A wind turbine main control system service domain data distributed storage method and system

Abstract

The application provides a kind of wind turbine master control system service domain data distributed storage method and system, the method comprises: in wind farm production control area deployment central pipe storage area, in each wind turbine master control system deployment data storage node;Central pipe storage area is responsible for distributing backup cluster configuration information and managing each node metadata and equipment state;Data storage node will be collected service domain data shunt into multistage cache partition, write into local persistent storage, and according to backup cluster configuration through inter-node synchronization service data backup to ring network designated off-site node;Central pipe storage area gathers the data location label and state information reported by each node, executes redundancy strategy and backup control.The application realizes data distributed redundancy storage using ring network node mutual backup mechanism without increasing hardware facilities conditions, effectively avoids single point failure and data island problem.

Description

Technical Field

[0001] This invention relates to the field of wind turbine data storage technology, and in particular to a distributed storage method and system for business domain data of a wind turbine main control system. Background Technology

[0002] The data storage architecture of wind farms is mainly divided into two categories: central monitoring system storage at the wind farm site and local storage at the wind turbine site. At the central monitoring system site, the data storage environment is relatively closed and has a high level of security protection, in accordance with power monitoring system standards such as GB / T36572-2018. However, due to limitations in the real-time communication efficiency and network bandwidth of industrial field communication protocols (such as Modbus / Tcp, OPC UA, and 104 protocol), the data acquisition granularity of the central monitoring system is usually coarse, with sampling frequencies often below 1Hz. This low-frequency acquisition method cannot meet the urgent need for refined analysis of special operating conditions in the context of the large-scale and intelligent development of wind turbines. For example, the central monitoring system struggles to effectively capture and store high-frequency fault waveform data or instantaneous operating condition data for key components such as gearbox vibration and generator bearing temperature.

[0003] To compensate for the insufficient granularity of data collection on the central monitoring side, existing technologies primarily rely on local storage systems on the wind turbine side to collect and cache high-frequency or event-triggered data. The mainstream local storage solutions mainly include the following two types: Firstly, there's the issue of local storage based on the programmable logic controller (PLC) of the main control system: Existing wind turbine PLCs typically possess basic fault recording capabilities, caching trigger event data in their local storage. However, due to the inherent limitations of PLC system architecture, their storage space is extremely small and computing resources are scarce. Achieving high-frequency data acquisition for specific business domains often relies on cumbersome user-defined development, resulting in poor versatility and high costs. This leads to an extremely limited amount of locally stored data, with a large amount of valuable special operating condition event data being discarded due to storage resource depletion, failing to support multi-faceted, in-depth data analysis and fault reproduction.

[0004] Secondly, there is extended storage based on Remote Terminal Units (RTUs): some solutions expand data acquisition and storage capabilities by deploying additional RTUs. However, this approach not only requires additional hardware investment, increasing system construction and maintenance costs, but also the data acquisition frequency of the RTUs is limited by the transmission efficiency of communication protocols and network bandwidth, making it difficult to fundamentally solve the problem of real-time throughput of high-frequency, massive data.

[0005] Furthermore, both traditional localized storage architectures based on PLCs and RTUs suffer from severe single-point-of-failure risks and data silo effects. In existing architectures, each wind turbine is equipped with an independent storage device. If this device experiences hardware damage (such as hard drive failure), storage system crashes, or data loss, the corresponding historical data for that wind turbine will be permanently lost, directly impacting the reconstruction of major equipment problems and accident tracing. Simultaneously, during communication or equipment failures, critical data from that turbine cannot be uploaded to the central monitoring system in a timely manner, creating information silos and interfering with the overall optimized scheduling and group control operation and maintenance decisions of the wind farm.

[0006] In summary, the existing technology lacks a wind turbine main control data storage solution that can meet the localized acquisition and storage needs of high-frequency, high-capacity business data of wind turbines, effectively avoid single points of failure and break down data silos, and without significantly increasing hardware costs. Summary of the Invention

[0007] This invention addresses the technical problems existing in the prior art by providing a distributed storage method and system for business domain data of a wind turbine main control system. By constructing a distributed storage architecture of "centralized storage area + data storage nodes", it achieves multi-level caching, mutual backup between nodes, and centralized and unified management of business domain data of wind turbines without increasing hardware costs.

[0008] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows: A method for distributed storage of business domain data in a wind turbine main control system includes: A central storage area is deployed in the production control area of ​​the wind farm, and data storage nodes are deployed in the main control system of each wind turbine. The central storage area is used to distribute the generated backup cluster configuration information to each data storage node and manage the metadata information and equipment status of each data storage node. The data storage node will distribute the collected business domain data into a preset multi-level data cache partition, and write the data in the multi-level data cache partition to the local storage for persistent storage. According to the backup cluster configuration information, the data storage node will back up the locally stored business domain data to a designated remote data storage node in the same ring network through the data synchronization service between data storage nodes. The central storage area collects data location tag information and status information reported by each data storage node, and executes data redundancy strategies and backup controls.

[0009] As a further improvement to the method of the present invention: the data storage node will split the collected business domain data into a preset multi-level data cache partition, which is based on the data collection frequency or data type, storing low-frequency regular data into the regular data cache area and high-frequency real-time data into the high-frequency data cache area.

[0010] As a further improvement to the method of the present invention: the data storage node writes the data in the multi-level data cache partition to local persistent storage, including: Data from the data cache is divided into real-time data and time-series data. The real-time data includes second-level data and statistical data, and the time-series data includes regular data, high-frequency data, and event-triggered data. The real-time data is written into an embedded real-time database; The time series data is written into the embedded time series database, and overdue rolling deletion is performed according to the capacity and configuration of the external storage device.

[0011] As a further improvement to the method of the present invention: the method further includes: For time-series data, the data storage node obtains time-series data from a specified remote data storage node within the same ring network according to the metadata configuration information through the remote data synchronization service, and stores the obtained time-series data in the local embedded time-series database. For real-time data, the data storage node obtains real-time data from a designated remote data storage node within the same ring network through a data synchronization interface to meet the collaborative computing needs between wind turbine units.

[0012] As a further improvement to the method of the present invention: the method further includes that the data storage node receives a data access request through a unified data access interface service and routes and forwards the data access request to the data access interface proxy service of the data storage node. The data access interface proxy service performs user authentication for the data access request. If authentication fails, an error message should be reported to the upstream service provider. If authentication is successful, the data access interface proxy service acts as a data accessor to obtain request data from the downstream data interface, temporarily stores the obtained data, and then feeds it back to the unified data access interface service, which finally delivers it to the data access end.

[0013] As a further improvement to the method of the present invention: the data storage node collects service domain data from the wind turbine generator, including: Regular time-series data collection, continuously collecting data according to a predetermined sampling frequency; Operating condition triggers data acquisition. When the operating parameters of the wind turbine reach a preset threshold, high-frequency data acquisition within a preset time window is automatically triggered. Actively trigger data collection by responding to commands from remote or local applications and initiating data collection within a preset time window.

[0014] As a further improvement to the method of the present invention: the central storage area includes a metadata configuration management module, a data synchronization storage management module, and a device status monitoring module; The metadata configuration management module is used to maintain the cluster metadata information master table, which records the partitioning information of each backup cluster; The data synchronization and storage management module is used to maintain a storage metadata master table, which records the data location tag information corresponding to the business domain data stored by each data storage node. The device status monitoring module is used to maintain the node heartbeat table, which records the device status information reported by each data storage node.

[0015] As a further improvement to the method of the present invention: the central storage area and each data storage node interact in the following manner: The central storage area distributes basic wind turbine information and backup cluster configuration information to each data storage node to control the number of data storage backup nodes, the number of replica backups, and the network distribution of data nodes. Each data storage node reports its device status information to the central storage area based on a preset heartbeat monitoring mechanism; The central storage area aggregates the data location tag information and status information corresponding to the business domain data stored on each data storage node through a data synchronization mechanism, and manages the data redundancy and backup topology accordingly, without performing full centralized persistent storage of the business domain data.

[0016] The present invention also provides a distributed storage system for business domain data of a wind turbine main control system, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute the distributed storage method for business domain data of the wind turbine main control system.

[0017] The present invention also provides a computer-readable storage medium storing a computer program / instruction that is programmed or configured to execute the distributed storage method for business domain data of the wind turbine master control system via a processor.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention separates data storage management functions from data storage entities by deploying a central storage area in the wind farm's production control zone and data storage nodes in each wind turbine's main control system. The central storage area centrally manages the metadata, equipment status, and backup cluster configuration information of each data storage node. Each data storage node distributes the collected business domain data into multi-level data cache partitions for local persistent storage. Simultaneously, based on the backup cluster configuration information, it backs up the locally stored business domain data to a designated remote data storage node within the same ring network through inter-node data synchronization services. Finally, the central storage area aggregates the data location tag information and status information reported by each data storage node to execute data redundancy strategies and backup control. Compared with existing technologies that rely on single-point storage in the main control system or require additional remote terminal unit hardware, this invention achieves distributed redundant storage of business domain data without adding hardware facilities. It utilizes a mutual backup mechanism between data storage nodes within the ring network to effectively avoid data loss and data silos caused by the failure of a single storage device, ensuring the integrity and traceability of wind turbine operation data, thereby improving the reliability and data security of wind farm operation and maintenance. Attached Figure Description

[0019] Figure 1 This is a flowchart of a distributed storage method for business domain data in a wind turbine main control system according to a specific embodiment of the present invention.

[0020] Figure 2 This is a schematic diagram of the distributed storage network topology for wind farm master control data in a specific embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the data storage structure of the data storage node in a specific embodiment of the present invention.

[0022] Figure 4 This is a schematic diagram illustrating the distributed storage principle of central and local master control data for wind turbines in a specific embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of the data flow and storage logic of the wind turbine main control system in a specific embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] like Figure 1As shown in the figure, this embodiment provides a distributed storage method for business domain data of a wind turbine main control system, including: Step 1: Deploy a central storage area in the wind farm production control area and deploy data storage nodes in the main control system of each wind turbine; the central storage area is used to distribute the generated backup cluster configuration information to each data storage node and manage the metadata information and equipment status of each data storage node; Step 2: The data storage node will distribute the collected business domain data into a preset multi-level data cache partition, and write the data in the multi-level data cache partition to the local persistent storage. According to the backup cluster configuration information, the data storage node will back up the locally stored business domain data to a specified remote data storage node in the same ring network through the data synchronization service between data storage nodes. Step 3: The central storage area collects the data location tag information and status information reported by each data storage node, and executes data redundancy strategies and backup controls.

[0026] like Figure 2 As shown, in wind farms, wind turbines are typically networked in a loop-type ring network. This loop-type ring network is usually used as the infrastructure for transmitting data and control signals, responsible for connecting the station control-side monitoring network with the main control network of the wind turbine generators, providing real-time data communication and control services. Based on the characteristics of the wind farm's collector lines, wind turbine generators are generally grouped into loops according to their respective collector lines. Based on this ring topology, each turbine generator acts as a node, forming one or more closed ring networks according to its group. Each node within a ring network can communicate with two adjacent nodes, thus forming an interconnected communication link. According to the characteristics of the wind farm network topology, the main control data distributed storage system or device establishes a central storage area and data storage nodes within the wind farm production control area and within the local main control system of the wind turbines, respectively. One or more data backup cluster networks are deployed using the ring network. Data nodes within the clusters back each other up and synchronize data to the central storage area for centralized persistent storage, thus forming a central-local wind turbine business domain data distributed storage network.

[0027] Based on the characteristics of the intermediate control node architecture, this embodiment divides the wind turbine main control data storage system into a central management and storage integrated area and data storage nodes. A data management and storage integrated area is set up on the central side to manage data node metadata and cluster configuration information, and adopts data redundancy and backup strategies to parse and store business domain data and control user access. Data storage nodes are set up in the wind turbine main control system, and multi-level cache partitions are planned to efficiently store key data of the wind turbine business domain.

[0028] This embodiment fully adapts to the future predictive maintenance and high reliability requirements of wind turbine equipment. Without increasing hardware facilities, it realizes distributed storage of wind turbine business domain data, avoids single points of failure, continuously meets the increasingly refined operation and maintenance management needs of wind turbines, and improves the availability rate and power generation of wind turbine equipment.

[0029] like Figure 3 As shown, the data storage structure of the wind turbine main control system in this embodiment can be divided into the application layer, data persistence layer, data forwarding and transmission layer, data caching layer and data acquisition layer from top to bottom.

[0030] The application layer primarily encompasses applications, configuration, protocol suites, data access entry points, and node synchronization access terminals. This layer provides external interface functions and services for the entire system. Specifically, the application layer is one of the core components of the wind turbine main control system. It includes various applications and interfaces, providing interaction methods between users and the system. Through applications, users can perform various operations and monitor wind turbine data. The configuration layer is used to set and manage the parameters and options for data storage in the wind turbine main control system. Through the configuration layer, users can flexibly configure and adjust the system to meet specific needs. The protocol suite layer supports communication and data exchange with other systems and devices. Its main function is to provide protocol parsing and data conversion functions, uniformly parsing and processing data from different devices and systems, enabling data exchange and sharing between different systems. The data access entry layer is the interface for data interaction between the central monitoring layer client and the wind turbine main control system. It provides a unified access entry point for the central monitoring layer client, allowing it to easily obtain and manage wind turbine data. The central monitoring system can then use this to monitor and control the wind turbine in real time, perform fault diagnosis, and optimize performance. The node synchronization access terminal provides an access interface for local users to access data of remote wind turbine units within a specified ring network. Through embedded devices and node synchronization services, it enables data access and management of wind turbine units located in remote locations.

[0031] Below the application layer, the data persistence layer bears the core responsibility for data storage and access control. The data persistence layer is primarily responsible for implementing local persistence, access control, and remote data synchronization of wind turbine business data. The entire data persistence layer provides a unified data access interface service to the data access client. The data persistence layer holds the client's user information and can initiate data access requests to remote locations. When the unified data access interface service receives a request, it blocks the user request and routes it to the data access interface proxy service. The data access interface proxy service is a key component in implementing data access control and access security isolation.

[0032] In this embodiment, the data storage node receives data access requests through the unified data access interface service and routes and forwards the data access requests to the data access interface proxy service of the data storage node. The data access interface proxy service performs user authentication for data access requests; If authentication fails, the error information will be reported directly to the upstream service. If authentication is successful, the data access interface proxy service, acting as the actual data accesser, obtains the requested data from the downstream data interface, temporarily stores the obtained data, and then feeds it back to the unified data access interface service, which finally delivers it to the data access end.

[0033] After completing the security authentication and routing of data access requests, the data storage node also needs to formally write the preprocessed data in the cache layer to the persistent storage medium to achieve long-term preservation and subsequent traceability of business data. In this embodiment, the data storage node writes the data in the multi-level data cache partitions to local persistent storage, including: Data from the data cache is divided into real-time data and time-series data. Real-time data has high requirements for real-time writing and reading performance and mainly stores real-time second-level data and statistical data of the current wind turbine business. Time-series data includes regular data, high-frequency data and event-triggered data. Write real-time data into an embedded real-time database; Time series data is written to an embedded time series database for short-term storage, and expired data is rolled over and deleted according to the capacity and configuration of the external storage device. Real-time and time-series data mainly come from local data cache and are asynchronously batch-loaded into the database via the data transmission layer and data writing service.

[0034] In this embodiment, for time series data, the data storage node obtains time series data from a specified remote data storage node within the same ring network according to the metadata configuration information through the remote data synchronization service, and stores the obtained time series data in the local embedded time series database. For real-time data, the data storage node obtains real-time data from a designated remote data storage node within the same ring network through a data synchronization interface to meet the collaborative computing needs between wind turbine units.

[0035] Between the data persistence layer and the data caching layer, the data forwarding and transport layer acts as a bridge. In the entire data flow business, the data forwarding and transport layer plays a connecting role, responsible for formatting the data in the local cache according to the data entry standard, and classifying and forwarding the data to the persistence layer, providing it to the write service to perform the entry operation.

[0036] The data caching layer is the core buffer of the data storage node, employing a partitioning strategy to handle data of varying frequencies. In this embodiment, the data storage node distributes the collected business domain data into preset multi-level data caching partitions. Based on the data collection frequency or data type, low-frequency routine data is stored in the routine data caching area, while high-frequency real-time data is stored in the high-frequency data caching area.

[0037] Specifically, the data caching layer is divided into a regular data cache and a high-frequency data cache based on the data acquisition frequency. The regular data cache is mainly used to store low-frequency acquired standard measurement point data, such as the daily operating status and power consumption of wind turbines. The high-frequency data cache is used to store high-frequency acquired data on key indicators of critical components of wind turbines, such as real-time changes in wind speed, power, and torque. The high-frequency data cache is crucial for ensuring the real-time performance of the system. Due to the high acquisition frequency of high-frequency data, directly writing it to the database would place a significant load on the database server. Therefore, by caching this data first in the high-frequency data cache and then writing it to the database in batches, both the real-time performance of data acquisition and the efficiency of system operation can be improved.

[0038] The data caching layer receives data from the underlying data acquisition layer. The data acquisition layer is primarily responsible for collecting data from various sensors on the wind turbine equipment. This data mainly includes routine data and high-frequency data. In this embodiment, the data storage node collects service domain data from the wind turbine, including: (1) Periodic time-series data collection: Data is continuously collected according to a predetermined sampling frequency. This collection method can ensure the continuity and real-time nature of the data, and provide continuous data support for the operation of the system.

[0039] (2) Condition-triggered data acquisition: When the operating parameters of the wind turbine reach the preset threshold, high-frequency data acquisition within a preset time window is automatically triggered. Specifically, the acquisition method is based on specific operating conditions to trigger the acquisition of data within a certain time window. When certain operating parameters of the wind power equipment reach the preset threshold, the system will automatically trigger data acquisition, record and analyze the data, and provide a basis for equipment maintenance and fault early warning.

[0040] (3) Actively trigger data collection: respond to remote or local application commands to trigger data collection within a preset time window. This means that the remote or local application actively triggers the collection of data within a certain time window. When it is necessary to obtain data within a specific time period, data can be obtained by actively triggering data collection, which is beneficial for scenarios involving real-time iterative calculation and analysis of data.

[0041] The above layers constitute the internal structure of the data storage node, while the central storage area undertakes the functions of overall management and scheduling. In this embodiment, the central storage area includes a metadata configuration management module, a data synchronization storage management module, and a device status monitoring module; The metadata configuration management module is used to maintain the cluster metadata information master table, which records the partitioning information of each backup cluster; The data synchronization and storage management module is used to maintain the storage metadata master table, which records the data location tag information corresponding to the business domain data stored on each data storage node; The device status monitoring module is used to maintain the node heartbeat table, which records the device status information reported by each data storage node.

[0042] In this embodiment, the central storage area and each data storage node interact in the following ways: The central storage area distributes basic information of wind turbine units and configuration information of backup clusters to each data storage node in order to control the number of data storage backup nodes, the number of replica backups, and the network distribution of data nodes. Each data storage node reports its device status information to the central storage area based on a preset heartbeat monitoring mechanism; The central storage area aggregates the data location tag information and status information corresponding to the business domain data stored on each data storage node through a data synchronization mechanism, and manages the data redundancy and backup topology accordingly, without performing full centralized persistent storage of the business domain data.

[0043] In specific application embodiments, based on the intermediate control node architecture and combined with the data storage characteristics of the wind power business domain, to avoid single points of failure and data silos, the main control data of the wind turbine business domain is redundantly backed up and stored in both the central storage area and the wind turbine data storage nodes. The entire system is divided into two main parts: the central storage area and the data storage nodes. The two work together through three mechanisms: distribution, synchronization and aggregation, and heartbeat monitoring.

[0044] The central storage area is primarily responsible for the metadata configuration management and distribution of data storage nodes and backup cluster information, the persistent storage management of main control business domain data, and the monitoring of data storage node device status, providing users with a unified interface for interaction, management, and monitoring. For example... Figure 4As shown, the user application layer of the central storage area integrates three core management functions. Metadata and configuration management maintains a master table of cluster metadata information, used to record the partitioning information of each wind turbine backup cluster (e.g., wind turbine backup cluster 01, wind turbine backup cluster 02, etc.), thereby clarifying the backup cluster to which each data node belongs and its topology within the ring network. Storage metadata management corresponds to maintaining a master table of storage metadata, used to record the data location tags corresponding to the business data stored by each wind turbine data node (e.g., wind turbine 001 data location tag 1, wind turbine 002 data location tag 1, etc.), thereby achieving global positioning and indexing of distributed storage data. Equipment status monitoring corresponds to maintaining a node heartbeat table, used to record the heartbeat status reported by each wind turbine node in real time (e.g., wind turbine 001 heartbeat, wind turbine 002 heartbeat, etc.), achieving real-time monitoring of the data node operating status and storage capacity. The central storage area controls the number of data storage backup nodes, the number of replica backups, and the network distribution of data nodes by configuring and distributing basic wind turbine information and cluster information.

[0045] The central storage area distributes wind turbine basic information tables and wind turbine storage metadata tables to each data node. The wind turbine basic information table clearly defines the primary and backup roles of each node (e.g., wind turbine 001 is the primary, wind turbine 002 is the backup), while the storage metadata table specifies the allocation relationship of data location tags. Simultaneously, the central storage area, through a data synchronization mechanism, aggregates the business data, data location tag information, and data record information stored by the data nodes to the central storage area, forming a globally unified data directory. Furthermore, each data storage node reports equipment status information to the central storage area based on a predetermined heartbeat monitoring mechanism. This information includes heartbeat and storage capacity information, and the central storage area updates the node heartbeat table accordingly.

[0046] The data storage node side includes multiple node instances such as Wind Turbine 001 data node and Wind Turbine 002 data node. Each data storage node is deployed within the main control system of each wind turbine unit and is responsible for the data storage management of business data within its cluster, based on the cluster backup information and storage strategy distributed by the central storage management area. Specifically, the central storage management area distributes basic and backup cluster information of the wind turbines to the data storage nodes. After receiving and initializing the information, the data storage nodes collect, classify, and dump data, and persistently write it to the local embedded database of the storage node, recording the data storage location tag and log information. During the data collection process, the nodes acquire operational data from various sensors of the wind power equipment. Based on the data collection frequency or type, low-frequency routine data is stored in the routine data cache area, and high-frequency real-time data is stored in the high-frequency data cache area. In the classification and dumping stage, the cached data is formatted and classified for forwarding. In the persistent writing stage, real-time data is written to the embedded real-time database, and time-series data is written to the embedded time-series database. At the same time, each data storage node periodically reports equipment status information to the central storage management area and provides business data access services.

[0047] Through the above-mentioned central-local collaborative architecture design, this embodiment realizes distributed redundant storage and unified management and control of wind turbine business domain data, which fully adapts to the future predictive maintenance and high reliability requirements of wind turbine equipment, and avoids the risk of single point of failure without increasing hardware facilities.

[0048] The above describes the functional division and interaction mechanism between the central storage area and data storage nodes from a static architecture perspective. The following section, combined with the dynamic operation process after system startup, further illustrates the complete flow path of data from acquisition to persistent storage.

[0049] like Figure 5 As shown, after the system starts, it first loads the main control system program and completes configuration initialization, establishing the parameter environment for the subsequent operation of each module. Next, the local data interface service is activated, and the data acquisition program starts, beginning to collect various operational data from the wind power equipment. The collected data, based on its type and triggering mechanism, is sent to the trigger data cache stage. The system determines the subsequent processing path based on the cached data trigger type. During the caching stage, data is temporarily stored in a temporary storage area and processed differently according to the cached data type: one part of the data is used for application-layer data access, providing a standardized data outlet for real-time monitoring and persistence of data in the central storage area through the unified data access interface service; the other part of the data is used for local or remote data node access, supporting remote data synchronization services and real-time data collaborative computing. The remote data synchronization service is responsible for backing up data to remote node data backup storage, achieving cross-regional redundancy and disaster recovery. Simultaneously, the local data dumping service runs according to a set cycle, dumping the cached data to the central storage area and further writing it to the local embedded database to complete persistent data storage. The entire process involves periodic read and write operations of local data, ensuring efficient data transfer, backup, and access between local and remote locations, ultimately achieving distributed storage management of wind turbine operation data.

[0050] This embodiment also provides a distributed storage system for business domain data of a wind turbine main control system, including a microprocessor and a memory interconnected thereto, wherein the microprocessor is programmed or configured to execute a distributed storage method for business domain data of a wind turbine main control system.

[0051] This embodiment also provides a computer-readable storage medium storing a computer program / instruction that is programmed or configured to execute a distributed storage method for business domain data of a wind turbine main control system via a processor.

[0052] Those skilled in the art will understand that the above embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

[0053] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A distributed storage method for business domain data in a wind turbine main control system, characterized in that, include: A central storage area is deployed in the production control area of ​​the wind farm, and data storage nodes are deployed in the main control system of each wind turbine. The central storage area is used to distribute the generated backup cluster configuration information to each data storage node, and to manage the metadata information and device status of each data storage node; The data storage node will distribute the collected business domain data into a preset multi-level data cache partition, and write the data in the multi-level data cache partition to the local storage for persistent storage. According to the backup cluster configuration information, the data storage node will back up the locally stored business domain data to a designated remote data storage node in the same ring network through the data synchronization service between data storage nodes. The central storage area collects data location tag information and status information reported by each data storage node, and executes data redundancy strategies and backup controls.

2. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The data storage node distributes the collected business domain data into preset multi-level data cache partitions. Based on the data collection frequency or data type, low-frequency routine data is stored in the routine data cache area, and high-frequency real-time data is stored in the high-frequency data cache area.

3. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The data storage node writes data from the multi-level data cache partitions to local persistent storage, including: Data from the data cache is divided into real-time data and time-series data. The real-time data includes second-level data and statistical data, and the time-series data includes regular data, high-frequency data, and event-triggered data. The real-time data is written into an embedded real-time database; The time series data is written into the embedded time series database, and overdue rolling deletion is performed according to the capacity and configuration of the external storage device.

4. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The method further includes: For time-series data, the data storage node obtains time-series data from a specified remote data storage node within the same ring network according to the metadata configuration information through the remote data synchronization service, and stores the obtained time-series data in the local embedded time-series database. For real-time data, the data storage node obtains real-time data from a designated remote data storage node within the same ring network through a data synchronization interface to meet the collaborative computing needs between wind turbine units.

5. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The method further includes the data storage node receiving a data access request through a unified data access interface service, and routing and forwarding the data access request to the data access interface proxy service of the data storage node. The data access interface proxy service performs user authentication for the data access request. If authentication fails, an error message should be reported to the upstream service provider. If authentication is successful, the data access interface proxy service acts as a data accessor to obtain request data from the downstream data interface, temporarily stores the obtained data, and then feeds it back to the unified data access interface service, which finally delivers it to the data access end.

6. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The data storage node collects service domain data from the wind turbine, including: Regular time-series data collection, continuously collecting data according to a predetermined sampling frequency; Operating condition triggers data acquisition. When the operating parameters of the wind turbine reach a preset threshold, high-frequency data acquisition within a preset time window is automatically triggered. Actively trigger data collection by responding to commands from remote or local applications and initiating data collection within a preset time window.

7. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The central storage area includes a metadata configuration management module, a data synchronization storage management module, and a device status monitoring module. The metadata configuration management module is used to maintain the cluster metadata information master table, which records the partitioning information of each backup cluster; The data synchronization and storage management module is used to maintain a storage metadata master table, which records the data location tag information corresponding to the business domain data stored by each data storage node. The device status monitoring module is used to maintain the node heartbeat table, which records the device status information reported by each data storage node.

8. The distributed storage method for business domain data of the wind turbine main control system according to claim 1, characterized in that, The central storage area interacts with each data storage node in the following ways: The central storage area distributes basic wind turbine information and backup cluster configuration information to each data storage node to control the number of data storage backup nodes, the number of replica backups, and the network distribution of data nodes. Each data storage node reports its device status information to the central storage area based on a preset heartbeat monitoring mechanism; The central storage area aggregates the data location tag information and status information corresponding to the business domain data stored on each data storage node through a data synchronization mechanism, and manages the data redundancy and backup topology accordingly, without performing full centralized persistent storage of the business domain data.

9. A distributed storage system for business domain data of a wind turbine main control system, comprising interconnected microprocessors and memory, characterized in that, The microprocessor is programmed or configured to execute the distributed storage method for business domain data of the wind turbine main control system according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program / instructions, characterized in that, The computer program / instructions are programmed or configured to execute, via a processor, the distributed storage method for business domain data of the wind turbine main control system as described in any one of claims 1 to 8.

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