A blockchain technology-based trusted big data storage and sharing system

Abstract

This invention relates to the field of blockchain technology and discloses a trusted big data storage and sharing system based on blockchain technology, comprising: a data acquisition module that collects data via an access protocol, generates data fingerprints, and transmits them to a hybrid storage module; a hybrid storage module that receives data transmitted from the data acquisition module and stores the data hierarchically according to importance; a consortium blockchain processing module that verifies the metadata generated by the hybrid storage module and records it to the blockchain; a privacy protection module; a cross-chain interaction module; and an application adaptation module. This invention introduces a trapdoor function through an improved Merkle-Hellman knapsack algorithm, combined with attribute-based proxy re-encryption in the privacy protection module, forming a dual encryption mechanism. The former utilizes the super-incrementing sequence characteristic to achieve efficient encryption, while the latter manages decryption permissions through access policies, preserving the unidirectionality of cross-chain data digests while ensuring that only authorized nodes can decrypt, significantly improving the security and privacy of cross-chain data transmission.

Description

Technical Field

[0001] This invention relates to the field of blockchain technology, specifically to a trusted big data storage and sharing system based on blockchain technology. Background Technology

[0002] Cross-chain data interaction is a core component of blockchain technology in multi-chain collaborative scenarios. It refers to the process of data transmission, asset transfer, and information sharing between blockchain networks with different architectures and protocols. Its core objective is to break down data barriers between chains and achieve efficient interconnection and interoperability of heterogeneous blockchain systems while ensuring data security, integrity, and privacy, thereby supporting complex business collaborations across industries and institutions.

[0003] In existing technologies, cross-chain interaction is mainly achieved through hash locking, notary mechanisms, or sidechain / relay chain technologies. Hash locking ensures transaction atomicity by verifying hash values, notary mechanisms rely on trusted third parties to relay data, and sidechain technology completes asset cross-chain operations through bidirectional anchoring with the main chain. At the encryption level, traditional asymmetric encryption algorithms such as RSA and ECC are mostly used to encrypt data, supplemented by hash functions to generate digests for integrity verification.

[0004] The most critical shortcoming of existing technologies lies in the lack of deep synergy between encryption mechanisms and access control. While traditional asymmetric encryption algorithms can ensure data confidentiality, they cannot achieve fine-grained access control and are difficult to adapt to the differentiated access requirements of multiple nodes and roles. At the same time, single encryption methods are weak against quantum attacks, and the data digest generation is disconnected from the access control policy, which makes cross-chain data susceptible to unauthorized access or privacy leaks during transmission, storage, and use, making it difficult to balance security, flexibility, and efficiency. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a trusted big data storage and sharing system based on blockchain technology, which solves the problem of insufficient collaboration between encryption mechanisms and access control.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a trusted big data storage and sharing system based on blockchain technology, comprising the following modules:

[0007] Data acquisition module: Collects data through a multi-source heterogeneous data access protocol, generates data fingerprints and transmits them to the hybrid storage module. The data fingerprint serves as a unique identifier for subsequent data association and verification by various modules.

[0008] Hybrid storage module: Receives data transmitted from the data acquisition module, classifies the data according to importance, stores key data in the blockchain storage unit, and generates metadata indexes for massive data after block-based redundant storage and sends them to the consortium blockchain processing module. The metadata includes data fingerprints and storage location information.

[0009] Consortium blockchain processing module: Constructs a dynamic node network, verifies the metadata generated by the hybrid storage module through an improved DPoS consensus mechanism and records it to the blockchain, and feeds back the generated block hash value as a data integrity proof to the privacy protection module;

[0010] Privacy protection module: The block hash value fed back by the consortium blockchain processing module is processed using an attribute-based proxy re-encryption algorithm, and verifiable ciphertext data is generated by combining zero-knowledge proofs. The ciphertext data is shared through the interface provided by the application adaptation module.

[0011] Cross-chain interaction module: A secure channel is established through quantum key distribution. An improved Merkle-Hellman knapsack algorithm is used to generate cross-chain data digests. The digests contain the ciphertext data features generated by the privacy protection module. Combined with hash time locking contracts, atomic data exchange with external blockchains is realized. The exchange results are synchronized to the system internally through the application adaptation module.

[0012] Application adaptation module: Provides a standardized RESTful interface, has a built-in multimodal data fusion algorithm to process data synchronized by the cross-chain interaction module, implements secure aggregation of model parameters based on the federated learning framework, and stores the aggregation results in encrypted form through a privacy protection module and updates them to the consortium blockchain processing module.

[0013] Preferably, the data acquisition module includes a smart contract-driven acquisition unit that obtains data quality scores in real time through an on-chain oracle, dynamically adjusts the sampling frequency and feature extraction dimensions, and generates a digital fingerprint using the BLAKE3 hash algorithm with added timestamp information. The timestamp is time-series correlated with the block timestamp of the consortium blockchain processing module.

[0014] Preferably, the blockchain storage unit of the hybrid storage module uses erasure coding technology for data redundancy, the distributed file storage unit implements adaptive sharding, dynamically adjusts the distribution of data blocks according to node load and network topology, and the metadata is stored in a directed acyclic graph structure, wherein the edge weights of the graph structure are dynamically determined by the node reputation value of the consortium blockchain processing module.

[0015] Preferably, the consensus mechanism of the consortium blockchain processing module introduces a reputation value decay factor, and the verification nodes compete for the right to record transactions by solving the quantum-resistant proof-of-work problem. The block generation time variance is controlled within ±50ms, and the reputation value is used as an access control parameter for the privacy protection module.

[0016] Preferably, the proxy re-encryption algorithm of the privacy protection module implements key segmentation management, the zero-knowledge proof uses the Groth16 protocol to generate concise proofs, the homomorphic encryption supports matrix operations and convolution operations, the ciphertext data supports k-anonymity and l-diversity protection, and the protection level is dynamically adjusted by the access request type of the application adaptation module.

[0017] Preferably, the quantum key distribution of the cross-chain interaction module uses the BB84 protocol to establish a secure channel, the improved Merkle-Hellman knapsack algorithm achieves asymmetric encryption by introducing a trapdoor function, the hash time locking contract adopts a multi-timestamp verification mechanism, supports atomic exchange and version control of cross-chain data, and the version control forms a mapping relationship with the data block version number of the hybrid storage module.

[0018] Preferably, the multimodal data fusion algorithm of the application adaptation module is based on the attention mechanism to realize the dynamic allocation of feature weights, the federated learning framework uses differential privacy to protect client data, the momentum term is introduced in the model parameter aggregation process to accelerate convergence, and the attention weights are trained by the historical transaction frequency of the consortium blockchain processing module.

[0019] Preferably, the smart contract driving unit of the data acquisition module is linked with the consensus mechanism of the consortium blockchain processing module. When the data quality score is lower than the threshold, the abnormal data marking process of the consortium blockchain processing module is triggered, and the acquisition task of the corresponding data source is suspended.

[0020] Preferably, the adaptive sharding mechanism of the hybrid storage module is linked with the network status monitoring of the cross-chain interaction module. When the cross-chain data transmission bandwidth is insufficient, the data block compression ratio is automatically increased and the redundant storage strategy is adjusted.

[0021] Preferably, the federated learning framework of the application adaptation module works in conjunction with the cross-chain interaction module. The model parameters obtained across the chain are processed by a multimodal data fusion algorithm, and the resulting aggregate parameters are encrypted by a privacy protection module and then written into the block of the consortium chain processing module.

[0022] This invention provides a trusted big data storage and sharing system based on blockchain technology. It has the following beneficial effects:

[0023] 1. This invention introduces a trapdoor function through an improved Merkle-Hellman knapsack algorithm, combined with attribute-based proxy re-encryption in the privacy protection module, forming a dual encryption mechanism. The former utilizes the super-incrementing sequence characteristic to achieve efficient encryption, while the latter manages decryption permissions through access policies, preserving the one-way nature of cross-chain data digests while ensuring that only authorized nodes can decrypt, significantly improving the security and privacy of cross-chain data transmission.

[0024] 2. This invention utilizes a multimodal fusion algorithm with an application adaptation module, linked with cross-chain Merkle-Hellman parameters, and dynamically allocates weights based on an attention mechanism to weightedly fuse cross-chain and local public key sequences. This linkage preserves the business characteristics of external parameters while adapting to the local system, resolving the issue of parameter format differences between different blockchain networks and improving algorithm compatibility and data reuse rate.

[0025] 3. This invention, through the synergy of an improved Merkle-Hellman knapsack algorithm and a federated learning framework, generates aggregated parameters from cross-chain acquired algorithm parameters via multimodal fusion, and then encrypts and writes them to the consortium blockchain through a privacy protection module. The attention weights are trained using the historical transaction frequency of the consortium blockchain, and the parameter aggregation introduces a momentum term to accelerate convergence. This approach not only enriches the local model with cross-chain parameters but also protects data through differential privacy, thereby improving model training efficiency and parameter security. Attached Figure Description

[0026] Figure 1 This is a flowchart of a trusted big data storage and sharing system based on blockchain technology. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Example:

[0029] Please see the appendix Figure 1 This invention provides a trusted big data storage and sharing system based on blockchain technology, comprising the following modules:

[0030] Data Acquisition Module: This module collects data through a multi-source heterogeneous data access protocol, generates data fingerprints, and transmits them to the hybrid storage module. The data fingerprint serves as a unique identifier for subsequent data association and verification by various modules. The data acquisition module includes a smart contract-driven acquisition unit that obtains data quality scores in real time through an on-chain oracle, dynamically adjusts the sampling frequency and feature extraction dimensions, and generates digital fingerprints using the BLAKE3 hash algorithm with added timestamp information. The timestamps are time-series correlated with the block timestamps of the consortium blockchain processing module. The smart contract-driven unit of the data acquisition module is linked with the consensus mechanism of the consortium blockchain processing module. When the data quality score is lower than a threshold, the abnormal data marking process of the consortium blockchain processing module is triggered, and the acquisition task of the corresponding data source is suspended.

[0031] Hybrid storage module: Receives data transmitted from the data acquisition module, classifies the data according to importance, stores key data in the blockchain storage unit, and generates metadata indexes for massive amounts of data after block-based redundant storage, which are then sent to the consortium blockchain processing module. The metadata includes data fingerprints and storage location information. The blockchain storage unit of the hybrid storage module uses erasure coding technology for data redundancy, and the distributed file storage unit implements adaptive sharding, dynamically adjusting the data block distribution according to node load and network topology. The metadata is stored using a directed acyclic graph structure, and the edge weights of the graph structure are dynamically determined by the node reputation value of the consortium blockchain processing module. The adaptive sharding mechanism of the hybrid storage module is linked with the network status monitoring of the cross-chain interaction module. When the cross-chain data transmission bandwidth is insufficient, it automatically increases the data block compression ratio and adjusts the redundant storage strategy.

[0032] Consortium blockchain processing module: Constructs a dynamic node network, verifies the metadata generated by the hybrid storage module through an improved DPoS consensus mechanism and records it to the blockchain. The generated block hash value serves as a data integrity proof and is fed back to the privacy protection module. The consensus mechanism of the consortium blockchain processing module introduces a reputation value decay factor. Verification nodes compete for the right to record transactions by solving a quantum-resistant proof-of-work problem. The block generation time variance is controlled within ±50ms. The reputation value serves as an access control parameter for the privacy protection module.

[0033] Privacy Protection Module: The module uses an attribute-based proxy re-encryption algorithm to process the block hash value fed back by the consortium blockchain processing module, and combines it with zero-knowledge proofs to generate verifiable ciphertext data. The ciphertext data is shared through the interface provided by the application adaptation module. The proxy re-encryption algorithm of the privacy protection module implements key segmentation management, the zero-knowledge proof uses the Groth16 protocol to generate concise proofs, the homomorphic encryption supports matrix operations and convolution operations, and the ciphertext data supports k-anonymity and l-diversity protection. The protection level is dynamically adjusted by the access request type of the application adaptation module.

[0034] Cross-chain interaction module: A secure channel is established through quantum key distribution. An improved Merkle-Hellman knapsack algorithm is used to generate cross-chain data digests. The digests contain ciphertext data features generated by the privacy protection module. Combined with a hash time-locked contract, atomic data exchange with external blockchains is achieved. The exchange results are synchronized to the system internally through the application adaptation module. The quantum key distribution of the cross-chain interaction module uses the BB84 protocol to establish a secure channel. The improved Merkle-Hellman knapsack algorithm achieves asymmetric encryption by introducing a trapdoor function. The hash time-locked contract adopts a multi-timestamp verification mechanism, supporting atomic exchange and version control of cross-chain data. The version control is mapped to the data block version number of the hybrid storage module. The implementation steps of the improved Merkle-Hellman knapsack algorithm are as follows:

[0035] The improved Merkle-Hellman knapsack algorithm enhances security by introducing a trapdoor function. It uses a public key to convert plaintext to ciphertext and a private key for decryption. Based on the mathematical properties of super-increasing sequences, the algorithm ensures one-way encryption and efficient decryption.

[0036] Step 1: Generate private key parameters

[0037] Choose a superincreasing sequence Each element All are greater than the sum of all preceding elements. Simultaneously, choose the modulus. Sum of multipliers ,satisfy and .

[0038] Algorithm formula:

[0039] ( )in:

[0040] The super-incrementing sequence in the private key is used for decryption;

[0041] : The first super-increasing sequence One element;

[0042] The modulus must be greater than the sum of the super-increasing sequences;

[0043] Multiplier, and Coprime, used to generate public keys;

[0044] The properties of superincreasing sequences make the Subset Sum Problem easy to solve when the sequence structure is known, but difficult otherwise, the modulus... Sum of multipliers This is used to transform a super-incrementing sequence into a seemingly random public key sequence, thus hiding its structural characteristics.

[0045] Step 2: Generate public key

[0046] We choose to convert the superincrementing sequence in the private key into a public key sequence using modular multiplication. .

[0047] Algorithm formula:

[0048]

[0049] in:

[0050] The original public key sequence, used to encrypt plaintext;

[0051] The i-th element of the public key sequence;

[0052] : The multiplier from the private key parameter;

[0053] The i-th element of the super-increasing sequence;

[0054] : The modulus derived from the private key parameter;

[0055] Modular multiplication will superincreasing sequences Converting it into a normal sequence hides its structural characteristics, ensuring that only those who know the private key can decrypt it. The public key is used in the encryption process and is publicly available information, while the private key is used for decryption and must be kept strictly confidential.

[0056] Step 3: Introduce trapdoor functions to enhance security

[0057] The public sequence B is transformed using a one-way transformation function T to generate the enhanced public key B′.

[0058] Algorithm formula:

[0059]

[0060] in:

[0061] T: One-way transformation function (trapdoor function), which increases the security of the encryption process;

[0062] B: Original public key sequence;

[0063] B′: The enhanced public key sequence after processing with the trapdoor function;

[0064] The trapdoor function is a one-way transformation. When the private key is known, it can be efficiently reversed to obtain the original public key sequence. However, it is difficult to reverse when the private key is unknown. This transformation further hides the association between the public key and the super-incrementing sequence, thereby improving the algorithm's resistance to cracking.

[0065] Step 4: The encryption process involves a weighted sum of the plaintext bit sequence and the enhanced public key elements to generate ciphertext C. Algorithm formula:

[0066]

[0067] in:

[0068] Plaintext bit sequence, each ;

[0069] Enhanced public key sequence The i-th element;

[0070] The ciphertext generated after encryption;

[0071] The encryption process utilizes the properties of an enhanced public key sequence to convert plaintext bits into an integer ciphertext. Since the enhanced public key sequence appears random, it is computationally difficult to deduce the plaintext from the ciphertext without knowing the private key and trapdoor function, thus achieving one-way encryption.

[0072] Step 5: Decryption process

[0073] We first convert the ciphertext into an intermediate value using modular inverse operations, and then use the properties of super-increasing sequences to solve for the plaintext using a greedy algorithm.

[0074] Algorithm formula:

[0075]

[0076]

[0077] in:

[0078] The modular inverse of a satisfies ;

[0079] Intermediate values ​​during the decryption process;

[0080] The i-th element of the plaintext bit sequence;

[0081] Modular inverse operation can reduce ciphertext based on enhanced public key encryption to a subset sum problem under a superincreasing sequence. By taking advantage of the property that each element in a superincreasing sequence is greater than the sum of all preceding elements, a greedy algorithm can efficiently solve for the plaintext bits, thus realizing the restoration from ciphertext to plaintext.

[0082] Application Adaptation Module: Provides a standardized RESTful interface, incorporates a multimodal data fusion algorithm to process data synchronized from the cross-chain interaction module, and implements secure aggregation of model parameters based on a federated learning framework. The aggregation results are encrypted and stored through a privacy protection module and updated to the consortium blockchain processing module. The multimodal data fusion algorithm of the application adaptation module dynamically allocates feature weights based on an attention mechanism. The federated learning framework uses differential privacy to protect client data. A momentum term is introduced during the model parameter aggregation process to accelerate convergence. The attention weights are trained using the historical transaction frequency of the consortium blockchain processing module. The federated learning framework of the application adaptation module collaborates with the cross-chain interaction module. The Merkle-Hellman knapsack algorithm parameters obtained from the cross-chain are processed by the multimodal data fusion algorithm, and the generated aggregation parameters are encrypted through the privacy protection module and written to the block of the consortium blockchain processing module. This includes the following algorithms:

[0083] Step 1: Obtain Merkle-Hellman algorithm parameters via cross-chain.

[0084] The cross-chain interaction module obtains improved Merkle-Hellman algorithm parameters from the blockchain network, including enhanced public key sequences. and ciphertext

[0085] in:

[0086] The enhanced public key sequence used by the external blockchain, via a trapdoor function Generated after processing;

[0087] : The Each element corresponds to the result of the outer chain superincreasing sequence after modular multiplication and trapdoor transformation;

[0088] External chains encrypt business data (such as transaction credentials and model parameters) using the Merkle-Hellman algorithm.

[0089] Simultaneously obtain metadata Meta It includes information such as source chain ID, generation timestamp, and data credibility score, which are used for subsequent fusion weight calculation. These parameters are transmitted to the application adaptation module through cross-chain protocols (such as hash time lock contracts) to ensure the atomicity and security of the transmission process.

[0090] Step 2: Attention weight calculation (multimodal fusion)

[0091] The application adaptation module directly calculates the weights of cross-chain parameters based on the attention mechanism, first concatenating the original parameters and metadata:

[0092]

[0093] in:

[0094] : The concatenated vector of the i-th parameter, which integrates numerical features and metadata;

[0095] The vector concatenation operation merges the original parameters (numerical type) with metadata (structured information) into a unified feature vector.

[0096] Attention scores are calculated using a multilayer perceptron (MLP):

[0097]

[0098] in:

[0099] : Attention score of the i-th cross-chain parameter; the higher the score, the more important the parameter.

[0100] MLP: A two-layer perceptron model used to learn parameter importance;

[0101] The weight matrices of the perceptron correspond to the mappings from the input layer to the hidden layer and from the hidden layer to the output layer, respectively.

[0102] The bias vector of the perceptron;

[0103] ReLU(x): Activation function, defined as ReLU(x) = max(0,x), introducing a nonlinear transformation;

[0104] Step 3: Parameter aggregation and fusion

[0105] By combining attention weights, cross-chain parameters and local parameters are directly weighted and fused:

[0106] in:

[0107] The i-th enhanced public key element after fusion;

[0108] Attention weights for cross-chain parameters;

[0109] Local system enhanced public key sequence The i-th element;

[0110] The weights of local parameters are complementary to the weights of cross-chain parameters.

[0111] Repeat this operation for all elements to obtain the aggregated public key sequence. This sequence retains both the business characteristics of cross-chain parameters and the system adaptability of local parameters;

[0112] Step 4: Encrypt the aggregation parameters (Privacy Protection)

[0113] Will The data is transmitted to the privacy protection module and encrypted using an attribute-based proxy re-encryption algorithm.

[0114] in:

[0115] Enc: Proxy re-encryption function, supporting attribute-based access control;

[0116] PK: System master public key, used for encryption operations;

[0117] Access Policy For example, "Node Role = Verification Node and Institutional Rating ≥ AA" defines decryptable node attributes;

[0118] The encrypted aggregate parameter ciphertext only satisfies The nodes can be decrypted.

[0119] Encryption process combined with zero-knowledge proof Groth16 protocol), generating proof Used to verify the validity of ciphertext and ensure that the encryption operation has not been tampered with;

[0120] Step 5: On-chain evidence storage and consensus

[0121] Will and proof Write a block to the consortium blockchain; the block structure is defined as follows:

[0122]

[0123] in:

[0124] The hash value of the previous block ensures the continuity and immutability of the blockchain;

[0125] Block generation timestamp, accurate to the millisecond level;

[0126] : The digital signature of the i-th verification node, which is the signature of the k nodes elected through the improved DPoS consensus mechanism;

[0127] The number of verification nodes participating in consensus is usually 1 / 3 of the total number of nodes.

[0128] After the validator nodes reach a consensus on the block content, they calculate the current block hash:

[0129] in:

[0130] : The unique hash identifier of the current block;

[0131] SHA-256: A hash function that outputs a 256-bit digest, ensuring that the block content cannot be tampered with;

[0132] After the block is added to the consortium blockchain, Feedback is sent to the hybrid storage module to update the metadata index.

[0133] Step 6: Decryption and Application (On-Demand Access) When a node that meets access policy Λ (such as a regulatory node or authorized agency) needs to use the aggregation parameter, the privacy protection module generates re-encrypted ciphertext:

[0134]

[0135] in:

[0136] ReEnc: A proxy re-encryption function that converts ciphertext from policy A to node-specific policy A';

[0137] SKproxy: The private key of the proxy node, used to perform re-encryption operations;

[0138] A': A refined strategy for adapting to the request node (e.g., "Node ID=Node-001").

[0139] The node uses its own private key to decrypt:

[0140] in:

[0141] Dec: Decryption function;

[0142] SKnode: Request the private key of the node.

[0143] get Afterwards, it can be used for encryption / decryption operations of the local Merkle-Hellman algorithm (such as verifying cross-chain transactions and generating new ciphertext), realizing secure linkage between cross-chain parameters and the local system.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A trusted big data storage and sharing system based on blockchain technology, characterized in that, Includes the following modules: Data acquisition module: Collects data through a multi-source heterogeneous data access protocol, generates data fingerprints and transmits them to the hybrid storage module. The data fingerprint serves as a unique identifier for subsequent data association and verification by various modules. Hybrid storage module: Receives data transmitted from the data acquisition module, classifies the data according to importance, stores key data in the blockchain storage unit, and generates metadata indexes for massive data after block-based redundant storage and sends them to the consortium blockchain processing module. The metadata includes data fingerprints and storage location information. Consortium blockchain processing module: Constructs a dynamic node network, verifies the metadata generated by the hybrid storage module through an improved DPoS consensus mechanism and records it to the blockchain, and feeds back the generated block hash value as a data integrity proof to the privacy protection module; Privacy protection module: The block hash value fed back by the consortium blockchain processing module is processed using an attribute-based proxy re-encryption algorithm, and verifiable ciphertext data is generated by combining zero-knowledge proofs. The ciphertext data is shared through the interface provided by the application adaptation module. Cross-chain interaction module: A secure channel is established through quantum key distribution. An improved Merkle-Hellman knapsack algorithm is used to generate cross-chain data digests. The digests contain the ciphertext data features generated by the privacy protection module. Combined with hash time locking contracts, atomic data exchange with external blockchains is realized. The exchange results are synchronized to the system internally through the application adaptation module. Application adaptation module: Provides a standardized RESTful interface, has a built-in multimodal data fusion algorithm to process data synchronized by the cross-chain interaction module, implements secure aggregation of model parameters based on the federated learning framework, and stores the aggregation results in encrypted form through a privacy protection module and updates them to the consortium blockchain processing module.

2. The trusted big data storage and sharing system based on blockchain technology according to claim 1, characterized in that, The data acquisition module includes a smart contract-driven acquisition unit that obtains data quality scores in real time through an on-chain oracle, dynamically adjusts the sampling frequency and feature extraction dimensions, and generates digital fingerprints using the BLAKE3 hash algorithm with added timestamp information. The timestamps are time-series correlated with the block timestamps of the consortium blockchain processing module.

3. The trusted big data storage and sharing system based on blockchain technology according to claim 1, characterized in that, The hybrid storage module's blockchain storage unit uses erasure coding technology for data redundancy, the distributed file storage unit implements adaptive sharding, dynamically adjusts the distribution of data blocks according to node load and network topology, and the metadata is stored using a directed acyclic graph structure, the edge weights of which are dynamically determined by the node reputation values ​​of the consortium blockchain processing module.

4. The trusted big data storage and sharing system based on blockchain technology according to claim 1, characterized in that, The consensus mechanism of the consortium blockchain processing module introduces a reputation value decay factor. Verification nodes compete for the right to record transactions by solving a quantum-resistant proof-of-work problem. The block generation time variance is controlled within ±50ms. The reputation value serves as an access control parameter for the privacy protection module.

5. A trusted big data storage and sharing system based on blockchain technology according to claim 1, characterized in that, The privacy protection module's proxy re-encryption algorithm implements key segmentation management, zero-knowledge proof uses the Groth16 protocol to generate concise proofs, homomorphic encryption supports matrix operations and convolution operations, and ciphertext data supports k-anonymity and l-diversity protection. The protection level is dynamically adjusted by the access request type of the application adaptation module.

6. A trusted big data storage and sharing system based on blockchain technology according to claim 1, characterized in that, The quantum key distribution of the cross-chain interaction module uses the BB84 protocol to establish a secure channel. The improved Merkle-Hellman knapsack algorithm achieves asymmetric encryption by introducing a trapdoor function. The hash time locking contract adopts a multi-timestamp verification mechanism, supports atomic exchange and version control of cross-chain data, and the version control is mapped to the data block version number of the hybrid storage module.

7. A trusted big data storage and sharing system based on blockchain technology according to claim 1, characterized in that, The multimodal data fusion algorithm of the application adaptation module is based on the attention mechanism to dynamically allocate feature weights. The federated learning framework uses differential privacy to protect client data. The model parameter aggregation process introduces a momentum term to accelerate convergence. The attention weights are trained by the historical transaction frequency of the consortium blockchain processing module.

8. A trusted big data storage and sharing system based on blockchain technology according to claim 2, characterized in that, The smart contract driving unit of the data acquisition module is linked with the consensus mechanism of the consortium blockchain processing module. When the data quality score is lower than the threshold, the abnormal data marking process of the consortium blockchain processing module is triggered, and the acquisition task of the corresponding data source is suspended.

9. A trusted big data storage and sharing system based on blockchain technology according to claim 3, characterized in that, The adaptive sharding mechanism of the hybrid storage module is linked with the network status monitoring of the cross-chain interaction module. When the cross-chain data transmission bandwidth is insufficient, it automatically increases the data block compression ratio and adjusts the redundant storage strategy.

10. A trusted big data storage and sharing system based on blockchain technology as described in claim 7, characterized in that, The application adaptation module's federated learning framework collaborates with the cross-chain interaction module. The model parameters obtained across the chain are processed by a multimodal data fusion algorithm, and the resulting aggregate parameters are encrypted by a privacy protection module and then written into the block of the consortium chain processing module.

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