data between the source blockchain and the target blockchain based on the first target summary value and the second target summary value, the method further comprises:

[0174] When the first target summary value and the second target summary value are consistent, the carbon data consistency proof information between the source blockchain and the target blockchain is generated, and the carbon data consistency proof information is loaded into the contract agreement between the source blockchain and the target blockchain.

[0175] Specifically, when the first target digest value is inconsistent with the second target digest value, the transmission consistency of the carbon emission data between the source blockchain and the target blockchain fails to pass the verification; when the first target digest value is consistent with the second target digest value, the transmission consistency of the carbon emission data between the source blockchain and the target blockchain passes the verification. At this time, the carbon data consistency proof information between the source blockchain and the target blockchain can be generated, wherein the carbon data consistency proof information is used to prove that the carbon data across the blockchains is completely equivalent in structure and meaning. Then, the carbon data consistency proof information is loaded into the smart contract protocol between the source blockchain and the target blockchain for subsequent carbon data transmission.

[0176] In practical applications, the above scheme in the embodiment can be provided to a carbon transaction, regulatory audit, carbon asset right confirmation and the like platform for subsequent use.

[0177] In the above embodiment, when the first target digest value is consistent with the second target digest value, by loading the carbon data consistency proof information into the contract protocol between the source blockchain and the target blockchain, the subsequent carbon data transmission process can be referenced.

[0178] In one exemplary embodiment, as Figure 4As shown, when carbon data needs to be transmitted from the source blockchain to the target area chain, first, the cross-chain function is obtained, then a plurality of source semantic data is extracted from the carbon emission data, based on the plurality of source semantic data, a source semantic structure body is generated, based on the source semantic structure body, a source semantic hash tree is generated, and based on four leaf nodes h1, h2, h3 and h4 in the source semantic hash tree, the first target summary value SMTH-Root of the source semantic hash tree is detected, and the plurality of source semantic data is equivalently converted into target semantic data in the target blockchain, the target semantic hash tree corresponding to the target semantic data is generated in the same way, and the second target summary value SMTH'-Root of the target semantic hash tree is detected, the source semantic hash tree and the target semantic hash tree are checked for semantics, that is, it is judged whether the first target summary value SMTH-Root and the second target summary value SMTH'-Root are consistent, to obtain the verification result of the consistency of the transmission of the carbon emission data between the source blockchain and the target blockchain, the verification result represents that the first target summary value SMTH-Root and the second target summary value SMTH'-Root are consistent and the first target summary value SMTH-Root and the second target summary value SMTH'-Root are inconsistent, when the verification result represents that the first target summary value SMTH-Root and the second target summary value SMTH'-Root are inconsistent, the verification result also includes the conflict field that causes the verification inconsistency. Further, in the case where the verification result represents that the first target summary value SMTH-Root and the second target summary value SMTH'-Root are consistent, the carbon emission data is transmitted from the source blockchain to the target blockchain based on the cross-chain function.

[0179] To solve the semantic inconsistency problem existing in the process of cross-blockchain transmission of carbon emission data in the prior art, the present application proposes a carbon data mapping verification method based on semantic hash tree. This method can also extract, standardize and encrypt the structure fields and semantic attributes of carbon emission data, and construct a hash tree structure with structure traceability and semantic consistency, thereby realizing semantic-level mapping and trusted verification of carbon data between different blockchain institutions. Let the source blockchain be blockchain A and the target blockchain be blockchain B, and the carbon data needs to be transmitted from blockchain A to blockchain B, then the following is Figure 5 As shown, a carbon data verification method for cross-blockchain transmission is described in detail in a most detailed embodiment, which includes the technical principles of the internal modules and modules executed:

[0180] 1. Semantic information extraction and standardization processing module:

[0181] The system receives the carbon emission data (such as electricity carbon emission, green electricity consumption record, etc.) to be verified in blockchain A, and automatically extracts the fields with semantic attributes to obtain a plurality of source semantic data.

[0182] To ensure semantic consistency, the system calls the carbon data semantic dictionary and the field mapping table at this stage to perform semantic standardization processing (such as unit unification, naming consistency, value domain conversion, etc.) on the source semantic data. In the process of calling the carbon data semantic dictionary and the field name mapping table to perform semantic standardization processing on the source semantic data, the field name and field value of each piece of source semantic data are read, the field name is mapped and replaced by calling the field name mapping table, and the carbon data semantic dictionary is called to perform unit conversion (such as kWh→MWh), numerical standardization (precision retention), and text consistency (such as unifying simplified and traditional Chinese, and capitalization specification, etc.) on the field value. Finally, data segments with unified semantic expression are formed for subsequent hashing.

[0183] 2. Semantic hash tree construction module:

[0184] After completing semantic field extraction, the source semantic data is organized into semantic information corresponding to the leaf nodes of the source semantic hash tree in a predefined order, and an encryption hash algorithm is used to calculate the digest value corresponding to each leaf node. Generally, the predefined order can be determined by the importance quantification value of the semantic field and the semantic structure level, ensuring that different institutions use consistent field ordering when constructing the semantic hash tree.

[0185] The above hash values are combined layer by layer according to the hash tree rules to generate the hash values of the intermediate nodes, and finally the hash value of the semantic hash root node in the source semantic hash tree is generated. The hash value of the semantic hash root node is taken as the first target digest value SMTH-Root of the source semantic hash tree, which is used as the unique identifier of the carbon emission data semantic structure.

[0186] 3. Cross-chain semantic mapping verification module:

[0187] Obtain the semantic conversion relationship between blockchain A and blockchain B, wherein the semantic conversion relationship includes at least one of unit conversion factor conversion relationship, carbon factor standard version conversion relationship, and field name conversion relationship; for each source semantic data, according to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship, the source semantic data is converted to obtain the target semantic data in the blockchain B.

[0188] Convert multiple target semantic data into a target semantic hash tree, and calculate the second target digest value SMTH'-Root of the target semantic hash tree in the same way as calculating the first target digest value of the source semantic hash tree.

[0189] The verification process is as follows: compare whether SMTH-Root and SMTH'-Root are consistent. If they are consistent, it means that the semantics and structure of the carbon emission data in blockchain A and blockchain B are consistent, and the transmission consistency verification between blockchain A and blockchain B is passed. If they are not consistent, it means that the semantics and structure of the carbon emission data in blockchain A or blockchain B are different, and the transmission consistency verification between blockchain A and blockchain B is not passed.

[0190] Further, if SMTH-Root and SMTH'-Root are not consistent, the hash values of each leaf node of the source semantic hash tree and the target semantic hash tree are recorded, and the matching leaf nodes are compared layer by layer in the comparison stage to find the first inconsistent leaf node, which is the location of the semantic conflict field.

[0191] 4. Verification result output and optional on-chain storage module:

[0192] When the verification is passed, a semantic consistency verification proof file is automatically generated, and the target chain smart contract can be optionally written;

[0193] When the verification fails, a structure comparison report with difference explanation is generated, including field name, field value, and conflict reason (such as unit inconsistency, statistical dimension mismatch, etc.).

[0194] Based on the above analysis, the present application innovatively combines semantic modeling, hash coding, and cross-blockchain verification mechanism by constructing a semantic hash tree of carbon emission data, realizes double consistency verification of cross-institution carbon emission data structure layer + semantic layer, and breaks through the technical bottleneck that existing cross-blockchain technology can only compare data structure and cannot verify meaning consistency.

[0195] The technical means of the present application has the following advantages:

[0196] 1. Standardized extraction method of carbon emission data semantic fields, including extraction and unified formatting of carbon emission index name, unit, time period, carbon factor source, statistical model and other semantic elements, to ensure semantic structure comparability and verifiability;

[0197] 2. Method for constructing semantic hash tree based on semantic field data, including generating a multi-layer hash tree according to fixed logic after hash processing of each semantic field data, and taking the root node of the semantic hash tree as the unique digest identifier of the semantic structure;

[0198] 3. Cross-blockchain semantic mapping and verification mechanism, including the semantic conversion relationship between the source blockchain and the target blockchain, the construction of the target semantic hash tree, and the comparison process of the target semantic hash tree and the source semantic hash tree, solving the semantic difference problem of carbon emission data of different institutions;

[0199] 4. Carbon data semantic consistency verification process, including source blockchain semantic structure generation, target blockchain semantic structure reconstruction, hash root node comparison, consistency judgment and verification result output, etc. It supports semantic trusted verification in chain-to-chain data transmission;

[0200] 5. Semantic verification result generation and storage method, including automatic generation of semantic consistency proof, abnormal field identification and interpretation, structured output of verification report, and optional on-chain storage, result pushing and other functional modules;

[0201] As can be seen, for the present application, first, in the data trusted verification layer, the technology no longer relies on the way of only comparing the hash of the field value, but introduces the context information of carbon data (such as carbon factor source, unit type, calculation model, time period, etc.) as semantic field, which is uniformly organized into a multi-layer hash tree structure, so as to ensure that the data in different chains is consistent in structure, and the semantic consistency can be judged. This processing mechanism effectively prevents the problem of "looking consistent, actually inconsistent" carbon data error circulation.

[0202] Secondly, in the system interconnection and agency interface level, the present application supports the realization of semantic automatic conversion and semantic hash alignment between different carbon data agencies through the configuration of semantic conversion relationship. This mechanism can adapt to regional power grid platform, carbon trading system, green certificate authentication chain and other typical blockchain systems, and provides standardized interface for carbon data cross-blockchain trusted exchange. The system can also output verification failure information, carbon data consistency proof information and other verification reports, which are convenient for user audit and subsequent correction, and improve the intelligence and explainability of the system.

[0203] In the business landing level, the technical solution provides bottom trusted support for carbon asset right confirmation, green electricity performance verification and carbon audit compliance judgment under multi-chain cooperation, especially suitable for mutual recognition and reference of key business data such as carbon emission data, green electricity consumption data and emission reduction accounting results between different agencies, effectively guaranteeing the legality, authenticity and non-repudiation of carbon trading data in the process of circulation.

[0204] In addition, the method of the present application has good engineering expansion ability and universal semantic adaptation ability, can support the access of national encryption algorithm, adapt to various carbon factor libraries, semantic models and statistical period templates, and has the basic conditions for evolution to industrial internet, energy big data agencies and international carbon data universal protocol.

[0205] In summary, the application not only solves the problem of "consistent structure but inconsistent semantics" in traditional cross-blockchain data verification, but also improves the credibility of carbon emission data sharing, circulation and auditing among multiple institutions, has significant technical advancement and industry applicability, and provides key support for building a trusted, unified and verifiable carbon data mutual recognition system. Therefore, the application is applicable to the application framework of carbon emission data semantic mapping and verification in a multi-blockchain system (heterogeneous trust domain), has adaptation capability for cross-institution, multi-model and multi-factor library, and can be popularized to various business scenarios such as carbon trading, green electricity right confirmation, carbon verification and carbon auditing.

[0206] It should be understood that although each step in the flowchart involved in the above embodiments is displayed in sequence according to the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least part of the steps in the flowchart involved in the above embodiments can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or steps or stages in other steps.

[0207] Based on the same inventive concept, the application embodiments also provide a cross-blockchain transmission carbon data verification device for implementing the above-mentioned cross-blockchain transmission carbon data verification method. The implementation scheme for solving problems provided by the device is similar to the implementation scheme described in the above method, so the specific limitations in one or more cross-blockchain transmission carbon data verification device embodiments provided below can refer to the limitations of the cross-blockchain transmission carbon data verification method in the above text, which will not be repeated here.

[0208] In one exemplary embodiment, as shown in Figure 6 A cross-blockchain transmission carbon data verification device is provided, comprising: an initial data acquisition module 100, a semantic data acquisition module 200, a first summary generation module 300, a second summary generation module 400 and a verification module 500, wherein:

[0209] The initial data acquisition module 100 is configured to acquire carbon emission data to be verified in a source blockchain, wherein the carbon emission data is data to be transmitted to a target blockchain;

[0210] The semantic data acquisition module 200 is configured to extract a plurality of source semantic data from the carbon emission data, and map each source semantic data to target semantic data in the target blockchain respectively;

[0211] The first abstract generation module 300 is configured to convert the plurality of pieces of source semantic data into a source semantic hash tree, and detect a first target abstract value of the source semantic hash tree.

[0212] The second abstract generation module 400 is configured to convert the plurality of pieces of target semantic data into a target semantic hash tree, and detect a second target abstract value of the target semantic hash tree.

[0213] The verification module 500 is configured to verify the consistency of the carbon emission data transmission between the source blockchain and the target blockchain based on the first target abstract value and the second target abstract value.

[0214] In an embodiment, the first abstract generation module 300 is further configured to, for each piece of source semantic data, detect an importance quantization value of the source semantic data and a semantic structure level corresponding to the source semantic data; and convert the source semantic data into semantic information corresponding to a leaf node in the source semantic hash tree based on the importance quantization value and the semantic structure level.

[0215] In an embodiment, the first abstract generation module 300 is further configured to detect a sub-abstract value of the semantic information corresponding to each leaf node in the source semantic hash tree; detect a root abstract value of a root node in the source semantic hash tree based on each sub-abstract value; and determine the root abstract value as the first target abstract value of the source semantic hash tree.

[0216] In an embodiment, the cross-blockchain carbon data transmission verification apparatus further includes a standardization module configured to obtain field names and field values of the plurality of pieces of source semantic data; and map the field names into target field names in a same format, and map the field values into target field values in the same format.

[0217] In an embodiment, the source semantic hash tree includes a plurality of first leaf nodes, and the target semantic hash tree includes a plurality of second leaf nodes, the first leaf nodes and the second leaf nodes corresponding to each other in a one-to-one manner; the verification module 500 is configured to, in a case where the first target abstract value and the second target abstract value are inconsistent, obtain a first sub-abstract value corresponding to semantic information of each first leaf node, and a second sub-abstract value corresponding to semantic information of each second leaf node; for any leaf node combination, query a target leaf node combination in which the first sub-abstract value and the second sub-abstract value are different, wherein the leaf node combination includes any first target leaf node in the plurality of first leaf nodes and a second target leaf node in the plurality of second leaf nodes that matches the first target leaf node; obtain a first target node identifier corresponding to the first target leaf node and a second target node identifier corresponding to the second target leaf node in the target leaf node combination, and combine the first target node identifier, the second target node identifier, source semantic data corresponding to the first target node identifier, and target semantic data corresponding to the second target node identifier, to obtain verification failure information of the carbon emission data.

[0218] In an embodiment, the semantic data obtaining module 200 is further configured to obtain a semantic conversion relationship between the source blockchain and the target blockchain, wherein the semantic conversion relationship comprises at least one of a unit conversion factor conversion relationship, a carbon factor standard version conversion relationship, and a field name conversion relationship; and for each source semantic data, the source semantic data is mapped to target semantic data in the target blockchain according to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship.

[0219] In an embodiment, the cross-blockchain-transmitted carbon data verification apparatus further comprises a loading module configured to, when the first target digest value is consistent with the second target digest value, generate carbon data consistency proof information between the source blockchain and the target blockchain, and load the carbon data consistency proof information into a contract protocol between the source blockchain and the target blockchain.

[0220] The modules in the above-described cross-blockchain-transmitted carbon data verification apparatus can be implemented in whole or in part by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform operations corresponding to the modules.

[0221] In an exemplary embodiment, a computer device, which can be a server, is provided, and an internal structure diagram of the computer device can be as shown in Figure 7 The computer device comprises a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The database of the computer device is configured to store carbon emission data to be verified in a source blockchain, etc. The input / output interface of the computer device is configured to exchange information between the processor and external devices. The communication interface of the computer device is configured to communicate with external terminals through a network connection. The computer program is executed by the processor to implement a cross-blockchain-transmitted carbon data verification method.

[0222] Those skilled in the art can understand that Figure 7The structure shown in the figure is a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0223] In an embodiment, a computer device is also provided, including a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method embodiments when executing the computer program.

[0224] In an embodiment, a computer readable storage medium is provided, storing a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0225] In an embodiment, a computer program product is provided, including a computer program, and the computer program implementing the steps in the above method embodiments when executed by a processor.

[0226] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., without being limited thereto.

[0227] The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0228] The above embodiments only express several implementation ways of the present application, and the description is specific and detailed, but it should not be understood as a limitation to the patent scope of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for verifying carbon data transmitted across blockchains, characterized in that, The method includes: Obtain carbon emission data to be verified from the source blockchain, wherein the carbon emission data is data to be transmitted to the target blockchain; Multiple source semantic data are extracted from the carbon emission data, and each source semantic data is mapped to target semantic data in the target blockchain. The multiple source semantic data are converted into a source semantic hash tree, and the first target digest value of the source semantic hash tree is detected; The multiple pieces of target semantic data are converted into a target semantic hash tree, and the second target digest value of the target semantic hash tree is detected; Based on the first target digest value and the second target digest value, the consistency of the carbon emission data transmission between the source blockchain and the target blockchain is verified. The source semantic hash tree includes multiple first leaf nodes, and the target semantic hash tree includes multiple second leaf nodes, with a one-to-one correspondence between the first leaf nodes and the second leaf nodes. The verification of the consistency of the carbon emission data transmission between the source blockchain and the target blockchain based on the first target digest value and the second target digest value includes: When the first target summary value and the second target summary value are inconsistent, obtain the first sub-summary value corresponding to the semantic information of each first leaf node, and the second sub-summary value corresponding to the semantic information of each second leaf node; for any combination of leaf nodes, query the target leaf node combination where the first sub-summary value and the second sub-summary value are different, wherein the leaf node combination includes any first target leaf node among the plurality of first leaf nodes, and a second target leaf node among the plurality of second leaf nodes that matches the first target leaf node; obtain the first target node identifier corresponding to the first target leaf node and the second target node identifier corresponding to the second target leaf node in the target leaf node combination, and combine the first target node identifier, the second target node identifier, the source semantic data corresponding to the first target node identifier and the target semantic data corresponding to the second target node identifier to obtain the verification failure information of the carbon emission data.

2. The method according to claim 1, characterized in that, The step of converting the multiple source semantic data into a source semantic hash tree includes: For each source semantic data, detect the quantification value of the importance of the source semantic data and the semantic structure level corresponding to the source semantic data; Based on the importance quantification value and the semantic structure hierarchy, the source semantic data is converted into semantic information corresponding to the leaf nodes in the source semantic hash tree.

3. The method according to claim 2, characterized in that, The detection of the first target digest value of the source semantic hash tree includes: Detect the sub-summary value of the semantic information corresponding to each leaf node in the source semantic hash tree; Based on each of the sub-digest values, detect the root digest value corresponding to the root node of the source semantic hash tree; The root digest value is determined as the first target digest value of the source semantic hash tree.

4. The method according to claim 2, characterized in that, Before converting the multiple source semantic data into a source semantic hash tree, the method further includes: The multiple source semantic data are subjected to semantic standardization processing; The semantic standardization process for the multiple source semantic data includes: Obtain the field names and field values ​​of each of the multiple source semantic data; Map the field names to target field names of the same format, and map the field values ​​to target field values ​​of the same format.

5. The method according to claim 1, characterized in that, The step of mapping each of the source semantic data to the target semantic data in the target blockchain includes: Obtain the semantic conversion relationship between the source blockchain and the target blockchain, wherein the semantic conversion relationship includes at least one of the following: unit conversion factor conversion relationship, carbon factor standard version conversion relationship, and field name conversion relationship; For each source semantic data, the source semantic data is mapped to target semantic data in the target blockchain according to at least one of the unit conversion factor conversion relationship, the carbon factor standard version conversion relationship, and the field name conversion relationship.

6. The method according to claim 1, characterized in that, After verifying the consistency of the carbon emission data transmission between the source blockchain and the target blockchain based on the first target digest value and the second target digest value, the method further includes: When the first target digest value is consistent with the second target digest value, carbon data consistency proof information between the source blockchain and the target blockchain is generated, and the carbon data consistency proof information is loaded into the contract protocol between the source blockchain and the target blockchain.

7. A carbon data verification device for cross-blockchain transmission, characterized in that, The device includes: The initial data acquisition module is used to acquire carbon emission data to be verified in the source blockchain, wherein the carbon emission data is the data to be transmitted to the target blockchain; The semantic data acquisition module is used to extract multiple source semantic data from the carbon emission data and map each source semantic data to target semantic data in the target blockchain. The first digest generation module is used to convert the multiple source semantic data into a source semantic hash tree and detect the first target digest value of the source semantic hash tree; The second summary generation module is used to convert multiple pieces of target semantic data into a target semantic hash tree, and to detect the second target summary value of the target semantic hash tree; A verification module is used to verify the consistency of the carbon emission data transmission between the source blockchain and the target blockchain based on the first target digest value and the second target digest value. The source semantic hash tree includes multiple first leaf nodes, and the target semantic hash tree includes multiple second leaf nodes, with a one-to-one correspondence between the first leaf nodes and the second leaf nodes; the verification module is further configured to: When the first target summary value and the second target summary value are inconsistent, obtain the first sub-summary value corresponding to the semantic information of each first leaf node, and the second sub-summary value corresponding to the semantic information of each second leaf node; for any combination of leaf nodes, query the target leaf node combination where the first sub-summary value and the second sub-summary value are different, wherein the leaf node combination includes any first target leaf node among the plurality of first leaf nodes, and a second target leaf node among the plurality of second leaf nodes that matches the first target leaf node; obtain the first target node identifier corresponding to the first target leaf node and the second target node identifier corresponding to the second target leaf node in the target leaf node combination, and combine the first target node identifier, the second target node identifier, the source semantic data corresponding to the first target node identifier and the target semantic data corresponding to the second target node identifier to obtain the verification failure information of the carbon emission data.

8. The apparatus according to claim 7, characterized in that, The first summary generation module is further configured to detect, for each of the source semantic data, the importance quantification value of the source semantic data and the semantic structure level corresponding to the source semantic data; and based on the importance quantification value and the semantic structure level, convert the source semantic data into semantic information corresponding to the leaf nodes in the source semantic hash tree.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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