Energy data consensus and efficient storage system based on hierarchical multi-chain architecture

Abstract

The invention discloses an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture. The energy data consensus and efficient storage system comprises a main chain consensus module, a side chain cluster module, a storage fragmentation module and a data migration module. The main chain consensus module processes the energy data through a Byzantine fault-tolerant algorithm and generates a global consensus signal containing a timestamp and an energy data fingerprint. The side chain cluster module receives the global consensus signal and then generates a local consensus result, and the side chain cluster module forms a first fragment processing instruction. The storage fragmentation module configures a feature analysis engine to analyze the service attribute of the energy data in real time, and generates a fragmentation instruction signal containing a storage type identifier. And the data migration module executes cold and hot data unloading according to the fragmentation instruction signal, and forms a second fragmentation processing instruction according to the access frequency of the data migration module. According to the energy data consensus and efficient storage system based on the hierarchical multi-chain architecture, the problems of low energy data storage efficiency, low consensus speed and difficulty in cross-chain verification can be solved.

Description

Technical Field

[0001] The present invention relates to an innovative application of blockchain technology in the field of energy data management, and particularly to an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture. Background Art

[0002] With the rapid development of the energy Internet and the construction of a new power system, the energy industry is facing the dual challenges of a sharp increase in data scale and the complexity of processing requirements. The data volume generated in business scenarios such as power trading, smart meters, new energy equipment monitoring, and carbon emission rights management has increased exponentially, and the data types cover multi-modal information such as real-time stream data, high-precision sensing data, structured transaction records, and unstructured operation and maintenance logs. Although traditional centralized data management systems can achieve basic storage, they have inherent defects in data credibility, anti-tampering, and cross-subject collaboration.

[0003] Blockchain technology has been introduced into the energy field due to its distributed and immutable characteristics. However, the existing blockchain architectures have significant limitations when adapting to the special requirements of the energy industry: First, the single-chain homogeneous consensus mechanism is difficult to cope with the heterogeneous characteristics of energy data. For example, the power trading scenario requires processing thousands of real-time transactions per second, while the device status monitoring data requires high-frequency on-chain and quick verification. Traditional blockchains using fixed consensus algorithms (such as PoW or PBFT) cannot achieve a dynamic balance between performance and security, resulting in processing delays for high-value real-time data due to network congestion, seriously affecting the timeliness of key services such as power grid dispatching. Second, there are trust transfer barriers in data interconnection across business chains. The energy industry chain involves multiple links such as power generation, transmission and distribution, power consumption, and carbon trading. Data from each link needs to be cross-chain interactively verified. However, existing cross-chain solutions mostly rely on relay chains or hash time locks. The verification process requires multiple inter-chain communications and complex state synchronization, which not only increases delays of more than one second but also introduces security risks such as double-spending attacks and data interception due to the vulnerability of intermediate links. Third, there is a lack of intelligent strategies for the full life cycle management of energy data. Summary of the Invention

[0004] In view of the above disadvantages of the prior art, the purpose of the present invention is to provide an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture to solve the problems of low energy data storage efficiency, slow consensus speed, and difficult cross-chain verification. The present invention separates global and local consensus through a hierarchical multi-chain architecture, realizes intelligent migration of hot and cold data through dynamic sharding storage, and ensures cross-chain credibility through main-side chain cross-verification, synergistically improving storage efficiency and processing speed, and solving the contradiction between the growth of energy data scale and the system performance bottleneck.

[0005] The present invention provides an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture, including: The main-chain consensus module processes energy data through the Byzantine fault tolerance algorithm and generates a global consensus signal containing a timestamp and an energy data fingerprint; The side-chain cluster module includes multiple heterogeneous blockchain units. Each unit configures a different consensus algorithm according to the preset energy data type. After receiving the global consensus signal, the side-chain cluster module generates a local consensus result and transmits the block header hash value of the side-chain cluster module to the main-chain consensus module to form a cross-verification chain. The side-chain cluster module forms a first sharding processing instruction; The storage sharding module configures a feature analysis engine to parse the business attributes and access characteristics of energy data in real time and generate a sharding instruction signal containing a storage type identifier; The data migration module performs cold and hot data transfer according to the sharding instruction signal and forms a second sharding processing instruction according to the access frequency of the data migration module; Among them, the storage sharding module receives the first sharding processing instruction and the second sharding processing instruction and corrects the sharding instruction signal in real time.

[0006] In an embodiment of the present invention, the heterogeneous blockchain units of the side-chain cluster module dynamically adapt different consensus mechanisms according to the energy data type. When processing real-time transaction data, the Byzantine fault tolerance algorithm optimized based on a time window is adopted to improve the response speed by compressing the consensus rounds and presetting a timeout mechanism. When processing device status data, the proof-of-authority mechanism with multi-party joint authentication is adopted, and the device manufacturer, regulatory agency, and operator jointly participate in node verification. When processing historical audit data, the proof-of-work mechanism with dynamic difficulty adjustment is adopted, and the computational complexity is automatically adjusted according to the data scale; when the side-chain cluster module generates the first sharding processing instruction, an adaptive time window sharding strategy is adopted for real-time data streams, and the window size dynamically expands and contracts with the network state. For batch data, a feature extraction sharding mode is adopted, and a sharding key is generated based on the data source attributes; the block header hash value undergoes multiple layers of encryption processing before being transmitted to the main-chain consensus module, including basic encryption using the asymmetric encryption algorithm of the main-chain consensus module node, and secondary reinforcement using the national cryptography algorithm of the side-chain supervisor to form an encryption anchor with anti-quantum characteristics.

[0007] In an embodiment of the present invention, the feature analysis engine of the storage sharding module generates a sharding strategy through semantic recognition and heat prediction model, and performs multi-dimensional feature analysis on energy data, including extracting business key tags, calculating the access heat value after time decay, and identifying the data source type; the sharding instruction signal contains multi-level storage identifiers, and divides the data into a high-response layer, an intermediate layer, and an archival layer, corresponding to storage media with different performances respectively. The high-response layer uses a low-latency solid-state storage cluster, the intermediate layer deploys a balanced storage pool, and the archival layer uses a mechanical storage array with high-density redundant coding; after receiving the first sharding instruction from the side-chain cluster module, the storage sharding module dynamically adjusts the feature sampling frequency according to the change of sharding granularity, and optimizes the storage layer configuration based on real-time feedback.

[0008] In an embodiment of the present invention, the cold and hot data transfer process of the data migration module includes an intelligent hierarchical migration mechanism. When it is detected that the heat of the data migration module continues to be lower than the set threshold, a phased migration process is triggered: first, the data is transferred to a temporary buffer for lossless compression and attribute-based permission encapsulation to ensure that only authorized entities can access it; the migration task priority is dynamically calculated according to the storage layer load, network status, and data volume. When it is detected that the storage pressure exceeds the standard, the emergency mode is automatically activated, and large-volume and low-heat data is preferentially migrated and a parallel transmission channel is enabled. At the same time, the maximum execution time of a single task is limited to avoid resource competition.

[0009] In an embodiment of the present invention, the main-chain consensus module uses the block fingerprint aggregation technology when generating the global consensus signal. After dividing the energy data by device group, it calculates the hash tree structure and forms the global data fingerprint through layer-by-layer aggregation; the construction of the cross-validation chain includes a version synchronization mechanism. The main-chain consensus module and the side-chain cluster module achieve block status alignment by embedding version identifiers. If it is detected that the version deviation exceeds the allowable range, a forced synchronization process is triggered; the cross-chain verification process uses privacy protection technology to verify the data validity through zero-knowledge proof and complete the trusted confirmation on the premise that the transaction content is not leaked.

[0010] In an embodiment of the present invention, the real-time correction logic of the storage sharding module integrates a multi-objective optimization strategy, incorporates the consensus constraints of the side-chain cluster module and the migration cost limit into the dynamic scoring model, and adjusts the sharding rules by synthesizing the weights of real-time performance, storage efficiency, and reliability; when it is identified that the energy data contains security critical tags, the anti-destruction storage mode is forcibly enabled, redundant copies are saved at multiple isolated nodes, and migration operations are prohibited until the preset security life cycle ends; the feature analysis engine continuously optimizes the sharding decision through machine learning and regularly updates the policy model to adapt to the change of system performance.

[0011] In one embodiment of the present invention, the side-chain cluster module implements a data redundancy protection mechanism in the sharding process, generates distributed check blocks for each data shard, and the number of check blocks is dynamically configured according to the storage level and scattered and stored in different regional nodes; the block header hash transmission adopts an intelligent routing strategy, selects the optimal network path according to the service type and implements transmission quality monitoring, and automatically switches to the backup link when the communication quality deteriorates; the local consensus process of the side-chain cluster module introduces a lightweight verification mechanism, and only needs to verify the key hash path to participate in the consensus.

[0012] In one embodiment of the present invention, the data migration module implements full-link resource monitoring, dynamically collects storage and network metrics during the transfer process, automatically triggers current limiting protection when the resource load is detected to exceed the safety threshold, and reduces the migration rate proportionally; the migration data packet is attached with an aggregated integrity proof, and the group signature technology is used to ensure that the transfer process cannot be tampered with; the archival layer implements automated lifecycle management, executes a secure destruction process for expired data, and needs to obtain a digital certificate issued by the main chain and record an irreversible operation voucher before destruction.

[0013] In one embodiment of the present invention, the storage sharding module executes a preprocessing process during the data access phase, including filtering abnormal data, standardizing the format, and extracting metadata indexes; the sharding instruction generation process includes a conflict arbitration mechanism. When multiple side-chains request the same data resource, the storage permission is allocated according to the service priority and a lock signal is issued through the main chain; a status synchronization channel is established between the storage sharding module and the data migration module to regularly exchange storage node information and trigger data replica reconstruction when a node is abnormal.

[0014] In one embodiment of the present invention, the main-chain consensus module adopts an incremental fingerprint update technology. When the sharding strategy is corrected, only the hash aggregation tree of the affected data blocks is recalculated and synchronized to the consensus network through a quick verification mechanism; the generation of the global consensus signal adopts a multi-party joint signature mechanism, and the main-chain nodes, side-chain representatives, and regulatory nodes participate in the signature according to their weights, and it can only take effect when the minimum number of node thresholds is met; the main chain has a built-in cross-chain arbitration contract. When there are irreparable conflicts between the side-chain data and the cross-verification chain, the side-chain write permission is frozen and the optimal replica is elected from the backup for state recovery.

[0015] An energy data consensus and efficient storage system based on a hierarchical multi-chain architecture provided by the present invention separates global and local consensus through a hierarchical multi-chain architecture, realizes intelligent migration of hot and cold data through dynamic sharding storage, ensures cross-chain trust through cross-verification of the main and side chains, and collaboratively improves storage efficiency and processing speed, solving the contradiction between the growth of energy data scale and the system performance bottleneck. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a system architecture diagram of an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture; Figure 2 It is a diagram showing the working process of an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture. Specific embodiments

[0018] The following illustrates the embodiments of the present invention through specific examples. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0019] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0020] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0021] Please refer to Figure 1-2, shown is an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture of the present invention. An energy data consensus and efficient storage system based on a hierarchical multi-chain architecture of the present invention includes a main chain consensus module, a side chain cluster module, a storage sharding module, and a data migration module. The main chain consensus module processes energy data through the Byzantine fault tolerance algorithm and generates a global consensus signal containing a timestamp and an energy data fingerprint. The side chain cluster module includes multiple heterogeneous blockchain units, and each unit configures a different consensus algorithm according to the preset energy data type. After receiving the global consensus signal, the side chain cluster module generates a local consensus result and transmits the block header hash value of the side chain cluster module to the main chain consensus module to form a cross-verification chain, and the side chain cluster module forms a first sharding processing instruction. The storage sharding module configures a feature analysis engine to parse the service attributes and access characteristics of energy data in real time and generate a sharding instruction signal containing a storage type identifier. The data migration module performs cold and hot data transfer according to the sharding instruction signal and forms a second sharding processing instruction according to the access frequency of the data migration module. Among them, the storage sharding module receives the first sharding processing instruction and the second sharding processing instruction and corrects the sharding instruction signal in real time.

[0022] As Figure 1As shown in the figure, the present invention realizes the efficient processing and trustworthy interaction of energy data by constructing a multi-dimensional collaborative side-chain cluster architecture. The heterogeneous blockchain units in the side-chain cluster module dynamically select the most optimized consensus mechanism according to the essential differences in energy business scenarios: when processing data with extremely high real-time requirements such as electricity transactions, the Byzantine Fault Tolerance algorithm optimized based on time windows is adopted. By reconstructing the communication timing of consensus nodes, the multi-round message interaction of the traditional Byzantine Fault Tolerance algorithm is compressed to be completed within a preset time threshold. A dynamic timeout detection mechanism is set for each consensus cycle. When the node response delay exceeds the window period, abnormal nodes are automatically removed and a fast recovery process is started to ensure that the real-time transaction is finally confirmed within the time constraint of the power grid dispatching. For the high-trust requirement of energy device status monitoring data, an authoritative proof mechanism for multi-party joint authentication is designed. The device manufacturer provides the hardware identity key, the regulatory agency issues digital certificates, and the operator verifies the operating status. The three-party nodes jointly participate in block verification through threshold signature technology. Only when the verification results of the three parties reach the preset weight ratio, the block is recognized as valid. This mechanism not only prevents the risk of a single entity acting maliciously but also ensures the authenticity and traceability of device data. For the long-term archiving requirement of historical audit data, a Proof of Work mechanism with dynamic difficulty adjustment is adopted. The system automatically adjusts the target difficulty value of hash calculation according to the total amount of archived data and the computing power distribution of storage nodes. When it detects a sharp increase in data scale, the computing complexity is appropriately reduced to avoid storage delays, while when the risk of network attacks increases, the difficulty value is increased to strengthen security protection. At the level of sharding processing, the side-chain cluster module implements an intelligent sharding strategy according to the characteristics of energy data streams: for real-time data streams, an adaptive time window sharding mode is adopted. The initial value of the window is set to the reference value of typical network latency. By continuously monitoring the data transmission rate and node load status, the window coverage range is dynamically expanded or contracted. When network congestion is detected, the window is automatically reduced to increase the sharding generation frequency to ensure that key data is processed first. For batch historical data, the internal attributes of the data are identified based on a feature extraction engine. By analyzing metadata such as device numbers, geographical location tags, and business type identifiers, a multi-dimensional sharding key space is constructed, and data with similar characteristics is clustered and stored to form logically coherent data sharding units. At the level of cross-chain interaction security, the block header hash value generated by the side-chain is protected by composite encryption before being transmitted to the main chain: first, the hash value is encrypted based on the asymmetric encryption algorithm of the main-chain node, and a first-layer ciphertext is generated using an encryption scheme based on elliptic curve cryptography; then, the national cryptographic standard algorithm specified by the side-chain regulatory party is superimposed for secondary reinforcement, and the first-layer ciphertext is diffused and confused through the block cipher working mode; the final encrypted anchor point not only has the security of the traditional encryption system but also enhances the anti-quantum computing attack ability through algorithm combination design to ensure the long-term reliability of the cross-verification chain in the future evolution of cryptographic technologies.

[0023] Furthermore, the system realizes the dynamic optimization of the energy data storage strategy and the precise allocation of resources through an intelligent storage sharding engine. The feature analysis engine of the storage sharding module adopts multi-modal data processing technology to construct an analysis model that combines semantic recognition and heat prediction: at the semantic level, it uses natural language processing technology to parse the business description fields of energy data, identifies key business tags such as power grid dispatching instructions, fault alarm codes, carbon emission rights trading identifiers, etc., and establishes a data importance grading system; at the heat prediction level, it introduces a time decay function to quantify the data access value, calculates the decay coefficient based on the time difference between the recent data access time and the current time, and constructs a heat prediction curve in combination with the historical access frequency to accurately predict the future access probability of the data. Based on the above analysis results, the system divides the energy data into a three-level storage system: the high-response layer deploys a distributed cluster constructed by non-volatile high-speed storage media, uses the memory-mapped file technology to achieve microsecond-level data access, and is specially used to store real-time monitoring data and high-frequency trading records; the middle layer configures a balanced hybrid storage pool to integrate the advantages of solid-state storage and high-speed mechanical disks, and is used to store operation and maintenance logs accessed periodically and business data with medium heat; the archival layer uses a mechanical storage array with high-density redundant coding, and improves the storage density through advanced data chunking and erasure coding technology, which is suitable for long-term storage of historical audit data and backup files with low-frequency calls. When receiving the sharding instruction from the side-chain cluster module, the storage sharding module starts a dynamic adjustment mechanism: if it detects that the side-chain sharding granularity switches from the fixed-capacity mode to the dynamic time window mode, it synchronously increases the sampling frequency of the feature analysis engine, captures more fine-grained access mode changes by increasing the number of data collection points; at the same time, according to the real-time feedback of storage performance indicators such as read and write latency, space utilization rate, and error rate of each storage layer, it dynamically adjusts the capacity allocation ratio between storage layers. When the utilization rate of the high-response layer exceeds the warning threshold, it automatically demotes some edge hot data to the middle layer to maintain the access performance of core data.

[0024] In an embodiment of the present invention, through an intelligent hierarchical migration mechanism, efficient transfer of hot and cold data and optimal utilization of storage resources are achieved. The data migration module is built with a multi-dimensional monitoring system that continuously tracks the data access characteristics of each storage layer: when the heat value of a specific data shard continuously remains below the dynamic threshold within consecutive monitoring cycles, the hierarchical migration process is triggered. The migration process is divided into four stages: First, the target data is marked as pending migration, and the system creates a temporary buffer between storage layers to transfer the data copy to the buffer for preprocessing; then, a lossless compression algorithm is executed, and a compression technique based on dictionary coding is used to eliminate data redundancy while maintaining the integrity of the data structure for subsequent retrieval; subsequently, attribute-based permission encapsulation is implemented, and the data access permission is bound to a preset attribute through an encryption algorithm. For example, only regulatory nodes or data owners holding specific digital certificates are allowed to decrypt and access the data, ensuring data confidentiality during the migration process; finally, the formal transfer operation is initiated to write the processed data packet to the target storage layer and update the global storage index. The priority of the migration task is dynamically calculated by an intelligent decision algorithm. The system real-time collects parameters such as the remaining capacity of the storage layer, network bandwidth utilization rate, and migration queue depth to construct a multi-dimensional evaluation matrix: when it is detected that the storage pressure of the archive layer is approaching the critical value, the algorithm automatically raises the migration priority of large-volume and low-heat data and enables parallel transmission channels to accelerate data transfer. At the same time, a maximum execution time threshold for a single task is set to prevent individual large migration tasks from blocking system resources. To ensure the security and reliability of the migration process, the system attaches integrity verification information during data packet transfer and adopts an aggregation proof mechanism based on group signature technology, allowing the verifier to verify the integrity of batch data through a single signature, significantly reducing the verification overhead. In addition, the archive layer implements full-life cycle management, and starts an automated destruction process for data that exceeds the retention period. Before destruction, a digital destruction certificate needs to be applied to the main chain consensus module. This certificate contains the data fingerprint, operation timestamp, and authorized node signature. After the destruction operation is completed, irreversible deposit information is recorded in the cross-verification chain to form a complete audit trail.

[0025] As Figure 1As shown in the figure, by constructing a full-process data governance system, refined control of energy data from access to storage is achieved. In the data access stage, the storage sharding module starts a multi-level preprocessing pipeline: First, a data cleaning unit is deployed to screen the quality of the original energy flow. Based on the preset sensor range threshold and data pattern recognition algorithm, abnormal values beyond physical possibility (such as a negative power factor or a voltage magnitude exceeding the live network standard) are filtered, and at the same time, duplicate data packets caused by network jitter are eliminated. Subsequently, it enters the format standardization unit, which converts heterogeneous data formats from different device manufacturers (including binary protocols, custom messages, and unstructured logs) into a unified semantic description framework. By defining a device type mapping table and dimension conversion rules, it ensures that all data has resolvable structured features. Finally, through the metadata extraction unit, the data content is deeply parsed, and core metadata including timestamp sequences, device unique identifiers, and geographic coordinate information is extracted to generate a shard index strongly associated with the business scenario. When multiple side-chain concurrent requests for the same data resource are detected during the shard instruction generation process, the system starts a conflict arbitration mechanism: Based on the preset business priority matrix (such as grid security control instructions taking precedence over ordinary metering data, and real-time transaction records taking precedence over historical archiving requests), the storage resource allocation strategy is dynamically adjusted, and at the same time, a storage lock request signal is sent to the main-chain consensus module. The main chain generates a global storage lock identifier through a smart contract and broadcasts it to the relevant side-chains to prevent other side-chains from preempting the same resource. A two-way state synchronization channel is established between the storage sharding module and the data migration module. Through the heartbeat monitoring protocol, the health status and load information of the storage nodes are exchanged at fixed intervals. When it is detected that a certain storage node fails to respond to the heartbeat packet continuously for multiple times, it is automatically marked as a faulty state and the data replica reconstruction process is triggered: First, the affected data shards and their redundant check block distribution positions are located according to the shard index, then the optimal data source is elected from the surviving nodes for replica regeneration, and finally, the new replicas are distributed to the preset geographically redundant storage nodes. The entire process completes self-healing of faults while ensuring service continuity.

[0026] As Figure 2As shown, through an innovative consensus optimization mechanism, the efficient maintenance of global data fingerprints and the trustworthy arbitration of cross-chain conflicts are achieved. The main-chain consensus module adopts an incremental fingerprint update technique. When the storage sharding module modifies the sharding strategy, causing a change in data distribution, the system only performs local hash recomputation on the affected data blocks: First, locate the leaf node position of the changed data block in the global Merkle tree, and update the hash values of the path nodes layer by layer from bottom to top until the root node. Subsequently, submit the difference value between the old and new root hashes to the consensus network for quick verification. The verification nodes only need to compare the hash chain of the difference path to confirm the legality of the change, reducing the consensus overhead by approximately 80% compared to full recomputation. The generation of the global consensus signal introduces a multi-party joint signature mechanism, which is jointly participated by three types of core nodes: The main-chain verification nodes are responsible for the signature weight of the basic consensus result, the side-chain representative nodes provide endorsements for business scenario compliance, and the regulatory nodes inject policy compliance verification elements. The three parties participate in the threshold signature process according to the dynamic weight ratio. The signature scheme sets the minimum number of participating nodes threshold to ensure that even if some nodes are offline, an effective consensus signal can still be generated. The cross-chain arbitration smart contract built into the main chain continuously monitors the consistency status of the cross-verification chain. When an irreconcilable deviation is detected between the block header hash of a certain side chain and the main-chain record, it automatically starts a multi-stage processing process: First, freeze the write permission of the problematic side chain and issue an exception status notice on the main chain. Subsequently, retrieve the latest valid state snapshot of the side chain from the backup network, generate an integrity proof by comparing multiple geographically dispersed backup copies, and finally select the consensus version confirmed by the majority of nodes for state rollback. During the rollback process, the global data fingerprint is updated synchronously to maintain system consistency. In addition, the system implements intelligent lifecycle management at the data archiving layer, starting an automated destruction process for data shards that reach the preset retention period. The destruction operation requires authorization verification through a digital certificate issued by the main-chain consensus module. The certificate contains elements such as data fingerprints, operation timestamps, and authorized node signatures. After the destruction is completed, an irreversible operation voucher containing the Merkle proof is recorded in the cross-verification chain to ensure the auditability of the entire lifecycle operation.

[0027] Furthermore, the present invention realizes the dynamic optimization of the entire system resources and the autonomous fault tolerance of abnormal working conditions by constructing an adaptive elastic architecture. An elastic capacity planning algorithm is introduced into the storage sharding module to monitor the utilization fluctuation trend of each storage layer in real time. When the utilization rate of the high-response layer continuously exceeds the warning threshold, the capacity expansion process is automatically activated: dynamically add new nodes to the distributed storage cluster, and redistribute data shards using the consistent hashing algorithm to ensure that the service is not interrupted during the expansion process; when it is detected that the utilization rate of the archival layer is lower than the economic operation threshold for a long time, the node sleep mechanism is started, redundant nodes are switched to the low-power state and data is migrated to active nodes to reduce the overall energy consumption. The data migration module integrates an intelligent traffic scheduling engine, which analyzes the network topology status and bandwidth utilization in real time during the transfer process and automatically selects the optimal transmission path: migration tasks with high real-time requirements are preferentially assigned dedicated network channels, and batch migration tasks adopt a time-sharing peak-shifting transmission strategy, while implementing dynamic bandwidth allocation to ensure that the transmission quality of critical business links is not affected. The system exception handling mechanism adopts a hierarchical response strategy: when a node-level failure is detected, the local replica recovery process is started; when a regional network interruption is identified, switch to the disaster recovery data center to take over the service; when a full-chain consensus attack is encountered, trigger the Byzantine node isolation program, identify malicious nodes through the behavior analysis model and implement dynamic blacklist control. In addition, the system implements full-link performance optimization. In the storage sharding stage, predictive cache preheating technology is adopted to preload high-probability accessed data to the cache layer based on historical access patterns; in the consensus process, an asynchronous verification mechanism is introduced to decouple transaction verification and block generation to improve the system throughput; in the cross-chain interaction layer, compressed relay nodes are deployed to aggregate batch verification requests and reduce network communication overhead. Through the above multi-dimensional optimization measures, the system can still maintain a sub-second response delay and 99.99% service availability in the scenario of explosive growth of energy data scale.

[0028] An energy data consensus and efficient storage system based on a hierarchical multi-chain architecture of the present invention separates global and local consensus through the hierarchical multi-chain architecture, realizes intelligent migration of hot and cold data through dynamic sharding storage, and ensures cross-chain credibility through cross-verification of the main and side chains, collaboratively improving storage efficiency and processing speed, and solving the contradiction between the growth of energy data scale and the system performance bottleneck.

[0029] Therefore, through an energy data consensus and efficient storage system based on a hierarchical multi-chain architecture of the present invention, the problems of low energy data storage efficiency, slow consensus speed, and difficult cross-chain verification can be solved.

[0030] The above embodiments are only illustrative of the principles and effects of the present invention and are not intended to limit the present invention. Any person familiar with this technology can modify or change the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or changes made by those with ordinary knowledge in the technical field without departing from the spirit and technical idea disclosed by the present invention should still be covered by the claims of the present invention.

Claims

1. An energy data consensus and efficient storage system based on a layered multi-chain architecture, characterized by: include: A main chain consensus module, which processes energy data through a Byzantine fault-tolerant algorithm and generates a global consensus signal including a timestamp and energy data fingerprint; A sidechain cluster module, wherein the sidechain cluster module comprises a plurality of heterogeneous blockchain units, each unit is configured with a different consensus algorithm according to a preset energy data type, the sidechain cluster module generates a local consensus result after receiving the global consensus signal, and transmits the block header hash value of the sidechain cluster module to the main chain consensus module to form a cross-validation chain, and the sidechain cluster module forms a first shard processing instruction; A storage sharding module, wherein the storage sharding module is configured with a feature analysis engine to analyze the business attributes and access features of the energy data in real time and generate a sharding instruction signal including a storage type identifier; A data migration module, which performs cold and hot data transfer according to the sharding instruction signal and forms a second sharding processing instruction according to the access frequency of the data migration module; The storage sharding module receives the first sharding processing instruction and the second sharding processing instruction and modifies the sharding instruction signal in real time.

2. According to claim 1, an energy data consensus and efficient storage system based on a layered multi-chain architecture is characterized in that: The heterogeneous blockchain units of the side chain cluster module dynamically adapt to different consensus mechanisms according to the type of energy data. When processing real-time transaction data, a Byzantine fault-tolerant algorithm based on time window optimization is adopted. The response speed is improved by compressing consensus rounds and presetting timeout mechanisms. When processing device status data, an authoritative proof mechanism of multi-party joint authentication is adopted, and the device manufacturer, regulatory agency and operator jointly participate in node verification. When processing historical audit data, a proof-of-work mechanism with dynamic difficulty adjustment is adopted, and the calculation complexity is automatically adjusted according to the data scale. When the side chain cluster module generates the first sharding processing instruction, an adaptive time window sharding strategy is adopted for the real-time data stream, and the window size is dynamically expanded and contracted with the network status. A feature extraction sharding mode is adopted for batch data, and a sharding key is generated based on the data source attribute. The block header hash value undergoes multi-layer encryption processing before being transmitted to the main chain consensus module, including basic encryption using the asymmetric encryption algorithm of the main chain consensus module node, and secondary reinforcement is performed by superimposing the national secret algorithm of the side chain regulator to form an encryption anchor with anti-quantum characteristics.

3. According to claim 1, an energy data consensus and efficient storage system based on a layered multi-chain architecture is characterized in that: The feature analysis engine of the storage sharding module generates a sharding strategy through semantic recognition and heat prediction models, and performs multi-dimensional feature analysis on the energy data, including extracting business-critical tags, calculating access heat values ​​after time decay, and identifying data source types; the sharding instruction signal includes a multi-level storage identifier, which divides the data into a high-response layer, an intermediate layer, and an archive layer, which correspond to storage media of different performances, respectively. The high-response layer adopts a low-latency solid-state storage cluster, the intermediate layer deploys a balanced storage pool, and the archive layer adopts a mechanical storage array with high-density redundant coding; After receiving the first sharding instruction from the side chain cluster module, the storage sharding module dynamically adjusts the feature sampling frequency according to the change of sharding granularity, and optimizes the storage level configuration based on real-time feedback.

4. According to claim 1, an energy data consensus and efficient storage system based on a layered multi-chain architecture is characterized in that: The cold and hot data transfer process of the data migration module includes an intelligent hierarchical migration mechanism. When the temperature of the data migration module is continuously lower than the set threshold, a phased migration process is triggered: first, the data is transferred to a temporary buffer for lossless compression and attribute-based permission encapsulation to ensure that only authorized entities can access it; The migration task priority is dynamically calculated based on the storage layer load, network status and data volume. When it is detected that the storage pressure exceeds the standard, the emergency mode is automatically activated, and large-volume low-temperature data is migrated first and parallel transmission channels are enabled. At the same time, the maximum execution time of a single task is limited to avoid resource competition.

5. According to claim 1, an energy data consensus and efficient storage system based on a layered multi-chain architecture is characterized in that: The main chain consensus module adopts block fingerprint aggregation technology when generating a global consensus signal, divides the energy data by device group, calculates the hash tree structure, and forms a global data fingerprint through layer-by-layer aggregation; the construction of the cross-validation chain includes a version synchronization mechanism, and the main chain consensus module and the side chain cluster module realize block status alignment by embedding version identifiers, and triggers a forced synchronization process if it is detected that the version deviation exceeds the allowable range; The cross-chain verification process uses privacy protection technology to verify data validity through zero-knowledge proof, ensuring that credible confirmation is completed without leaking transaction content.

6. The energy data consensus and efficient storage system based on a layered multi-chain architecture according to claim 1 is characterized in that: The real-time correction logic of the storage sharding module integrates a multi-objective optimization strategy, incorporates the consensus constraints and migration cost restrictions of the side chain cluster module into a dynamic scoring model, and adjusts the sharding rules based on real-time, storage efficiency and reliability weights; when it is identified that the energy data contains security-critical tags, the indestructible storage mode is forcibly enabled, redundant copies are saved in isolated nodes in multiple locations, and migration operations are prohibited until the preset security lifecycle ends; The feature analysis engine continuously optimizes sharding decisions through machine learning and regularly updates the strategy model to adapt to changes in system performance.

7. The energy data consensus and efficient storage system based on a layered multi-chain architecture according to claim 1 is characterized in that: The side chain cluster module implements a data redundancy protection mechanism in sharding processing, generates distributed check blocks for each data shard, and the number of check blocks is dynamically configured according to the storage level and distributedly stored in nodes in different regions; the block header hash transmission adopts an intelligent routing strategy, selects the optimal network path according to the business type and implements transmission quality monitoring, and automatically switches to the backup link when the communication quality is detected to be deteriorated; The local consensus process of the sidechain cluster module introduces a lightweight verification mechanism, which only requires verifying the key hash path to participate in the consensus.

8. The energy data consensus and efficient storage system based on a layered multi-chain architecture according to claim 1 is characterized in that: The data migration module implements full-link resource monitoring, dynamically collects storage and network indicators during the transfer process, and automatically triggers current limiting protection when it detects that the resource load exceeds the safety threshold, and proportionally reduces the migration rate; The migration data package is attached with an aggregated integrity certificate, and group signature technology is used to ensure that the transfer process cannot be tampered with; the archive layer implements automated lifecycle management and executes a secure destruction process for expired data. Before destruction, it is necessary to obtain a digital certificate issued by the main chain and record irreversible operation credentials.

9. The energy data consensus and efficient storage system based on a layered multi-chain architecture according to claim 1 is characterized in that: The storage sharding module performs a preprocessing process during the data access phase, including filtering abnormal data, standardizing formats, and extracting metadata indexes; The sharding instruction generation process includes a conflict arbitration mechanism. When multiple side chains request the same data resource, storage permissions are allocated based on business priorities and locking signals are issued through the main chain. A state synchronization channel is established between the storage sharding module and the data migration module to periodically exchange storage node information and trigger data replica reconstruction when a node is abnormal.

10. The energy data consensus and efficient storage system based on a layered multi-chain architecture according to claim 1 is characterized in that: The main chain consensus module adopts incremental fingerprint update technology. When the sharding strategy is revised, only the hash aggregation tree of the affected data block is recalculated and synchronized to the consensus network through a fast verification mechanism. The generation of the global consensus signal adopts a multi-party joint signature mechanism, in which the main chain nodes, side chain representatives and regulatory nodes participate in the signature according to the weight, and the minimum node number threshold must be met before it can take effect. The main chain has a built-in cross-chain arbitration contract. When there is an irreparable conflict between the side chain data and the cross-validation chain, the side chain write permission is frozen and the best copy is selected from the backup for status recovery.

Images