A compliance type data credible storage and traceability system based on digital fingerprints and alliance chains

Abstract

The application belongs to the technical field of blockchains, and particularly relates to a compliance data credible storage and traceability system based on digital fingerprints and alliance chains, which comprises a data preprocessing and structured slicing module, a multi-level digital fingerprint construction module, an alliance chain consensus storage module and a compliance verification and traceability module. The data preprocessing and structured slicing module is used for data standardization and slicing. The multi-level digital fingerprint construction module is used for constructing a Merkle tree based on a slicing fingerprint and extracting a global fingerprint. The alliance chain consensus storage module is used for encapsulating the global fingerprint and metadata and storing encrypted slices in a distributed file system. The compliance verification and traceability module is used for realizing data verification and tampering accurate positioning by using a Merkle path proof. The multi-level hash structure improves large-scale data verification efficiency, the selective disclosure mechanism solves the conflict between privacy protection and compliance audit, and the alliance chain consensus mechanism ensures the whole life cycle safety and credible traceability of electronic data storage.

Description

Technical Field

[0001] This invention belongs to the field of blockchain technology, specifically a compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchains. Background Technology

[0002] With the profound development of the digital economy, data has become a core production factor. Ensuring the authenticity, integrity, and traceability of data during its collection, storage, and flow is the cornerstone of building a digital trust system. In highly sensitive areas such as financial transactions, government offices, and supply chain management, reliable electronic data storage and traceability technologies are crucial for preventing data fraud, supporting compliance audits, and clarifying legal responsibilities. Currently, utilizing distributed ledger and cryptographic technologies to achieve decentralized data storage has become the mainstream technological approach for safeguarding data sovereignty and security.

[0003] Among them, the distributed evidence storage solution based on consortium blockchains provides a technical framework that combines security and regulatory compliance for multi-party collaboration scenarios through a pre-set consensus mechanism and access control. This type of technology typically uses digital fingerprints to abstract the original data and stores the generated hash digest in an immutable blockchain ledger, thereby establishing a unique correspondence between the original data and the on-chain certificate, providing underlying support for subsequent data verification and full lifecycle traceability.

[0004] Existing technologies for implementing blockchain electronic data storage typically employ a model of performing a single hash operation on the entire electronic file and then uploading it to the blockchain. However, this approach faces significant challenges when dealing with large-scale or complex data structures. When the amount of evidence data is large and contains a large number of detailed fields, even if only a partial data needs to be verified for authenticity, the verifier must obtain and process the entire original data, resulting in an extreme waste of bandwidth resources and computing costs. Verification using a single hash value can only determine whether the overall data has changed, but it cannot accurately pinpoint the location of the tampering, resulting in coarse-grained and inefficient traceability auditing. Furthermore, there is a significant contradiction between privacy protection and verification requirements in traditional solutions. Auditors find it difficult to complete partial data verification without accessing all sensitive information in restricted access scenarios, which violates the security principle of minimizing data exposure and makes it difficult to meet increasingly stringent compliance requirements.

[0005] Therefore, a compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain is proposed to address the above problems. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by establishing a compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchains, thereby solving the technical problems mentioned in the background.

[0007] To address the above technical issues, the following technical solution is adopted: A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchains, comprising: The data preprocessing and structured fragmentation module is used to receive raw electronic data, standardize the format of the raw electronic data according to the preset data protocol, and then divide the standardized data into several independent data fragments based on the data logical correlation. A multi-level digital fingerprint construction module is used to perform hash operations on each data shard to generate the corresponding shard-level digital fingerprint, and to use a recursive hash iteration algorithm to construct all shard-level digital fingerprints into a Merkle tree with a multi-level structure, and extract the root node hash value of the Merkle tree as the global digital fingerprint. The consortium blockchain consensus storage module consists of a consortium blockchain network composed of pre-set regulatory nodes, audit nodes, and data providing nodes. It is used to encapsulate global digital fingerprints, data element information, and timestamps into block headers through a consensus mechanism, and to store encrypted data fragments in a distributed file system. At the same time, it records the index address pointing to the distributed file system on the chain. The compliance verification and traceability module is used to respond to verification requests by retrieving the Merkel path proof corresponding to the target data shard from the consortium blockchain consensus storage module. By reconstructing the local hash chain and comparing it with the global digital fingerprint stored on the chain, the module can verify the authenticity of a specific data shard and pinpoint the location of any tampering.

[0008] Preferably, the execution process of the data preprocessing and structured sharding module is as follows: The system first parses the input unstructured or semi-structured raw electronic data to identify key fields and business logic levels; then, using a dynamic sharding algorithm, the raw data is divided into data shards 1 to 9 based on the data volume and the minimum granularity requirements for compliance auditing, where N is a positive integer greater than one; each data shard is assigned a unique globally unique identifier and a mapping relationship is established with the metadata of the raw data to ensure that the sharded data maintains logical integrity.

[0009] Preferably, when constructing the Merkle tree, the multi-level digital fingerprint construction module uses the first to Nth data shards as leaf nodes of the tree structure; performs hash calculation on each leaf node to obtain an initial hash value; then, concatenates a predetermined number of adjacent initial hash values ​​and performs hash calculation again to generate the intermediate node hash value of the next higher level; this process continues to loop until a unique root node hash value is generated; if the total number of shards is odd, the hash value of the last shard is copied to make an even number of pairs, thereby ensuring the symmetry of the tree structure and the continuity of hash calculation; this multi-level structure ensures that any slight change in any data shard will be uploaded to the root node level by level, causing the global digital fingerprint to change.

[0010] Preferably, the consortium blockchain consensus storage module adopts a practical Byzantine fault-tolerant consensus algorithm or an improved algorithm to ensure that the system can still achieve data consistency even in the presence of some faulty nodes. The consensus process includes a pre-preparation phase, a preparation phase, and a confirmation phase. In the pre-preparation phase, the master node sends a proposal containing a global digital fingerprint to all slave nodes. In the preparation phase, each node verifies the signature of the proposal and broadcasts the verification results to each other. In the confirmation phase, when a node receives a preparation message that meets the preset ratio requirement, it writes the evidence storage record into its local ledger. In addition, the module also introduces an identity-based access control mechanism, allowing only nodes holding valid digital certificates to participate in consensus and data reading, ensuring the closed and compliant nature of the evidence storage environment.

[0011] Preferably, the compliance verification and traceability module supports selective disclosure. When the auditor only needs to verify a portion of the data shards, the system only provides the auditor with the target data shard and its corresponding completion path node hash value in the Merkle tree, without exposing other unrelated data shard content. The verifier performs layer-by-layer synthesis calculations by combining the hash value of the target data shard with the provided completion path node hash value. If the final hash value is completely consistent with the global digital fingerprint recorded on the consortium blockchain, the local data is determined to be authentic and trustworthy. This mechanism meets the requirements of compliance auditing while minimizing the exposure scope of sensitive data.

[0012] Preferably, the compliance verification and traceability module also integrates a tampering location engine; when the global verification fails, the system starts a recursive search program, comparing the hash values ​​of intermediate nodes at each level from the root node of the Merkle tree downwards; by identifying branches with inconsistent hash values, the system can quickly lock the specific leaf node that caused the verification failure, that is, accurately locate the tampered data shard; subsequently, the system automatically extracts the historical change records of the data shard on the chain, combines the timestamp and operator signature information, reconstructs the context of the tampering, and provides conclusive evidence for legal accountability.

[0013] Preferably, the system also includes a privacy computing gateway, which is used to perform homomorphic encryption or differential privacy processing on sensitive fields before data is uploaded to the blockchain. The privacy computing gateway ensures that even if the storage nodes in the distributed file system are illegally compromised, attackers cannot obtain the plaintext content of the original data. At the same time, the compliance verification and traceability module can perform specific logical operations in the encrypted state to achieve compliance judgment without leaking privacy.

[0014] Preferably, the consortium blockchain consensus storage module further includes a smart contract layer for presetting evidence storage rules and automated auditing logic. Before data enters the consensus process, the smart contract automatically checks the compliance indicators of the data, such as whether the data format meets the standards, whether key fields are missing, and whether the signature is valid. Only data that passes the automatic verification by the smart contract can enter the subsequent consensus and storage stages. In addition, the smart contract is also used to record the operation log of each traceability query to ensure that the auditing process itself is also auditable and traceable.

[0015] Preferably, the distributed file system adopts a hierarchical storage strategy; for recently generated evidence data with high access frequency, it is stored in a cache layer based on high-performance media to improve the verification response speed; for evidence data from a long time ago, it is migrated to a large-capacity storage layer; the system maintains the association between data fragments in physical storage location and logical structure in real time through a global file index table to ensure consistency when retrieving data across levels.

[0016] As one embodiment of the present invention, the system operates in a distributed network environment jointly maintained by multiple regulatory agencies; each regulatory agency, as a peer node of the consortium blockchain, jointly exercises the right to supervise data storage behavior; the system requires, through multi-signature technology, any sensitive configuration change or data cleanup operation must be authorized by more than a preset proportion of regulatory nodes before it can take effect, thereby technically eliminating the possibility of a single node acting maliciously.

[0017] Preferably, when performing verification, the compliance verification and traceability module also retrieves the digital signature information associated with the global digital fingerprint; it verifies the source identity of the stored data by verifying the public key signature of the data providing node; if the signature verification fails, the system will issue a warning signal indicating that the data may be at risk of impersonation or forgery; this dual verification mechanism combining digital fingerprint and digital signature constructs a comprehensive trust link from data content to data source.

[0018] Preferably, the data preprocessing and structured sharding module also has a data integrity self-check function; after the data is sharded, the module automatically calculates the sum of the preset length features of all shards and compares it with the total length features of the original data; at the same time, by performing a fast logical check on all shards, it ensures that no bit faults or data loss are introduced during the sharding process; only the shard set that passes the self-check will be passed to the multi-level digital fingerprint construction module.

[0019] Preferably, the multi-level digital fingerprint construction module adopts a hardware-accelerated hash engine that supports a variety of high-strength secure hash algorithms. The system dynamically adjusts the preset bit width of the hash algorithm according to the security level requirements to resist potential collision attacks. After generating the global digital fingerprint, the system encapsulates the algorithm version number and parameter information used into the metadata to facilitate algorithm alignment during subsequent verification.

[0020] Preferably, the system is also equipped with an automatic compliance report generation unit; after the traceability verification is completed, the unit automatically generates a legally valid electronic certificate of evidence based on the verification results, timestamp, node signature, Merkel path, and on-chain consensus record; the certificate adopts a standardized format, includes an anti-counterfeiting QR code and an electronic seal, and can be directly used as judicial evidence or audit report.

[0021] Preferably, the consortium blockchain consensus storage module provides read-only data synchronization services to non-consensus participants by setting up observer nodes; the observer nodes synchronize the on-chain ledger in real time, but do not participate in consensus voting, thereby expanding the transparency and scope of supervision of the stored data without affecting system performance.

[0022] Preferably, the compliance verification and traceability module supports cross-chain interoperability protocols; when the evidence storage data needs to be transferred or verified between different consortium blockchain networks, the system converts the global digital fingerprint and consensus proof of this chain into a format that can be recognized by the target chain through a cross-chain gateway, so as to realize cross-regional and cross-industry compliance evidence storage and mutual recognition.

[0023] The beneficial effects of this invention are: 1. By introducing a multi-level digital fingerprint construction module, the original data is transformed into a Merkle tree with a multi-level structure, fundamentally solving the verification efficiency problem in large-scale data storage. The verifier does not need to obtain all the original data; it can complete the authenticity verification within a predetermined low time complexity based only on the target fragment and its corresponding Merkle path. This technical path of reconstructing global trust based on local evidence greatly reduces network bandwidth consumption and computational overhead, making real-time and high-frequency verification of large-scale data possible, and significantly improving the system's throughput and response speed.

[0024] 2. By leveraging the characteristics of a multi-level hash structure, precise location of tampering is achieved. Compared to the limitations of traditional solutions that can only determine whether the overall data has changed, this system can quickly pinpoint specific data fragments by following branches with inconsistent hashes, achieving a leap from macro-level evidence preservation to micro-level tracing. This fine-grained location capability not only improves the efficiency of auditing in discovering problems but also provides precise directional basis for subsequent legal evidence collection, liability determination, and data repair, greatly enhancing the practical value of the evidence preservation system.

[0025] 3. By working in tandem with the selective disclosure mechanism and the privacy computing gateway, the conflict between data privacy protection and compliance auditing has been successfully resolved. Auditors can verify the compliance of specific business data through Merkel path proof without accessing unrelated sensitive information, strictly adhering to the security principle of minimizing data exposure. This design not only meets the stringent privacy protection compliance requirements of the financial and government sectors, but also provides solid technical support for building a digital trust ecosystem with multi-party participation and balanced interests.

[0026] 4. Leveraging the decentralized consensus mechanism and immutability of consortium blockchains, a highly transparent and multi-party supervised evidence storage environment is constructed. By anchoring global digital fingerprints, timestamps, and node signatures on the chain, the risks of single points of failure and data forgery are eliminated. The introduction of smart contracts automates and standardizes the evidence storage process, ensuring that every piece of data uploaded to the chain undergoes strict filtering according to preset rules. This system architecture, which combines underlying cryptographic tools with an upper-level distributed governance framework, provides legally valid technical protection for the full lifecycle management of electronic data, significantly reducing social trust costs. Attached Figure Description

[0027] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] In the attached diagram: Figure 1 This is a schematic diagram of the overall system flow of the present invention; Figure 2 This is a flowchart of the multi-level digital fingerprint construction process in this invention; Figure 3 This is a flowchart of the compliance verification and traceability process in this invention. Detailed Implementation

[0029] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0030] Specific implementation examples are given below.

[0031] Example The compliant data trust storage and traceability system based on digital fingerprints and consortium blockchains deeply integrates cryptography, distributed storage, and consensus algorithms in its overall architecture design. It aims to build a trustworthy, transparent, and legally valid electronic data storage environment throughout its entire lifecycle. The system comprises a data preprocessing and structured sharding module, a multi-level digital fingerprint construction module, a consortium blockchain consensus storage module, a compliance verification and traceability module, a privacy computing gateway, a smart contract layer, a distributed file system, and an automatic compliance report generation unit.

[0032] As the system's entry point, the data preprocessing and structured fragmentation module undertakes the key tasks of accessing, cleaning, standardizing, and logically segmenting raw electronic data. When the system receives raw electronic data from different business systems, the data preprocessing and structured fragmentation module first starts the format parsing program. The raw electronic data includes unstructured data, semi-structured data, and structured data, and the formats cover text, images, audio and video, log files, and database records. The data preprocessing and structured fragmentation module has built-in multiple data protocol protocols. By identifying the metadata information of the raw electronic data, it uniformly converts data with different encoding formats and different encapsulation protocols into the system's preset standard intermediate format. During processing, this module performs character set alignment, removes redundant fields, and fills in null values ​​to ensure consistency in subsequent processing.

[0033] After standardizing the data format, the data preprocessing and structured sharding module performs dynamic sharding based on the logical relationships between the data. This module divides the standardized data into several independent data shards according to preset business logic rules. The sharding process is not a simple physical cutting, but rather incorporates the minimum granularity requirements of compliance auditing. For financial transaction data, the sharding logic may be based on a single transaction record; for government approval documents, the sharding logic may be based on the document's chapters or attachments. The system uses a dynamic sharding algorithm to divide the original data into data shards 1 to 9 based on the total amount of data and a preset storage load balancing strategy, where N is a positive integer greater than 1. At the moment of generation, each data shard is assigned a globally unique identifier by the data preprocessing and structured sharding module. This identifier is composed of a timestamp, node number, and a random number sequence to ensure uniqueness throughout the distributed network. At the same time, this module establishes a shard index mapping table to record the logical correspondence between each data shard and the original data, including the shard number, offset, and metadata of the original file to which it belongs, thereby ensuring the logical integrity and reconfigurability of the data. In addition, the module has a data integrity self-check function. After the fragmentation is completed, it verifies whether there is data loss or bit error by calculating the sum of the byte lengths of all fragments and comparing it with the original data length.

[0034] The multi-level digital fingerprint construction module receives a set of data fragments from the data preprocessing and structured fragmentation module and initiates high-strength cryptographic operations. This module has a built-in hardware-accelerated hash engine that supports a variety of secure hash algorithms. The core task of the multi-level digital fingerprint construction module is to construct a Merkle tree with a multi-level structure. This module performs independent hash operations on the first to Nth data shards to generate corresponding shard-level digital fingerprints, which constitute the leaf nodes of the Merkle tree. ; The above formula describes the generation logic of the parent node hash value in a Merkle tree, where Represents the hash value of the parent node. and These represent the hash values ​​of its left and right child nodes, respectively. The multi-level digital fingerprint construction module adopts a recursive hash iteration algorithm, which concatenates the hash values ​​of two adjacent leaf nodes and performs hash operation on the concatenated result again to generate the hash value of the intermediate node of the next level. If the total number of shards in a certain tree level is odd, the module will automatically copy the hash value of the last node to make it an even number of pairs, ensuring the symmetry of the tree structure. This process continues to iterate upwards until a unique root node hash value is generated. This root node hash value serves as the global digital fingerprint. This multi-level structure ensures that any small change in any underlying data shard will cause the hash values ​​of all intermediate nodes on its path to change, and will ultimately be reflected in the global digital fingerprint. The system dynamically adjusts the bit width of the hash algorithm according to the preset security level requirements to enhance the ability to resist collision attacks. The generated global digital fingerprint, the algorithm version number used, and the algorithm parameter information are encapsulated in the evidence storage metadata data package to prepare for subsequent consensus on-chain processing.

[0035] The consortium blockchain consensus storage module is a distributed network consisting of regulatory nodes, audit nodes, and data providing nodes. This module adopts a practical Byzantine fault-tolerant consensus algorithm to ensure that the system can still achieve strong consistency even if there are no more than one-third of the faulty or malicious nodes in the network. The consensus process is strictly divided into a pre-preparation phase, a preparation phase, and a confirmation phase. During the pre-preparation phase, the master node is responsible for collecting the global digital fingerprint, data element information, and timestamp of the data to be stored, and encapsulating them into a proposal package, which is then broadcast to all slave nodes. During the preparation phase, after receiving the proposal, each node first verifies the identity of the master node using its built-in digital certificate, and then verifies the integrity of the proposal content. After verification, nodes digitally sign the proposal and broadcast the signed preparation message to each other across the network. During the confirmation phase, when a node receives more than a preset threshold of preparation messages (usually two-thirds of the total number of nodes), that node enters the confirmation state and broadcasts a confirmation message. Once a node receives a sufficient number of confirmation messages again, it formally writes the evidence record into its local distributed ledger. In addition, the consortium blockchain consensus storage module introduces an identity-based access control mechanism. All participating nodes must hold digital certificates issued by an authoritative institution and communicate through a two-way secure transport layer protocol. This module also stores encrypted data in fragments in a distributed file system and records the index address pointing to the physical storage location on the chain, realizing the separate storage of ledger data and large amounts of original data, which ensures both the immutability of the evidence and storage efficiency.

[0036] The distributed file system employs a tiered storage strategy to optimize the balance between storage cost and access performance. For recently generated, frequently accessed evidence data, the system stores it in a cache layer based on high-performance solid-state storage media to provide millisecond-level response speeds. For historical evidence data that has been stored for longer than a preset threshold and has a low access frequency, the system automatically executes a data migration procedure to transfer it to a large-capacity mechanical storage layer. The distributed file system maintains a global file index table to track the specific physical storage location of each data shard in real time, including server number, disk path, and sector offset. To prevent data loss due to single-point failure of storage nodes, the distributed file system uses a multi-replica fault-tolerant mechanism or erasure coding technology to redundantly store data shards on multiple geographically dispersed nodes.

[0037] The privacy computing gateway is located before data is uploaded to the blockchain and is specifically designed to protect the privacy of sensitive data. Before data enters the consensus process, the privacy computing gateway performs homomorphic encryption or differential privacy processing on specific fields involving personal privacy and trade secrets. Homomorphic encryption technology enables compliance verification and traceability modules to directly perform specific logical operations or aggregation operations on encrypted data without decryption, thereby ensuring that the original sensitive information is invisible to all on-chain nodes while meeting audit requirements. Differential privacy processing introduces controlled noise into the data to prevent attackers from deducing individual sensitive attributes through reverse engineering. The privacy computing gateway ensures that even if the storage nodes in the distributed file system are illegally compromised, attackers will only obtain unrecoverable encrypted fragments.

[0038] The smart contract layer is deployed on the consortium blockchain and has pre-set strict evidence storage rules and automated auditing logic. Whenever an evidence storage request is initiated, the smart contract automatically triggers the execution program to perform multi-dimensional checks on the compliance indicators of the input data. The checks include whether the data format conforms to the predefined pattern, whether the key business fields are complete, whether the signature of the data providing node is within the validity period, and whether the evidence storage frequency exceeds the quota. Only data that passes the automatic verification of the smart contract is allowed to enter the subsequent consensus process. In addition, the smart contract also undertakes the task of operation auditing. It automatically records detailed logs of every traceability query, data retrieval, and configuration change, including the operator's identity, operation time, query parameters, and a summary of the returned results. These audit logs, as special evidence storage data, are also recorded in the blockchain ledger, ensuring that the audit process itself is auditable and traceable.

[0039] The compliance verification and traceability module is the core component of the system to realize regulatory and audit functions. When the auditor or regulatory agency initiates a verification request, the module first retrieves the corresponding evidence storage record from the consortium blockchain consensus storage module based on the globally unique identifier in the request, and obtains the global digital fingerprint stored on the chain. Subsequently, the system retrieves the target data fragment from the distributed file system. To achieve efficient compliance verification, the compliance verification and traceability module supports selective disclosure. ; The above formula describes the verification logic of local verification, where For full-cycle digital fingerprints stored on the heap, The target data to be verified is fragmented. The system provides the set of hash values ​​of the supplementary path nodes corresponding to the shard in the Merkle tree. The verifier does not need to obtain the full amount of original data; the system only provides the target data shard and its corresponding Merkle path proof. The verifier performs layer-by-layer synthesis calculations by combining the hash value of the target data shard with the provided supplementary path node hash values ​​to obtain a calculation result. The compliance verification and traceability module compares the calculation result with the global digital fingerprint recorded on the chain. If the two are completely consistent, it is determined that the local data shard has not been tampered with after the proof is stored, and its logical source is authentic and reliable.

[0040] If global verification fails, the tampering location engine integrated into the compliance verification and traceability module will automatically activate. This engine executes a recursive search procedure, starting from the root node of the Merkle tree and comparing the hash values ​​of intermediate nodes at each level downwards. By identifying which branch's hash value does not match the original evidence record, the system can quickly eliminate irrelevant data branches, narrowing the search scope. This process continues until the specific leaf node causing the verification failure is located, i.e., the tampered data fragment is precisely located. After the target is located, the system automatically extracts all historical change records of that data fragment on the chain, and reconstructs the context in which the tampering occurred by combining timestamp information, node signature information, and operation logs. The compliance verification and traceability module will also retrieve the digital signature information associated with the global digital fingerprint and verify the public key of the node providing the data to confirm the true source of the data. If signature verification fails, the system immediately issues a warning signal, indicating that the data is at risk of being impersonated.

[0041] After completing the traceability verification, the compliance report automatic generation unit is responsible for transforming the verification process and results into a legally binding formal document. Based on the verification results, timestamps, list of nodes participating in the consensus, Merkel path proof, and hash digest of the on-chain consensus record, this unit automatically generates an electronic certificate of evidence. The certificate adopts a standardized document format and embeds an anti-counterfeiting QR code and a system electronic seal. The QR code contains a query link to the blockchain ledger, allowing third parties to quickly verify the authenticity of the certificate by scanning the QR code. The electronic certificate of evidence records in detail the complete evidence chain from data access, sharding, on-chain to verification, and can be directly used as judicial evidence, administrative regulatory reports, or internal audit reports.

[0042] The system operates in a distributed network environment jointly maintained by multiple regulatory agencies. Each regulatory agency acts as a peer node in the consortium blockchain, jointly exercising the right to supervise data storage activities. Through multi-signature technology, the system stipulates that any changes involving the core configuration of the system, the clearing of historical data, or the adjustment of node permissions must be authorized by more than a preset proportion (such as more than 60%) of the regulatory nodes before they can take effect. This distributed governance architecture technically eliminates the possibility of illegal operations by a single agency or a single node, thereby enhancing the credibility of the system.

[0043] In addition, the system supports cross-chain interoperability protocols to address cross-industry and cross-regional evidence storage needs. When evidence storage data needs to be transferred or verified between different consortium blockchain networks, the compliance verification and traceability module uses a cross-chain gateway and relay chain technology or hash locking technology to convert the global digital fingerprint and consensus proof of this chain into a format recognizable by the target chain. The cross-chain interoperability protocol ensures the consistency and traceability of data during cross-chain migration and realizes the transfer of trust between different compliance domains.

[0044] During the data acquisition phase, the data preprocessing and structured sharding modules interface with the business system through a standardized application programming interface (API). This interface supports both synchronous and asynchronous data access modes. For transaction data with high real-time requirements, the synchronous mode is used to ensure that the data is preprocessed the instant the business occurs. For large volumes of log data, the asynchronous mode is used, with buffering via a message queue to avoid putting performance pressure on the business system. During preprocessing, the module monitors the data stream in real time. If any abnormal data format or missing fields are detected, an alarm is immediately triggered and recorded in the exception log.

[0045] To further enhance security during the construction of multi-level digital fingerprints, the system introduces a salting mechanism. Before hashing each data shard, the system generates a random salt value based on the shard's globally unique identifier and timestamp, and mixes it with the data content. This mechanism effectively prevents rainbow table attacks and pre-mapping attacks. After generating the Merkle tree, the multi-level digital fingerprint construction module records the tree's structural parameters, such as its height, the total number of leaf nodes, and the padding strategy used, in the metadata. These parameters are crucial for subsequent compliance verification, ensuring that the verifier can accurately reconstruct the hash calculation path.

[0046] The observer nodes in the consensus storage module of the consortium blockchain provide a lightweight participation method. Observer nodes do not participate in consensus voting or undertake accounting tasks, but they synchronize the ledger data of the entire network in real time. Regulatory agencies can deploy a large number of observer nodes to achieve real-time monitoring and data backup of the evidence storage environment. This design greatly expands the transparency of the system and the redundancy of data without increasing the complexity of consensus.

[0047] In the management of the distributed file system, the system employs data fingerprint deduplication technology. Before storing a new data shard, the system first checks the global index table to see if a duplicate shard-level digital fingerprint exists. If it does, only one logical reference pointing to the existing physical storage location is added, without storing the physical data again. This technology significantly reduces storage space usage, especially when processing highly repetitive business documents.

[0048] The smart contract layer also integrates lifecycle management logic. Based on the data retention requirements of different laws and regulations, smart contracts can preset the validity period of data. Once the stored data exceeds its validity period, the smart contract automatically triggers an archiving process, migrating the data from online storage to offline backup media and recording the archived data on the blockchain. If laws and regulations require the complete destruction of data, the smart contract, after obtaining authorization from multiple regulatory nodes, will perform an irreversible erasure operation and generate a destruction certificate.

[0049] When performing cross-chain verification, the compliance verification and traceability module invokes cross-chain smart contracts. These smart contracts are responsible for verifying the validity of evidence from other chains and checking whether the evidence has been signed by a legitimate consensus node on the target chain. In this way, the system builds an interconnected digital trust network, enabling electronic evidence to be recognized more broadly.

[0050] The automatic compliance report generation unit also supports custom report templates. Auditors can select different report dimensions according to different audit scenarios, such as summarizing by time period, by business type, or by specific node. The report generation engine will automatically retrieve the original evidence storage data and verification logs on the blockchain, fill them into the template, and generate visual charts to intuitively show the compliance trends and potential risk points of the data.

[0051] The system employs software-defined networking (SDN) technology at the network layer to achieve logical isolation of evidence storage traffic. The evidence storage data stream, consensus control stream, and audit query stream run in different virtual network channels without interfering with each other. By configuring strict access control lists and intrusion detection systems, the system can effectively resist denial-of-service attacks and network sniffing attacks, ensuring the availability and privacy of the evidence storage channel.

[0052] At the hardware level, the system supports deployment on domestically produced servers and operating systems, and is fully compatible with domestically produced cryptographic algorithm hardware acceleration cards. By utilizing a trusted execution environment at the hardware level, the system places core hash operations, digital signatures, and key management tasks within a protected hardware security module, preventing malicious code from the operating system layer from stealing keys or tampering with the computation process. This integrated hardware and software security design provides solid underlying support for compliant data storage.

[0053] When processing large-scale datasets, the data preprocessing and structured sharding module adopts a parallel processing architecture. By distributing the data stream to multiple processing cores, the system can simultaneously perform standardization and formatting operations on multiple data shards. Each processing core has an independent buffer, avoiding performance bottlenecks caused by data contention. In terms of sharding logic, the system also supports content-based sharding algorithms, which determine the sharding points by identifying specific feature markers in the data. This makes the processing of variable-length records more flexible and accurate.

[0054] When constructing the Merkle tree, the multi-level digital fingerprint building module also considers the dynamic update requirements of the tree. When a certain piece in the original data needs to be updated, the system does not need to recalculate the hash value of the entire tree. By utilizing the local update characteristics of the Merkle tree, the system only needs to recalculate the hash values ​​of the affected piece and its ancestor nodes to obtain a new global digital fingerprint. This incremental update mechanism greatly improves the efficiency of processing dynamically changed data, while also preserving the historical traceability capability before and after the change.

[0055] The ledger structure of the consortium blockchain consensus storage module adopts an improved Merkel-Patricia tree, which not only records the current evidence storage status but also preserves the historical trajectory of state changes. Each block contains not only transaction data but also the root hash of the state tree, which allows auditors to easily query the evidence storage status at any historical moment, achieving true all-time and all-space traceability.

[0056] The distributed file system employs erasure coding technology at the physical layer, further encoding each encrypted data fragment into multiple data blocks and check blocks. These blocks are distributed and stored in different racks and data centers. Even if multiple storage nodes fail simultaneously, the system can recover the original data fragment from the remaining blocks. This extremely high reliability ensures that the evidence data will not be lost due to hardware aging or accidental disasters during a retention period of up to several decades.

[0057] When performing differential privacy processing, the privacy computing gateway employs an adaptive noise injection algorithm. This algorithm dynamically adjusts the distribution and intensity of noise based on the sensitivity of the data and the accuracy requirements of the query. For insensitive statistical data, less noise is injected to maintain high availability; for highly sensitive personally identifiable information, stronger noise is injected to ensure privacy. This balancing strategy allows the system to produce valuable audit statistics while protecting privacy.

[0058] The automated auditing logic at the smart contract layer also includes in-depth checks on data consistency. For example, in supply chain finance evidence storage scenarios, smart contracts automatically verify the logical consistency between invoice storage, logistics document storage, and payment voucher storage. If inconsistencies are found, such as discrepancies between the logistics document amount and the invoice amount, the smart contract will reject the request to add that batch of data to the blockchain and send a risk warning to the regulatory authorities. This business-level compliance check elevates the function of the evidence storage system from passive recording to proactive supervision.

[0059] When handling high-concurrency verification requests, the compliance verification and traceability module employs load balancing and distributed caching technologies. Commonly used Merkel path proofs are cached in an in-memory database to reduce the number of accesses to the distributed file system. Meanwhile, the verification logic is designed to be stateless and can be horizontally scaled to execute on multiple verification servers, ensuring that the system can still provide stable verification services under high-load scenarios such as regulatory censuses.

[0060] The electronic certificates generated by the automatic compliance report generation unit are encapsulated using digital signature technology. Each page of the certificate contains an invisible digital watermark. Even if the certificate is printed as a paper document, the anti-counterfeiting information can still be extracted through high-precision scanning and image processing technology. In addition, the system also connects with external timestamp service agencies to provide authoritative third-party time verification for each report, enhancing the legal persuasiveness of the evidence.

[0061] In terms of operation and maintenance monitoring, the system integrates a full-link tracing system. From the first millisecond of data entering the system to the final completion of on-chain storage, the time consumption, resource usage, and status changes of each processing step are recorded in detail. Operation and maintenance personnel can monitor the health status of the system through a visual interface and promptly identify and address potential performance bottlenecks or faults. This high degree of observability ensures the stable operation of the evidence storage system as infrastructure.

[0062] In terms of enhanced security, the system introduces multi-signature and threshold signature mechanisms. For operations involving extremely high privileges, such as updating the system root key and modifying regulatory rules, the system requires the participation of multiple authorized personnel distributed in different geographical locations and belonging to different organizations. The operation instruction will only be activated when the number of collected signatures reaches the set threshold value. This mechanism prevents systemic risks caused by internal personnel committing malicious acts or the theft of a single account from both institutional and technical perspectives.

[0063] The data preprocessing and structured fragmentation module also features automatic retry and breakpoint resume capabilities. When receiving large volumes of data, if the network connection is interrupted, the system automatically records the current transmission progress; once the connection is restored, the system continues receiving data from the point of interruption without starting from the beginning. This greatly improves data access efficiency in unstable network environments. At the same time, the module performs real-time cyclic redundancy checks on the received data blocks to ensure that no random errors are introduced during transmission.

[0064] After generating a global digital fingerprint, the multi-level digital fingerprint construction module binds it to the evidence storage sequence number. The evidence storage sequence number is a monotonically increasing counter that records the total number of evidence storage requests processed by the system. By introducing the sequence number into hash calculation, the system ensures that even if two identical data fragments are stored at different times, the generated global digital fingerprint is still unique, thus effectively preventing replay attacks.

[0065] The consortium blockchain consensus storage module further enhances transaction processing capabilities by adopting sharding technology. The system divides all network nodes into multiple consensus shards, each of which independently processes a portion of the evidence storage requests. Different shards maintain data synchronization through cross-shard communication protocols. This architecture enables the overall throughput of the system to increase linearly with the number of nodes, easily handling the pressure of tens of thousands of evidence storage requests per second.

[0066] The distributed file system also integrates a data cleaning and integrity scanning program, which runs periodically in the background of the system. It reads each data fragment in storage and recalculates its hash value, then compares it with the records on the chain. If a physical copy is found to be damaged, the system will automatically use redundant copies to repair it, ensuring the long-term static security of the stored data.

[0067] When processing cross-institutional collaborative queries, privacy-preserving computation gateways utilize secure multi-party computation technology. This allows multiple institutions to collaboratively calculate a single statistical result without exchanging their original data. For example, multiple banks can jointly query whether a specific customer is on an industry-wide blacklist without disclosing their individual blacklist details. This technology makes it possible to build cross-industry credit compliance systems.

[0068] The rule base of the smart contract layer supports dynamic upgrades. As regulatory policies change, regulatory agencies can release new versions of smart contracts. The system adopts a proxy contract model to ensure that existing evidence data and business logic remain continuous during contract upgrades. All contract upgrade operations are recorded on the blockchain, making them transparent and traceable, and meeting the dynamic requirements of compliance management.

[0069] When performing source tracing, the compliance verification and traceability module can not only locate the tampered fragment, but also trace all historical versions of that fragment. The system uses backtracking pointers to link evidence records from different periods, forming a complete data evolution map. This is of great value for handling complex, long-cycle business source tracing, and can clearly show the compliance status of data at different points in time.

[0070] The automatic compliance report generation unit supports multilingual and multi-standard output. In addition to meeting domestic judicial evidence preservation standards, the system can also generate reports according to international auditing standards. The reports include detailed technical descriptions and cryptographic proof processes, enabling judges or auditors without technical backgrounds to understand the principles of evidence preservation reliability.

[0071] In terms of environmental adaptability, the system supports a cloud-native deployment architecture. All functional modules are encapsulated in containers and managed uniformly through an orchestration engine. This allows the system to be quickly deployed in private, public, or hybrid cloud environments and automatically scale elastically according to business load. This flexible deployment approach reduces the initial construction cost and subsequent maintenance difficulty of the system.

[0072] At the user interaction level, the system provides a comprehensive monitoring dashboard and management backend. Supervisors can view the total amount of evidence stored across the entire network, node distribution, consensus latency, and anomaly alerts in real time, while data providers can query their own evidence storage records and verification logs. This multi-dimensional information display enhances the system's usability and management efficiency.

[0073] To further enhance the authority of the evidence, the system can also connect with national-level timestamp centers and notary offices. While completing on-chain evidence storage, the system automatically initiates a secondary evidence storage request to external authoritative institutions to obtain third-party certifications with higher legal validity. This multi-layered evidence storage mechanism constructs a complete closed loop from technological trust to legal trust.

[0074] The data preprocessing and structured segmentation module introduces a sliding window mechanism when processing streaming data. This allows the system to perform real-time slicing and fingerprint extraction on continuously generated data streams, achieving near real-time evidence storage of dynamic data. This is of great significance for the reliable recording of surveillance video streams and industrial sensor data streams.

[0075] In terms of algorithm selection, the multi-level digital fingerprint construction module prioritizes domestic cryptographic algorithms approved by the State Cryptography Administration. This not only meets the national requirements for the security and control of critical information infrastructure, but also reaches the international leading level in terms of algorithm performance and anti-attack strength. The system has reserved an algorithm upgrade interface, which can smoothly transition to the next generation of cryptographic algorithms resistant to quantum computing attacks.

[0076] The consensus storage module of the consortium blockchain introduces a weighted voting mechanism, which gives regulatory nodes higher decision-making weight. When a consensus is reached, the voting results of regulatory nodes have a key influence. This ensures that the system is always under regulatory control during operation, preventing the technological decentralization from evolving into regulatory loss of control.

[0077] The distributed file system employs a geo-aware allocation strategy in its storage layout. Data copies are preferentially stored in areas with different earthquake zones and different power grid coverage, greatly enhancing the system's survivability in the face of natural disasters or regional power outages. This high-availability design is the cornerstone of the long-term stable service of the compliant evidence storage system.

[0078] When performing data anonymization, the privacy computing gateway uses format-preserving encryption technology. The encrypted data retains its original format characteristics, such as length and character type. This allows existing business systems to process encrypted evidence data without large-scale modifications, reducing the difficulty of system integration.

[0079] The smart contract layer also has an automatically triggered compliance warning function. When the system detects abnormal fluctuations in certain types of business data, such as a sudden increase in the number of evidence stored or an increase in the verification failure rate, the smart contract will automatically push warning information to the regulatory node and take restrictive actions according to the preset script, such as temporarily freezing the evidence storage permissions of relevant accounts, thereby realizing the leap from post-audit to forward-looking supervision.

[0080] When performing verification, the compliance verification and traceability module takes into account network latency and node load. The system will automatically select the storage node and verification node that are closest to the verifier and have the lowest load to process the request. Through distributed collaborative computing, the efficiency and smoothness of the verification process are ensured.

[0081] The automatic compliance report generation unit has strict access control. Only authorized auditors can initiate report generation requests. The system assigns a unique code to each generated report and records its whereabouts in the database. This closed-loop management prevents compliance reports from being forged or misused.

[0082] The system is designed with scalability in mind, employing a plug-in architecture that allows third-party developers to add new data parsing protocols, storage engines, or verification logic. This open ecosystem design enables the system to continuously absorb the latest technological advancements and maintain its longevity.

[0083] In terms of data consistency assurance, the system adopts a collaborative mechanism of two-phase commit protocol and blockchain consensus. While data is written to the distributed file system, it ensures that its hash digest reaches consensus on the blockchain in sync. If either party fails, the system will automatically perform a rollback operation to ensure absolute synchronization between the ledger and the physical data.

[0084] When performing data cleaning, the data preprocessing and structured sharding module uses machine learning algorithms to identify potential data anomalies. By learning from historical data, the algorithm can automatically discover outliers that do not conform to business logic and mark them as pending verification. This intelligent preprocessing improves the overall quality of the evidence data.

[0085] When dealing with ultra-large-scale Merkle trees, the multi-level digital fingerprint building module adopts a block-based construction and on-demand loading strategy. The system does not need to store the entire tree in memory, but dynamically loads the relevant node hashes according to the current calculation path. This enables the system to support ultra-large-scale evidence storage tasks with hundreds of millions of leaf nodes without causing memory overflow.

[0086] The consortium blockchain consensus storage module supports dynamic node admission. When a new organization applies to join the consortium, the system will initiate an automated review process to evaluate the applicant node's hardware performance, network bandwidth, and security qualifications. After obtaining approval from existing members, the new node can quickly access the network through state synchronization.

[0087] During data transmission, the distributed file system employs an end-to-end encrypted channel. The data remains encrypted throughout the entire process from the preprocessing module to storage on the disk. The storage nodes themselves do not hold the decryption key; only authorized verifiers in a specific execution environment can obtain decryption privileges.

[0088] The privacy computing gateway also supports zero-knowledge proof technology, which allows the prover to prove to the verifier that the data meets specific attributes without providing specific data. For example, it can prove that someone's income exceeds a preset threshold without disclosing the specific income amount. This provides a powerful tool for compliance verification with extremely high privacy requirements.

[0089] The code of the smart contract layer has undergone rigorous formal verification, and mathematical methods have been used to prove that the contract logic will not have logical loopholes or infinite loops under all possible inputs. This extremely high level of code security prevents hackers from attacking by exploiting contract vulnerabilities.

[0090] When performing source tracing, the compliance verification and traceability module generates a detailed traceability path diagram, which clearly marks the data flow time, processing actions, and responsible persons at each node. This visualized chain of evidence greatly reduces the difficulty for auditors to understand complex business processes.

[0091] The automatic compliance report generation unit supports deep integration with e-government systems. The generated reports can be directly pushed to the database of the government regulatory platform, realizing the automated collection and reporting of regulatory data and reducing the compliance burden on enterprises.

[0092] The system also has a robust disaster recovery and failover mechanism. In the event of a failure in the primary data center, the backup center can take over the evidence storage business within seconds. Through real-time status synchronization technology, the backup center has ledgers and file indexes that are completely consistent with those of the primary data center, ensuring business continuity and zero data loss.

[0093] In terms of key management, the system adopts a hierarchical management scheme based on secret sharing. The root key is split into multiple fragments and kept separately by multiple core regulatory agencies. Only in an emergency can the root key be reconstructed by collecting enough key fragments. This design prevents the risk of key leakage due to the error of a single person or the damage of a single institution.

[0094] The system introduces a multi-level digital fingerprint construction module to transform the raw data into a Merkle tree with a multi-level structure, fundamentally solving the verification efficiency problem in large-scale data storage. The verifier does not need to obtain the full amount of raw data; it can complete the authenticity verification within a predetermined low time complexity based solely on the target fragment and its corresponding Merkle path. This technical approach of reconstructing global trust based on local evidence greatly reduces network bandwidth consumption and computational overhead, making real-time and high-frequency verification of large-scale data possible and significantly improving the system's throughput and response speed.

[0095] Leveraging the characteristics of its multi-level hash structure, the system achieves precise location of tampering. Compared to traditional solutions that can only determine whether the overall data has changed, this system can quickly pinpoint specific data fragments by following branches with inconsistent hashes. This enables a leap from macro-level evidence preservation to micro-level tracing. This fine-grained location capability not only improves the efficiency of auditing in discovering problems but also provides precise guidance for subsequent legal evidence collection, liability determination, and data restoration, greatly enhancing the practical value of the evidence preservation system.

[0096] By working in tandem with the selective disclosure mechanism and the privacy computing gateway, the system successfully resolves the conflict between data privacy protection and compliance auditing. Auditors can verify the compliance of specific business data through Merkel path proof without accessing unrelated sensitive information, strictly adhering to the security principle of minimizing data exposure. This design not only meets the stringent privacy protection compliance requirements of the financial and government sectors, but also provides solid technical support for building a digital trust ecosystem with multi-party participation and balanced interests.

[0097] Leveraging the decentralized consensus mechanism and immutability of consortium blockchains, the system constructs a highly transparent and multi-party supervised evidence storage environment. By anchoring global digital fingerprints, timestamps, and node signatures on the chain, the risks of single points of failure and data forgery are eliminated. The introduction of smart contracts automates and standardizes the evidence storage process, ensuring that every piece of data uploaded to the chain undergoes strict filtering according to preset rules. This system architecture, which combines underlying cryptographic tools with an upper-level distributed governance framework, provides legally valid technical protection for the full lifecycle management of electronic data, significantly reducing social trust costs.

[0098] During the execution of the data preprocessing and structured sharding module, the system first parses the input unstructured or semi-structured raw electronic data to identify key fields and business logic levels. Then, using a dynamic sharding algorithm, the raw data is divided into data shards 1 to 9 based on the data volume and the minimum granularity requirements for compliance auditing, where N is a positive integer greater than 1. Each data shard is assigned a unique globally unique identifier and a mapping relationship is established with the metadata of the raw data to ensure that the sharded data maintains logical integrity.

[0099] When constructing the Merkle tree, the multi-level digital fingerprint construction module treats the first to Nth data shards as leaf nodes of the tree structure. Each leaf node is hashed to obtain an initial hash value. Then, a predetermined number of adjacent initial hash values ​​are concatenated and hashed again to generate the hash value of the intermediate node at the next higher level. This process continues until a unique root node hash value is generated. If the total number of shards is odd, the hash value of the last shard is copied to make an even number of pairs, thus ensuring the symmetry of the tree structure and the continuity of hash calculations. This multi-level structure ensures that any subtle change in a data shard is uploaded to the root node level by level, causing a change in the global digital fingerprint.

[0100] The consortium blockchain consensus storage module employs a practical Byzantine fault-tolerant consensus algorithm or an improved version thereof to ensure that the system can still achieve data consistency even with some faulty nodes. The consensus process includes a pre-preparation phase, a preparation phase, and a confirmation phase. In the pre-preparation phase, the master node sends a proposal containing a global digital fingerprint to all slave nodes. In the preparation phase, each node verifies the proposal by signing it and broadcasts the verification results to each other. In the confirmation phase, when a node receives a preparation message that meets the preset proportion requirements, it writes the evidence storage record into its local ledger. In addition, the module introduces an identity-based access control mechanism, allowing only nodes holding valid digital certificates to participate in consensus and data reading, ensuring the closed and compliant nature of the evidence storage environment.

[0101] The compliance verification and traceability module supports selective disclosure. When the auditor only needs to verify a portion of the data shards, the system only provides the auditor with the target data shard and its corresponding completion path node hash value in the Merkle tree, without exposing other unrelated data shard content. The verifier performs layer-by-layer synthesis calculations by combining the hash value of the target data shard with the provided completion path node hash value. If the final hash value is completely consistent with the global digital fingerprint recorded on the consortium blockchain, the local data is determined to be authentic and trustworthy. This mechanism meets compliance audit requirements while minimizing the exposure of sensitive data.

[0102] The compliance verification and traceability module also integrates a tampering location engine. When the global verification fails, the system starts a recursive search program, comparing the hash values ​​of intermediate nodes at each level from the root node of the Merkle tree downwards. By identifying branches with inconsistent hash values, the system can quickly locate the specific leaf node that caused the verification failure, that is, accurately locate the tampered data shard. Subsequently, the system automatically extracts the historical change records of the data shard on the chain, combines the timestamp and operator signature information, reconstructs the context of the tampering, and provides conclusive evidence for legal accountability.

[0103] The privacy computing gateway is used to perform homomorphic encryption or differential privacy processing on sensitive fields before data is uploaded to the blockchain. The privacy computing gateway ensures that even if the storage nodes in the distributed file system are illegally compromised, attackers cannot obtain the plaintext content of the original data. At the same time, the compliance verification and traceability module can perform specific logical operations in the encrypted state to achieve compliance judgment without leaking privacy.

[0104] The smart contract layer is used to pre-set evidence storage rules and automated auditing logic. Before data enters the consensus process, the smart contract automatically checks the compliance indicators of the data, such as whether the data format meets the standards, whether key fields are missing, and whether the signature is valid. Only data that passes the automatic verification of the smart contract can enter the subsequent consensus and storage stages. In addition, the smart contract is also used to record the operation log of each traceability query to ensure that the audit process itself is also auditable and traceable.

[0105] The distributed file system adopts a hierarchical storage strategy. For recently generated and frequently accessed evidence data, it is stored in a cache layer based on high-performance media to improve the verification response speed. For historical evidence data, it is migrated to a large-capacity storage layer. The system maintains the association between data fragments in physical storage location and logical structure in real time through a global file index table to ensure consistency when retrieving data across levels.

[0106] The system operates in a distributed network environment jointly maintained by multiple regulatory agencies. Each regulatory agency acts as a peer node of the consortium blockchain, jointly exercising the right to supervise data storage activities. Through multi-signature technology, the system requires that any sensitive configuration changes or data cleanup operations must be authorized by more than a preset proportion of regulatory nodes before they can take effect, thereby technically eliminating the possibility of a single node acting maliciously.

[0107] When performing verification, the compliance verification and traceability module also retrieves the digital signature information associated with the global digital fingerprint. By verifying the public key signature of the data provider node, it confirms the source identity of the stored data. If the signature verification fails, the system will issue a warning signal, indicating that the data may be at risk of impersonation or forgery. This dual verification mechanism, which combines digital fingerprint and digital signature, builds a comprehensive trust link from data content to data source.

[0108] The data preprocessing and structured sharding module also has a data integrity self-check function. After the data is sharded, the module will automatically calculate the sum of the preset length features of all shards and compare it with the total length features of the original data. At the same time, by performing a fast logical check on all shards, it ensures that no bit faults or data loss are introduced during the sharding process. Only the shard set that passes the self-check will be passed to the multi-level digital fingerprint construction module.

[0109] The multi-level digital fingerprint building module uses a hardware-accelerated hash engine that supports a variety of high-strength secure hash algorithms. The system dynamically adjusts the preset bit width of the hash algorithm according to the security level requirements to resist potential collision attacks. After generating the global digital fingerprint, the system will encapsulate the algorithm version number and parameter information used into the metadata to facilitate algorithm alignment during subsequent verification.

[0110] After completing the traceability verification, the compliance report automatic generation unit automatically generates a legally valid electronic certificate of evidence based on the verification results, timestamps, node signatures, Merkel paths, and on-chain consensus records. The certificate adopts a standardized format, includes an anti-counterfeiting QR code and an electronic seal, and can be directly used as judicial evidence or audit reports.

[0111] The consortium blockchain consensus storage module provides read-only data synchronization services to non-consensus participants by setting up observer nodes. Observer nodes synchronize the on-chain ledger in real time but do not participate in consensus voting, thereby expanding the transparency and scope of supervision of the stored data without affecting system performance.

[0112] The compliance verification and traceability module supports cross-chain interoperability protocols. When evidence storage data needs to be transferred or verified between different consortium blockchain networks, the system converts the global digital fingerprint and consensus proof of this chain into a format that can be recognized by the target chain through the cross-chain gateway, so as to achieve cross-regional and cross-industry compliance evidence storage and mutual recognition.

[0113] In the description of this invention, it should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions provided in this disclosure can be achieved, and no limitation is imposed herein.

[0114] The above description is merely a preferred embodiment of the present invention and does not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain, characterized in that, include: The data preprocessing and structured sharding module is used to receive unstructured or semi-structured raw electronic data, identify key fields and business logic levels in the raw electronic data, and perform format standardization processing on the raw electronic data according to a preset data specification protocol. Then, based on the data logic correlation and the minimum granularity requirements for compliance auditing, the standardized data is divided into several independent data shards using a dynamic sharding algorithm, and each data shard is assigned a globally unique identifier to establish a sharding index mapping table. A multi-level digital fingerprint construction module is used to perform hash operations on each of the data fragments to generate corresponding fragment-level digital fingerprints, and to use a recursive hash iteration algorithm to construct a Merkle tree with a multi-level structure by using all the fragment-level digital fingerprints as leaf nodes. The hash value of the root node of the Merkle tree is extracted as the global digital fingerprint, and the odd number of nodes are copied and padded during the construction process to maintain the symmetry of the tree structure. The consortium blockchain consensus storage module consists of a consortium blockchain network composed of pre-set regulatory nodes, audit nodes, and data providing nodes. It is used to encapsulate the global digital fingerprint, data element information, and timestamp into the block header through the pre-preparation, preparation, and confirmation stages using a practical Byzantine fault-tolerant consensus algorithm. The encrypted data fragments are stored in a distributed file system, and the index address pointing to the physical storage location of the distributed file system is recorded on the chain. The compliance verification and traceability module is used to respond to verification requests by retrieving the Merkel path proof corresponding to the target data shard from the consortium blockchain consensus storage module. By reconstructing and comparing the hash value of the target data shard with the hash value of the path completion node, the authenticity of the specific data shard is verified. When the global verification fails, a recursive search program is started to compare the hash values ​​of intermediate nodes at each level from the root node of the Merkel tree to accurately locate the tampered data shard.

2. The compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: The data preprocessing and structured fragmentation module is also used to perform a data integrity self-check after the data is fragmented. This is achieved by calculating the sum of the preset length characteristics of all the data fragments and comparing it with the total length characteristics of the original electronic data, as well as performing logical checks on all fragments, to ensure that no bit errors or data loss are introduced during the fragmentation process.

3. The compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: The multi-level digital fingerprint construction module includes a hardware-accelerated hash engine, which generates a random salt value based on the globally unique identifier and timestamp and mixes it with the data content before performing hash operations on each data fragment, in order to resist rainbow table attacks and pre-mapping attacks, and dynamically adjusts the preset bit width of the hash algorithm according to security level requirements.

4. The compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: The consortium blockchain consensus storage module also includes an identity-based access control mechanism, which requires all participating nodes to hold digital certificates issued by an authoritative institution and to communicate through a two-way secure transport layer protocol, allowing only nodes holding valid digital certificates to participate in consensus, data reading, and evidence storage.

5. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: The distributed file system adopts a hierarchical storage strategy, including: A cache layer is used to store recently generated evidence data that is accessed more frequently than a preset threshold. The cache layer is built on a high-performance solid-state storage medium. A storage layer is used to store historical evidence data whose access frequency is lower than the preset threshold. The storage layer is constructed based on a high-capacity mechanical storage medium. A global file index table is used to maintain the association between the physical storage location and logical structure of the data fragments in real time, ensuring consistency when retrieving data across levels.

6. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 5, characterized in that: The distributed file system is also used to employ erasure coding technology to encode the encrypted data into multiple data blocks and check blocks, and distribute them across multiple nodes with different geographical locations to achieve redundancy and fault tolerance of the storage nodes.

7. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: Also includes: A privacy computing gateway is used to perform homomorphic encryption or differential privacy processing on sensitive fields in the original electronic data before the data enters the consortium blockchain consensus storage module, so that the compliance verification and traceability module can perform logical operations in an encrypted state without disclosing the plaintext content of the original data.

8. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: Also includes: The smart contract layer is used to pre-set evidence storage rules and automated audit logic. Before the data enters the consensus process, it automatically checks the compliance of the data format, key fields and signatures, and records the operation log of each traceability query to achieve the traceability of the audit process.

9. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: The compliance verification and traceability module is also used to support selective disclosure. By providing the auditing party with the target data shard and its corresponding completion path node hash value in the Merkle tree, it enables local data authenticity verification without exposing other unrelated data shard content.

10. A compliant data trusted storage and traceability system based on digital fingerprints and consortium blockchain as described in claim 1, characterized in that: The compliance verification and traceability module also includes a tampering location engine, which is used to automatically extract the historical change records of the data shard on the chain after locking the tampered data shard, and reconstruct the context of the tampering by combining the timestamp and operator signature information.

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