Offshore wind power plant operation and maintenance data management system and method based on block chain

Abstract

The invention provides an offshore wind farm operation and maintenance data management system and method based on a block chain, and relates to the technical field of offshore wind farms. Comprising a data acquisition module, a storage management module, a block chain module, an index optimization module and a report generation module. High-efficiency management of hot, warm and cold data is realized through a multi-level storage architecture, credibility and security of the data are ensured by adopting a block chain network, query performance is optimized in combination with adaptive indexing and dynamic fragmentation, and a full-life-cycle operation and maintenance data report is generated. According to the method, the data storage efficiency can be improved, the data credibility is enhanced, the large-scale data management capability is optimized, and scientific decision support is provided for intelligent operation and maintenance of the offshore wind plant.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind farms, and in particular to a blockchain-based offshore wind farm operation and maintenance data management system and method. Background Art

[0002] With the continued expansion of offshore wind farm construction, the importance of operation and maintenance data management has become increasingly prominent. Traditional offshore wind farm operation and maintenance data management models suffer from data silos, difficulty in information sharing, and insufficient data credibility. These issues severely impact operation and maintenance efficiency and the effectiveness of decision-making. Blockchain technology, with its advantages of decentralization, immutability, and transparency, is increasingly being applied to offshore wind farm operation and maintenance data management. However, existing patent solutions for offshore wind farms based on blockchain technology still have certain limitations in terms of functional coverage and technical implementation details.

[0003] After searching, an intelligent management and control system for offshore wind power project construction based on blockchain technology was disclosed with publication number CN108985593B, and the publication date was September 28, 2021. This patent realizes the intelligent management and control of the construction process by applying blockchain technology to multiple links of offshore wind power project construction (such as construction preparation, transportation, foundation engineering, equipment installation, etc.). However, this technical solution mainly focuses on the management and control of the construction phase, and involves less data management in the operation and maintenance phase of the wind farm, and fails to fully cover the operation and maintenance needs of the wind farm throughout its life cycle. In addition, the application of blockchain technology in this solution is mainly concentrated in the protocol layer, extension layer and application layer, and lacks the design of operation and maintenance data storage optimization and efficient query mechanism, which may lead to performance bottlenecks in large-scale operation and maintenance data processing. Summary of the Invention

[0004] The present invention discloses a blockchain-based offshore wind farm operation and maintenance data management system, which aims to achieve efficient management of wind farm operation and maintenance data throughout its life cycle, optimize data storage and query performance, and enhance the scalability of the system, thereby meeting the offshore wind farm's needs for intelligent and highly reliable operation and maintenance data management.

[0005] The present invention adopts the following scheme: This application provides a blockchain-based offshore wind farm operation and maintenance data management method, which includes the following steps: Obtain and classify the operation and maintenance data of offshore wind farms throughout their life cycle. This data includes equipment status data, environmental monitoring data, fault record data, and maintenance plan data. A distributed storage architecture is built based on classified and labeled operation and maintenance data, dividing the data into hot data, warm data, and cold data, and storing them in the cache layer, distributed database layer, and archive storage layer respectively; Build a blockchain network, define data writing rules and access permissions through smart contracts, store key data summaries on the chain, and form tamper-proof data records; Introducing an adaptive index structure to optimize the indexing of warm data in distributed databases, and combining dynamic sharding technology to divide data storage areas to improve data query efficiency; Generate operation and maintenance data reports, including equipment status trend analysis, failure frequency statistics, maintenance plan execution status, and data query response time.

[0006] Furthermore, the distributed storage architecture is constructed based on the classified and labeled operation and maintenance data, including: Data classification and labeling: Operation and maintenance data is divided into hot data, warm data, and cold data based on data access frequency and importance. Hot data includes real-time monitoring data, warm data includes historical operation data, and cold data includes long-term archived data. Storage layer configuration: The cache layer uses an in-memory database to store hot data, the distributed database layer uses a distributed file system to store warm data, and the archive storage layer uses a low-cost tape library or cloud storage service to store cold data; Data migration strategy: Set a data migration threshold. When the data access frequency falls below the preset threshold, the data is automatically migrated from the cache layer to the distributed database layer. When the data access frequency decreases further, it is migrated to the archive storage layer.

[0007] Furthermore, the building of a blockchain network includes: Node deployment: Blockchain nodes are deployed at each operation and maintenance site of the offshore wind farm, with each node equipped with independent data storage units and computing resources; Consensus mechanism selection: An improved Byzantine fault-tolerant algorithm is used as the consensus mechanism, and the validity of data writing is determined through multiple rounds of voting; Smart contract design: Define data writing rules, including data format verification, timestamp marking, and signature verification; at the same time, design access control logic to limit the access scope of different roles to data.

[0008] Furthermore, the introducing of the adaptive index structure includes: Index initialization: Create an initial index for warm data in the distributed database. The index fields include device number, timestamp, and data type. Index update: Dynamically adjust the index structure based on data access patterns. When the access frequency of a certain type of data increases significantly, it is assigned a higher index priority. Dynamic sharding technology: Divide the distributed database into multiple shards, each of which stores operation and maintenance data for a specific time period or type; the shard size is dynamically adjusted based on the data volume to avoid overloading a single shard.

[0009] Furthermore, generating the operation and maintenance data report includes: Data collection: Extracting required operation and maintenance data from the distributed storage architecture, including equipment status data, environmental monitoring data, fault record data, and maintenance plan data; Data analysis: Statistical analysis of the extracted data is performed to calculate equipment status change trends, fault frequency distribution, and maintenance plan execution rates; Report generation: Integrate analysis results into visual charts, including line charts, bar charts, and pie charts, and generate operation and maintenance data reports containing detailed data and analysis conclusions.

[0010] Furthermore, the present application provides a blockchain-based offshore wind farm operation and maintenance data management system, the system comprising: The data acquisition module is used to obtain and classify the operation and maintenance data of the offshore wind farm throughout its life cycle. The operation and maintenance data includes equipment status data, environmental monitoring data, fault record data, and maintenance plan data. The storage management module is used to build a distributed storage architecture based on classified and labeled operation and maintenance data, divide the data into hot data, warm data, and cold data, and store them in the cache layer, distributed database layer, and archive storage layer respectively; The blockchain module is used to build a blockchain network, define data writing rules and access permissions through smart contracts, and store key data summaries on the chain to form tamper-proof data records; The index optimization module is used to introduce an adaptive index structure to optimize the index of warm data in the distributed database, and divide the data storage area into dynamic sharding technologies to improve data query efficiency; The report generation module is used to generate operation and maintenance data reports. The report content includes equipment status trend analysis, failure frequency statistics, maintenance plan execution status and data query response time.

[0011] Beneficial effects: 1. The present invention builds a multi-level data storage architecture, divides operation and maintenance data into hot data, warm data, and cold data, and stores them in the cache layer, distributed database layer, and archive storage layer respectively, effectively improving data storage efficiency. By setting data migration thresholds, automatic data migration between different storage layers is achieved, reducing the waste of storage resources. 2. This invention builds a blockchain network and adopts an improved Byzantine fault-tolerant algorithm as a consensus mechanism to ensure the validity and consistency of data writing. It also defines data writing rules and access rights through smart contracts, thereby enhancing the security and credibility of data. 3. This invention optimizes the storage and query performance of warm data in distributed databases by introducing an adaptive index structure and dynamic sharding technology. The adaptive index structure dynamically adjusts index priorities based on data access patterns, and dynamic sharding technology avoids overloading a single shard by flexibly dividing data storage areas, significantly improving the management capabilities of large-scale operation and maintenance data. 4. The present invention generates operation and maintenance data reports, presenting key information such as equipment status trends, fault frequency statistics, and maintenance plan execution status in a visual form, providing a scientific basis for operation and maintenance decisions; by integrating full life cycle data, data continuity and integrity are achieved, meeting the needs of offshore wind farms for intelligent and highly reliable operation and maintenance data management. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the structure of a blockchain-based offshore wind farm operation and maintenance data management system according to an embodiment of the present invention; Figure 2 This is a schematic structural diagram of a blockchain-based offshore wind farm operation and maintenance data management method according to an embodiment of the present invention; DETAILED DESCRIPTION Example 1 Combine Figure 1 This embodiment provides an offshore wind farm operation and maintenance data management system based on blockchain, the system comprising: a data acquisition module for acquiring operation and maintenance data throughout the entire life cycle of the offshore wind farm and classifying and labeling the data; wherein the operation and maintenance data includes equipment status data, environmental monitoring data, fault record data, and maintenance plan data; a storage management module for constructing a distributed storage architecture based on the classified and labeled operation and maintenance data, dividing the data into hot data, warm data, and cold data, and storing them in a cache layer, a distributed database layer, and an archive storage layer, respectively; a blockchain module for building a blockchain network, defining data writing rules and access rights through smart contracts, storing key data summaries on the chain, and forming tamper-proof data records; an index optimization module for introducing an adaptive index structure, performing index optimization on warm data in a distributed database, and dividing data storage areas in combination with dynamic sharding technology to improve data query efficiency; and a report generation module for generating operation and maintenance data reports, the report content of which includes equipment status trend analysis, fault frequency statistics, maintenance plan execution status, and data query response time.

[0013] The data collection module collects data such as device status, environmental monitoring, fault records, and maintenance plans through sensors and logging devices, and categorizes it according to pre-set tags. This can be implemented using IoT protocols and metadata tagging technology to ensure data source integrity and classification accuracy. The storage management module stores hot, warm, and cold data in in-memory databases, distributed file systems, and low-cost storage media based on data access frequency. This can be implemented using Redis, HDFS, and cloud storage services to optimize storage resource allocation and reduce data access latency. The blockchain module deploys a blockchain network across multiple operation and maintenance nodes and performs data validation and permission management through smart contracts. This can be implemented using the Hyperledger Fabric framework to ensure data immutability and access security. The index optimization module dynamically adjusts index priorities and allocates storage shards based on data access patterns. This can be implemented using Elasticsearch's adaptive indexing mechanism to improve query efficiency for warm data. The report generation module generates reports containing trend charts and statistical conclusions through aggregated analysis. This can be implemented using visualization tools such as Apache Superset to support data-driven operation and maintenance decision-making.

[0014] In this embodiment, the data acquisition module first collects operating parameters in real time through sensors deployed on equipment such as wind turbines and substations, and integrates maintenance logs and plan files to complete data classification and labeling. The storage management module automatically stores hot data in the memory database according to the data access frequency threshold, migrates warm data to the distributed file system, and archives cold data to cloud storage. The blockchain module establishes a consensus mechanism between each operation and maintenance node, and uses smart contracts to verify the signature and timestamp the key data summary to ensure the legitimacy of the data on the chain. The index optimization module continuously monitors hot spots of warm data access, dynamically adjusts the weight of index fields, and divides the database into scalable shards by time or device type. The report generation module regularly extracts data from each storage layer, analyzes equipment status trends and fault distribution, and generates visual reports containing line charts and bar charts.

[0015] By combining blockchain with distributed storage, data silos and credibility issues are resolved; through tiered storage of hot, warm, and cold data and adaptive indexing, the storage efficiency and query performance of large-scale operation and maintenance data are optimized; through dynamic sharding technology, system scalability is enhanced, avoiding the performance bottleneck of a single database; full-cycle coverage and trusted storage of offshore wind farm operation and maintenance data are achieved, improving real-time access to high-frequency data, optimizing the query efficiency of historical data, and reducing manual analysis costs through automated report generation, providing reliable data support for operation and maintenance decisions.

[0016] The storage management module includes the following functions: it can divide operation and maintenance data into hot data, warm data and cold data according to data access frequency and importance, hot data includes real-time monitoring data, warm data includes historical operation data, and cold data includes long-term archived data; configure the cache layer to use an in-memory database to store hot data, the distributed database layer to use a distributed file system to store warm data, and the archive storage layer to use a low-cost tape library or cloud storage service to store cold data; set a data migration threshold, and automatically migrate data from the cache layer to the distributed database layer when the data access frequency is lower than the preset threshold, and migrate it to the archive storage layer when the data access frequency further decreases.

[0017] Hot data refers to real-time monitoring data that requires frequent access. This can be achieved using Redis or Memcached in-memory databases, accelerating the rapid response of real-time data through in-memory reading and writing. Warm data refers to historical operational data with a moderate access frequency. This can be achieved using HDFS or Ceph distributed file systems, balancing storage capacity and query efficiency. Cold data refers to long-term archived data with infrequent access. This can be achieved using AWS Glacier or Alibaba Cloud archiving storage services to reduce long-term storage costs. The data migration threshold refers to the critical value of the access frequency that triggers the transfer of data levels. This can be achieved by using a sliding window algorithm to count the number of accesses within a period. When the number of accesses falls below the set threshold, the migration operation is triggered, enabling dynamic allocation of storage resources.

[0018] The storage management module monitors data access frequency in real time, retaining active real-time monitoring data in the in-memory database to support millisecond-level responses, moving historical operational data to the distributed file system to balance storage costs and query performance, and transferring archived data that has exceeded its retention period to low-cost storage media. When data access frequency continues to drop below the preset migration threshold, the system automatically performs data tier migration. For example, when the weekly access count for a device's status data falls below five, it is migrated from the in-memory database to the distributed file system; when the access count further drops to less than one per month, it is transferred to cloud archive storage. This dynamic migration mechanism dynamically matches storage resources with access requirements by configuring migration trigger conditions and the target storage tier.

[0019] By establishing a three-tiered storage system and setting dynamic migration rules, the low storage resource utilization and high access latency issues of traditional solutions are resolved. Automated migration mechanisms also reduce manual maintenance costs. Tiered storage and dynamic migration of operational data are implemented, effectively improving access speeds for high-frequency data and reducing storage costs for low-frequency data, thus avoiding the resource waste associated with the mixing of hot and cold data in traditional storage architectures. Automated management with pre-set migration thresholds reduces the need for manual intervention, ensuring that storage resource configuration remains synchronized with data access patterns, and resolving the storage performance and cost imbalance inherent in existing technologies.

[0020] In this embodiment, blockchain nodes are deployed at various operation and maintenance sites of offshore wind farms, and each node is equipped with independent data storage units and computing resources. An improved Byzantine fault-tolerant algorithm is used as a consensus mechanism, and the validity of data writing is determined through multiple rounds of voting. Data writing rules are defined through smart contracts, including data format verification, timestamp marking, and signature verification. At the same time, access control logic is designed to limit the access scope of different roles to data.

[0021] Blockchain nodes refer to computing units deployed at physically dispersed operation and maintenance sites. Each node can be equipped with independent storage devices and processors, for example, using server clusters with redundant storage capabilities, to achieve distributed data storage and localized processing of computing tasks. Improved Byzantine fault tolerance refers to a consensus protocol that adds a multi-round voting process to the traditional fault tolerance mechanism. For example, data validity verification is completed through three stages: pre-submission, preparation, and confirmation. This is used to ensure the reliability of data writing when nodes may be faulty or malicious. Smart contracts are programmatic protocols that are automatically executed based on preset conditions. For example, data verification rules and permission determination logic written in Solidity are used to implement format standardization and access control during the data upload process.

[0022] Blockchain nodes are deployed on physical servers at each operation and maintenance site, each connected via a dedicated network to form a peer-to-peer communication structure. When operation and maintenance data needs to be written to the blockchain, a modified Byzantine fault-tolerance algorithm is triggered, and each node performs multiple rounds of cross-validation on the data packet. For example, suspicious packets are screened out in the first round of voting, and valid data is confirmed through a threshold judgment mechanism in the second round of voting. The smart contract program automatically performs format verification before data is written, for example, verifying that temperature sensor data contains a complete timestamp and device ID. It also dynamically adjusts data access permissions based on the role information in the operator's digital certificate, for example, allowing only maintenance engineers to view fault records for specific equipment. By deploying a multi-node architecture combined with a multi-stage voting mechanism, consensus efficiency is improved while maintaining decentralization. Smart contracts enable dynamic permission management based on data content and operator identity, effectively preventing malicious nodes from tampering with operation and maintenance data and ensuring the trusted storage of critical data in a distributed environment. This dynamic permission control mechanism prevents unauthorized access to sensitive operation and maintenance information, and the multi-stage consensus process shortens the verification time for data on-chain, providing technical support for the real-time sharing and traceability of offshore wind farm operation and maintenance data.

[0023] The index optimization module includes the following functions: establishing an initial index for warm data in the distributed database, with index fields including device number, timestamp and data type; dynamically adjusting the index structure according to the data access pattern, and assigning a higher index priority to a certain type of data when the access frequency increases significantly; dividing the distributed database into multiple shards, each shard storing operation and maintenance data for a specific time period or a specific type, and the shard size dynamically adjusting according to the data volume.

[0024] An adaptive index structure is a mechanism that automatically adjusts index priorities based on data access patterns. This can be achieved using a weighted allocation algorithm based on access frequency statistics. This structure dynamically optimizes the index hierarchy by monitoring data query requests in real time, addressing the efficiency degradation of fixed index structures when access patterns change. Dynamic sharding technology divides database storage units based on data attributes and storage requirements. This can be achieved using a hash sharding algorithm combined with a time window partitioning strategy. This technology improves data storage locality and query efficiency by flexibly adjusting shard boundaries.

[0025] When the temperature data is stored in the distributed database, a composite index is first established according to the device number, timestamp and data type. The combination of index fields can be configured according to actual query requirements. For example, the device number can be used as the primary index key to support data retrieval in the device dimension. As the system runs, when it is monitored that the data access volume of a certain type of equipment continues to increase, the index optimization module automatically increases the weight of the device number in the index structure so that it is retrieved first during query. At the same time, the distributed database is divided into multiple logical shards. Each shard can be set to store the operating data of the last three months or the monitoring data of the same model of fan. The shard capacity threshold can be set to 500GB, for example. When the data volume reaches the threshold, the shard expansion operation is automatically triggered.

[0026] In some specific implementations, index weight adjustment can utilize a sliding time window to calculate access frequency, for example, counting data queries per device on an hourly basis. Sharding can also incorporate geographic location information, centralizing data from wind farms in the same offshore area to specific shards. When data volume fluctuates significantly, shard expansion can utilize online migration technology to dynamically expand storage space.

[0027] By monitoring data access characteristics in real time and establishing an indexing mechanism with adaptive adjustment capabilities, the system can automatically optimize retrieval paths for frequently accessed data. At the same time, dynamic sharding technology can flexibly adjust the size of storage units based on data growth trends, avoiding the waste of storage space or frequent capacity expansion caused by traditional static sharding. This effectively addresses the inefficient query and storage management challenges inherent in offshore wind farm operation and maintenance data management. The dynamically optimized index structure significantly shortens retrieval time for frequently accessed data, while the intelligent sharding mechanism improves storage resource utilization, enabling the system to efficiently process large-scale operation and maintenance data and providing reliable data support for real-time monitoring and fault analysis.

[0028] The report generation module described in this embodiment includes the following functions: extracting the required operation and maintenance data from the distributed storage architecture, including equipment status data, environmental monitoring data, fault record data, and maintenance plan data; performing statistical analysis on the extracted data to calculate the equipment status change trend, fault frequency distribution, and maintenance plan execution rate; integrating the analysis results into visual charts, including line charts, bar charts, and pie charts, to generate an operation and maintenance data report containing detailed data and analysis conclusions.

[0029] Among them, operation and maintenance data refers to structured data such as equipment operating status, environmental parameters, fault events, and maintenance plans generated throughout the life cycle of an offshore wind farm. Specifically, it can be extracted from a distributed storage architecture using a database query interface to address the low integration efficiency caused by decentralized data storage in traditional models. Statistical analysis refers to the mathematical modeling and computational processing of operation and maintenance data. Specifically, it can be implemented using time series analysis algorithms and probability distribution models to reveal the patterns of equipment performance degradation and the characteristics of fault occurrence. Visualization charts refer to the conversion of analysis results into graphical presentations. Specifically, they can be generated using a data visualization tool library to intuitively present the inherent correlations and trend changes of complex data.

[0030] During operation, the report generation module first extracts cross-timeframe O&M data from the cache layer, distributed database layer, and archive storage layer through data interfaces, such as equipment vibration monitoring data from the last three months or fault records from the past five years. It then uses a sliding window algorithm to calculate the mean rate of change of equipment status indicators, employs a Poisson distribution model to calculate the failure probability of different components, and evaluates plan execution based on maintenance work order completion rates. Finally, a visualization engine is used to map the calculated results into a time series line chart, a pie chart of fault type percentages, and a Gantt chart of maintenance progress, generating a structured report document containing a data summary, analysis conclusions, and improvement recommendations. By establishing a data extraction mechanism covering the entire lifecycle, combined with multi-dimensional statistical models and dynamic chart generation technology, this system achieves end-to-end analysis of O&M data, addressing the coarse-grained data analysis and insufficient decision support inherent in existing technologies. This solution automatically integrates distributed O&M data and generates structured analysis reports, significantly improving the accuracy of equipment status assessments and helping O&M personnel quickly identify frequently failing components. Visual charts also intuitively display deviations from maintenance plan execution, providing data support for optimizing O&M strategies.

[0031] In a preferred embodiment, edge computing nodes can be deployed on offshore wind turbines and maintenance vessels, each equipped with a small cache and a lightweight blockchain client. These edge computing nodes first store hot data in a local cache, ensuring local data processing and analysis during network outages. The system dynamically adjusts data synchronization frequency and priority based on network quality. When network conditions are good, edge computing nodes frequently synchronize data to the onshore control center. When the network is unstable, the system reduces synchronization frequency and prioritizes critical data. Incremental synchronization technology is employed to transmit only data that has changed since the last synchronization, reducing data transmission volume and improving synchronization efficiency. During network outages, a temporary local blockchain network is formed between edge computing nodes, maintaining data consistency using a simplified consensus algorithm (such as Raft). Upon network restoration, these local consensus results are merged with the main blockchain network. This solution addresses the unstable network connectivity issues often encountered in data transmission due to the unique environment of offshore wind farms. This makes the system more adaptable to the geographical characteristics and harsh marine environment of offshore wind farms, especially in inclement weather, where data synchronization between offshore equipment and the onshore control center may be delayed or interrupted.

[0032] Example 2 Combine Figure 2 This embodiment discloses a method for managing offshore wind farm operation and maintenance data based on blockchain, the method comprising the following steps: Obtain and classify the operation and maintenance data of offshore wind farms throughout their life cycle. This data includes equipment status data, environmental monitoring data, fault record data, and maintenance plan data. A distributed storage architecture is built based on classified and labeled operation and maintenance data, dividing the data into hot data, warm data, and cold data, and storing them in the cache layer, distributed database layer, and archive storage layer respectively; Build a blockchain network, define data writing rules and access permissions through smart contracts, store key data summaries on the chain, and form tamper-proof data records; Introducing an adaptive index structure to optimize the indexing of warm data in distributed databases, and combining dynamic sharding technology to divide data storage areas to improve data query efficiency; Generate operation and maintenance data reports, including equipment status trend analysis, failure frequency statistics, maintenance plan execution status, and data query response time. Among them, the classification and tagging of operation and maintenance data refers to the structured processing of data according to the four dimensions of equipment status, environmental monitoring, fault records and maintenance plans. This can be achieved specifically by using a metadata tag system to establish the data attribute foundation for subsequent tiered storage. The distributed storage architecture divides the data storage levels by access frequency thresholds. For example, real-time monitoring data is defined as hot data and stored in the memory database, historical operation data is stored as warm data in the distributed file system, and long-term archive data is stored in a tape library to achieve optimized storage resource configuration. The blockchain network deployment adopts a multi-node consensus mechanism, such as the improved Byzantine fault tolerance algorithm, to ensure the non-tamperability of key data summaries. The adaptive index structure adjusts the index priority by dynamically monitoring the data access pattern. For example, when the query frequency of a certain type of equipment fault data increases, its index level is automatically upgraded, and the storage sharding layout is optimized in conjunction with the time slice partitioning strategy.

[0033] During the data collection phase, IoT sensors and the SCADA system acquire equipment status data, such as wind turbine vibration and temperature. This data is then combined with environmental data collected by meteorological monitoring stations to form a structured dataset. The classification and tagging module adds a device number, timestamp, and data type tag to each piece of data. For example, gearbox oil temperature data is labeled "WTG-07# Equipment Status - 2023Q3." Based on a preset access frequency threshold, the storage management module identifies data accessed more than 100 times in the past seven days as hot data and stores it in a Redis cluster. Data not accessed for more than 30 days is migrated to GlusterFS distributed storage, and data older than one year is archived to Alibaba Cloud OSS object storage. Blockchain nodes are deployed in the onshore centralized control center and on offshore maintenance vessels. The PBFT consensus algorithm is used to verify data hash values ​​across multiple nodes before uploading them to the blockchain. The index optimization engine analyzes SQL query logs in real time. When it detects a sudden increase in queries for fault records for a particular inverter model, it automatically creates a composite index for that data and reallocates storage shards.

[0034] This embodiment breaks through the limitations of a single blockchain architecture, and by integrating tiered storage with blockchain technology, it solves the problem of large-scale data storage efficiency while ensuring data credibility. By uploading key data summaries to the chain instead of storing the full amount of data, the traceability of core data is retained while reducing the storage pressure on the blockchain. It effectively solves the problem of low collaborative efficiency caused by the decentralized storage of offshore wind farm operation and maintenance data. The application of blockchain technology ensures the integrity and auditability of key data. The tiered storage architecture reduces long-term data storage costs, and the dynamic migration strategy improves storage resource utilization. Adaptive indexing and sharding technology significantly speeds up the retrieval of historical operation and maintenance data. The visual report generation module provides multi-dimensional data support for equipment health status assessment, assisting operation and maintenance personnel in formulating precise maintenance strategies.

[0035] In a preferred embodiment, a distributed storage architecture is constructed based on classified and labeled operation and maintenance data, including the following steps: dividing the operation and maintenance data into hot data, warm data, and cold data according to the frequency and importance of data access, where hot data includes real-time monitoring data, warm data includes historical operation data, and cold data includes long-term archived data; configuring the cache layer to use an in-memory database to store hot data, the distributed database layer to use a distributed file system to store warm data, and the archive storage layer to use a low-cost tape library or cloud storage service to store cold data; setting a data migration threshold, and automatically migrating the data from the cache layer to the distributed database layer when the data access frequency is lower than the preset threshold, and migrating the data to the archive storage layer when the data access frequency further decreases. For example, a weekly access threshold can be set, such as a hot data migration threshold of ≤5 weekly accesses and a warm data migration threshold of ≤2 monthly accesses).

[0036] Operation and maintenance data is first divided into hot, warm, and cold data through a classification and tagging module. Real-time monitoring data is marked as hot data and stored in the in-memory database to meet millisecond-level response requirements; historical operation data is marked as warm data and stored in the distributed file system to support batch query and analysis; long-term archived data is marked as cold data and transferred to low-cost storage media. When the data access frequency monitoring module detects that the access frequency of hot data is lower than the preset threshold, it triggers the migration program to transfer it to the distributed file system; if the access frequency of warm data further decreases, it is transferred to the archive storage layer. The entire process achieves optimal allocation of storage resources through automated migration strategies.

[0037] This solution uses a dynamic data tiering and migration mechanism to match data with different access requirements to corresponding storage tiers, ensuring efficient real-time data processing while reducing cold data storage overhead. This balances storage efficiency and cost control. The rapid response of hot data supports real-time diagnosis of device failures, while the distributed storage of warm data meets the needs of historical data analysis. Low-cost archiving of cold data frees up core storage resources, and the data migration threshold mechanism reduces manual intervention through automated scheduling.

[0038] In this embodiment, the method for building a blockchain network includes deploying blockchain nodes at each operation and maintenance site of an offshore wind farm, with each node equipped with an independent data storage unit and computing resources; using an improved Byzantine fault-tolerant algorithm as a consensus mechanism, and determining the validity of data writing through multiple rounds of voting; defining data writing rules through smart contracts, including data format verification, timestamp marking, and signature verification, and designing access control logic to limit the access scope of different roles to data.

[0039] Blockchain nodes refer to computing units running on physical servers or in virtualized environments. Specifically, they can be implemented as dedicated devices equipped with solid-state drives and independent processors. Each node independently stores a complete copy of the data and participates in consensus verification, thereby ensuring decentralized data storage and resistance to single points of failure. Improved Byzantine fault-tolerance algorithms can employ consensus protocols that optimize voting rounds and message verification processes. Specifically, this can be implemented using predefined groups of validating nodes and a dynamic weight adjustment mechanism, improving network throughput by reducing redundant communications. For example, the consensus condition can be set as: N ≥ 3f+1 (N = total number of nodes, f = number of faulty nodes). Smart contracts are executable code modules deployed on the blockchain. They can be written in Solidity and compiled into Ethereum Virtual Machine bytecode. Data verification and permission verification processes are triggered by pre-set conditions.

[0040] During the blockchain network deployment phase, nodes with independent storage and computing capabilities are installed at each operation and maintenance site, forming a peer-to-peer communication architecture. During the consensus mechanism, validating nodes verify data validity through multiple rounds of interaction. A new block is generated when a predetermined percentage of nodes reach consensus. Smart contracts perform format checks and timestamps before data is written, and verify the identity of operators through digital signatures. Access control logic dynamically assigns data query and modification permissions based on role attributes. Independent node deployment enhances data storage reliability. An improved Byzantine fault-tolerance algorithm ensures data consistency while improving processing efficiency. The smart contract's rule engine enables fine-grained permission management. This addresses existing issues such as the lack of standardized verification during the operation and maintenance data writing process, insufficient cross-node data consistency, and crude permission control. The independent deployment of blockchain nodes prevents single-point data loss. The multi-round voting mechanism effectively identifies anomalous node behavior. The smart contract's automated validation rules reduce the risk of manual intervention errors. The hierarchical access control logic precisely matches the operational requirements of different roles.

[0041] The adaptive index structure introduced in this embodiment includes the following steps: establishing an initial index for warm data in a distributed database, where the index fields include device number, timestamp, and data type; dynamically adjusting the index structure according to the data access pattern, and assigning a higher index priority to a certain type of data when the access frequency increases significantly; dividing the distributed database into multiple shards, each shard storing operation and maintenance data for a specific time period or a specific type, and dynamically adjusting the shard size according to the data volume, for example, the shard capacity threshold setting formula is: S=β×(C / N), where β=expansion factor 1.2, C=current total data volume, and N=number of existing shards.

[0042] The adaptive index structure is a mechanism that automatically adjusts index priorities based on data access frequency. This is achieved using a dynamic indexing algorithm based on a hash tree. By monitoring data query requests in real time and counting access frequencies, the weight distribution of index nodes is dynamically adjusted. This structure is used in the solution to address the problem of fluctuating query efficiency for warm data as access patterns change. Priority adjustments are used to reduce the length of search paths for high-frequency data.

[0043] Dynamic sharding is a method that automatically adjusts the storage area based on changes in data volume. This is achieved by combining a consistent hashing algorithm with a load balancing strategy, with automatic expansion or merging triggered by preset shard capacity thresholds. This technology is used in the solution to address the dynamic growth of distributed database storage, avoiding wasted storage resources by flexibly partitioning data areas.

[0044] First, an initial index is established for temperature data in the distributed database, using device number, timestamp, and data type as fields to form a basic query path. When the system detects that the access frequency of a certain type of data exceeds a preset threshold, such as a surge in query requests for a device's historical operating data, the adaptive index structure automatically increases the index priority of that data type and assigns it a position closer to the root node in the hash tree, thereby shortening the search path. For example, when the query frequency of a certain type of data exceeds three standard deviations from the mean, the index weight multiplication mechanism is triggered. At the same time, based on the current total amount and type distribution of data, dynamic sharding technology divides the database into multiple independent storage areas, such as time slices by quarter or functional slices by device type. The storage capacity limit of each shard can be set to a dynamic value, such as automatically triggering horizontal expansion when the data volume of a single shard reaches a preset ratio. The adaptive index structure achieves dynamic optimization of the query path. Combined with the flexible storage area division of sharding technology, this effectively solves the problem of balancing query efficiency and storage scalability in large-scale data environments for offshore wind farms. Significantly improve the query response speed of distributed databases for warm data, especially reducing the index traversal level when access hotspots are concentrated; at the same time, optimize storage resource utilization through a dynamic sharding mechanism to avoid local storage overload problems caused by uneven data distribution, providing scalable technical support for the long-term accumulation and efficient access of offshore wind farm operation and maintenance data.

[0045] The steps for generating an operation and maintenance data report described in this embodiment include extracting the required operation and maintenance data from a distributed storage architecture, performing statistical analysis on the extracted data and calculating equipment status change trends, fault frequency distribution, and maintenance plan execution rates, integrating the analysis results into visual charts, and generating an operation and maintenance data report containing detailed data and analysis conclusions. The operation and maintenance data report refers to a structured document generated based on multi-dimensional data analysis. Specifically, an automated script can be used to periodically trigger the data extraction process and generate quantitative indicators in combination with a preset statistical model, such as using the Python Pandas library to implement data aggregation and calculation. Statistical analysis refers to mathematical modeling and trend prediction of operation and maintenance data. Specifically, a sliding window algorithm can be used to calculate the equipment status change rate, or a Poisson distribution model can be used to analyze the fault frequency distribution, such as using Spark MLlib for distributed computing. Visual charts refer to converting abstract data into a graphical representation. Specifically, tools such as ECharts or Tableau can be used to generate a line chart to display equipment status trends, and Matplotlib can be used to generate a bar chart to compare fault frequencies in different time periods, such as presenting the maintenance plan execution rate as a pie chart.

[0046] The O&M data report generation process first extracts categorized data, such as equipment status and environmental monitoring data, on demand from the cache layer, distributed database layer, and archive storage layer. Subsequently, the statistical analysis module performs time series analysis on equipment operating status to identify abnormal fluctuation trends. It also clusters and analyzes fault log data to generate heat maps of fault distribution by equipment type or time dimension. Furthermore, maintenance plan execution is calculated by comparing the deviation rate between the planned time window and the actual maintenance timestamp. Finally, the analysis results are automatically generated into interactive charts using a visualization engine and integrated with the text analysis conclusions into a report document in PDF or web format. By building a data statistical model covering the entire lifecycle, continuous tracking of equipment status trends is achieved. Furthermore, the integrated display of multiple charts improves data readability and decision support efficiency. This solves the existing problem of manual processing and single-dimensional analysis in O&M data report generation. By implementing automatic data extraction, parallel calculation of multiple indicators, and integrated visualization results, it can quickly generate comprehensive reports that include equipment health assessment, fault pattern identification, and maintenance efficiency analysis, providing real-time data support for O&M decision-making.

[0047] It should be understood that the above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention.

[0048] The above description of the drawings used in the implementation manner only shows certain embodiments of the present invention and should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without making any creative efforts.

Claims

1. A blockchain-based offshore wind farm operation and maintenance data management method, characterized in that: The following steps are involved: Acquire and classify the operation and maintenance data of the offshore wind farm throughout its life cycle, including equipment status data, environmental monitoring data, fault record data, and maintenance plan data; A distributed storage architecture is built based on classified and labeled operation and maintenance data, dividing the data into hot data, warm data, and cold data, and storing them in the cache layer, distributed database layer, and archive storage layer respectively; Build a blockchain network, define data writing rules and access permissions through smart contracts, and store key data summaries on the chain; Introducing an adaptive index structure to optimize the indexing of warm data in distributed databases, and combining dynamic sharding technology to divide data storage areas; Generate operation and maintenance data reports, including equipment status trend analysis, failure frequency statistics, maintenance plan execution status, and data query response time.

2. The method for managing offshore wind farm operation and maintenance data based on blockchain according to claim 1, characterized in that: The distributed storage architecture based on the classified and labeled operation and maintenance data includes the following steps: Based on data access frequency and importance, operation and maintenance data is divided into hot data, warm data, and cold data. Hot data includes real-time monitoring data, warm data includes historical operation data, and cold data includes long-term archived data. Configure the cache layer to use an in-memory database to store hot data, the distributed database layer to use a distributed file system to store warm data, and the archive storage layer to use a low-cost tape library or cloud storage service to store cold data; Set a data migration threshold. When the data access frequency falls below the preset threshold, the data will be automatically migrated from the cache layer to the distributed database layer. When the data access frequency decreases further, it will be migrated to the archive storage layer.

3. The method for managing offshore wind farm operation and maintenance data based on blockchain according to claim 1, characterized in that: The construction of the blockchain network includes the following steps: Deploy blockchain nodes at each operation and maintenance site of the offshore wind farm, with each node equipped with independent data storage units and computing resources; An improved Byzantine fault-tolerant algorithm is used as the consensus mechanism, and the validity of data writing is determined through multiple rounds of voting; Data writing rules are defined through smart contracts, including data format verification, timestamp marking, and signature verification. At the same time, access control logic is designed to limit the access scope of different roles to data.

4. The method for managing offshore wind farm operation and maintenance data based on blockchain according to claim 1, characterized in that: The introduction of the adaptive index structure includes the following steps: Establish an initial index for the temperature data in the distributed database. The index fields include device number, timestamp and data type. Dynamically adjust the index structure based on data access patterns. When the access frequency of a certain type of data increases significantly, it is assigned a higher index priority. The distributed database is divided into multiple shards. Each shard stores operation and maintenance data for a specific time period or a specific type. The shard size is dynamically adjusted according to the amount of data.

5. The method for managing offshore wind farm operation and maintenance data based on blockchain according to claim 1, characterized in that: Generating the operation and maintenance data report includes the following steps: Extract required operation and maintenance data from the distributed storage architecture, including equipment status data, environmental monitoring data, fault record data, and maintenance plan data; Perform statistical analysis on the extracted data to calculate equipment status change trends, failure frequency distribution, and maintenance plan execution rate; Integrate analysis results into visual charts, including line charts, bar charts, and pie charts, and generate operation and maintenance data reports containing detailed data and analysis conclusions.

6. A blockchain-based offshore wind farm operation and maintenance data management system, characterized in that: Includes the following modules: A data acquisition module is used to obtain and classify the operation and maintenance data of the offshore wind farm throughout its life cycle. The operation and maintenance data includes equipment status data, environmental monitoring data, fault record data, and maintenance plan data. The storage management module is used to build a distributed storage architecture based on classified and labeled operation and maintenance data, divide the data into hot data, warm data, and cold data, and store them in the cache layer, distributed database layer, and archive storage layer respectively; The blockchain module is used to build a blockchain network, define data writing rules and access permissions through smart contracts, and store key data summaries on the chain; Index optimization module, which is used to introduce adaptive index structure, optimize the index of warm data in distributed database, and divide the data storage area by combining dynamic sharding technology; The report generation module is used to generate operation and maintenance data reports. The report content includes equipment status trend analysis, failure frequency statistics, maintenance plan execution status and data query response time.

7. The blockchain-based offshore wind farm operation and maintenance data management system according to claim 6 is characterized in that: The storage management module includes the following functions: Based on data access frequency and importance, operation and maintenance data is divided into hot data, warm data, and cold data. Hot data includes real-time monitoring data, warm data includes historical operation data, and cold data includes long-term archived data. Configure the cache layer to use an in-memory database to store hot data, the distributed database layer to use a distributed file system to store warm data, and the archive storage layer to use a low-cost tape library or cloud storage service to store cold data; Set a data migration threshold. When the data access frequency falls below the preset threshold, the data will be automatically migrated from the cache layer to the distributed database layer. When the data access frequency decreases further, it will be migrated to the archive storage layer.

8. The blockchain-based offshore wind farm operation and maintenance data management system according to claim 6 is characterized in that: The blockchain module includes the following functions: Deploy blockchain nodes at each operation and maintenance site of the offshore wind farm, with each node equipped with independent data storage units and computing resources; An improved Byzantine fault-tolerant algorithm is used as the consensus mechanism, and the validity of data writing is determined through multiple rounds of voting; Data writing rules are defined through smart contracts, including data format verification, timestamp marking, and signature verification. At the same time, access control logic is designed to limit the access scope of different roles to data.

9. The blockchain-based offshore wind farm operation and maintenance data management system according to claim 6, characterized in that: The index optimization module includes the following functions: Establish an initial index for the temperature data in the distributed database. The index fields include device number, timestamp and data type. Dynamically adjust the index structure based on data access patterns. When the access frequency of a certain type of data increases significantly, it is assigned a higher index priority. The distributed database is divided into multiple shards. Each shard stores operation and maintenance data for a specific time period or a specific type. The shard size is dynamically adjusted according to the amount of data.

10. The blockchain-based offshore wind farm operation and maintenance data management system according to claim 6, characterized in that: The report generation module includes the following functions: Extract required operation and maintenance data from the distributed storage architecture, including equipment status data, environmental monitoring data, fault record data, and maintenance plan data; Perform statistical analysis on the extracted data to calculate equipment status change trends, failure frequency distribution, and maintenance plan execution rate; Integrate analysis results into visual charts, including line charts, bar charts, and pie charts, and generate operation and maintenance data reports containing detailed data and analysis conclusions.

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