A blockchain-based real-world study database management method and system

Abstract

The application provides a kind of based on blockchain real world research database management method and system, belong to blockchain storage technology field, its method includes collecting hospital related data, the type of the related data is divided, according to type classification result corresponding hash storage is generated;According to the abstract of hash storage generation chain, the abstract is fragmented, and the effective verification of the fragmentation result is carried out, and the effective fragmentation information verified is written into new block;Based on new block, establish anti quantum Merkle tree, add time stamp anchor, smart contract automatically analyzes zk-SNARK proof in block, and establishes cross-chain hash index;According to cross-chain hash index setting hierarchical data release rule, form research database, finally form research database.This is conducive to improving the transparency, credibility, security and shareability of data, so as to promote the development of real world research.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of blockchain storage, and particularly relates to a real world research database management method and system based on a blockchain. BACKGROUND

[0002] With the surge in data scale and complexity, the real world research market is expanding, and the data types have expanded from structured electronic medical records to unstructured data such as Internet of Things devices and imageomics, which makes it difficult for traditional manual governance models to cope with the cleaning, standardization and cross-domain integration of massive data. Currently, the technology is facing challenges such as the surge in data scale and complexity, data island and collaboration bottleneck, and research reproducibility crisis.

[0003] Therefore, the present application provides a real world research database management method and system based on a blockchain. SUMMARY

[0004] The real world research database management method and system based on a blockchain provided by the present application collects hospital related data through blockchain technology, classifies the types of the data, and generates corresponding hash storage according to the type classification results. The data digest is chained and fragmented, an anti-quantum Merkle tree is established, a time stamp anchor is added, a smart contract automatically analyzes the zk-SNARK proof in the block, a cross-chain hash index is established, and finally a research database is formed. This is conducive to improving the transparency, credibility, security and shareability of the data, thereby promoting the development of real world research.

[0005] The real world research database management method based on a blockchain provided by the present application comprises:

[0006] Step 1: Collecting hospital related data, classifying the types of the related data, and generating corresponding hash storage according to the type classification results;

[0007] Step 2: Chaining the digest according to the hash storage, fragmenting the digest, verifying the fragmentation results, and writing the verified effective fragmentation information into a new block;

[0008] Step 3: Establishing an anti-quantum Merkle tree based on the new block, adding a time stamp anchor, automatically analyzing the zk-SNARK proof in the block by a smart contract, and establishing a cross-chain hash index;

[0009] Step 4: Setting a hierarchical data release rule according to the cross-chain hash index to form a research database.

[0010] The real world research database management method based on a blockchain provided by the present application collects hospital related data, classifies the types of the related data, and generates corresponding hash storage according to the type classification results, which comprises:

[0011] The interface of each business system of the hospital is connected, and the interface condition is adapted and judged, the corresponding adaptation tool is selected from the format-adaptation table according to the adaptation judgment result, and the hospital related data is collected based on the adaptation tool;

[0012] The related data is type-divided, standard conversion is carried out based on the type division result, a conversion result is obtained, and a hash storage certificate is generated according to the conversion result.

[0013] The application provides a blockchain-based real world research database management method, which divides the related data into types, converts standards based on the type division result, obtains a conversion result, and generates a hash storage certificate according to the conversion result, comprising:

[0014] The related data is input into a feature analysis engine, the related data type is parsed and the corresponding type label is added, and a type division result is obtained;

[0015] The structured data in the type division result is matched with the real-time syntax of the knowledge graph, and then unit intelligent conversion is carried out, to obtain a first conversion result;

[0016] The unstructured data in the type division result is annotated by multi-modal AI and converted into a standard result through cross-modal alignment, to obtain a second conversion result;

[0017] The first conversion result and the second conversion result are serialized, and a hash value is calculated for the serialized result using an encryption hash algorithm, to generate a hash storage certificate.

[0018] The application provides a blockchain-based real world research database management method, which generates an abstract chain according to the hash storage certificate, performs sharding processing on the abstract, performs effective verification on the sharding result, and writes the verified effective sharding information into a new block, comprising:

[0019] The key fields of the hash storage certificate are converted into a compact JSON format, a JSON string is obtained, a second hash is calculated for the JSON string, and a chain identifier is generated;

[0020] An abstract is generated according to the chain identifier, the abstract is grouped, a time sequence group and a verification group are obtained, the time sequence group is sharded according to a fixed length, the shortage is supplemented with zero, the sharding is encoded, and the metadata of all sharding is calculated according to the encoding;

[0021] The verification group nodes are determined according to VRF random allocation, the metadata is broadcasted to the verification group nodes by the time sequence group, each verification group node verifies a specific sharding according to VRF allocation, and a signature proof is generated;

[0022] The time sequence group receives the signature proof of all verification group nodes, and counts the valid shard proportion of the signature proof, packs the corresponding valid shard information based on the valid shard proportion to generate transaction data, submits the transaction to the blockchain, and performs malicious node processing on the blockchain to form network consensus, and writes the valid shard information into a new block.

[0023] The application provides a real world research database management method based on a blockchain, determines verification group nodes according to VRF random allocation, and broadcasts metadata to the verification group nodes by a time sequence group, each verification group node verifies a specific shard according to VRF allocation, and generates a signature proof, including:

[0024] A dynamic staking mechanism is set according to the hospital level, and a variable candidate node list is determined by combining the dynamic staking mechanism, the medical special-purpose blockchain network and the hospital characteristics;

[0025] The candidate nodes in the variable candidate node list are calculated by VRF based on the hash evidence, and the nodes with the last two bits of the VRF output value less than a preset value become the verification group, and the remaining nodes become the time sequence group;

[0026] The time sequence group packs the metadata of the current time window and sends it to all verification group nodes through the medical special-purpose network, each verification group node calculates a shard index according to its own VRF output value, and determines the specific shard verification right of each verification group node according to the shard index;

[0027] The verification group node performs medical data verification according to the specific shard verification right, and generates a signature proof after verification.

[0028] The application provides a real world research database management method based on a blockchain, establishes an anti-quantum Merkle tree based on a new block, adds a timestamp anchor, and automatically parses the zk-SNARK proof in the block by a smart contract, and establishes a cross-chain hash index, including:

[0029] The verified specific shard in the new block is used as a leaf node, an anti-quantum Merkle tree is constructed using an XMSS algorithm, a root hash is generated, and the root hash is timestamp anchored;

[0030] The pre-stored zk-SNARK proof in the new block is parsed by a smart contract, one-way hash matching is performed on the specific shard, and a first matching result is obtained;

[0031] The consistency of the root hash and the block header is verified according to the Merkle tree, and a second verification result is obtained;

[0032] If the first matching result and the second verification result are both passed, a cross-chain hash index is generated.

[0033] The application provides a blockchain-based real world research database management method, which sets hierarchical data release rules according to a cross-chain hash index, forms a research database, and comprises the following steps:

[0034] An index key value is constructed according to the cross-chain hash index, the index key value is written into an index mapping table of a target chain, preset hierarchical conditions and corresponding hierarchical data release rules are set in a smart contract, and a research database is formed.

[0035] The application provides a blockchain-based real world research database management system, which comprises the following modules:

[0036] The hash evidence module collects hospital-related data, classifies the related data according to types, and generates corresponding hash evidence according to the type classification results;

[0037] The sharding module generates a digest on-chain according to the hash evidence, performs sharding processing on the digest, performs effective verification on the sharding results, and writes the verified effective sharding information into a new block;

[0038] The cross-chain module establishes an anti-quantum Merkle tree based on the new block, adds a timestamp anchor, automatically parses zk-SNARK proof in the block through a smart contract, and establishes a cross-chain hash index;

[0039] The database module sets hierarchical data release rules according to the cross-chain hash index, and forms a research database.

[0040] Compared with the prior art, the application has the following beneficial effects: hospital-related data is collected through blockchain technology, and type classification and hash evidence are performed; the data digest is on-chain and sharding processing is performed; an anti-quantum Merkle tree is established, a timestamp anchor is added, zk-SNARK proof in the block is automatically parsed through a smart contract, and a cross-chain hash index is established; and finally a research database is formed. This is conducive to improving the transparency, credibility, security and shareability of data, thereby promoting the development of real world research.

[0041] Other features and advantages of the application will be set forth in the following description of the application, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings.

[0042] The technical solutions of the application will be further described in detail below with reference to the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings are used to provide a further understanding of the application, and constitute a part of the specification, together with the embodiments of the application, to explain the application, and do not constitute a limitation on the application. In the drawings:

[0044] Figure 1 is a flowchart of a blockchain-based real-world research database management method provided by an embodiment of the present application;

[0045] Figure 2 is a structural diagram of a blockchain-based real-world research database management system provided by an embodiment of the present application;

[0046] Figure 3 is a flowchart of a medical special-purpose blockchain network data sharing process provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0048] Embodiment 1: The present application provides a blockchain-based real-world research database management method, as shown in Figure 1 , which includes:

[0049] Step 1: Collecting hospital-related data, classifying the related data by type, and generating corresponding hash records according to the type classification results;

[0050] Step 2: Generating a summary on-chain according to the hash records, performing sharding processing on the summary, performing effective verification on the sharding results, and writing the verified effective sharding information into a new block;

[0051] Step 3: Based on the new block, an anti-quantum Merkle tree is established, a time stamp anchor is added, a smart contract automatically parses the zk-SNARK proof in the block, and a cross-chain hash index is established;

[0052] Step 4: Setting a hierarchical data release rule according to the cross-chain hash index, and forming a research database.

[0053] In this embodiment, the hospital-related data refers to the data collected from various business systems of the hospital, including the medical records of patients, the inventory information of drugs, the laboratory test results, etc., such as the diagnosis records of patients, the medication history, and the laboratory test results.

[0054] In this embodiment, the type classification refers to the process of classifying the collected data according to different types, such as classifying the data into text data, numerical data, and time series data.

[0055] In this embodiment, the type classification result refers to the result after data classification, which describes the type of each piece of data, such as a patient information record being classified as text data.

[0056] In this embodiment, hash storage refers to the process of using a hash function to process data and generate a fixed-length string, which is used to verify the integrity and authenticity of the data. For example, using the SHA-256 hash function to generate a hash value for the converted data, and storing the value on the blockchain.

[0057] In this embodiment, where, represents the hash value calculated according to the Pareto optimal calculation; represents the intermediate hash value of structured data in the i-th iteration; represents the intermediate hash value of unstructured data in the i-th iteration; K represents the additional key; i represents the iteration index; p represents a large prime number; represents the random coefficient of structured data; m represents the vector dimension of structured data; represents a nonlinear activation function; represents the weight matrix of structured data; represents the vectorization of structured data; represents vector splicing; represents the dynamic salt value of the i-th iteration; represents the vectorization of unstructured data; represents the weight matrix of unstructured data represents the random coefficient of unstructured data; represents the rounding function.

[0058] In this embodiment, generating an abstract refers to the process of abstracting data and extracting key information from the data. This usually involves using a hash function or other abstract algorithm, such as using the SHA-256 hash function to generate a fixed-length abstract for a large data file.

[0059] In this embodiment, the process of abstracting on-chain is to extract key fields: extract key information from hash storage, such as data hash value, timestamp, etc.; Convert to compact JSON format: convert these key information to compact JSON format, reduce data size; Generate JSON string: convert JSON format data to string for hash calculation; Calculate the second hash: apply a hash function to the JSON string to generate a hash value, which is the on-chain identifier; On-chain: store the on-chain identifier on the blockchain as a permanent record of the data abstract.

[0060] In this embodiment, the process of sharding is to generate summaries: generate data summaries based on on-chain identifiers; grouping: divide summaries into timing groups and verification groups. The timing group is responsible for broadcasting data shards to the verification group, which is responsible for verifying shards; sharding: shard data in the timing group by fixed length, and fill zeros if data is insufficient; encoding: encode each shard, for example using Base64 encoding, to facilitate transmission in the network; calculate metadata: calculate the metadata of all shards, such as the size and hash value of each shard, based on the encoded shards.

[0061] In this embodiment, the process of effective verification is VRF allocation: randomly allocate verification group nodes using a verifiable random function (VRF), and determine the authority of each node to verify specific shards; broadcast metadata: the timing group broadcasts shards and metadata to all verification group nodes; verify shards: each verification group node verifies specific shards according to the VRF allocation result and generates a signed proof; collect signed proofs: the timing group collects signed proofs from all verification group nodes; count the proportion of valid shards: count the proportion of valid shards in the signed proofs; package transaction data: according to the proportion of valid shards, package the corresponding valid shard information into transaction data; submit transaction: submit transaction data to the blockchain; handle malicious nodes: handle malicious nodes on the blockchain to ensure network security; form consensus: through the consensus mechanism of the blockchain, ensure that all nodes agree on valid shard information; write new block: write valid shard information into a new block to complete permanent storage of data.

[0062] In this embodiment, valid shard information refers to the data and information contained in the shards that are valid during the verification process, such as a shard containing valid transaction records.

[0063] In this embodiment, the anti-quantum Merkle tree refers to a Merkle tree constructed using an anti-quantum signature algorithm, which can resist attacks by quantum computers and ensure data security in the quantum computing era, such as a Merkle tree constructed using the XMSS algorithm.

[0064] In this embodiment, the new block refers to the block on the blockchain where the verified specific shard information is packaged into transaction data, confirmed through the consensus mechanism of the blockchain network, and finally added. This new block contains the hash value and metadata of these shard information, as well as possibly other related information such as timestamp and block creator's signature, etc.

[0065] In this embodiment, timestamp anchoring refers to associating the hash value or summary of data with time information, usually using a timestamp service to ensure the existence and status of data at a specific point in time, such as associating the root hash of a Merkle tree with a timestamp service to prove that the data has not been tampered with at a specific point in time.

[0066] In this embodiment, zk-SNARK proof is a kind of zero-knowledge proof, which allows the prover to prove that a statement is true without revealing any specific information about the statement, for example, a medical data owner proves its ownership of the data without revealing the actual content of the data.

[0067] In this embodiment, smart contract parsing refers to using a smart contract to automatically execute and verify the processing logic of data, for example, a smart contract is used to verify the consistency of medical data shard hash and Merkle tree root hash.

[0068] In this embodiment, cross-chain hash index refers to establishing an index between different blockchains to facilitate data sharing and interoperability, for example, a cross-chain hash index that associates medical data shards on different blockchains to facilitate data sharing and querying.

[0069] In this embodiment, hierarchical data release rules refer to rules defined in a smart contract to control the release and access of data based on the level and access conditions of the data, for example, a rule that allows researchers to access medical data at the public level, but requires additional authorization to access data at the restricted level.

[0070] In this embodiment, research database refers to a well-organized collection of data that contains data records and metadata for research purposes, for example, a database containing medical records, patient information, and clinical research results to support medical research.

[0071] The working principle and beneficial effects of the above technical solution are: through the blockchain technology to collect hospital related data, and type division and hash storage, the data summary is chained and fragmented, an anti-quantum Merkle tree is established, a time stamp anchor is added, a smart contract automatically parses the zk-SNARK proof in the block, and a cross-chain hash index is established, and finally a research database is formed. This helps to improve the transparency, credibility, security and shareability of data, thereby promoting the development of real-world research.

[0072] Embodiment 2: The present application provides a blockchain-based real-world research database management method, which collects hospital-related data, classifies the related data by type, and generates corresponding hash storage according to the type classification results, including:

[0073] Interface with hospital business system interface, and adapt the interface condition to judge, according to the adaptation judgment result, select the corresponding adaptation tool from the format-adaptation table, and collect the hospital related data based on the adaptation tool;

[0074] The related data is classified by type, and a standard conversion is performed based on the type classification result to obtain a conversion result, and a hash record is generated according to the conversion result.

[0075] In this embodiment, the hospital business system interface refers to the interface provided by different information systems (such as electronic medical record system, pharmacy management system, laboratory information system, etc.) in the hospital for data exchange and communication, such as the API interface of the electronic medical record system, which is used to query and update patient information.

[0076] In this embodiment, the interface status refers to indicators such as health status, availability, performance, and security of the interface, such as whether the interface is online, response time, error rate.

[0077] In this embodiment, the adaptation judgment refers to the process of evaluating whether the interface can be compatible with the data collection system, such as checking whether the protocol, data format, and data transmission method of the interface meet the requirements.

[0078] In this embodiment, the format-adaptation table is a mapping table that maps different interface formats to corresponding adaptation tools. This table is usually constructed based on the characteristics of the interface and the requirements of the data collection system, such as a table listing interface formats (such as HL7, FHIR, DICOM) and corresponding adaptation tools (such as data converters, protocol converters).

[0079] In this embodiment, the adaptation tool refers to a tool for converting data in different formats into a format that can be processed by the data collection system, such as a converter that converts HL7 format data into FHIR format.

[0080] In this embodiment, the standard conversion refers to the process of converting different types of data into a unified format or standard, such as converting dates in different formats into the ISO8601 standard format.

[0081] In this embodiment, the conversion result refers to the data after standard conversion, such as the unified format patient information record after conversion.

[0082] The working principle and beneficial effects of the above technical solution are: by evaluating the compatibility of the hospital business system interface, selecting appropriate adaptation tools to collect data, classifying and standardizing the data, and generating a hash record to ensure data integrity and traceability, improving data processing efficiency, promoting data sharing and collaboration, and enhancing the repeatability and credibility of research.

[0083] Embodiment 3: The embodiment of the present application provides a blockchain-based real-world research database management method. The related data is classified by type, the standard conversion is performed based on the type classification result, the conversion result is obtained, and the hash evidence is generated according to the conversion result, comprising:

[0084] The related data is input into a feature analysis engine, the type of the related data is analyzed and a corresponding type label is added, and the type classification result is obtained;

[0085] The structured data in the type classification result is matched with the real-time terminology of the knowledge graph, and then unit intelligent conversion is performed, and the first conversion result is obtained;

[0086] The unstructured data in the type classification result is subjected to multi-modal AI labeling and cross-modal alignment conversion to a standard result, and then the second conversion result is obtained;

[0087] The first conversion result and the second conversion result are serialized, and the hash value is calculated using an encryption hash algorithm on the serialized result, and then the hash evidence is generated.

[0088] In this embodiment, the feature analysis engine refers to a tool or software for analyzing and extracting data features, identifying the type, structure and other attributes of data. For example, a natural language processing (NLP) engine is used to parse text data and extract keywords and entities.

[0089] In this embodiment, the type label refers to a label or classification used to identify the type of data. These labels can help the system understand the meaning and purpose of the data, such as numerical data, which can be labeled as numerical type; for text data, the type label can be text type.

[0090] In this embodiment, structured data refers to data with a clear format and structure, such as data in a spreadsheet or records in a database, such as a patient information table containing fields such as name, age, gender, etc.

[0091] In this embodiment, the real-time terminology matching of the knowledge graph refers to the use of a knowledge graph to understand and interpret the terms and concepts in the data. This usually involves matching the terms in the data with the entities in the knowledge graph, such as using a medical knowledge graph to parse disease names and symptom descriptions in an electronic medical record.

[0092] In this embodiment, unit intelligent conversion refers to the process of converting the units in the data to standard units. This usually involves using rules or algorithms to identify and convert different units of measurement, such as converting body temperature from Celsius to Fahrenheit.

[0093] In this embodiment, the first conversion result refers to the structured data after unit intelligent conversion, such as converting patient weight from kilograms to pounds.

[0094] In this embodiment, unstructured data refers to data without explicit format and structure, such as text, image, audio, etc., such as doctors' diagnosis notes, X-ray images, patient interview recordings.

[0095] In this embodiment, multi-modal AI labeling refers to the process of labeling unstructured data using artificial intelligence technology. This usually involves using machine learning models to identify and label different elements in the data, such as using image recognition models to label fractures in X-ray images.

[0096] In this embodiment, cross-modal alignment refers to the process of aligning or synchronizing data from different modalities. This usually involves using algorithms to match timestamps or events in different modal data, such as aligning the timestamps of patient interview recordings with event records in electronic medical records.

[0097] In this embodiment, the second conversion result refers to the unstructured data after multi-modal AI labeling and cross-modal alignment, such as extracting keywords and entities from patient interview recordings and aligning them with text data in electronic medical records.

[0098] In this embodiment, serialization refers to the process of converting data into a format that can be stored or transmitted. This usually involves converting data structures into byte streams or other formats, such as converting patient information tables into JSON format.

[0099] The working principle and beneficial effects of the above technical solution are: using the feature analysis engine to analyze the type of data and add labels, matching structured data with knowledge graph for intelligent conversion, converting unstructured data into standard results through multi-modal AI labeling and cross-modal alignment, and finally serializing the conversion results and calculating the hash value to generate a hash evidence, improving the accuracy and reliability of the data.

[0100] Embodiment 4: The present application provides a blockchain-based real-world research database management method, which generates a summary on-chain according to a hash evidence, performs sharding processing on the summary, performs effective verification on the sharding results, and writes the verified effective sharding information into a new block, including:

[0101] Extract the key fields of the hash evidence and convert them into compact JSON format to obtain a JSON string, calculate the second hash of the JSON string, and generate a chain identifier;

[0102] Generate a summary according to the chain identifier, group the summary, obtain a time sequence group and a verification group, slice the time sequence group according to a fixed length, pad zeros for the insufficient part, encode the slices, and calculate the metadata of all slices according to the encoding;

[0103] According to the VRF random allocation determination, the verification group nodes are determined, the time sequence group broadcasts the metadata to the verification group nodes, each verification group node verifies a specific shard according to the VRF allocation, and generates a signature proof;

[0104] The time sequence group receives the signature proofs of all verification group nodes, counts the proportion of valid shards of the signature proofs, packs the corresponding valid shard information based on the proportion of valid shards to generate transaction data, submits the transaction to the blockchain, and processes malicious nodes on the blockchain to form network consensus and write the valid shard information to a new block.

[0105] In this embodiment, the key field refers to the most important information in the hash record, which is used to identify and verify the integrity and authenticity of the data, such as the hash value, timestamp, and data type of the data.

[0106] In this embodiment, the compact JSON format refers to representing data as a JSON string in a concise and efficient manner. This usually involves removing unnecessary spaces and line breaks, and using short key names.

[0107] In this embodiment, the JSON string refers to a string representing data in JSON format. JSON is a lightweight data exchange format that is easy for humans to read and machines to parse, such as {key1:value1, key2:value2}.

[0108] In this embodiment, the secondary hash refers to applying a hash function to the JSON string again to generate a new hash value. This is usually used to generate a unique identifier for the data, such as using the SHA-256 hash function to generate a hash value for the JSON string.

[0109] In this embodiment, the on-chain identifier refers to a unique identifier used to identify data on the blockchain. This is usually a hash value or address, such as an Ethereum address, used to identify a smart contract.

[0110] In this embodiment, the time sequence group refers to a data grouping arranged in chronological order. This is usually used to record the order and timeliness of data, such as a series of transaction records arranged in chronological order.

[0111] In this embodiment, the verification group refers to a collection of nodes used to verify the integrity and authenticity of data. These nodes usually need to be authenticated and authorized, such as a group of verification nodes participating in the blockchain network.

[0112] In this embodiment, the fixed length refers to the fixed length of the data, and the insufficient part needs to be filled with zeros or other fillers. This is usually used to ensure the consistency and comparability of data, such as a 128-bit hash value with zeros filled in the insufficient part.

[0113] In this embodiment, encoding refers to the process of converting data into a specific format or encoding scheme. This often involves using a specific encoding scheme or algorithm, such as converting text data into UTF-8 encoding.

[0114] In this embodiment, metadata refers to data about data, such as the source, format, size, and other attributes of data. For example, the metadata of a file includes the file name, size, creation date, etc.

[0115] In this embodiment, VRF random allocation refers to the use of a verifiable random function (VRF) to randomly allocate tasks or resources, such as using VRF to randomly allocate a verification task to a specific verification node.

[0116] In this embodiment, verification group node refers to a node that participates in the data verification process. These nodes usually need to be authenticated and authorized, such as a medical node participating in a certain network.

[0117] In this embodiment, specific shard refers to a part of data used for verification or storage, such as a fixed-size shard in a large data file.

[0118] In this embodiment, signature proof refers to the use of digital signature technology to prove the integrity and authenticity of data, such as a digital signature using a private key to sign a data digest.

[0119] In this embodiment, effective shard ratio refers to the proportion of effective shards in the total shards in the verification process, such as in 100 shards, 95 shards are verified as effective, and the effective shard ratio is 95%.

[0120] In this embodiment, transaction data refers to the data of transactions or operations carried out on the blockchain, such as a certain network transaction, including the sender, receiver, and transaction amount.

[0121] In this embodiment, network consensus refers to the process of all nodes in the blockchain network reaching agreement on the data state, such as the proof of work (PoW) consensus mechanism in a certain network.

[0122] In this embodiment, malicious node processing refers to the process of identifying and handling malicious nodes in the blockchain network, such as identifying and punishing a malicious node that tries to double-spend in a certain network.

[0123] The working principle and beneficial effects of the above technical solution are: converting the key fields of the hash evidence into a compact JSON format, calculating a second hash to generate an on-chain identifier, generating a digest and grouping it into a time sequence group and a verification group, calculating metadata for the time sequence group, VRF randomly assigning verification group nodes to verify the shards and generating a signature proof, the time sequence group calculating the proportion of valid shards and packaging transaction data for submission to the blockchain, processing malicious nodes to form network consensus, writing to a new block, ensuring data tamper resistance and traceability, and promoting data sharing and collaboration.

[0124] Embodiment 5: The present embodiment provides a blockchain-based real-world research database management method, which determines verification group nodes according to VRF random allocation, and the time sequence group broadcasts metadata to the verification group nodes. Each verification group node verifies a specific shard according to the VRF allocation, and generates a signature proof, including:

[0125] A dynamic staking mechanism is set according to the hospital level, and a variable candidate node list is determined based on the dynamic staking mechanism, the medical special-purpose blockchain network, and the hospital characteristics;

[0126] VRF calculation is performed on the candidate nodes in the variable candidate node list based on hash evidence, and the nodes with the last two bits of the VRF output value less than the preset value become the verification group, and the remaining nodes become the time sequence group;

[0127] The time sequence group packages the metadata of the current time window and sends it to all verification group nodes through the medical special-purpose network. Each verification group node calculates a shard index according to its VRF output value, and determines the specific shard verification right of each verification group node according to the shard index;

[0128] The verification group node performs medical data verification according to the specific shard verification right, and generates a signature proof after verification.

[0129] In this embodiment, the hospital level refers to the different levels of hospitals divided according to their size, service capacity, technical level, etc., such as a tertiary A-level hospital and a secondary B-level hospital. For example, a tertiary A-level hospital and a secondary B-level hospital.

[0130] In this embodiment, the dynamic staking mechanism refers to dynamically adjusting the number of tokens that a node needs to stake according to certain characteristics of the node (such as reputation, performance, participation, etc.), in order to ensure the security and incentive compatibility of the network. For example, in a medical blockchain network, the number of staked tokens is dynamically adjusted according to the level and participation of the hospital.

[0131] In this embodiment, the medical special-purpose blockchain network refers to a blockchain network designed specifically for medical data sharing and transactions, such as Figure 3 As shown, it provides high security, privacy protection, and data tamper resistance, such as an Ethereum-based medical data sharing platform.

[0132] In this embodiment, hospital features refer to certain attributes or indicators of a hospital, such as its geographical location, specialty area, patient volume, etc., such as the hospital's location, service range, bed number.

[0133] In this embodiment, the variable candidate node list refers to a list of candidate nodes in the medical blockchain network that can dynamically change according to certain rules, which can be hospitals, research institutions or other medical entities, such as a candidate node list containing different levels of hospitals and medical research institutions.

[0134] In this embodiment, VRF calculation refers to the use of a verifiable random function (VRF) to calculate a certain input value (such as a global random seed) to produce a random output value and a proof that other nodes can verify whether the output value is correct, such as using VRF to calculate the global random seed to produce a random number and a proof.

[0135] In this embodiment, the last two digits of the VRF output value are less than the preset value, which refers to the last two digits of the output value in the VRF calculation being less than the preset threshold, and these nodes will be selected as the verification group, such as the preset threshold being 50 and the VRF output value being 1234567890abcdef, the last two digits being ef, which is less than 50, so the node is selected as the verification group.

[0136] In this embodiment, the shard index refers to an index value used to identify the location of a data shard in the overall data, such as a file's shard index indicating the location of the shard in the entire file.

[0137] In this embodiment, the specific shard verification right refers to the right and ability of the verification group node to verify its specific shard, such as the right of a verification node to verify the medical data in its shard.

[0138] In this embodiment, medical data verification refers to checking and confirming medical data to ensure its accuracy and integrity, such as verifying the accuracy of patient information, drug dosage, etc. data in medical records.

[0139] In this embodiment, the signature proof refers to the verification group node signing the data it has verified to prove the validity and authenticity of the data, such as the verification node using its private key to sign the medical record to generate a digital signature.

[0140] The working principle and beneficial effects of the above technical solution are: setting a dynamic pledge mechanism according to the hospital level, generating a global random seed, and using a private key for VRF calculation by the candidate node, the last two bits of the output value are less than the preset value, and the node becomes a verification group, and the remaining is a time sequence group; the metadata is packaged and sent to the verification group, the verification group node calculates the slice index and verifies the specific slice, generates a signature proof after verification, ensures the integrity and non-tamperability of the data, and promotes medical data sharing and cooperation.

[0141] Embodiment 6: The present application provides a blockchain-based real-world research database management method, which establishes an anti-quantum Merkle tree based on a new block, adds a timestamp anchor, and automatically parses the zk-SNARK proof in the block through a smart contract, and establishes a cross-chain hash index, including:

[0142] The verified specific slice in the new block is used as a leaf node, and an XMSS algorithm is used to construct an anti-quantum Merkle tree to generate a root hash, and the root hash is timestamp anchored;

[0143] The pre-stored zk-SNARK proof in the new block is parsed using a smart contract, and a one-way hash matching is performed on the specific slice to obtain a first matching result;

[0144] The consistency of the root hash and the block header is verified according to the Merkle tree to obtain a second verification result;

[0145] If the first matching result and the second verification result are both passed, a cross-chain hash index is generated.

[0146] In this embodiment, the leaf node refers to the node at the bottom of the Merkle tree structure, which usually contains actual data slices or data hash values, for example, a hash value containing a medical record slice.

[0147] In this embodiment, the XMSS algorithm is a digital signature algorithm resistant to quantum computing attacks, which can be used to construct an anti-quantum Merkle tree, for example, the XMSS algorithm is used to generate the signature of the leaf node.

[0148] In this embodiment, the root hash refers to the hash value at the top of the Merkle tree, which is a summary of the entire Merkle tree content, used to verify the integrity and consistency of the data, for example, the top hash value of the Merkle tree is used to verify the hash value of all leaf nodes.

[0149] In this embodiment, one-way hash matching refers to using a hash function to compare the hash values of two data slices to determine whether they are the same without comparing the actual data content, for example, using a SHA-256 hash function to compare the hash values of two medical record slices.

[0150] In this embodiment, the first matching result refers to the result obtained by one-way hash matching, which indicates whether the hash values of the two data shards are consistent, for example, the hash values of the two shards match, indicating that the data has not been tampered with.

[0151] In this embodiment, the block header refers to the header information of each block in the blockchain, which contains the metadata of the block, such as timestamp, difficulty target, hash value of the previous block, etc., for example, the header information of a certain network block contains the creation time and difficulty target of the block.

[0152] In this embodiment, the second verification result refers to the result obtained by verifying the consistency of the root hash of the Merkle tree and the block header, for example, the root hash of the Merkle tree is consistent with the hash value in the block header, indicating that the block data has not been tampered with.

[0153] The working principle and beneficial effects of the above technical solution are: the verified specific shard in the new block is used as a leaf node, an XMSS algorithm is used to construct a quantum-resistant Merkle tree, a root hash is generated and timestamped, a smart contract analyzes the pre-stored zk-SNARK proof in the new block, and one-way hash matching is performed on the specific shard; verify the consistency of the root hash of the Merkle tree and the block header; if both matching and verification pass, generate a cross-chain hash index to promote data sharing between different blockchains.

[0154] Embodiment 7: The present embodiment provides a blockchain-based real-world research database management method, which sets hierarchical data release rules according to the cross-chain hash index to form a research database, including:

[0155] According to the cross-chain hash index, an index key value is constructed, the index key value is written into the index mapping table of the target chain, and hierarchical conditions and corresponding hierarchical data release rules are pre-set in the smart contract to form a research database.

[0156] In this embodiment, the index key value refers to a key-value pair used to uniquely identify a data record in a database or index mapping table. In the blockchain, it is usually a hash value or address, for example, a key-value pair containing the hash value of a medical record shard and its location in the blockchain.

[0157] In this embodiment, the pre-set hierarchical condition refers to a condition set in the smart contract, which is used to divide data into different levels or categories according to different data access requirements, for example, according to the sensitivity and privacy level of medical data, the data is divided into three levels of public, restricted and confidential.

[0158] In this embodiment, the index mapping table refers to a data structure that maps index key values to the actual location of data records, such as block positions in a blockchain or row numbers in a database. For example, a table that maps the hash value of a medical record shard to its corresponding location on the blockchain.

[0159] The working principle and beneficial effects of the above technical solution are: through cross-chain hash index, the index is established between different blockchains, which is convenient for data sharing and query; through the preset hierarchical condition and hierarchical data release rule of the smart contract, the security and privacy of the data are protected, at the same time, a research database is formed, which provides more convenient data access and analysis tools for researchers, and promotes data sharing and interoperability.

[0160] Embodiment 8: The present application provides a blockchain-based real world research database management system, as shown in Figure 2 , which includes:

[0161] Hash evidence module: collect hospital-related data, classify the related data by type, and generate corresponding hash evidence according to the type classification result;

[0162] Sharding module: generate a summary on the chain according to the hash evidence, perform sharding processing on the summary, and perform effective verification on the sharding result. The verified effective sharding information is written into a new block;

[0163] Cross-chain module: based on the new block, an anti-quantum Merkle tree is established, a time stamp anchor is added, a smart contract automatically analyzes the zk-SNARK proof in the block, and a cross-chain hash index is established;

[0164] Database module: set hierarchical data release rules according to the cross-chain hash index, and form a research database.

[0165] The working principle and beneficial effects of the above technical solution are: through the blockchain technology, the hospital-related data is collected, classified and hashed, the data summary is chained and sharded, the anti-quantum Merkle tree is established, the time stamp anchor is added, the smart contract automatically analyzes the zk-SNARK proof in the block, and the cross-chain hash index is established, and finally the research database is formed. This is conducive to improving the transparency, credibility, security and shareability of data, thereby promoting the development of real world research.

[0166] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A blockchain-based real world study database management method, characterized by, The application relates to a hospital data collection method based on a quantum-resistant blockchain, and belongs to the technical field of data collection and blockchain. Step 1: collecting hospital-related data, classifying the related data according to types, and generating corresponding hash storage certificates according to the type classification results; Step 2: generating a summary on a chain according to the hash storage certificates, performing sharding processing on the summary, performing effective verification on the sharding results, and writing the verified effective sharding information into a new block; Step 3: establishing an anti-quantum Merkle tree based on the new block, adding a time stamp anchor, automatically analyzing zk-SNARK proof in the block by an intelligent contract, and establishing a cross-chain hash index; Step 4: setting a hierarchical data release rule according to the cross-chain hash index, and forming a research database. The method comprises the following steps: extracting key fields of the hash storage certificates and converting the key fields into a compact JSON format to obtain a JSON string, calculating a second hash of the JSON string, and generating an on-chain identifier; generating a summary according to the on-chain identifier, grouping the summary, obtaining a time sequence group and a verification group, sharding the time sequence group according to a fixed length, supplementing zeros for the sharding, encoding the sharding, and calculating metadata of all the sharding according to the encoding; determining a verification group node according to VRF random distribution, broadcasting the metadata of the time sequence group to the verification group node, and generating a signature proof of a specific sharding according to VRF distribution by each verification group node; receiving the signature proof of all the verification group nodes by the time sequence group, and counting a valid sharding proportion of the signature proof, packaging corresponding valid sharding information based on the valid sharding proportion to generate transaction data, submitting the transaction data to a blockchain, performing malicious node processing on the blockchain, forming network consensus, and writing the valid sharding information into a new block. 2.The blockchain-based real world research database management method of claim 1, wherein, The method comprises the following steps: adapting an interface state of each business system interface of a hospital, selecting a corresponding adaptation tool from a format-adaptation table according to an adaptation judgment result, and collecting hospital-related data based on the adaptation tool; classifying the related data according to types, converting standards based on the type classification results, obtaining a conversion result, and generating a hash storage certificate according to the conversion result. 3.The blockchain-based real world research database management method of claim 2, wherein, The method comprises the following steps: inputting the related data into a feature analysis engine, analyzing the type of the related data, and adding a corresponding type label to obtain a type classification result; matching the structured data in the type classification result with real-time terminology of a knowledge graph, and then performing unit intelligent conversion to obtain a first conversion result; performing multi-modal AI labeling on the unstructured data in the type classification result, and converting the unstructured data into a standard result through cross-modal alignment to obtain a second conversion result; serializing the first conversion result and the second conversion result, calculating a hash value of the serialized result by using an encryption hash algorithm, and then generating a hash storage certificate. 4.The blockchain-based real world research database management method of claim 1, wherein, According to the VRF random distribution determination, the verification group nodes are determined, the metadata is broadcast to the verification group nodes, each verification group node verifies a specific shard according to the VRF distribution, a signature certificate is generated, including: According to the hospital level setting dynamic staking mechanism, combined with the dynamic staking mechanism and the medical special block chain network and the hospital characteristics to determine the variable candidate node list; Based on the hash storage, the VRF calculation is performed on the candidate nodes in the variable candidate node list, and the last two bits of the VRF output value are less than the preset value, which becomes the verification group, and the remaining nodes become the time sequence group; The time sequence group packs the metadata of the current time window and sends it to all verification group nodes through the medical special network, each verification group node calculates the shard index according to its VRF output value, and determines the specific shard verification right of each verification group node according to the shard index; The verification group node performs medical data verification according to the specific shard verification right, and generates a signature certificate after verification. 5.The blockchain-based real world research database management method of claim 4, wherein, Based on the new block, an anti-quantum Merkle tree is established, a timestamp anchor is added, a smart contract automatically parses the zk-SNARK proof in the block, and a cross-chain hash index is established, including: The verified specific shard in the new block is used as a leaf node, an anti-quantum Merkle tree is constructed using the XMSS algorithm, a root hash is generated, and the root hash is timestamp anchored; The smart contract parses the pre-stored zk-SNARK proof in the new block, performs one-way hash matching on the specific shard, and obtains a first matching result; According to the consistency of the Merkle tree verification root hash and the block header, a second verification result is obtained. If the first matching result and the second verification result are both passed, a cross-chain hash index is generated. 6.The blockchain-based real world research database management method of claim 1, wherein, According to the cross-chain hash index, a hierarchical data release rule is set, and a research database is formed, including: According to the cross-chain hash index, an index key value is constructed, the index key value is written into the index mapping table of the target chain, the hierarchical conditions and the corresponding hierarchical data release rules are pre-set in the smart contract, and the research database is formed.

7. A blockchain-based real world study database management system, characterized by, Including: Hash storage module: collect hospital related data, classify the related data according to type, and generate corresponding hash storage according to type classification result; Sharding module: generate digest on chain according to hash storage, perform sharding on digest, and write verified effective shard information into new block; Cross-chain module: based on the new block, an anti-quantum Merkle tree is established, a timestamp anchor is added, a smart contract automatically parses the zk-SNARK proof in the block, and a cross-chain hash index is established; Database module: according to the cross-chain hash index, a hierarchical data release rule is set, and a research database is formed; The sharding module includes: First generation unit: extract the key field of hash storage and convert it into compact JSON format, obtain JSON string, calculate second hash of JSON string, and generate on-chain identifier; Second generation unit: generate digest according to on-chain identifier, group digest, obtain time sequence group and verification group, slice time sequence group according to fixed length, fill zero for deficiency, encode shard, and calculate metadata of all shards according to encoding; The third generation unit: according to the VRF random distribution, determine the verification group node, the time sequence group broadcasts the metadata to the verification group node, each verification group node verifies the specific shard according to the VRF distribution, and generates the signature proof; The writing unit: the time sequence group receives the signature proof of all verification group nodes, and counts the valid shard proportion of the signature proof, packs the corresponding valid shard information based on the valid shard proportion to generate transaction data, submits the transaction to the block chain, and processes the malicious node in the block chain to form network consensus, and writes the valid shard information into a new block.

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