Wind power plant data optimization storage management system and method

Abstract

The invention discloses a wind power plant data optimization storage management system and method, and relates to the technical field of data storage management. Comprising a data acquisition module used for acquiring and storing operation data of a wind power plant to a storage node, performing copy number evaluation on the operation data, and distributing the copy number according to a copy number evaluation result; the storage evaluation module is used for acquiring storage node information, performing storage space evaluation on the storage node information, performing migration evaluation on the copies according to a storage space evaluation result, and determining the copies required to be migrated according to a migration evaluation result; and the copy optimization module is used for carrying out copy placement evaluation on the storage node information. According to the method, the problems of storage space waste and insufficient system fault-tolerant capability caused by a fixed number of copies in a traditional wind power plant data storage management system are solved, the utilization efficiency of storage resources is improved, the fault-tolerant capability of the system is enhanced, and the storage cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data storage management, and specifically to a wind farm data optimization storage management system and method. Background Art

[0002] Wind farm data usually includes real-time monitoring data and historical records, covering information such as wind speed, wind direction, temperature, humidity, fan performance, power generation, etc. These data are collected in real time by sensors, devices, and wind turbine monitoring systems distributed at various locations in the wind farm, and are transmitted to the storage system through wired or wireless networks for processing and storage. Real-time data is used to monitor the operating status of the wind farm, while historical data is used to analyze the long-term trends of the wind farm, equipment maintenance, and optimize operating strategies.

[0003] For example, a Chinese patent with the publication number CN117633881A, a method for optimizing the processing of power data, relates to the field of power data processing. The key points of its technical solution include: the user verifies the identity through the Web browsing page of the client, determines whether to allow logging in to the system, and obtains the corresponding access and management permissions according to the role assigned to the user identity. The user unlocks the power data of the corresponding security level by carrying the corresponding key assigned to the role; the file packages at each collection point are encrypted with the public key issued by the central processing unit and uploaded to the cloud through the wireless network. The cloud decrypts the file packages with the private key and stores them; the improved K-means clustering algorithm uses the median of the power data in each file package as the clustering centroid, iteratively aggregates the power data in the file package, and displays the power data not included in the clustering value set as abnormal, and generates warning information and new commands to achieve encrypted transmission and access of power data and highlighting of abnormal data.

[0004] For example, a Chinese patent with the publication number CN117743876B, a method for optimizing the management of intelligent warehouse data based on cloud computing, includes: mapping each temperature and humidity data into the clustering space; obtaining the minimum neighborhood number and neighborhood radius of the DBSCAN clustering algorithm; obtaining the abnormality degree of each temperature and humidity data in the clustering space to obtain the updated neighborhood radius of each temperature and humidity data; obtaining the discrete evaluation value of each temperature and humidity data in the clustering space to obtain discrete data; obtaining the first-dimensional isolation evaluation criterion and the second-dimensional isolation evaluation criterion of each discrete data in the clustering space to obtain the isolation evaluation value of each discrete data, and then obtaining non-isolated discrete data; adaptively updating the minimum neighborhood number of non-isolated discrete data and combining the updated neighborhood radius of temperature and humidity data to cluster and compress the storage of temperature and humidity data. The present invention aims to reduce redundant data in temperature and humidity data and improve the compression efficiency.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of the present application, it is found that the above technologies have at least the following technical problems: In a traditional wind farm data storage management system, the number of data copies is usually preset and fixed. The update frequency and importance of data vary, and a fixed number of copies may result in too many copies for data with low update frequency, thus occupying a large amount of storage space. For data with high update frequency and high value, a fixed number of copies may not fully guarantee its reliability, resulting in insufficient system fault tolerance. Summary of the Invention

[0006] Technical Problem to be Solved

[0007] In view of the deficiencies of the prior art, the present invention provides a wind farm data optimized storage management system and method, which solves the problems of wasted storage space and insufficient system fault tolerance caused by a fixed number of copies in a traditional wind farm data storage management system, improves the utilization efficiency of storage resources, enhances the system's fault tolerance, and reduces the storage cost.

[0008] Technical Solution

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions: A wind farm data optimized storage management system includes: a data acquisition module, configured to collect and store the operation data of a wind farm in a storage node, and evaluate the number of copies of the operation data, and allocate the number of copies according to the evaluation result of the number of copies; a storage evaluation module, configured to obtain storage node information, evaluate the storage space of the storage node information, and evaluate the migration of copies according to the evaluation result of the storage space, and determine the copies to be migrated according to the evaluation result of the migration; a copy optimization module, configured to evaluate the placement of copies of the storage node information, and determine the storage node for storing the copies according to the evaluation result of the placement.

[0010] Further, the specific steps of collecting and storing the operation data of a wind farm in a storage node are as follows: The operation data includes: real-time data of the wind farm and system operation log records, specifically the basic number of copies, data access frequency, and data update frequency of different real-time data, and also includes the system's maximum access frequency, system's maximum update frequency, and system's maximum number of copies operation data; and store the operation data in the storage node.

[0011] Further, the specific steps of evaluating the number of copies of the operation data are as follows: Comprehensively calculate the basic number of copies, data access frequency, system's maximum access frequency, system's maximum number of copies, data update frequency, and system's maximum update frequency of different storage information to obtain an evaluation value of the number of copies.

[0012] Further, the specific steps for allocating the number of replicas according to the evaluation result of the number of replicas are as follows: Compare the replica number evaluation value with the number interval threshold in real time. If the replica number evaluation value is less than or equal to the number interval threshold, the system creates a new replica and reallocates storage nodes for the new replica. If the replica number evaluation value is greater than the number interval threshold, the system deletes historical data replicas according to the data access frequency.

[0013] Further, the specific steps for evaluating the storage space of storage node information are as follows: The storage node information includes: the used storage capacity and the total storage capacity of all current storage nodes; Traverse the storage nodes, comprehensively calculate the used storage capacity and the total storage capacity to obtain a space evaluation value; And comprehensively calculate the space evaluation value, the used storage capacity, and the total storage capacity to obtain a storage evaluation value.

[0014] Further, the specific steps for evaluating replica migration according to the storage space evaluation result are as follows: Obtain the data access frequency, the system maximum access frequency, the data update frequency, and the system maximum update frequency, and comprehensively calculate the storage evaluation value, the data access frequency, the system maximum access frequency, the data update frequency, and the system maximum update frequency to obtain a storage adjustment value.

[0015] Further, the specific steps for determining the replicas to be migrated according to the migration evaluation result are as follows: Compare the storage adjustment value with the storage threshold in real time. If the storage adjustment value is greater than or equal to the storage threshold, determine the replicas to be migrated according to the load and space conditions of the source storage node, and record the number of migrated replicas and the replica changes of the source node. If the storage adjustment value is less than the storage threshold, it means that the storage resources are sufficient and storage can continue.

[0016] Further, the specific steps for evaluating replica placement of storage node information are as follows: Traverse the storage nodes, obtain the load data and space evaluation value of each storage node, and comprehensively analyze the load data of the storage nodes to obtain a node load value; The load data includes: memory usage rate and CPU usage rate. Comprehensively calculate the node load value, the space evaluation value, and the storage adjustment value to obtain a replica placement evaluation value.

[0017] Further, the specific steps for determining the storage node for storing replicas according to the placement evaluation result are as follows: Traverse the replica placement evaluation values, compare the replica placement evaluation values of each storage node, record the node with the largest replica placement evaluation value as the target storage node for placing replicas, and migrate the replica data to be migrated from the source storage node to the target storage node. After the replica migration storage is completed, update the replica information of the source storage node and continue to monitor the space condition of the replicas on the target storage node.

[0018] A method for optimizing the storage management of wind farm data includes the following steps: Step 1, collect and store the operation data of the wind farm in the storage nodes, evaluate the number of replicas of the operation data, and allocate the number of replicas according to the evaluation result of the number of replicas; Step 2, obtain the storage node information, evaluate the storage space of the storage node information, and perform a migration evaluation on the replicas according to the evaluation result of the storage space, and determine the replicas to be migrated according to the migration evaluation result; Step 3, perform a replica placement evaluation on the storage node information, and determine the storage node for storing the replicas according to the evaluation result of the placement evaluation.

[0019] Beneficial effects

[0020] The present invention has the following beneficial effects:

[0021] (1) For the wind farm data optimization storage management system and method, the number of replicas can be dynamically adjusted by comparing the replica number evaluation value with the number interval threshold. When the replica number evaluation value is low, the number of replicas is increased to improve data redundancy and fault tolerance ability, and ensure data reliability; when the evaluation value is high, the number of replicas is reduced to avoid waste of storage resources and optimize resource utilization, so that when optimizing the storage management of wind farm data, while ensuring the security and reliability of data, storage resources can be efficiently utilized and management costs can be reduced.

[0022] (2) For the wind farm data optimization storage management system and method, the obtained storage evaluation value can be used to judge the space tension degree of the storage node, help the system reasonably allocate storage resources, and avoid the problems of overloading and insufficient space of some nodes caused by blind storage.

[0023] (3) For the wind farm data optimization storage management system and method, through the obtained storage adjustment value, it can be used to accurately judge whether data needs to be migrated. By comparing the storage adjustment value with the storage threshold in real time, when the storage adjustment value is greater than or equal to the storage threshold, the situation that the current storage node has a heavy load or high data access frequency can be timely detected, prompting the system to migrate data and avoiding the decline of system performance; and when the storage adjustment value is less than the storage threshold, data migration is not required, saving resources.

[0024] (4) For the wind farm data optimization storage management system and method, through the comprehensively calculated replica placement evaluation value, the storage node most suitable for storing the replicas to be migrated can be accurately obtained, so that the system preferentially selects a node with better performance and more matching resources to store the replicas, improving the data reading speed and enhancing the overall stability and reliability of the wind farm data storage system.

[0025] Of course, it is not necessary for any product implementing the present invention to achieve all the above advantages simultaneously. Description of the drawings

[0026] Figure 1 This is a structural diagram of a wind farm data optimization storage management system of the present invention;

[0027] Figure 2 A flow chart of a method for optimizing storage and management of wind farm data according to the present invention;

[0028] Figure 3 A schematic diagram of replica placement evaluation values ​​in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] The embodiment of the present invention provides a technical solution: a wind farm data optimization storage management system, such as Figure 1 As shown, including:

[0031] The data collection module is used to collect and store the operation data of the wind farm in the storage node, evaluate the number of copies of the operation data, and allocate the number of copies according to the evaluation result;

[0032] The specific steps of collecting and storing the wind farm operation data in the storage node are as follows:

[0033] Preset collection time period, operation data includes: wind farm real-time data collected by various sensors within the preset collection time period and system operation log records, specifically the number of basic copies of different real-time data, data access frequency and data update frequency, and also includes the system maximum access frequency, system maximum update frequency and system maximum number of copies. The system maximum access frequency, system maximum update frequency and system maximum number of copies are all obtained through the system configuration file;

[0034] Eliminate outliers from the collected operating data, and fill in missing data during the collection process through interpolation and mean filling; store the operating data in different storage nodes.

[0035] The specific steps for evaluating the number of copies of running data are as follows: comprehensively calculate the number of basic copies of different real-time data, the maximum number of copies of the system, the data access frequency, the maximum access frequency of the system, the data update frequency, and the maximum update frequency of the system to obtain the evaluation value of the number of copies;

[0036] The method to obtain the estimated value of the number of replicas is as follows:

[0037]

[0038] In the formula, A represents the evaluation value of the number of replicas. The evaluation value of the number of replicas is comprehensively calculated based on the data access frequency, data update frequency, and the basic number of replicas, and is used to evaluate how many replicas are needed for the current data to ensure reliability. The larger the evaluation value of the number of replicas, the higher the demand for replicas for the data, and the system should allocate more replicas; the smaller the evaluation value of the number of replicas, the lower the demand for replicas for the data, and the number of replicas can be reduced. F represents the data access frequency, which is mainly used to reflect the activity of accessing the data. V represents the maximum access frequency of the system, specifically the maximum access frequency that the system can bear. By comparing the data access frequency with the maximum access frequency of the system, the proportion of the data access frequency occupying the maximum access frequency of the system can be obtained, and the proportion of the maximum bearing capacity of the system can be calculated. G represents the data update frequency, specifically the update frequency of the data within the preset acquisition time period. B represents the maximum update frequency of the system, specifically the maximum update frequency of the data allowed by the system. By comparing the data update frequency with the maximum update frequency of the system, the demand for the number of replicas for the data can be evaluated. H represents the basic number of replicas, specifically the number of replicas of the current data in the initial state. N represents the maximum number of replicas of the system, specifically the maximum number of replicas that the system can support, which is used to limit the upper limit of the replicas and avoid overloading system resources.

[0039] The specific steps for allocating the number of replicas according to the evaluation result of the number of replicas are as follows:

[0040] Compare the calculated evaluation value of the number of replicas with the threshold of the number interval in real time. If the evaluation value of the number of replicas is less than the threshold of the number interval, the system creates a new replica and reallocates storage nodes for the new replica. Because when the evaluation value of the number of replicas is less than the threshold of the number interval, it means that the number of replicas of the data is insufficient and cannot meet the reliability requirements of the data, and the system needs to increase the number of replicas to improve the redundancy and fault tolerance of the data. If the evaluation value of the number of replicas is greater than the threshold of the number interval, the system deletes the historical data replicas according to the data access frequency. Because when the evaluation value of the number of replicas exceeds the upper limit of the threshold, it means that the number of replicas is too large, which will waste storage resources and cause redundant replicas. The system reduces the number of replicas by one to optimize the use of storage resources until the evaluation value of the number of replicas is within the threshold of the number interval.

[0041] In the solution of this embodiment, the number of replicas can be dynamically adjusted by comparing the evaluation value of the number of replicas with the threshold of the number interval. When the evaluation value of the number of replicas is low, replicas are increased to improve the data redundancy and fault tolerance and ensure data reliability; when the evaluation value is high, replicas are reduced to avoid wasting storage resources and optimize resource utilization, so that when optimizing the storage management of wind farm data, while ensuring the security and reliability of the data, the storage resources can be efficiently utilized and the management cost can be reduced.

[0042] A storage evaluation module, which is used to obtain storage node information, evaluate the storage space of the storage node information, perform migration evaluation on the replicas according to the storage space evaluation result, and determine the replicas to be migrated according to the migration evaluation result;

[0043] The specific steps for evaluating the storage space of the storage node information are as follows:

[0044] The storage node information includes: the used storage capacity and the total storage capacity of all current storage nodes; traverse all storage nodes, normalize the used storage capacity of the current storage node and the total storage capacity of the current storage node, and perform comprehensive calculation on the normalized used storage capacity of the current storage node and the total storage capacity of the current storage node to obtain a space evaluation value; and perform comprehensive calculation on the space evaluation value, the used storage capacity of the current storage node, and the total storage capacity of the current storage node to obtain the storage evaluation value of the current storage node;

[0045] The way to obtain the storage evaluation value is as follows:

[0046]

[0047] In the formula, Q i represents the storage evaluation value of the i-th storage node, which is mainly used to represent the load situation of the current storage node. The higher the storage evaluation value, the greater the load of the current storage node and the tighter the space. W i represents the used storage capacity of the i-th storage node, specifically the occupied storage space of the i-th storage node. E i represents the total storage capacity of the i-th storage node, specifically the total storage capacity of the storage node of the i-th storage node. R i represents the space evaluation value of the i-th storage node, which mainly represents the ratio of the remaining storage space to the total storage space. Specifically, it is obtained through ratio calculation. The larger the space evaluation value, the more sufficient the space; the smaller the space evaluation value, the tighter the space.

[0048] In the solution of this embodiment, the obtained storage evaluation value can be used to judge the tightness of the storage space of the storage node, help the system reasonably allocate storage resources, and avoid problems such as overloading and insufficient space of some nodes caused by blind storage.

[0049] The specific steps for performing migration evaluation on the replicas according to the storage space evaluation result are as follows:

[0050] Obtain the data access frequency, the maximum system access frequency, the data update frequency, and the maximum system update frequency, and perform comprehensive calculation on the storage evaluation value, the data access frequency, the maximum system access frequency, the data update frequency, and the maximum system update frequency to obtain a storage adjustment value;

[0051] The method for obtaining the storage adjustment value is as follows:

[0052]

[0053] In the formula, T i represents the storage adjustment value of the i-th storage node, which is obtained by comprehensively calculating the storage evaluation value and the data access frequency. The larger the storage adjustment value, the more it means that the data needs to be migrated to a more suitable storage node. Q i represents the storage evaluation value of the i-th storage node, which is mainly used to represent the load condition of the i-th storage node. The higher the storage evaluation value, the greater the load and the more tense the space of the i-th storage node. F represents the data access frequency, which is mainly used to reflect the activity of accessing this data. V represents the maximum system access frequency, specifically the maximum access frequency that the system can bear. By comparing the data access frequency with the maximum system access frequency, the proportion of the data access frequency occupying the maximum system access frequency can be obtained, and the proportion of the maximum system bearing capacity can be calculated. By comparing the data access frequency with the maximum system access frequency, the impact of the data access frequency on the migration requirement can be obtained.

[0054] The specific steps for determining the replicas to be migrated according to the migration evaluation result are as follows:

[0055] Compare the storage adjustment value with the storage threshold in real time. If the storage adjustment value is greater than or equal to the storage threshold, determine the replicas to be migrated according to the load and space conditions of the source storage node, and record the number of migrated replicas and the replica changes of the source node. Because when the storage adjustment value is greater than or equal to the storage threshold, it means that the current storage node has a heavy load or a high data access frequency, and data migration is required. The number of migrated replicas can be determined according to the proportion of the load. If the storage adjustment value is less than the storage threshold, it means that the storage resources are sufficient for storage. Because when the storage adjustment value is less than the storage threshold, it means that the current storage node has a light load or a low data access frequency, and the system does not need to immediately migrate the data, and the data can continue to be retained on the current node.

[0056] In the solution of this embodiment, the obtained storage adjustment value can be used to accurately judge whether data needs to be migrated. By comparing the storage adjustment value with the storage threshold in real time, when the storage adjustment value is greater than or equal to the storage threshold, the situation that the current storage node has a heavy load or a high data access frequency can be detected in time, prompting the system to migrate the data and avoiding the decline of system performance; and when the storage adjustment value is less than the storage threshold, data migration is not required, saving resources.

[0057] The replica optimization module is used to evaluate the replica placement of the storage node information and determine the storage node for storing the replica according to the placement evaluation result.

[0058] The specific steps for evaluating replica placement of stored node information are as follows:

[0059] Traverse all storage nodes to obtain the load data and space evaluation value of each storage node. The load data includes: memory usage rate and CPU usage rate; comprehensively calculate the load data to obtain the node load value. Conduct a comprehensive analysis of the load data of the storage node to obtain the node load value;

[0060] The method for obtaining the node load value is as follows:

[0061]

[0062] In the formula, L i represents the node load value of the i-th storage node, which is mainly used to represent the workload of the current storage node, I i represents the memory usage rate of the i-th storage node, specifically representing the proportion of the used memory of the i-th storage node to the total memory, O i represents the CPU usage rate of the i-th storage node, specifically representing the usage of the CPU resources of the i-th storage node, and both the memory usage rate and the CPU usage rate are between 0 and 1, and are obtained through system monitoring tools;

[0063] Comprehensively calculate the node load value, space evaluation value, and storage adjustment value to obtain the replica placement evaluation value. Traverse the replica placement evaluation value, compare the replica placement evaluation values of each storage node, record the node with the largest replica placement evaluation value as the target storage node for placing the replica, and migrate the required replica data from the source storage node to the target storage node. After the replica migration storage is completed, update the replica information of the source storage node, and continue to monitor the space situation of the replicas on the target storage node;

[0064] The method for obtaining the replica placement evaluation value is as follows:

[0065] Y i =(L i ×(1 - R i ))×(1 - T i );

[0066] In the formula, Y i represents the replica placement evaluation value of the i-th storage node, which is mainly used to evaluate which node among all storage nodes is suitable for placing replicas. The larger this value is, the more suitable the storage node is for placing replicas, L i represents the node load value of the i-th storage node, which is mainly used to represent the workload of the i-th storage node, R i represents the space evaluation value of the i-th storage node, mainly representing the proportion of the remaining storage space of the i-th storage node to the total storage space, through The ratio is calculated. The larger the space evaluation value, the more sufficient the space is. The smaller the space evaluation value, the more tense the space is. T i represents the storage adjustment value of the i-th storage node, which is comprehensively calculated by the storage evaluation value and the data access frequency. The larger the storage adjustment value, the more it indicates that the data needs to be migrated to a more suitable storage node. The calculation of 1 - R is used to reflect the occupancy of the storage space of the current storage node.

[0067] Table 1 Replica Placement Evaluation Value

[0068] Storage node Node load value Space evaluation value Storage adjustment value Replica placement evaluation value Node 1 0.6 0.3 0.8 0.08 Node 2 0.4 0.6 0.4 0.09 Node 3 0.5 0.4 0.6 0.18

[0069] In this embodiment, as Figure 3 shown, three different storage nodes are collected for evaluation, namely Node 1, Node 2, and Node 3. The storage node load values, space evaluation values, and storage adjustment values of Node 1, Node 2, and Node 3 are comprehensively analyzed and calculated respectively, so as to obtain the replica placement evaluation values of Node 1, Node 2, and Node 3. The replica placement evaluation value reflects the characteristic differences of the nodes in terms of load, space utilization rate, storage adjustment, and replica placement.

[0070] The node load value of Node 1 reaches 0.6, indicating that its workload is heavy and it may face greater pressure when processing tasks. The space evaluation value is only 0.3, meaning that the remaining storage space is limited. If a large amount of data continues to be stored, the space may soon become insufficient. The storage adjustment value is 0.8, indicating that this node has a strong need for data migration and is not very suitable for accepting new replicas in the current state. Its replica placement evaluation value is only 0.08. Considering all indicators, Node 1 performs poorly in resource allocation and storage efficiency;

[0071] For Node 2, the node load value is 0.4, the workload is relatively light, and there are more resources available for allocation. The space evaluation value is 0.6, indicating that the space is sufficient to accommodate more data. The storage adjustment value is 0.4, the migration requirement is small, and the replica placement evaluation value is 0.09, slightly higher than that of Node 1, showing relatively balanced resource utilization;

[0072] The node load value of Node 3 is 0.5, and the workload is at a moderate level, neither causing resource waste due to too light a load nor resulting in low efficiency due to too heavy a load. The space evaluation value is 0.4, and there is a certain amount of storage space available. The storage adjustment value is 0.6, and the replica placement evaluation value is as high as 0.18, standing out among the three nodes. This indicates that Node 3 has good comprehensive performance and is more suitable for placing the replicas that need to be migrated;

[0073] By comparing the replica placement evaluation values of different storage nodes, the system can intuitively understand the resource allocation and storage efficiency of each node, thereby achieving more efficient storage management. Moreover, the system can optimize and adjust according to the resource usage of different nodes, further improving the performance and stability of the overall storage system. Optimizing the load balance, space utilization, and replica placement strategy of storage nodes can not only improve the system response speed, reduce resource waste, but also effectively reduce storage costs and ensure data security and high availability.

[0074] In the solution of this embodiment, through the replica placement evaluation value obtained by comprehensive calculation, the storage node most suitable for storing the required migrated replica can be accurately obtained, enabling the system to preferentially select a node with better performance and more matching resources to store the replica, improving the data reading speed, and enhancing the overall stability and reliability of the wind farm data storage system.

[0075] A method for optimizing the storage management of wind farm data, as Figure 2 shown, includes the following steps:

[0076] Step 1, collect and store the operation data of the wind farm in the storage nodes, evaluate the number of replicas of the operation data, and allocate the number of replicas according to the evaluation result of the number of replicas;

[0077] Step 2, obtain the storage node information, evaluate the storage space of the storage node information, and perform a migration evaluation on the replicas according to the evaluation result of the storage space, and determine the replicas to be migrated according to the migration evaluation result;

[0078] Step 3, perform a replica placement evaluation on the storage node information, and determine the storage node for storing the replicas according to the evaluation result of the placement evaluation.

[0079] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device.

[0080] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific embodiments described. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the present invention, so that those skilled in the art can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A wind farm data optimization storage management system, characterized in that: include: The data collection module is used to collect and store the operation data of the wind farm in the storage node, evaluate the number of copies of the operation data, and allocate the number of copies according to the evaluation result; The storage evaluation module is used to obtain storage node information, perform storage space evaluation on the storage node information, and perform migration evaluation on the replicas according to the storage space evaluation results, and determine the replicas to be migrated according to the migration evaluation results; The replica optimization module is used to perform replica placement evaluation on storage node information and determine the storage node where the replica is stored based on the placement evaluation result.

2. A wind farm data optimization storage management system according to claim 1, characterized in that: The specific steps of collecting and storing the wind farm operation data in the storage node are as follows: Operational data includes: The real-time data of the wind farm and the system operation log records include the basic number of copies of different real-time data, data access frequency and data update frequency, as well as the system maximum access frequency, system maximum update frequency and system maximum number of copies of operation data; and the operation data is stored in the storage node.

3. The wind farm data optimization storage management system according to claim 1, characterized in that: The specific steps of evaluating the number of replicas of running data are as follows: The basic number of copies of different storage information, data access frequency, system maximum access frequency, system maximum number of copies, data update frequency and system maximum update frequency are comprehensively calculated to obtain the copy number evaluation value.

4. A wind farm data optimization storage management system according to claim 3, characterized in that: The specific steps of allocating the number of replicas according to the replica number evaluation result are as follows: The replica quantity evaluation value is compared with the quantity interval threshold in real time. If the replica quantity evaluation value is less than or equal to the quantity interval threshold, the system creates a new replica and reallocates the storage node to the new replica. If the replica quantity evaluation value is greater than the quantity interval threshold, the system deletes a replica based on the data access frequency and evaluates the replica quantity evaluation value again until the replica quantity evaluation value is within the quantity interval threshold.

5. The wind farm data optimization storage management system according to claim 1, characterized in that: The specific steps of performing storage space evaluation on storage node information are as follows: The storage node information includes: the used storage capacity and total storage capacity of all current storage nodes; traversing the storage nodes, comprehensively calculating the used storage capacity and the total storage capacity to obtain the space evaluation value; and comprehensively calculating the space evaluation value, the used storage capacity and the total storage capacity to obtain the storage evaluation value.

6. A wind farm data optimization storage management system according to claim 5, characterized in that: The specific steps of performing migration evaluation on the replica according to the storage space evaluation result are as follows: The data access frequency, the system maximum access frequency, the data update frequency and the system maximum update frequency are obtained, and the storage evaluation value, the data access frequency, the system maximum access frequency, the data update frequency and the system maximum update frequency are comprehensively calculated to obtain the storage adjustment value.

7. The wind farm data optimization storage management system according to claim 5, characterized in that: The specific steps of determining the replica to be migrated according to the migration assessment result are as follows: The storage adjustment value is compared with the storage threshold in real time. If the storage adjustment value is greater than or equal to the storage threshold, the replicas that need to be migrated are determined based on the load and space conditions of the source storage node, and the number of migrated replicas and the replica changes of the source node are recorded. If the storage adjustment value is less than the storage threshold, it means that the storage resources are sufficient and storage can continue.

8. The wind farm data optimization storage management system according to claim 1, characterized in that: The specific steps of performing replica placement evaluation on storage node information are as follows: Traverse the storage nodes, obtain the load data and space evaluation value of each storage node, and perform a comprehensive analysis on the load data of the storage nodes to obtain the node load value; the load data includes: memory usage and CPU usage, and the node load value, space evaluation value and storage adjustment value are comprehensively calculated to obtain the replica placement evaluation value.

9. A wind farm data optimization storage management system according to claim 8, characterized in that: The specific steps of determining the storage node where the replica is stored according to the placement evaluation result are as follows: Traverse the replica placement evaluation values, compare the replica placement evaluation values ​​of each storage node, record the node with the largest replica placement evaluation value as the target storage node for placing the replica, and migrate the replica data to be migrated from the source storage node to the target storage node. After the replica migration storage is completed, update the replica information of the source storage node and continue to monitor the space situation of the target storage node replica.

10. A method for optimizing storage and management of wind farm data, used to implement a wind farm data optimization storage and management system as described in any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect and store the operation data of the wind farm in the storage node, evaluate the number of copies of the operation data, and allocate the number of copies according to the evaluation result; Step 2: Obtain storage node information, perform storage space evaluation on the storage node information, perform migration evaluation on the replicas based on the storage space evaluation results, and determine the replicas to be migrated based on the migration evaluation results; Step three: perform replica placement evaluation on the storage node information, and determine the storage node where the replica is stored based on the placement evaluation result.

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