An energy data consensus and efficient storage system based on a layered multi-chain architecture

Abstract

This invention discloses an energy data consensus and high-efficiency storage system based on a layered multi-chain architecture, comprising 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 using a Byzantine fault-tolerant algorithm and generates a global consensus signal containing timestamps and energy data fingerprints. The side chain cluster module receives the global consensus signal, generates a local consensus result, and forms a first sharding processing instruction. The storage sharding module is configured with a feature analysis engine to parse the business attributes of the energy data in real time, generating a sharding instruction signal containing a storage type identifier. The data migration module executes cold and hot data transfer according to the sharding instruction signal and forms a second sharding processing instruction based on the access frequency of the data migration module. This energy data consensus and high-efficiency storage system based on a layered multi-chain architecture can solve the problems of low energy data storage efficiency, slow consensus speed, and difficulty in cross-chain verification.

Description

Technical Field

[0001] This invention relates to the innovative application of blockchain technology in the field of energy data management, specifically to an energy data consensus and efficient storage system based on a layered multi-chain architecture. Background Technology

[0002] With the rapid development of the energy internet and the construction of new power systems, the energy industry is facing the dual challenges of a surge in data volume and increasingly complex processing demands. The amount of data generated by business scenarios such as electricity trading, smart meters, new energy equipment monitoring, and carbon emission management is growing exponentially, encompassing multimodal information including real-time streaming data, high-precision sensor data, structured transaction records, and unstructured operation and maintenance logs. While traditional centralized data management systems can provide basic storage, they have inherent deficiencies in data reliability, tamper resistance, and cross-entity collaboration.

[0003] Blockchain technology, due to its distributed and immutable characteristics, has been introduced into the energy sector. However, existing blockchain architectures have revealed significant limitations when adapting to the specific needs of the energy industry: First, the homogeneous consensus mechanism of a single chain is unable to cope with the heterogeneous nature of energy data. For example, electricity trading scenarios require processing thousands of real-time transactions per second, while equipment status monitoring data requires high-frequency on-chain recording and rapid 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, severely impacting the timeliness of critical operations such as power grid dispatch. Second, there are trust transmission barriers to data interoperability across business chains. The energy industry chain involves multiple stages, including power generation, transmission and distribution, electricity consumption, and carbon trading. Data from each stage needs cross-chain interaction verification, but existing cross-chain solutions mostly rely on relay chains or hash time locks. The verification process requires multiple inter-chain communications and complex state synchronizations, which not only increases latency by more than a 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 entire lifecycle management of energy data. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide an energy data consensus and high-efficiency storage system based on a layered multi-chain architecture to solve the problems of low energy data storage efficiency, slow consensus speed, and difficulty in cross-chain verification. This invention separates global and local consensus through a layered multi-chain architecture, achieves intelligent migration of hot and cold data through dynamic sharding storage, and ensures cross-chain trustworthiness through main-sidechain cross-verification, thereby synergistically improving storage efficiency and processing speed and resolving the contradiction between the growth of energy data scale and system performance bottlenecks.

[0005] This invention provides an energy data consensus and high-efficiency storage system based on a layered multi-chain architecture, comprising:

[0006] The main chain consensus module processes energy data using the Byzantine fault-tolerant algorithm and generates a global consensus signal containing timestamps and energy data fingerprints.

[0007] The sidechain cluster module contains multiple heterogeneous blockchain units. Each unit is configured with a different consensus algorithm according to the preset energy data type. After receiving the global consensus signal, the sidechain cluster module generates a local consensus result and transmits the block header hash value of the sidechain cluster module to the main chain consensus module to form a cross-validation chain. The sidechain cluster module forms the first sharding processing instruction.

[0008] The storage sharding module is configured with a feature analysis engine to analyze the business attributes and access characteristics of energy data in real time, and generate sharding instruction signals containing storage type identifiers.

[0009] The data migration module performs cold and hot data transfer according to the sharding instruction signal, and generates a second sharding processing instruction based on the access frequency of the data migration module.

[0010] 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.

[0011] In one embodiment of the present invention, the heterogeneous blockchain units of the sidechain cluster module dynamically adapt to different consensus mechanisms according to the energy data type. When processing real-time transaction data, a Byzantine fault-tolerant algorithm based on time window optimization is adopted to improve response speed by compressing consensus rounds and setting a timeout mechanism. When processing device status data, an authoritative proof mechanism with multi-party joint certification is adopted, with the device manufacturer, regulatory agency and operator jointly participating in node verification. When processing historical audit data, a proof-of-work mechanism with dynamic difficulty adjustment is adopted, which automatically adjusts the computational complexity according to the data scale. When the sidechain cluster module generates the first sharding processing instruction, an adaptive time window sharding strategy is adopted for real-time data streams, with the window size dynamically scaling with the network status. For batch data, a feature extraction sharding mode is adopted, and sharding keys are generated based on data source attributes. 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 by superimposing the national cryptographic algorithm of the sidechain regulator, forming an encryption anchor with quantum resistance.

[0012] In one embodiment of the present invention, the feature analysis engine of the storage sharding module generates a sharding strategy through semantic recognition and heat prediction models, performs multi-dimensional feature analysis on energy data, including extracting key business tags, calculating access heat values ​​after time decay, and identifying data source types; the sharding instruction signal contains multi-level storage identifiers, dividing the data into a high-response layer, an intermediate layer, and an archive layer, each corresponding to storage media with different performance. The high-response layer uses a low-latency solid-state storage cluster, the intermediate layer deploys a balanced storage pool, and the archive layer uses a high-density redundant coded mechanical storage array; after receiving the first sharding instruction from the sidechain cluster module, the storage sharding module dynamically adjusts the feature sampling frequency according to the change in sharding granularity, and optimizes the storage layer configuration based on real-time feedback.

[0013] In one embodiment of the present invention, the hot and cold data transfer process of the data migration module includes an intelligent hierarchical migration mechanism. When the hotness of the data migration module is detected to be continuously lower than a 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 priority of the migration task is dynamically calculated based on the storage layer load, network status and data volume. When the storage pressure is detected to be excessive, an emergency mode is automatically activated to prioritize the migration of large-volume low-hot data and enable parallel transmission channels, while limiting the maximum execution time of a single task to avoid resource contention.

[0014] In one embodiment of the present invention, the main chain consensus module uses block fingerprint aggregation technology to generate a global consensus signal. Energy data is divided into device groups and a hash tree structure is calculated. A global data fingerprint is formed by aggregating layer by layer. 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 state alignment by embedding version identifiers. If a version deviation is detected to exceed the allowable range, a forced synchronization process is triggered. The cross-chain verification process adopts privacy protection technology. Zero-knowledge proof is used to verify the validity of the data, and a trusted confirmation is completed under the premise that the transaction content is not disclosed.

[0015] In one 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 sidechain cluster module and migration cost limits into the dynamic scoring model, and adjusts the sharding rules by comprehensively considering the weights of real-time performance, storage efficiency, and reliability; when energy data is identified to contain a safety-critical label, a tamper-proof storage mode is forcibly enabled, redundant copies are saved on isolated nodes in multiple locations and migration operations are prohibited until the preset safety lifecycle ends; the feature analysis engine continuously optimizes sharding decisions through machine learning and periodically updates the strategy model to adapt to changes in system performance.

[0016] In one embodiment of the present invention, the sidechain cluster module implements a data redundancy protection mechanism in the sharding process, generating a distributed verification block for each data shard. The number of verification blocks is dynamically configured according to the storage level and distributed across different regional nodes. The block header hash transmission adopts an intelligent routing strategy, selecting the optimal network path according to the service type and implementing transmission quality monitoring. When communication quality degradation is detected, the backup link is automatically switched. The local consensus process of the sidechain cluster module introduces a lightweight verification mechanism, which only needs to verify the key hash path to participate in the consensus.

[0017] In one embodiment of the present invention, the data migration module implements full-link resource monitoring, dynamically collects storage and network indicators during the transfer process, and automatically triggers rate limiting protection when the resource load exceeds the safety threshold, reducing the migration rate proportionally; the migration data packet is attached with aggregated integrity proof, and the transfer process is ensured to be tamper-proof through group signature technology; the archiving layer implements automated lifecycle management, performs a secure destruction process for expired data, and obtains a digital certificate issued by the main chain and records irreversible operation credentials before destruction.

[0018] In one embodiment of the present invention, 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, in which storage permissions are allocated according to business priority and a locking signal is issued through the main chain when multiple sidechains request the same data resource; a state synchronization channel is established between the storage sharding module and the data migration module, and storage node information is exchanged periodically and data copy reconstruction is triggered when a node is abnormal.

[0019] In one embodiment of the present invention, the main chain consensus module adopts 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 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 their weights. It can only take effect if the minimum number of nodes threshold is met. 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 elected from the backup for state restoration.

[0020] This invention provides an energy data consensus and high-efficiency storage system based on a layered multi-chain architecture. By separating global and local consensus through the layered multi-chain architecture, dynamic sharding storage enables intelligent migration of hot and cold data, and cross-verification between main and side chains ensures cross-chain trustworthiness. This collaboratively improves storage efficiency and processing speed, resolving the contradiction between the growth of energy data scale and system performance bottlenecks. Attached Figure Description

[0021] 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.

[0022] Figure 1 This is a system architecture diagram of an energy data consensus and high-efficiency storage system based on a layered multi-chain architecture;

[0023] Figure 2 This diagram illustrates the workflow of an energy data consensus and efficient storage system based on a layered multi-chain architecture. Detailed Implementation

[0024] The following specific examples illustrate the implementation of the present invention. 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, and 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, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0025] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0026] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0027] Please see Figure 1-2The diagram illustrates an energy data consensus and efficient storage system based on a layered multi-chain architecture, as described in this invention. This system 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 using a Byzantine fault-tolerant algorithm and generates a global consensus signal containing timestamps and energy data fingerprints. The side chain cluster module comprises multiple heterogeneous blockchain units, each configured with a different consensus algorithm based on a preset energy data type. After receiving the global consensus signal, the side chain cluster module generates a local consensus result and transmits its block header hash value to the main chain consensus module to form a cross-validation chain. The side chain cluster module then generates a first sharding processing instruction. The storage sharding module is equipped with a feature analysis engine to analyze the business attributes and access characteristics of the energy data in real time, generating 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 generates a second sharding processing instruction based on the access frequency of the data migration module. The storage sharding module receives the first and second sharding processing instructions and corrects the sharding instruction signal in real time.

[0028] like Figure 1As shown, this invention achieves efficient processing and reliable interaction of energy data by constructing a multi-dimensional collaborative sidechain cluster architecture. The heterogeneous blockchain units in the sidechain cluster module dynamically select the optimal consensus mechanism based on the inherent differences in energy business scenarios: when processing data with extremely high real-time requirements, such as electricity trading, a Byzantine fault-tolerant algorithm optimized based on a time window is adopted. By reconstructing the communication sequence of consensus nodes, the multi-round message interaction of the traditional Byzantine fault-tolerant algorithm is compressed to 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 rapid recovery process is initiated, ensuring that real-time transactions are finally confirmed within the grid dispatch time constraints. For the high reliability requirements of energy equipment status monitoring data, a multi-party joint authentication authoritative proof mechanism is designed, with the equipment manufacturer... The system involves three parties: the vendor provides the hardware identity key, the regulatory agency issues the digital certificate, and the operator verifies the operational status. These three parties jointly participate in block verification using threshold signature technology. Only when the verification results of the three parties reach a preset weight ratio is the block considered valid. This mechanism not only prevents the risk of malicious actions by a single entity but also ensures the authenticity and traceability of device data. For the long-term storage needs of historical audit data, a dynamic difficulty-adjusted proof-of-work mechanism is adopted. The system automatically adjusts the target difficulty value of hash calculation based on the total amount of archived data and the computing power distribution of storage nodes. When a surge in data volume is detected, the computational complexity is appropriately reduced to avoid storage delays, while the difficulty value is increased to strengthen security when the risk of network attacks rises. At the sharding processing level, the sidechain cluster module implements an intelligent sharding strategy based on the characteristics of energy data streams: For real-time data streams, an adaptive time window sharding mode is adopted, with the initial window value set as a baseline value for typical network latency. By continuously monitoring the data transmission rate and node load status, the window coverage is dynamically expanded or contracted. When network congestion is detected, the window is automatically reduced to increase the sharding generation frequency, ensuring that critical data is processed first. For batch historical data, the feature extraction engine identifies the inherent attributes of the data. By analyzing metadata such as device number, geographic location tag, and business type identifier, a multi-dimensional sharding key space is constructed, and data with similar characteristics are clustered and stored to form logically coherent data sharding units. At the cross-chain interaction security level, the block header hash value generated by the sidechain is protected by composite encryption before being transmitted to the main chain: First, the hash value is encrypted using the asymmetric encryption algorithm of the main chain node, and the first layer of ciphertext is generated using an encryption scheme based on elliptic curve cryptography; then, a second layer of reinforcement is carried out by superimposing the national cryptographic standard algorithm specified by the sidechain regulator, and the first layer of ciphertext is subjected to diffusion obfuscation processing through the block cipher working mode; the final encrypted anchor not only has the security of the traditional encryption system, but also enhances the resistance to quantum computing attacks through algorithm combination design, ensuring the long-term reliability of the cross-validation chain in the future evolution of cryptographic technology.

[0029] Furthermore, the system utilizes an intelligent storage sharding engine to achieve dynamic optimization of energy data storage strategies and precise resource allocation. The feature analysis engine of the storage sharding module employs multimodal data processing technology to construct an analysis model combining semantic recognition and popularity prediction: at the semantic level, it uses natural language processing to parse the business description fields of energy data, identifying key business tags such as grid dispatch instructions, fault alarm codes, and carbon emission trading identifiers, and establishing a data importance classification system; at the popularity prediction level, it introduces a time decay function to quantify the value of data access, calculates the decay coefficient based on the time difference between the most recent access time and the current time, and constructs a popularity prediction curve based on historical access frequencies to accurately predict the future access probability of the data. Based on the above analysis, the system divides energy data into a three-tier storage architecture: the high-response layer deploys a distributed cluster built with non-volatile high-speed storage media, using memory-mapped file technology to achieve microsecond-level data access, specifically for storing real-time monitoring data and high-frequency transaction records; the middle layer is configured with a balanced hybrid storage pool, integrating the advantages of solid-state storage and high-speed mechanical disks, used to store periodically accessed operation and maintenance logs and moderately frequent business data; the archive layer uses a high-density redundant coding mechanical storage array, improving storage density through advanced data block and erasure coding technology, suitable for long-term storage of historical audit data and low-frequency backup files. When a sharding instruction is received from the sidechain cluster module, the storage sharding module initiates a dynamic adjustment mechanism: if it detects that the sidechain sharding granularity has switched from a fixed capacity mode to a dynamic time window mode, it synchronously increases the sampling frequency of the feature analysis engine to capture more granular access pattern changes by increasing the number of data collection points; at the same time, based on real-time feedback of storage performance indicators, such as read / write latency, space utilization, 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 degrades some edge hot data to the intermediate layer to maintain the access performance of core data.

[0030] In one embodiment of the present invention, an intelligent hierarchical migration mechanism is used to achieve efficient transfer of hot and cold data and optimal utilization of storage resources. The data migration module has a built-in multi-dimensional monitoring system that continuously tracks the data access characteristics of each storage layer: when the popularity value of a specific data fragment is continuously lower than the dynamic threshold within a continuous monitoring period, the hierarchical migration process is triggered. The migration process is divided into four stages: First, the target data is marked as to be migrated, and the system creates a temporary buffer between storage layers, transferring data copies to the buffer for preprocessing; then, a lossless compression algorithm is executed, using dictionary-based compression technology to eliminate data redundancy while maintaining the integrity of the data structure for subsequent retrieval; subsequently, attribute-based permission encapsulation is implemented, binding data access permissions to preset attributes through encryption algorithms, such as only allowing supervisory nodes holding specific digital certificates or data owners to decrypt and access data, ensuring data confidentiality during the migration process; finally, the formal transfer operation is initiated, writing the processed data packets to the target storage layer and updating the global storage index. Migration task priorities are dynamically calculated by an intelligent decision-making algorithm. The system collects parameters such as remaining storage capacity, network bandwidth utilization, and migration queue depth in real time to construct a multi-dimensional evaluation matrix. When the storage pressure of the archive layer approaches a critical value, the algorithm automatically increases the migration priority of large-volume, low-frequency data and enables parallel transmission channels to accelerate data transfer. Simultaneously, 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, employing an aggregation proof mechanism based on group signature technology. This allows verifiers to verify the integrity of batch data through a single signature, significantly reducing verification overhead. Furthermore, the archive layer implements full lifecycle management, initiating an automated destruction process for data exceeding its retention period. Before destruction, a digital destruction certificate must be applied for from the main chain consensus module. This certificate contains data fingerprints, operation timestamps, and authorized node signatures. After the destruction operation is completed, irreversible evidence information is recorded in the cross-validation chain, forming a complete audit trail.

[0031] like Figure 1As shown, by constructing a full-process data governance system, refined management and control of energy data from access to storage has been achieved. During the data access phase, the storage sharding module initiates a multi-level preprocessing pipeline: First, a data cleaning unit is deployed to screen the raw energy stream for quality. Based on preset sensor range thresholds and data pattern recognition algorithms, it filters out abnormal values ​​exceeding physical possibilities (such as negative power factors or voltage levels exceeding current network standards), while eliminating duplicate data packets caused by network jitter. Then, the data enters the format standardization unit, which converts heterogeneous data formats from different equipment manufacturers (including binary protocols, custom messages, and unstructured logs) into a unified semantic description framework. By defining a device type mapping table and unit conversion rules, it ensures that all data possesses parsable structured characteristics. Finally, the metadata extraction unit deeply analyzes the data content, extracting core metadata such as timestamp sequences, unique device identifiers, and geographic coordinate information, generating a sharding index strongly correlated with the business scenario. When multiple sidechains concurrently request the same data resource during the sharding instruction generation process, the system initiates a conflict arbitration mechanism: based on a preset business priority matrix (e.g., power grid safety control instructions take precedence over ordinary metering data, and real-time transaction records take precedence over historical archive requests), the system dynamically adjusts the storage resource allocation strategy and simultaneously sends a storage lock request signal 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 sidechains to prevent other sidechains from preempting the same resource. A two-way state synchronization channel is established between the storage sharding module and the data migration module. Through a heartbeat monitoring protocol, the system exchanges the health status and load information of storage nodes at fixed intervals. When a storage node is detected to have failed to respond to heartbeat packets multiple times consecutively, it is automatically marked as faulty and a data replica reconstruction process is triggered: first, the affected data shard and its redundant check block distribution location are located based on the sharding index; then, the optimal data source is elected from the surviving nodes for replica regeneration; finally, the new replica is distributed to preset geographically redundant storage nodes. The entire process completes fault self-healing while ensuring service continuity.

[0032] like Figure 2As shown, an innovative consensus optimization mechanism enables efficient maintenance of global data fingerprints and reliable arbitration of cross-chain conflicts. The main chain consensus module employs incremental fingerprint update technology. When the storage sharding module modifies the sharding strategy, causing changes in data distribution, the system only performs local hash recalculation on the affected data blocks: first, it locates the leaf node position of the changed data block in the global Merkle tree, updates the hash values ​​of path nodes layer by layer from bottom to top until the root node, and then submits the difference between the old and new root hashes to the consensus network for rapid verification. Verification nodes only need to compare the hash chains of the difference paths to confirm the legality of the change, reducing consensus overhead by approximately 80% compared to full recalculation. The generation of the global consensus signal introduces a multi-party joint signature mechanism, with three types of core nodes participating: the main chain verification node is responsible for the signature weight of the basic consensus result, the side chain representative node provides compliance endorsement for business scenarios, and the regulatory node injects policy compliance verification elements. The three parties participate in the threshold signature process according to dynamic weight ratios. The signature scheme sets a minimum threshold for the number of participating nodes to ensure that a valid consensus signal can still be generated even if some nodes are offline. The main chain's built-in cross-chain arbitration smart contract continuously monitors the consistency status of the cross-validation chains. When an irreconcilable discrepancy is detected between the block header hash of a sidechain and the main chain record, a multi-stage processing procedure is automatically initiated: First, write permissions for the problematic sidechain are frozen, and an abnormal status announcement is published on the main chain. Then, the most recent valid state snapshot of the sidechain is retrieved from the backup network. Integrity proofs are generated by comparing multiple geographically dispersed backup copies. Finally, the consensus version confirmed by a majority of nodes is selected 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. Data shards that have reached the preset retention period initiate an automated destruction process. The destruction operation requires authorization and verification through a digital certificate issued by the main chain consensus module. The certificate contains elements such as data fingerprint, operation timestamp, and authorized node signature. After destruction, an irreversible operation credential containing Merkel proof is recorded in the cross-validation chain to ensure the auditability of the entire lifecycle operation.

[0033] Furthermore, this invention achieves dynamic optimization of system resources and autonomous fault tolerance under abnormal operating 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 consistently exceeds the warning threshold, the capacity expansion process is automatically activated: new nodes are dynamically added to the distributed storage cluster, and data shards are redistributed using a consistent hashing algorithm to ensure uninterrupted service during the expansion process. When the utilization rate of the archive layer is detected to be consistently below the economic operating threshold, a node hibernation mechanism is initiated, switching redundant nodes to a low-power state and migrating data to active nodes to reduce overall energy consumption. The data migration module integrates an intelligent traffic scheduling engine, which analyzes the network topology and bandwidth utilization in real time during the migration process and automatically selects the optimal transmission path: dedicated network channels are prioritized for migration tasks with high real-time requirements, and a time-sharing peak-shifting transmission strategy is adopted for batch migration tasks. Simultaneously, dynamic bandwidth allocation is implemented to ensure that the transmission quality of critical business links is not affected. The system's anomaly handling mechanism employs a tiered response strategy: when a node-level failure is detected, a local replica recovery process is initiated; when a regional network outage is identified, the system switches to a disaster recovery data center to take over services; and when a full-chain consensus attack is encountered, a Byzantine node isolation procedure is triggered, using a behavioral analysis model to identify malicious nodes and implement dynamic blacklist management. Furthermore, the system implements end-to-end performance optimization. Predictive caching preheating technology is used in the storage sharding stage, preloading high-probability access data to the high-speed cache layer based on historical access patterns. An asynchronous verification mechanism is introduced during the consensus process, decoupling transaction verification from block generation to improve system throughput. Compressed relay nodes are deployed in the cross-chain interaction layer to aggregate batch verification requests, reducing network communication overhead. Through these multi-dimensional optimization measures, the system can maintain sub-second response latency and 99.99% service availability even under scenarios of explosive growth in energy data volume.

[0034] This invention discloses an energy data consensus and high-efficiency storage system based on a layered multi-chain architecture. By separating global and local consensus through the layered multi-chain architecture, dynamic sharding storage enables intelligent migration of hot and cold data, and cross-verification between main and side chains ensures cross-chain trustworthiness. This collaboratively improves storage efficiency and processing speed, resolving the contradiction between the growth of energy data scale and system performance bottlenecks.

[0035] Therefore, the energy data consensus and efficient storage system based on a layered multi-chain architecture of the present invention can solve the problems of low energy data storage efficiency, slow consensus speed and difficulty in cross-chain verification.

[0036] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An energy data consensus and high-efficiency storage system based on a layered multi-chain architecture, characterized in that, include: The main chain consensus module processes energy data using a Byzantine fault-tolerant algorithm and generates a global consensus signal containing timestamps and energy data fingerprints. The sidechain cluster module comprises multiple heterogeneous blockchain units. Each unit is configured with a different consensus algorithm based on a preset energy data type. After receiving the global consensus signal, the sidechain cluster module generates a local consensus result and transmits its block header hash value to the main chain consensus module to form a cross-validation chain. The sidechain cluster module then generates a first sharding processing instruction. The heterogeneous blockchain units of the sidechain cluster module dynamically adapt to different consensus mechanisms based on the energy data type. When processing real-time transaction data, a Byzantine fault-tolerant algorithm optimized based on a time window is used to improve response speed by compressing consensus rounds and using a preset timeout mechanism. When processing device status data, a multi-party joint authentication authoritative proof mechanism is used, with device manufacturers, regulatory agencies, and operators jointly participating in node verification. When processing historical audit data, a dynamically difficulty-adjusted proof-of-work mechanism is used to automatically adjust the computational complexity based on the data scale. When generating the first sharding processing instruction, the sidechain cluster module adopts an adaptive time window sharding strategy for real-time data streams, with the window size dynamically scaling with network status. For batch data, a feature extraction sharding mode is used, generating sharding keys based on data source attributes. The storage sharding module is equipped with a feature analysis engine that analyzes the business attributes and access characteristics of the energy data in real time and generates a sharding instruction signal containing a storage type identifier. 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 key business tags, calculating access heat values ​​after time decay, and identifying the data source type; the sharding instruction signal contains multi-level storage type identifiers, dividing the data into a high-response layer, an intermediate layer, and an archive layer, each corresponding to storage media with different performance. The high-response layer uses a low-latency solid-state storage cluster, the intermediate layer deploys a balanced storage pool, and the archive layer uses a high-density redundant coded mechanical storage array; After receiving the first sharding processing instruction from the sidechain cluster module, the storage sharding module dynamically adjusts the feature sampling frequency according to the sharding granularity change, and optimizes the storage layer configuration based on real-time feedback. The data migration module performs cold and hot data transfer according to the sharding instruction signal, and generates 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 corrects the sharding instruction signal in real time.

2. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, characterized in that, The block header hash value undergoes multiple layers of encryption before being transmitted to the main chain consensus module. This includes basic encryption using the asymmetric encryption algorithm of the main chain consensus module node, followed by secondary reinforcement using the national cryptographic algorithm of the side chain regulator, forming a quantum-resistant encryption anchor.

3. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, characterized in that, The hot and cold data transfer process of the data migration module includes an intelligent hierarchical migration mechanism. When the hotness of the data migration module is detected to be 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 storage layer load, network status, and data volume. When storage pressure is detected to be excessive, emergency mode is automatically activated to prioritize the migration of large-volume, low-intensity data and enable parallel transmission channels. At the same time, the maximum execution time of a single task is limited to avoid resource contention.

4. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, characterized in that, When generating global consensus signals, the main chain consensus module uses block fingerprint aggregation technology to divide the energy data into device groups and calculate the hash tree structure. The global data fingerprint is formed by 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 state alignment by embedding version identifiers. If the version deviation is detected to exceed the allowable range, a forced synchronization process is triggered. The cross-chain verification process employs privacy-preserving technology, using zero-knowledge proofs to verify data validity and ensure trusted confirmation without disclosing transaction content.

5. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, 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 limits of the sidechain cluster module into a dynamic scoring model, and adjusts the sharding rules by comprehensively considering the weights of real-time performance, storage efficiency, and reliability. When the energy data is identified to contain a safety-critical tag, a tamper-proof storage mode is forcibly enabled, redundant copies are saved on isolated nodes in multiple locations, and migration operations are prohibited until the preset safety 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.

6. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, characterized in that, The sidechain cluster module implements a data redundancy protection mechanism in the sharding process, generating a distributed verification block for each data shard. The number of verification blocks is dynamically configured according to the storage level and distributed across different regional nodes. The block header hash value transmission adopts an intelligent routing strategy, selecting the optimal network path according to the service type and implementing transmission quality monitoring. When communication quality degradation is detected, the backup link is automatically switched. The local consensus process of the sidechain cluster module introduces a lightweight verification mechanism, which only requires verification of the key hash path to participate in the consensus.

7. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, 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 rate limiting protection when the resource load exceeds the safety threshold, reducing the migration rate proportionally. The migration data package is attached with an aggregated integrity certificate, and the transfer process is ensured to be tamper-proof through group signature technology; the archive layer implements automated lifecycle management, and performs a secure destruction process for overdue data. Before destruction, a digital certificate issued by the main chain is obtained and an irreversible operation certificate is recorded.

8. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, 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 sidechains request the same data resource, storage permissions are allocated according to business priority and a locking signal is issued through the main chain. The storage sharding module and the data migration module establish a status synchronization channel to periodically exchange storage node information and trigger data copy reconstruction when a node fails.

9. The energy data consensus and high-efficiency storage system based on a layered multi-chain architecture according to claim 1, characterized in that, The main chain consensus module adopts 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 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 their weights. It can only take effect if the minimum number of nodes threshold is met. 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's write permissions are frozen and the best copy is elected from the backup for state restoration.

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