CN111917861A - Knowledge storage method and system based on block chain and knowledge graph and application thereof - Google Patents
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Abstract
The invention discloses a knowledge storage method, a knowledge storage system and application based on a block chain and a knowledge graph, wherein the storage method comprises the steps of preparing original data into knowledge metadata and storing the knowledge metadata through the block chain. The data or information stored by the method has the characteristics of being unforgeable, traceable in process, transparent in disclosure, capable of being maintained collectively and the like.
Description
Technical Field
The invention relates to the technical field of knowledge graphs.
Background
With the continuous improvement of the industrial automation degree, the data volume collected by the sensor in the automation process is remarkably increased, the data collection, arrangement, analysis and the like become more important, and the problem of how to ensure the authenticity of the data and the correct processing of the data becomes an urgent need to be solved.
For example, in the knowledge graph technology, which is one of the important means for processing industrial process data, in order to adapt to storage management and query processing of large-scale knowledge graph data, the prior art designs a special storage scheme and a query processing mechanism for a graph data model, including 3store based on a relational storage system, RDF4J based on an RDF triple library storage system, Neo4j based on a graph database system, and the like, wherein the storage scheme includes a raw graph storage, a distributed storage, a triple index, and the like. Under these schemes, databases with different characteristics were developed, such as Allegro Graph excelling in semantic reasoning using triple-indexed storage scheme; the Orient DB software using the native graph storage scheme supports multi-model data management; the Neo4j software stored by using the protograph storage scheme is the most popular graph database at present, and can realize the import and visualization operation of knowledge map data, so when a user inputs a relevant query instruction in the Neo4j software and queries required data, the result can be directly seen from a simple graph without reading a large amount of text information. In these schemes, the data of the knowledge graph is often stored by importing into a database, and if there is a data that is tampered or counterfeited by other means, there may be a significant misleading or damage to the overall query or analysis result, or even if all the results are not biased, if a certain user finds that one of the data is counterfeit, then all users suspect the authenticity of the entire knowledge graph database, and a huge loss is brought to the user of the database.
Disclosure of Invention
The invention aims to provide a knowledge storage method based on a block chain and a knowledge graph, and data or information stored by the method has the characteristics of impossibility, process traceability, public transparency, collective maintenance and the like.
The invention also aims to provide a system capable of realizing the storage method.
The invention also aims to provide application of the knowledge storage method or system.
The invention firstly provides the following technical scheme:
the knowledge storage method based on the block chain and the knowledge graph comprises the following steps:
the raw data is prepared into knowledge metadata, and then the knowledge metadata is stored through a block chain.
In some embodiments, the raw data is obtained by uploading by a user qualified.
In some embodiments, the storing of the knowledge metadata comprises:
providing a knowledge metadata to all nodes in the blockchain unit;
competing for uplink qualification by all nodes within a blockchain unit;
providing uplink information from the node acquiring uplink qualification;
verifying the uplink information by other nodes;
after the verification is passed, the node obtaining uplink qualification processes uplink of the generated blocks containing knowledge metadata.
In some embodiments, after storage, knowledge metadata is exported via the public interface of the blockchain and viewed graphically.
In some embodiments, the competition mode is: and in all nodes, the node with the minimum absolute value of the difference between the public key of the generated block and the hash value to be linked obtains the uplink qualification, wherein the hash value to be linked is a new hash value obtained by carrying out hash calculation on the hash value of the previous block and the random number generated by the previous block.
In some embodiments, the post-uplink information is a hash value of a current block linked to a previous block generated by a node obtaining uplink qualification, and is obtained by hashing knowledge metadata of the current block and the hash value of the previous block.
In some embodiments, the verifying comprises: and randomly selecting three other nodes except the node acquiring the uplink qualification, comparing the hash values of the current block, if the hash values of the entity data are consistent, passing the verification, signing by the three nodes, and agreeing to release the broadcast.
In some embodiments, a credit incentive is applied to the node that completed uplink as shown in the following equation:
where CV represents the credit of the node that completed the uplink, TN is the number of times it generated the correct block, and FN is the number of times it generated the error block.
In some embodiments, each chunk includes a local chunk name, a last chunk hash value, a local chunk hash value, a timestamp, a random number, a Merkle tree, and knowledge metadata.
In some embodiments, the preparing of the knowledge metadata comprises:
according to the semantic standard, performing semantization on all the obtained data through a semantic technology;
performing semantic encapsulation on all the obtained data, adding context semantic description, and converting the obtained data into a text;
performing entity identification and line relation extraction on the text to obtain total knowledge metadata of all data;
and dividing the total knowledge metadata according to different entity names to obtain a plurality of knowledge metadata.
In some embodiments, the aggregate knowledge metadata storage format is a relational triple, i.e., { head entity, relation, tail entity }.
The invention further provides a system applying the knowledge storage method, which comprises a user unit, a data unit and a block chain unit, wherein the block chain unit is provided by a plurality of decentralized nodes through distributed interconnection, the user unit comprises a user information database and a user information processor, the data unit comprises an upload data database, a data processing unit and a knowledge metadata base, the block chain unit comprises a block chain uplink unit and a block chain storage unit, and the data processing unit comprises a BiLSTM-CRF model for entity identification and an Attention-BiLSTM model for relation extraction.
In some embodiments, the subscriber units, data units, and blockchain units are all disposed within the decentralized node of the distributed interconnect.
The invention further provides an application method of the knowledge storage method or the system, and the application method is applied to industrial process data management.
The invention has the following beneficial effects: in the storage method, the original data is stored in a decentralized system in a knowledge element form, so that a user cannot rewrite the original data, and the authenticity of the data can be ensured; meanwhile, the stored knowledge metadata can be traced through the block chain, namely, all operation processes from generation to storage can be traced, and once the data is changed in the process, the data can be quickly and accurately identified, so that the authenticity and the stability of the data are further ensured.
Drawings
FIG. 1 is a schematic diagram of the system components in an embodiment of the present invention.
Fig. 2 is a schematic overall flow chart according to the embodiment of the present invention.
Fig. 3 is a block chain structure diagram according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a user qualification process according to an embodiment of the invention.
Fig. 5 is a hash solving algorithm according to the embodiment of the present invention.
Fig. 6 is a flowchart of the system according to embodiment 1 of the present invention.
Fig. 7 is a schematic diagram of entity identification according to embodiment 1 of the present invention.
Fig. 8 is a schematic diagram of relationship extraction according to embodiment 1 of the present invention.
Detailed Description
The present invention is described in detail below with reference to the following embodiments and the attached drawings, but it should be understood that the embodiments and the attached drawings are only used for the illustrative description of the present invention and do not limit the protection scope of the present invention in any way. All reasonable variations and combinations that fall within the spirit of the invention are intended to be within the scope of the invention.
Industrial knowledge storage is performed in a system as shown in fig. 1, the system comprising:
the system comprises a user unit, a data unit and a block chain unit, wherein the block chain unit is composed of a plurality of decentralized nodes which are connected with one another in a distributed mode. Wherein each node can generate a new block link after an existing block, thereby forming a block chain. The initial block is created by the system, i.e. the created block.
The user unit comprises a user information database, the data unit comprises a sensor database, a data processing unit and a knowledge metadata database, and the block chain unit comprises a block chain upper unit and a block chain storage unit.
Preferably, the subscriber unit, the data unit and the blockchain unit are all arranged in decentralized nodes of the distributed interconnection.
Outside the block chain unit, the user unit can complete the steps of registration, login and the like of the user, so that the user can be accepted by the whole system, and then the system can operate the sensor data uploaded correspondingly by the user. The data unit can carry out semantic recovery, entity extraction, relation extraction, single knowledge metadata segmentation and other operations on sensor data uploaded by a user, so that uplink, broadcast and other operations can be carried out in the block chain unit.
Wherein each new tile generated by a node should include the tile information and knowledge metadata stored within the tile.
The block information at least includes an identifier of a previous block to be linked, an identifier of the current block, and traceable characteristic values of all the linked blocks and traceable relationships between the characteristic values.
The last block id refers to a numerical value and/or text for indicating the identity and/or feature of the last block, and in the implementation, the name of the last block and/or a hash value thereof may be used.
The identifier of the block refers to a numerical value and/or text for indicating the identity and/or feature of the block, and in implementation, the name of the block and/or a hash value thereof may be used.
The traceable characteristic value refers to a value for indicating the independent identity and/or characteristic of all the linked blocks before the new block, and the traceable relationship between the characteristic values refers to a logic and/or link relationship for indicating the development of the characteristic values according to time and/or link sequence.
In addition to the above information, other information may be added to the block.
In a Block chain unit (partially shown) as shown in fig. 3, it generates a new Block by a node: 102 to the last generated Block on the existing Block chain: 101, a block chain is formed as shown. Each Block in the Block chain comprises Block information and industrial knowledge metadata (such as industrial knowledge metadata 1 or industrial knowledge metadata 2) stored in the Block, wherein the Block information comprises a Block name (such as Block: 101 or Block: 102), a last Block hash value, a timestamp, a random number and a Merkle tree. Such as the name including (block: 101), last chunk hash value, local chunk hash value, timestamp, random number.
Wherein the random number is obtainable by a verifiable random function. The verifiable random function comprises four functions, namely key generation, random number output generation, zero knowledge proof calculation and random number output verification. Where the process of generating the random number and its proof is performed within the system, the input is a private key and a value. The output is the random number and its zero knowledge proof. After receiving the input and the proof, the other nodes can verify the random number by combining the public key of the node generating the random number.
The knowledge storage is performed through the system shown in fig. 1 and the process shown in fig. 2, and the specific steps may be as follows:
s1: user qualification is accomplished within the subscriber unit as shown in fig. 4:
the subscriber unit includes a database for storing subscriber information and a subscriber information processor.
Wherein, if the user information with data uploading and/or inquiring qualification is stored in the user unit in advance, qualification can be completed through the following processes:
s11: and the user uploads the identity information and the public key information.
S12: and after receiving the identity information and the public key information, the user unit verifies whether the user is registered, and if the user passes the verification, the user is considered to complete qualification.
Wherein, the verification process can select: and matching the user uploading information with the stored information in the user unit database, and judging whether the user information is stored in the database according to a matching result.
Wherein, the matching process can select: and searching the public key information corresponding to the identity information in the user unit according to the identity information uploaded by the user, comparing the searched public key information with the public key information uploaded by the user, and if the information is consistent, determining that the matching is passed.
If the user information desired to be uploaded and/or queried for is not stored in the subscriber unit, the qualification may be accomplished by:
s11: and uploading the identity information by the user.
S12: the user unit judges whether the user is a registrable user according to whether the identity data of the user exists in the user information database.
S13: if the user is a registrable user, the user unit stores the identity information thereof in the user information database and returns an identity identifier to the user, and the user is considered to be qualified and passed.
S2: completing industrial knowledge metadata preparation in a data unit:
the data unit should at least comprise a data storage unit and a data processing unit.
The data storage unit is used for storing data uploaded by a user, such as data generated by an industrial sensor, and knowledge metadata after data processing is completed, and correspondingly, the data storage unit can comprise a sensor database and a knowledge metadata database.
The uploaded data, such as data generated by the sensor, should be directly uploaded to the storage unit of the data unit periodically without user operation.
In specific implementation, the storage unit may further set a deadline requirement for the uploaded data according to an actual situation, where the deadline is required to be agreed by most users, and then the system sets the data uploading interface to periodically close access, so that only data meeting the deadline requirement can be stored.
After a certain period of time expires, all the data to be processed uploaded to the storage unit by all the users are further input into the data processing unit.
The data processing unit is used for preparing the data stored in the data storage unit into knowledge metadata, and the processing process of the data processing unit can comprise the following steps:
according to the semantic standard, all industrial sensor data are semanticized through a semantic technology, and abstraction of the equipment of the Internet of things, namely the industrial sensor used in the embodiment, is realized.
Defining a data interface meeting the semantic standard, performing semantic encapsulation on the sensor data and adding context semantic description to the sensor data, namely converting the digital type data represented by the sensor data into a text description form.
Carrying out entity recognition on a text sentence converted from sensor data by using a Bi LSTM + CRF model, namely recognizing an entity name in the sentence; and then extracting the relation of the sentences of the identified entities by using an Attention-Bi LSTM model so as to extract and obtain the total industrial knowledge metadata of all data, wherein the storage format of the industrial knowledge metadata is set as a relation triple- - { head entity, relation, tail entity }.
Dividing the obtained total industrial knowledge metadata into a plurality of single knowledge metadata according to the name of the entity, namely dividing all relation triples containing the name of a certain entity into a single knowledge metadata, and storing each knowledge metadata in principle based on a primitive map.
S3: completing storage of industrial knowledge metadata within the blockchain unit
The method comprises the following steps:
s31: and selecting uplink qualification for the nodes in the block chain.
The process can comprise the following steps:
providing random numbers by the nodes participating in the transaction in the previous round of blocks;
obtaining a new hash value by the random number and the hash value of the previous block through an SHA256 algorithm;
for the nodes in all the block chain units, the absolute value of the difference between the node public key and the new hash value is calculated, and the node with the minimum absolute value is the new uplink node;
the new uplink node performs hash calculation on the obtained knowledge metadata and the hash value of the previous block by using a hash algorithm SHA-256, so as to obtain the hash value of the current block, as shown in fig. 5;
the remaining other nodes verify the hash value of the current block, and if the calculated hash value of the entity data is consistent with the hash value of the uplink node, the remaining other nodes approve releasing and signing; the release approval means that the node receives the new block after confirming that the added block is valid, and the node stores the new block and adds the storage of the new block to other blocks.
After the uplink node obtains the broadcast which agrees to be released, the acquired knowledge metadata is put into the data layer, the block is created and added into the main chain, the broadcast is carried out immediately, the node which receives the broadcast carries out verification and transmits the verification after passing the verification, and the uplink node completes the storage of the knowledge metadata in the block.
S32: credit incentives are provided for the generation of uplink nodes.
Setting the credit values of all nodes in the initialized blockchain unit to be 100, and adjusting the credit values of the nodes after one node becomes an uplink node and finishes blockchain entry as shown in formula (1):
wherein CV represents the credit value of the uplink node, TN is the number of times the uplink node generates the correct block, and FN is the number of times the uplink node generates the wrong block.
By the above formula, the credit value of the uplink node is decreased once the uplink node generates the error block, and is decreased to 0 when the number of times the uplink node generates the error block is greater than that of the correct block, while the credit value of the uplink node is constantly increased when no error block is generated.
And recording the credit value of the node in the system as a basis for rewarding the node.
In the above process, after the system creates the created block, each network node, i.e. all registered users, may obtain the qualification of the block chain record containing the knowledge metadata through competition.
S4: data query
The knowledge stored in the system can be queried in the following way:
inquiring through an open interface of the block chain, and inquiring the unique ID of the corresponding suspected block in the open interface; by this ID, the timestamp in the block and the knowledge metadata in the block can be viewed, wherein the knowledge metadata can be imported into Neo4j software, so that the entity data can be viewed graphically. When the authenticity of the entity data is suspected, the time stamp of the block chain can be checked and verified, and the data can be traced to the source to see whether a change record exists in the near future.
Example 1
With the above embodiments, knowledge storage is performed on certain lathe sensor data in the system shown in fig. 6. The specific process is as follows:
the system comprises an A node, a B node and a C node which are interconnected, and lathe sensor data are uploaded to the system by a qualified user at the A node and are stored in a data storage unit of the A node. The data on the lathe sensor of the node A is 1-7, and sentences generated by the lathe for seven times can be obtained by carrying out sentence reduction on the sensor data by using a semantic technology according to the rule that all nodes are unified. The sentence is subjected to entity recognition through a BilSTM model, and as shown in FIG. 6, entities including a number one, a lathe and a number seven can be obtained. Entity identification and relationship extraction are performed using the Attention BilSTM model, as shown in FIG. 7, the entities and relationships can be obtained as follows: lathe # one produces { entity } relations } seven times { entity }, which is a knowledgeelement containing a relational triple, which is stored in a knowledgeelement database in a protogram storage format. Thereafter, the node-B in the system acquires the uplink qualification through uplink qualification selection. The uplink node-B will: { lathe one, production, seventh } data in the primitive graph storage format is put into the data layer of the block chain, and the block is created and added into the main chain, and then broadcast is performed, and the node A and the node C which receive the broadcast perform verification, and further forwarding is performed after the verification is passed. When the data needs to be viewed, the data can be read into Neo4j software.
The whole process is completed by decentralized nodes, data is stored in the system in a knowledge metadata mode, a user cannot change the knowledge metadata easily, authenticity of the data is guaranteed, and if the authenticity of the knowledge metadata is suspected, operation before the knowledge metadata can be checked through a block chain tracing source to judge whether the knowledge metadata is changed or not.
The above is only a preferred embodiment of the present invention, and the scope of the present invention is not limited to the above-described examples. All technical schemes belonging to the idea of the invention belong to the protection scope of the invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention, and such modifications and embellishments should also be considered as within the scope of the invention.
Claims (10)
1. The knowledge storage method based on the block chain and the knowledge graph is characterized in that: it includes:
preparing the original data into knowledge metadata, and then storing the knowledge metadata through a block chain;
preferably, the raw data is obtained by uploading by a user qualified.
2. The knowledge storage method of claim 1, wherein: the storing of the knowledge metadata comprises:
providing a knowledge metadata to all nodes in the blockchain unit;
competing for uplink qualification by all nodes within a blockchain unit;
providing uplink information from the node acquiring uplink qualification;
verifying the uplink information by other nodes;
after the verification is passed, the node obtaining the uplink qualification carries out uplink on the generated blocks containing the knowledge metadata;
preferably, after storage, the knowledge metadata is exported through a public interface of the blockchain and is referred to graphically.
3. The knowledge storage method of claim 2, wherein: the competition mode is as follows: and in all nodes, the node with the minimum absolute value of the difference between the public key of the generated block and the hash value to be linked obtains the uplink qualification, wherein the hash value to be linked is a new hash value obtained by carrying out hash calculation on the hash value of the previous block and the random number generated by the previous block.
4. The knowledge storage method of claim 2, wherein: the information after uplink is a hash value of a current block linked with a previous block generated by a node obtaining uplink qualification, and the information after uplink is obtained by performing hash calculation on knowledge metadata of the current block and the hash value of the previous block.
5. The knowledge storage method of claim 4, wherein: the verification comprises: and randomly selecting three other nodes except the node acquiring the uplink qualification, comparing the hash values of the current block, if the hash values of the entity data are consistent, passing the verification, signing by the three nodes, and agreeing to release the broadcast.
6. The knowledge storage method of claim 2, wherein: performing credit stimulation on the node completing the uplink as shown in the following formula:
where CV represents the credit of the node that completed the uplink, TN is the number of times it generated the correct block, and FN is the number of times it generated the error block.
7. The knowledge storage method of claims 2-6, wherein: each block comprises a block name, a block hash value, a time stamp, a random number, a Merkle Tree and knowledge metadata.
8. The knowledge storage method of claim 1, wherein: the preparation of the knowledge metadata comprises:
according to the semantic standard, performing semantization on all the obtained data through a semantic technology;
performing semantic encapsulation on all the obtained data, adding context semantic description, and converting the obtained data into a text;
performing entity identification and relation extraction on the text to obtain total knowledge metadata of all data;
dividing the total knowledge metadata according to different entity names to obtain a plurality of knowledge metadata;
preferably, the overall knowledge metadata storage format is a relationship triple, i.e., { head entity, relationship, tail entity }.
9. The system of knowledge storage methods of claims 1-8, wherein: the system comprises a user unit, a data unit and a block chain unit, wherein the block chain unit is provided by a plurality of decentralized nodes through distributed interconnection, the user unit comprises a user information database and a user information processor, the data unit comprises an upload data database, a data processing unit and a knowledge metadata database, the block chain unit comprises a block chain uplink unit and a block chain storage unit, and the data processing unit comprises a BiLSTM-CRF model for entity identification and an Attention-BiLSTM model for relation extraction.
10. Use of the knowledge storage method of claims 1-8 or the system of claim 9 in industrial process data management.
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