DIKW graph-based blockchain content determination method for essential computing and reasoning

Abstract

The present invention provides a DIKW graph-based blockchain content determination method for essential computing and reasoning, comprising the following steps: obtaining blockchain typed data, and constructing a blockchain DIKW model according to the blockchain typed data; a user initiates a request for content to be added, obtains typed data of the user who initiated the request, and constructs a user DIKW model according to the user typed data; the blockchain DIKW model performs semantic analysis on the content to be added and obtains a blockchain semantic representation, and the user DIKW model performs semantic analysis on the content to be added and obtains a user semantic representation; a semantic comparison is performed on the blockchain semantic representation and the user semantic representation, and the content to be added is added to the end of the blockchain with a consistent semantic comparison result; the DIKW graph is combined with the blockchain, and semantic analysis of the content to be added can be performed according to the semantic systems contained in both parties, so that rapid matching can be performed, so as to quickly upload the content to the chain, thereby improving the upload speed and the speed of content consistency judgment.

Description

Technical Field

[0001] The present invention relates to the field of blockchain technology, and in particular to a DIKW graph-based blockchain content determination method for essential computing and reasoning. Background Art

[0002] Blockchain is essentially a shared database. The data or information stored in it has the characteristics of being unforgeable, traceable throughout the process, traceable, open and transparent, and collectively maintained. Based on these characteristics, blockchain technology has laid a solid foundation of trust, created a reliable cooperation mechanism, and has broad application prospects.

[0003] For blockchain, newly added content needs to be consistent with the content in the previous block in the chain. This process is called on-chaining. To ensure the accuracy of the content, the existing blockchain will identify the content on the chain and only allow it to be on-chain after the content is identified to be consistent. However, the current content identification and determination method is too complicated, and the attributes and ideas of the user themselves are not taken into account when the content is on-chain. As a result, the on-chain speed is slow and cannot be quickly on-chained.

[0004] Each object has its own data, information, knowledge, and wisdom. Combining data, information, knowledge, wisdom, and intention can form a DIKWP map. The DIKWP map contains detailed information about things. Through the DIKW map, the detailed content of an object can be quickly obtained. If the DIKW map can be applied to the content determination of the blockchain, it will greatly improve the speed of content consistency judgment and chain-up speed of the blockchain. Summary of the Invention

[0005] In view of this, the present invention proposes a DIKW graph-based blockchain content determination method for essential computing and reasoning. It performs semantic analysis on the content to be added based on the content of the user and the blockchain itself, and adds the content to be added to the blockchain module with consistent semantic comparison results after semantic comparison. This can greatly improve the speed of chain uploading and content consistency judgment.

[0006] The technical solution of the present invention is achieved as follows:

[0007] The DIKW graph-based blockchain content determination method for essential computing and reasoning includes the following steps:

[0008] Step S1: Obtain blockchain typed data and construct a blockchain DIKW model based on the blockchain typed data;

[0009] Step S2: The user initiates a request for content to be added, obtains the user typed data of the requesting user, and constructs a user DIKW model based on the user typed data;

[0010] Step S3: The blockchain DIKW model performs semantic analysis on the content to be added and obtains a blockchain semantic representation. The user DIKW model performs semantic analysis on the content to be added and obtains a user semantic representation.

[0011] Step S4: Perform a semantic comparison between the blockchain semantic representation and the user semantic representation, and add the content to be added to the end of the blockchain where the semantic comparison result is consistent.

[0012] Preferably, the blockchain DIKW model includes a block header DIKW model and a block body DIKW model. The user DIKW model, the block header DIKW model and the block body DIKW model all include a data model, an information model and a knowledge model. The block body DIKW model also includes a semantic model. The data model, the information model and the knowledge model can be converted into each other. The semantic model is obtained by combining the data model, the information model and the knowledge model.

[0013] Preferably, the specific steps of step S1 include:

[0014] Step S11: Obtain the block header and block body of each block in each blockchain;

[0015] Step S12: Map the contents contained in the block header and the block body into blockchain typed resources, where the blockchain typed resources include data resources, information resources, and knowledge resources;

[0016] Step S13: Constructing a block header DIKW model based on data resources, information resources, and knowledge resources;

[0017] Step S14: construct a semantic system based on data resources, information resources, and knowledge resources, and construct a block DIKW model based on data resources, information resources, knowledge resources, and the semantic system.

[0018] Preferably, the specific steps of step S2 are:

[0019] Step S21: The user initiates a request for content to be added, and the user account is obtained according to the initiated request;

[0020] Step S22: Obtain trace content left by the user on the Internet according to the user account, and map the trace content into user-typed resources, wherein the user-typed resources include data resources, information resources, and knowledge resources;

[0021] Step S23: construct a semantic system based on data resources, information resources and knowledge resources, and construct a user DIKW model based on the data resources, information resources, knowledge resources and semantic system.

[0022] Preferably, the trace content includes the user's information entry, browsing history, interest preferences, and public information interaction content on the Internet.

[0023] Preferably, in step S3, the blockchain DIKW model processes the content to be added and obtains the specific steps of blockchain semantic representation:

[0024] Step S31: The block header DIKW model identifies the relevance of the content to be added;

[0025] Step S32: Classify the blockchains into relevant blockchains and unrelated blockchains based on the correlation identification result;

[0026] Step S33: The DIKW model of the block body corresponding to the relevant blockchain performs semantic analysis on the content to be added based on the semantic system and obtains the blockchain semantic representation.

[0027] Preferably, in step S3, the user DIKW model processes the content to be added and obtains the user semantic representation in the following specific steps:

[0028] Step S34: The user DIKW model extracts keywords from the content to be added, and processes the keywords based on the semantic system to obtain user semantic representation.

[0029] Preferably, the specific steps of step S4 are:

[0030] Step S41: Determine the similarity between the blockchain semantic representation and the user semantic representation;

[0031] Step S42: Compare all similarity results and arrange them in descending order;

[0032] Step S43: The blockchain with the highest similarity result is used as the blockchain with the same semantic comparison result;

[0033] Step S44: Add the content to be added to the end of the blockchain with the consistent semantic comparison result.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention provides a DIKW graph-based blockchain content determination method for essential computing and reasoning. The method first constructs a blockchain DIKW model of all blockchains. After a user initiates a request for content to be added, a user DIKW model is constructed for the user who initiates the request. Then, the blockchain DIKW model and the user DIKW model process the content to be added respectively, and obtain a blockchain semantic representation and a user semantic representation respectively. Finally, after semantic comparison of the blockchain semantic representation and the user semantic representation, a blockchain consistent with the representation of the content to be added can be obtained, and the content to be added is added to the end of the blockchain to achieve chain-up. Since the DIKW graph is used for modeling, the content contained in the DIKW graph is related to the blockchain itself and the content of the user itself. Therefore, the speed of searching for the corresponding blockchain and chain-up will be greatly increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 This is a flowchart of the DIKW graph-based blockchain content determination method for essential computing and reasoning of the present invention;

[0038] Figure 2 This is a flowchart of step S1 of the DIKW graph-based blockchain content determination method for essential computing and reasoning of the present invention;

[0039] Figure 3 This is a flowchart of step S2 of the DIKW graph-based blockchain content determination method for essential computing and reasoning of the present invention;

[0040] Figure 4 This is a flowchart of step S3 of the DIKW graph-based blockchain content determination method for essential computing and reasoning of the present invention;

[0041] Figure 5 This is a flowchart of step S4 of the DIKW graphical blockchain content determination method for essential computing and reasoning of the present invention. DETAILED DESCRIPTION

[0042] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0043] See also Figures 1 to 5The DIKW graph-based blockchain content determination method for essential computing and reasoning provided by the present invention includes the following steps:

[0044] Step S1: Obtain blockchain typed data and construct a blockchain DIKW model based on the blockchain typed data;

[0045] Step S2: The user initiates a request for content to be added, obtains the user typed data of the requesting user, and constructs a user DIKW model based on the user typed data;

[0046] Step S3: The blockchain DIKW model performs semantic analysis on the content to be added and obtains a blockchain semantic representation. The user DIKW model performs semantic analysis on the content to be added and obtains a user semantic representation.

[0047] Step S4: Perform a semantic comparison between the blockchain semantic representation and the user semantic representation, and add the content to be added to the end of the blockchain where the semantic comparison result is consistent.

[0048] The present invention provides a DIKW graph-based blockchain content determination method for essential computing and reasoning, which combines the DIKW graph with blockchain technology to perform consistency judgment on the content that needs to be added to the blockchain. When the content is judged to be consistent with the content of the blockchain, the content to be added is chained to the corresponding blockchain end. When performing content consistency judgment, since the blockchain and the user are DIKW modeled separately, the DIKW graph contains relatively detailed private content of the blockchain and the user itself. Therefore, when performing content consistency judgment, it is possible to accurately judge whether the content is consistent, thereby ensuring that the content on the chain is consistent with the content of the blockchain itself.

[0049] Before determining the content, all blockchains need to be DIKW modeled first. After the modeling is completed, the blockchain DIKW model of each blockchain can be obtained. Whenever a new chain is generated, the new chain needs to be DIKW modeled. When the content of the old chain changes or increases, it will also be remodeled and updated. When a user initiates a request for content to be added, the user can be DIKW modeled and the corresponding user DIKW model can be obtained. The blockchain DIKW model and the user DIKW model then process the content to be added respectively, analyze the semantics of the content to be added in the blockchain and the user respectively, and obtain the semantic representation. Then, the user semantic representation is compared with the blockchain semantic representation one by one, and finally a blockchain with consistent semantic comparison results can be obtained, so that the content to be added can be linked to the end of the blockchain.

[0050] The purpose of obtaining semantic representations and performing semantic comparisons is that since the content contained in each thing is not exactly the same, different texts, pictures, and other contents have different meanings in the expression of different things. Therefore, it is necessary to obtain the semantic representations of the content to be added on the user side and the blockchain side to ensure that the content to be added can be accurately uploaded to the corresponding blockchain.

[0051] Preferably, the blockchain DIKW model includes a block header DIKW model and a block body DIKW model. The user DIKW model, the block header DIKW model and the block body DIKW model all include a data model, an information model and a knowledge model. The block body DIKW model also includes a semantic model. The data model, the information model and the knowledge model can be converted into each other. The semantic model is obtained by combining the data model, the information model and the knowledge model.

[0052] Both the blockchain DIKW model and the user DIKW model include basic data models, information models, and knowledge models. The blockchain DIKW model is divided into a block header DIKW model and a block body DIKW model. The block header DIKW model and the block body DIKW model respectively process the content to be added to ensure the accuracy of the content consistency judgment. Since semantic analysis of the content to be added is required, a semantic model is also constructed when constructing the user DIKW model and the block body DIKW model for semantic analysis.

[0053] For example, in the procurement of tools and equipment in the power grid, the power company has established a set of rules for the procurement of tools and equipment, and uses blockchain to store the procurement rules system. Each time a tool is procured, the tool will be uploaded to the blockchain. By performing DIKW modeling on the blockchain, the procurement rules system can be obtained and converted into a corresponding semantic system. When a staff member (i.e., a user) initiates a procurement request (i.e., a request for content to be added), the blockchain DIKW model and the user DIKW model can analyze the content to be added respectively, and obtain the semantic representation of the content on the user side and the blockchain side respectively. Different blockchains have different semantic representations for the same content to be added. Finally, the correct blockchain can be quickly obtained through semantic comparison.

[0054] Preferably, the specific steps of step S1 include:

[0055] Step S11: Obtain the block header and block body of each block in each blockchain;

[0056] Step S12: Map the contents contained in the block header and the block body into blockchain typed resources, where the blockchain typed resources include data resources, information resources, and knowledge resources;

[0057] Step S13: Constructing a block header DIKW model based on data resources, information resources, and knowledge resources;

[0058] Step S14: construct a semantic system based on data resources, information resources, and knowledge resources, and construct a block DIKW model based on data resources, information resources, knowledge resources, and the semantic system.

[0059] Since there are a large number of blockchains in the entire system, the blockchain is composed of a block header and a block body. The block header contains some basic content, and the block body contains more detailed content. In order to further improve the speed of content consistency judgment and chain-up speed, when the blockchain DIKW model processes the added content, the block header DIKW model and the block body DIKW model process it separately. First, the block header DIKW model performs preliminary recognition, thereby reducing the number of blockchains. Then, the blockchain DIKW model of the blockchain that has passed the recognition performs semantic analysis on the added content, avoiding the need for all blockchain DIKW models to perform semantic analysis on the added content, thereby improving the speed of content consistency judgment.

[0060] Preferably, the specific steps of step S2 are:

[0061] Step S21: The user initiates a request for content to be added, and the user account is obtained according to the initiated request;

[0062] Step S22: Obtain trace content left by the user on the Internet based on the user account, the trace content includes the user's information input, browsing history, interest preferences, and public information interaction content on the Internet, and map the trace content into user-typed resources, the user-typed resources including data resources, information resources, and knowledge resources;

[0063] Step S23: construct a semantic system based on data resources, information resources and knowledge resources, and construct a user DIKW model based on the data resources, information resources, knowledge resources and semantic system.

[0064] To construct a user DIKW model, we first need to know the subject of the DIKW model to be constructed. Therefore, after the user initiates a request for content to be added, the corresponding user's account is obtained according to the request. The user's account contains the user's personal information, and the trace content associated with it can be searched through the user's account. These trace contents can be mapped into user typed resources, and finally the user DIKW model can be constructed based on the typed resources.

[0065] Preferably, the specific steps of step S3 are:

[0066] Step S31: The block header DIKW model identifies the relevance of the content to be added;

[0067] Step S32: Classify the blockchains into relevant blockchains and unrelated blockchains based on the correlation identification result;

[0068] Step S33: The DIKW model of the block body corresponding to the relevant blockchain performs semantic analysis on the content to be added based on the semantic system and obtains the blockchain semantic representation;

[0069] Step S34: The user DIKW model extracts keywords from the content to be added, and processes the keywords based on the semantic system to obtain user semantic representation.

[0070] When acquiring the semantic representation, the block header DIKW model first performs relevance identification on the content to be added. The purpose of relevance identification is to determine which blockchains are relevant to the content to be added, so that irrelevant blockchains can be eliminated. After obtaining the relevant blockchains, the block body DIKW model of the relevant blockchain performs semantic analysis on the content to be added based on its internal semantic system, thereby obtaining the blockchain semantic representation. Similarly, when performing semantic analysis, the user DIKW model also performs analysis based on its internal semantic system to obtain the user semantic representation. Since the block header DIKW model has performed relevance identification on the blockchain in advance, some irrelevant blockchains can be eliminated, and then the blockchain DIKW model of the relevant blockchain performs semantic analysis. Since the number of blockchains used for semantic analysis is reduced, the speed of semantic analysis will be improved. After all relevant blockchains have performed semantic analysis on the content to be added and obtained the blockchain semantic representation, a semantic comparison can be performed between the blockchain semantic representation and the user semantic representation.

[0071] Preferably, the specific steps of step S4 are:

[0072] Step S41: Determine the similarity between the blockchain semantic representation and the user semantic representation;

[0073] Step S42: Compare all similarity results and arrange them in descending order;

[0074] Step S43: The blockchain with the highest similarity result is used as the blockchain with the same semantic comparison result;

[0075] Step S44: Add the content to be added to the end of the blockchain with the consistent semantic comparison result.

[0076] When performing semantic comparison, since there may be multiple blockchains with content similar to the content to be added, the similarity between the blockchain semantic representation and the user semantic representation is judged, and the similarity results are obtained. The similarity results are then sorted, and finally the blockchain corresponding to the highest similarity result is used as the blockchain with the consistent semantic comparison results, and then the content to be added is uploaded to the chain.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. The DIKW graph-based blockchain content determination method for essential computing and reasoning is characterized by: The following steps are involved: Step S1: Obtain blockchain typed data and construct a blockchain DIKW model based on the blockchain typed data; Step S2: The user initiates a request for content to be added, obtains the user typed data of the requesting user, and constructs a user DIKW model based on the user typed data; Step S3: The blockchain DIKW model performs semantic analysis on the content to be added and obtains a blockchain semantic representation. The user DIKW model performs semantic analysis on the content to be added and obtains a user semantic representation. Step S4: Perform a semantic comparison between the blockchain semantic representation and the user semantic representation, and add the content to be added to the end of the blockchain where the semantic comparison result is consistent; The blockchain DIKW model includes a block header DIKW model and a block body DIKW model. The user DIKW model, block header DIKW model, and block body DIKW model all include a data model, an information model, and a knowledge model. The block body DIKW model also includes a semantic model. The data model, information model, and knowledge model can be converted into each other. The semantic model is obtained by combining the data model, information model, and knowledge model. The specific steps of step S1 include: Step S11: Obtain the block header and block body of each block in each blockchain; Step S12: Map the contents contained in the block header and the block body into blockchain typed resources, where the blockchain typed resources include data resources, information resources, and knowledge resources; Step S13: Constructing a block header DIKW model based on data resources, information resources, and knowledge resources; Step S14: constructing a semantic system based on data resources, information resources, and knowledge resources, and constructing a block DIKW model based on the data resources, information resources, knowledge resources, and semantic system; In step S3, the blockchain DIKW model processes the content to be added and obtains the specific steps of blockchain semantic representation: Step S31: The block header DIKW model identifies the relevance of the content to be added; Step S32: Classify the blockchains into relevant blockchains and unrelated blockchains based on the correlation identification result; Step S33: The DIKW model of the block body corresponding to the relevant blockchain performs semantic analysis on the content to be added based on the semantic system and obtains the blockchain semantic representation; In step S3, the user DIKW model processes the content to be added and obtains the user semantic representation in the following specific steps: Step S34: The user DIKW model extracts keywords from the content to be added, and processes the keywords based on the semantic system to obtain user semantic representation.

2. The DIKW graph-based blockchain content determination method for essential computing and reasoning according to claim 1 is characterized in that: The specific steps of step S2 are: Step S21: The user initiates a request for content to be added, and the user account is obtained according to the initiated request; Step S22: Obtain trace content left by the user on the Internet according to the user account, and map the trace content into user-typed resources, wherein the user-typed resources include data resources, information resources, and knowledge resources; Step S23: construct a semantic system based on data resources, information resources and knowledge resources, and construct a user DIKW model based on the data resources, information resources, knowledge resources and semantic system.

3. The DIKW graph-based blockchain content determination method for essential computing and reasoning according to claim 2 is characterized in that: The trace content includes the information input, browsing history, interest preferences, and public information interaction content conducted by users on the Internet.

4. The DIKW graph-based blockchain content determination method for essential computing and reasoning according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41: Determine the similarity between the blockchain semantic representation and the user semantic representation; Step S42: Compare all similarity results and arrange them in descending order; Step S43: The blockchain with the highest similarity result is used as the blockchain with the same semantic comparison result; Step S44: Add the content to be added to the end of the blockchain with the consistent semantic comparison result.

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