Data storage method, device, equipment, storage medium and computer program product

Abstract

The application relates to the technical field of data storage, and discloses a data storage method, device, equipment, storage medium and computer program product, which comprises the following steps: acquiring power plant data generated in a current time period of a power plant, and initializing storage nodes and survival times corresponding to the storage nodes based on data compression results of a previous time period; determining data levels of each data object in the power plant data; storing the data objects into the storage nodes according to the data levels, and activating the survival times to obtain an initial storage set; when the survival times start to shorten, adjusting the initial storage set based on monitored changes of the survival times to obtain a target storage set. The initial storage set is adjusted according to changes of the survival times, the storage structure is dynamically optimized in real time according to changes of the timeliness and importance of data, important data is ensured to be always in a suitable storage node, waste of storage resources is avoided, and the overall performance of a storage system and the rationality of data management are improved.

Description

Technical Field

[0001] This application relates to the field of data storage technology, and in particular to a data storage method, apparatus, device, storage medium, and computer program product. Background Technology

[0002] In the field of power plant data management, with the rapid development of smart grids and the Industrial Internet of Things, the scale of data generated by power plant systems is growing exponentially. Existing storage nodes cannot dynamically adjust to changes in data importance or system load fluctuations, resulting in the premature obsolescence of critical data or the long-term retention of redundant data, affecting the storage of new data and overall storage performance. This fails to meet the data management and application needs of different sources, multiple types (including relational data, time-series data, and unstructured data), and different tenants. Summary of the Invention

[0003] The main purpose of this application is to provide a data storage method, apparatus, device, storage medium, and computer program product, which aims to solve the technical problem that existing storage nodes cannot dynamically adjust with changes in data importance or system load fluctuations, resulting in the premature elimination of critical data or the long-term retention of redundant data, affecting the storage of new data and the overall storage performance.

[0004] To achieve the above objectives, this application proposes a data storage method, which includes:

[0005] Acquire the power plant data generated within the current time period, and initialize the storage node and the corresponding lifetime of the storage node based on the data compression result of the previous time period;

[0006] Determine the data level of each data object in the power plant data;

[0007] The data object is stored in the storage node according to the data level, and the lifetime is activated to obtain the initial storage set;

[0008] When the lifetime begins to shorten, the initial storage set is adjusted based on the monitored changes in the lifetime to obtain the target storage set.

[0009] Optionally, the initial storage set includes core nodes, buffer nodes, and eviction nodes;

[0010] The step of adjusting the initial storage set based on the monitored changes in the lifetime when the lifetime begins to shorten, to obtain the target storage set, includes:

[0011] When the survival time begins to shorten, the changes in the survival time are monitored in real time.

[0012] If the change is that the first lifespan of the eliminated node decreases to the first safety threshold, then the first data object in the eliminated node is migrated to the buffer node, and the eliminated node is reclaimed to obtain the first adjustment result;

[0013] If the change is that the second lifetime of the core node decreases to the second safety threshold, then the second data object of the core node is migrated to the eviction node to obtain the second adjustment result;

[0014] The initial storage set is adjusted based on the first adjustment result and / or the second adjustment result to obtain the target storage set.

[0015] Optionally, after the step of migrating the first data object in the eliminated node to the buffer node and reclaiming the eliminated node to obtain the first adjustment result if the change is that the first lifetime of the eliminated node decreases to a first safety threshold, the method further includes:

[0016] Determine whether the fluctuation in the current lifetime of the buffer node exceeds a preset safety range;

[0017] When the fluctuation in the lifetime of the buffer node exceeds a preset safety range, the Shannon entropy value is calculated based on the distribution of the third data object in the buffer node.

[0018] Based on the Shannon entropy value, the buffer node is detected to see if it meets the preset reallocation conditions, and the entropy value detection result is obtained.

[0019] If the entropy detection result indicates that the buffer node meets the preset reallocation conditions, and the Shannon entropy value is greater than the high entropy alarm threshold, then the anti-entropy storage optimization algorithm is activated to reallocate the buffer node.

[0020] If the entropy detection result indicates that the buffer node meets the preset redistribution conditions, and the Shannon entropy value is less than the low entropy optimization threshold, then the buffer node is redistributed according to the data aggregation degree of the buffer node.

[0021] Optionally, the step of migrating the second data object of the core node to the eviction node to obtain the second adjustment result if the change is that the second lifetime of the core node decreases to the second safety threshold, includes:

[0022] If the change is that the second lifetime of the core node drops to the second safety threshold, a migration queue is generated based on the data level of the second data object stored in the core node;

[0023] A third survival time is set for the migration queue based on the second survival time;

[0024] Based on the configured migration queue, the second data object is migrated to the elimination node, and the first lifetime of the elimination node after the migration is completed is adjusted according to the third lifetime to obtain the second adjustment result.

[0025] Optionally, the step of acquiring power plant data generated within the current time period and initializing the storage node and its corresponding lifetime based on the data compression result of the previous time period includes:

[0026] The power plant data for the current time period is obtained from the business system through the data acquisition interface;

[0027] Analyze the data compression results of the previous time period to determine the remaining available resources and status of the storage nodes;

[0028] The initial lifetime and storage type of the storage node are determined based on the remaining available resources and the status.

[0029] Initialize the storage node according to the storage type.

[0030] Optionally, the step of determining the data level of each data object in the power plant data includes:

[0031] Obtain the data sensitivity of each data object in the power plant data;

[0032] Determine the first degree of association between the data object and the production business, and the second degree of association between the data object and the business system;

[0033] The scope of influence of the data object is determined based on a preset evaluation process;

[0034] The data objects are classified according to the data sensitivity, the first correlation, the second correlation, and the scope of influence to obtain the data level of each data object.

[0035] Furthermore, to achieve the above objectives, this application also proposes a data storage device, the data storage device comprising:

[0036] The node initialization module is used to obtain the power plant data generated in the current time period and initialize the storage node and the corresponding lifetime of the storage node based on the data compression result of the previous time period.

[0037] The grade determination module is used to determine the data grade of each data object in the power plant data;

[0038] The data storage module is used to store the data object into the storage node according to the data level, and activate the lifetime to obtain an initial storage set;

[0039] The storage adjustment module is used to adjust the initial storage set based on the monitored changes in the lifetime when the lifetime begins to shorten, so as to obtain a target storage set.

[0040] In addition, to achieve the above objectives, this application also proposes a data storage device, the device comprising: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the data storage method described above.

[0041] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the data storage method described above.

[0042] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the data storage method described above.

[0043] This application discloses a method for acquiring power plant data generated within the current time period, initializing storage nodes and their corresponding lifetimes based on data compression results from the previous time period; determining the data level of each data object in the power plant data; storing the data objects in the storage nodes according to their data levels and activating their lifetimes to obtain an initial storage set; and adjusting the initial storage set based on monitored changes in lifetimes when the lifetimes begin to shorten to obtain a target storage set. By adjusting the initial storage set according to changes in lifetimes, the storage structure is dynamically optimized in real time based on changes in data timeliness and importance, ensuring that important data is always stored in appropriate storage nodes, avoiding waste of storage resources, and improving the overall performance of the storage system and the rationality of data management. Attached Figure Description

[0044] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1This is a flowchart illustrating the first embodiment of the data storage method of this application;

[0047] Figure 2 This is a flowchart illustrating the second embodiment of the data storage method of this application;

[0048] Figure 3 This is a flowchart illustrating the third embodiment of the data storage method of this application;

[0049] Figure 4 This is a schematic diagram of the module structure of the data storage device according to an embodiment of this application;

[0050] Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the data storage method in this application embodiment.

[0051] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0052] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0053] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0054] The main solution of this application embodiment is as follows: acquire power plant data generated in the current time period, and initialize storage nodes and the lifetime of the storage nodes based on the data compression results of the previous time period; determine the data level of each data object in the power plant data; store the data objects in the storage nodes according to the data level, and activate the lifetime to obtain an initial storage set; when the lifetime begins to shorten, adjust the initial storage set based on the monitored changes in the lifetime to obtain a target storage set.

[0055] In the field of power plant data management, with the rapid development of smart grids and the Industrial Internet of Things, the scale of data generated by power plant systems is growing exponentially, covering high-value information such as real-time monitoring data, equipment logs, and user electricity consumption records. Traditional data storage solutions typically employ static tiered storage architectures (such as cold / hot data separation) and fixed Time To Live (TTL) management. However, these solutions have significant shortcomings in terms of dynamism, resource efficiency, and security, specifically: 1. Rigid storage tiers: Relying on predefined storage tiers (such as core storage areas and archive storage areas), data classification rules are fixed and cannot be dynamically adjusted according to data value. For example, highly sensitive real-time monitoring data is mixed with low-priority log data, leading to excessive storage pressure on core nodes and insufficient utilization of buffer nodes. 2. Fixed time-to-live management: Traditional solutions set a uniform TTL for storage nodes without considering differences in data levels. Critical data may be mistakenly deleted due to TTL expiration, while low-value data remains indefinitely, consuming resources.

[0056] Therefore, this application provides a data storage method that coordinates dynamic hierarchical storage and lifetime optimization. Through dynamic data level classification, lifetime self-initialization, and real-time feedback adjustment mechanism, it realizes elastic allocation of storage resources, precise control of data lifecycle, and self-optimized system operation, thereby meeting the high concurrency, high security, and low latency storage requirements of power plant data.

[0057] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, data migration, and program execution functions, such as a production and operation system, or an electronic device capable of performing the above functions. The following description uses a power plant production and operation management system as an example to illustrate this embodiment and the subsequent embodiments.

[0058] Based on this, the embodiments of this application provide a data storage method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the data storage method of this application.

[0059] In this embodiment, the data storage method includes:

[0060] Step S10: Obtain the power plant data generated within the current time period, and initialize the storage node and the corresponding lifetime of the storage node based on the data compression result of the previous time period.

[0061] It should be noted that power plant data refers to various types of data generated during the operation of a power plant, including but not limited to real-time monitoring data (such as equipment temperature and voltage), historical operation logs, operation records, and fault alarms. Data compression results are storage efficiency metrics obtained after compressing data from the previous time period, such as compression ratio, compression time, and remaining space utilization. Storage nodes are the basic logical storage units of a storage system and can be categorized according to data value or access frequency into core nodes (high-value data), buffer nodes (short- to medium-term data), and eviction nodes (low-value data). Lifetime can be the maximum time data is retained in a storage node, or the time a storage node can function normally under the current load, and is related to factors such as the storage node's performance and remaining capacity. Based on the changes in the lifetime of each storage node, the value of the data stored in the current storage node can be dynamically matched, and the storage node capacity allocation can be adjusted to avoid resource waste.

[0062] Understandably, when raw data for the current time period can be obtained in real time from the power plant business system through a standardized interface, it is necessary to analyze the data compression results of the storage nodes in the previous time period, extract key indicators, and predict the storage space requirements of the current data volume based on the key indicators, thereby initializing the storage nodes and the corresponding lifetime of the storage nodes.

[0063] Step S20: Determine the data level of each data object in the power plant data.

[0064] It should be noted that data level is a quantitative indicator of the importance of a data object.

[0065] In practical applications, the data level of each data object can be determined based on its impact on power plant operation, safety, or compliance; or it can be determined based on the data and the core business processes of the power plant; of course, in order to reflect the scope of business links that may be affected by data anomalies or loss, the data level can also be determined by the frequency and dependencies of data calls across modules in the power plant information system.

[0066] Step S30: Store the data object in the storage node according to the data level, and activate the lifetime to obtain the initial storage set.

[0067] It should be understood that when storing the data object in the storage node, node type matching can be used to select the target storage node according to the data level. Important data can be stored in the core node for high-performance storage and support real-time read and write. Medium data can be stored in the buffer node to support short-term caching while balancing performance and cost. Edge data (lower data level) needs to be stored in the eviction node for low-cost storage and can support fast erasure.

[0068] Understandably, different types of storage nodes have different lifespans, and even storage nodes of the same type can vary in lifespan depending on the data level of the stored data objects. Each storage node has a corresponding lifespan, which is activated when a data object is stored on the appropriate storage node.

[0069] After activating the time-to-live (TTL) of a storage node, the TTL dynamically decays as data accumulates. This decay mechanism ensures that low-value data is quickly discarded, reducing the attack surface and improving data storage security. Simultaneously, the system can take timely measures to migrate data or adjust storage strategies, improving the efficiency and flexibility of data management.

[0070] In one example, when storing real-time monitoring data of a thermal power plant based on a preset detection cycle, boiler temperature data and turbine pressure data are set as Class A data, and fan operation logs are set as Class C data. The target storage node is selected according to the data level (A / B / C level), such as: Class A: core node (high-performance storage, supports real-time read and write); Class B: buffer node (balances performance and cost, supports short-term caching); Class C: obsolete node (low-cost storage, supports fast erasure).

[0071] After real-time monitoring data is stored in the corresponding storage nodes, a corresponding TTL needs to be set for each storage node using a dynamic TTL formula, calculated as follows:

[0072] T = T base ×α×β

[0073] Where T represents the time to survival (TTL), T base The base lifetime can be preset according to the storage node type. α is the data level coefficient, and β is the node load coefficient, which is negatively correlated with the current node load rate (L).

[0074] After data is written to the storage node, a countdown timer starts, decaying the Time-To-Live (TTL) every second. Simultaneously, the remaining space on the node is updated in real time, and the TTL decay rate is automatically adjusted when a threshold is triggered. If the storage node failure rate exceeds 5%, its data can be migrated to a standby node and the TTL reset.

[0075] In high-load scenarios, such as when the load on core node 1 rises to 85%, the TTL decay rate accelerates, triggering a migration queue to downgrade Class A data (such as boiler temperature data) with less than 12 hours remaining in TTL to buffer nodes. By migrating the core node load down to 60%, the utilization rate of buffer nodes increases from 40% to 65%.

[0076] Step S40: When the lifespan begins to shorten, adjust the initial storage set based on the monitored changes in the lifespan to obtain the target storage set.

[0077] Understandably, when reallocating and adjusting data objects and storage nodes in the initial storage set based on changes in lifespan, the adjustments may include data migration, node reclamation, and node activation, to ensure that data is properly preserved in suitable storage nodes. When the lifespan of a storage node shortens, timely migration of data to a more reliable node can prevent data loss or corruption due to storage node failure, thereby improving data reliability and availability.

[0078] In this embodiment, power plant data generated within the current time period is acquired, and storage nodes and their corresponding lifetimes are initialized based on the data compression results of the previous time period. The data level of each data object in the power plant data is determined. The data objects are stored in the storage nodes according to their data levels, and their lifetimes are activated to obtain an initial storage set. When the lifetime begins to shorten, the initial storage set is adjusted based on the monitored changes in lifetime to obtain a target storage set. Adjusting the initial storage set based on changes in lifetime allows for real-time dynamic optimization of the storage structure according to changes in data timeliness and importance. This ensures that important data is always stored in suitable storage nodes, while unimportant data is promptly cleaned up or migrated, avoiding waste of storage resources and further improving the overall performance of the storage system and the rationality of data management.

[0079] Reference Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the data storage method of this application. Based on the first embodiment described above, a second embodiment of the data storage method of this application is proposed.

[0080] In the second embodiment, step S40 includes:

[0081] Step S401: When the survival time begins to shorten, monitor the changes in the survival time in real time.

[0082] Step S402: If the change is that the first lifetime of the eliminated node decreases to the first safety threshold, then the first data object in the eliminated node is migrated to the buffer node, and the eliminated node is reclaimed to obtain the first adjustment result.

[0083] It should be noted that the first lifetime is the initial lifespan of data in the eviction node, and the first safety threshold is the critical value that triggers data migration and resource reclamation in the eviction node, which can be a percentage of the first lifetime. The first data object is the data unit to be processed in the eviction node that meets the migration conditions, usually sorted by priority, such as generation time, access frequency, etc.

[0084] Understandably, when migrating the first data object from the eviction node to the buffer node, all data within the node will be deleted after the data migration is complete, physical resources will be released, and the node status will be marked as "idle" to facilitate storage allocation in the next cycle. The buffer node receiving the migrated data can then appropriately extend its TTL for troubleshooting and short-term backtracking.

[0085] Furthermore, in order to improve the stability and storage efficiency of the buffer nodes through an adaptive optimization strategy, thereby ensuring the performance of the entire storage system, the following steps are included after step S402:

[0086] Determine whether the fluctuation in the current lifetime of the buffer node exceeds a preset safety range;

[0087] When the fluctuation in the lifetime of the buffer node exceeds a preset safety range, the Shannon entropy value is calculated based on the distribution of the third data object in the buffer node.

[0088] Based on the Shannon entropy value, the buffer node is detected to see if it meets the preset reallocation conditions, and the entropy value detection result is obtained.

[0089] If the entropy detection result indicates that the buffer node meets the preset reallocation conditions, and the Shannon entropy value is greater than the high entropy alarm threshold, then the anti-entropy storage optimization algorithm is activated to reallocate the buffer node.

[0090] If the entropy detection result indicates that the buffer node meets the preset redistribution conditions, and the Shannon entropy value is less than the low entropy optimization threshold, then the buffer node is redistributed according to the data aggregation degree of the buffer node.

[0091] It should be noted that fluctuation represents the magnitude of change in lifetime over time, reflecting the stability of storage load. The preset safety range can be derived based on historical data statistics (e.g., mean ± 2 times the standard deviation). The third data object is a specific category of data stored in the buffer node, typically Class B or temporarily migrated data. Shannon entropy is used to quantify the degree of disorder in data distribution; a higher value indicates more dispersed data, while a lower value indicates more concentrated data. High entropy alarm thresholds and low entropy optimization thresholds are used to determine whether the data in the buffer node is randomly distributed or excessively clustered, respectively.

[0092] In one example, when calculating the TTL fluctuation size within a 5-minute time window, if the fluctuation size exceeds a preset safety range, the entropy value H(X) is calculated using the following formula:

[0093]

[0094] Where p(x) i ) represents the proportion of the i-th type of data in the buffer node.

[0095] When H(X) is greater than the high entropy alarm threshold, it indicates that the data distribution is disordered and the anti-entropy storage optimization algorithm needs to be activated. This algorithm splits similar data into multiple nodes, uses a consistent hashing algorithm to allocate storage locations, and updates the TTL to 80% of the original value to balance the load. This reduces the entropy value by distributing the data for storage.

[0096] When H(X) is less than the low-entropy optimization threshold, it indicates that the data is over-clustered and needs to be redistributed according to the clustering degree. This could involve merging contiguous storage blocks and adjusting the TTL to 120% of its original value to extend the retention time. The data clustering degree measures the concentration of data in the storage space and can be determined by the ratio of the number of contiguous storage nodes to the total number of data nodes.

[0097] Step S403: If the change is that the second lifetime of the core node decreases to the second safety threshold, then the second data object of the core node is migrated to the eviction node to obtain the second adjustment result.

[0098] It should be noted that the second lifetime is the dynamic lifetime of data in the core node, representing the remaining retention time of data from when it is written until it needs to be downgraded or cleaned up. The second security threshold is the critical value that triggers the core node to perform data degradation migration, and it can be a percentage of the second lifetime.

[0099] Specifically, the system monitors the second time-to-live (TTL) of core nodes in real time. When the TTL value drops to a preset second safety threshold (e.g., 10% of the remaining time), the system triggers a data migration process. Data that needs to be downgraded is selected based on the data object's level. For example, data with lower levels and data that has not been accessed for a long time are migrated first, or data is migrated in an ordered queue based on data attributes such as last access time, size, and importance of related business. This ensures that critical data is migrated last to minimize impact.

[0100] Furthermore, in order to achieve precise control over the data migration process and target node time management, and to ensure the security and timeliness of important data during the migration process, step S403 may include:

[0101] If the change is that the second lifetime of the core node drops to the second safety threshold, a migration queue is generated based on the data level of the second data object stored in the core node;

[0102] A third survival time is set for the migration queue based on the second survival time;

[0103] Based on the configured migration queue, the second data object is migrated to the elimination node, and the first lifetime of the elimination node after the migration is completed is adjusted according to the third lifetime to obtain the second adjustment result.

[0104] It should be noted that the migration queue is a list of data to be migrated, sorted by priority such as data level and access frequency, used to control the demotion order. The third lifetime is the new lifetime of data after being migrated to the eviction node, which is usually shorter than the original lifetime but longer than the default value of the eviction node.

[0105] Understandably, core nodes typically store high-priority, highly sensitive data, supporting real-time access and strong encryption. When the second lifetime of a core node falls below the second security threshold, a degradation process is initiated, filtering data that meets degradation criteria, such as data objects that were close to low-level in previous data classification but were assigned to core nodes. Simultaneously, a migration queue is generated in reverse chronological order of access time, and a third lifetime is generated based on the original lifetime and a decay coefficient. During data migration, the data in the migration queue is first copied to the corresponding eviction node, then data integrity is verified, and the original data in the core node is deleted.

[0106] It should be understood that the above-mentioned tiered and downgraded mechanism can ensure that high-value data is retained for a long time and low-value data is released as needed. At the same time, setting a third lifespan can also prevent migrated data from being cleaned up prematurely, thus achieving resource balance and data value continuation between core nodes and obsolete nodes, and significantly improving the efficiency and compliance of power plant storage systems.

[0107] In one example, a third time-to-live (TTL) is dynamically set based on the decay rate of the core node's original remaining TTL and the current load of the vacated node.

[0108] T new =T remaining ×γ

[0109] Where γ is the attenuation coefficient, and γ < 1, T remaining This indicates the original remaining TTL of the core node. If the core node stores boiler pressure data (Level A), the initial TTL is 24 hours. When the remaining TTL is detected to be 2.4 hours (i.e., dropped to the 10% threshold):

[0110] 1. Filter out B-level historical data that has not been accessed in the past week and generate a migration queue.

[0111] 2. Set the third survival time to 1.2 hours (γ = 0.5) and migrate to the elimination node.

[0112] 3. The elimination node adjusts the default cleanup cycle to 1 hour based on the new data TTL, accelerating the elimination of low-value data.

[0113] 4. 50GB of space is freed up on the core nodes to store newly generated real-time monitoring data.

[0114] Data migration can be performed using chunked transmission, copying data in the migration queue to the eviction node in chunks, and using incremental synchronization to reduce bandwidth consumption. Consistency checks are performed, using hash verification to ensure data integrity; if a check fails, a retry or rollback is initiated. After migration is complete, core node space is released, the node is marked as available, and the data chunks that failed to migrate are recorded for retry after system recovery. If the migration takes too long or the failure rate exceeds the limit, an alarm is triggered and the administrator is notified.

[0115] Step S404: Adjust the initial storage set based on the first adjustment result and / or the second adjustment result to obtain the target storage set.

[0116] Understandably, during the adjustment process, when receiving the first and second adjustment results to form an adjustment instruction set, if both are triggered simultaneously, the core node degradation migration (i.e., the second adjustment result) will be processed first to avoid the risk of high-value data being retained. If only a single adjustment result is received, it will be directly applied to the storage set.

[0117] Specifically, after inputting the adjustment results, the global storage topology needs to be updated to synchronize the data status, recording the latest data distribution, remaining capacity, and TTL of each node. When adjusting core nodes, migrated data is removed, and the remaining capacity and load rate of the core nodes are recalculated. If the core node load rate is less than 50%, extend the baseline TTL of newly written data (e.g., from 8h to 12h); conversely, shorten the TTL to accelerate data flow. When adjusting buffer nodes, if the load of a buffer node exceeds 80% due to receiving evicted data, horizontal scaling is triggered, automatically creating new buffer nodes. A consistent hashing algorithm is used to migrate some data to the new nodes, while updating the routing table to ensure that access requests are distributed to the new nodes. The buffer node TTL is dynamically adjusted according to the data access frequency. When adjusting evicted nodes, the TTL (Third Time To Live) of the data migrated from the core node is compared with the default TTL of the evicted node. If the median TTL of the new data is greater than the original evicted node's TTL, the overall TTL of the evicted node is increased to the new median. For example, if the original evicted node's TTL = 1h and the median TTL of the new data is 1.5h, the TTL is updated to 1.5h. Then, a background process is started to scan for data with a TTL less than 0.5h in the evicted node, immediately deletes it, and marks the storage block as "overwhelmable." Finally, storage set optimization and data consistency verification are required. If a buffer node's load is less than 30% for an extended period (e.g., three consecutive cycles), it is downgraded to a vacancy node. If a vacancy node's load consistently exceeds 90%, it is upgraded to a buffer node. Alternatively, it can automatically partition data based on access patterns, migrating cold data to vacancy nodes and hot data (retaining it on core / buffer nodes). After data migration, the hash values ​​of the source and target nodes are compared to ensure integrity, and all adjustment operations are logged. Rollback operations are supported; for example, if a node crashes during migration, the logs can be used to revert to the previous stable state.

[0118] In this embodiment, when the lifetime begins to shorten, the change in lifetime is monitored in real time. If the change is that the first lifetime of the evicted node drops to a first safety threshold, the first data object in the evicted node is migrated to the buffer node, and the evicted node is reclaimed, resulting in a first adjustment result. If the change is that the second lifetime of the core node drops to a second safety threshold, the second data object of the core node is migrated to the evicted node, resulting in a second adjustment result. Based on the first adjustment result and / or the second adjustment result, the initial storage set is adjusted to obtain the target storage set. By migrating evicted node data to the buffer node and reclaiming the evicted node, and migrating core node data to the evicted node, respectively, for different scenarios where the lifetime of evicted nodes and core nodes drop to the safety threshold, the operations of migrating evicted node data to the buffer node and reclaiming the evicted node, and migrating core node data to the evicted node are respectively adopted. This effectively balances the load of various types of storage nodes, ensures the stability of data storage, and achieves dynamic optimization of storage resources.

[0119] Reference Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the data storage method of this application. Based on the second embodiment described above, a third embodiment of the data storage method of this application is proposed.

[0120] In the third embodiment, step S10 includes:

[0121] Step S101: Obtain power plant data for the current time period from the business system through the data acquisition interface.

[0122] It should be noted that business systems refer to the various information systems used in a power plant for daily production, operation, and management. These systems cover all aspects of the power plant's business operations, such as power generation management systems, equipment monitoring systems, fuel management systems, and financial management systems. When acquiring power plant data, the time period can be determined based on specific business needs and data collection strategies.

[0123] Understandably, the data acquisition interface needs to be adapted to multiple protocols. The interface protocol should be automatically matched based on the business system type, and a protocol-system mapping table should be established. Alternatively, a factory pattern can be used to dynamically load protocol adapters. The data acquisition time window is configured with a preset acquisition period (e.g., 5-minute micro-batch processing, 1-hour batch processing). Clocks in each business system are synchronized via NTP service, and an alarm is triggered when the time deviation exceeds 500ms. Alternatively, power plant data can be read from the transaction log.

[0124] Step S102: Analyze the data compression results of the previous time period to determine the remaining available resources and status of the storage nodes.

[0125] As is understandable, remaining available resources refer to the amount of resources a storage node can still use to store new data in its current state. This mainly includes the remaining storage space, and may also involve the node's computing resources. Status information includes the storage node's current operating status or health indicators, such as performance status and data storage status.

[0126] In one example, analyzing data compression results could involve parsing compression logs generated by the storage system, extracting key fields, and aggregating compression metrics for each node using a time window. The deviation of the node's compression rate from the historical baseline (the average of the same period over the past 7 days) is then calculated to assess compression efficiency. Simultaneously, future storage requirements (S) can be predicted based on the compression results. remaining ,like:

[0127]

[0128] Among them, R i (t) represents the expected compression rate of node i at time t, which can be set based on historical compression data. S compressed,i S represents the expected compression requirement for node i. total This is the total storage space.

[0129] Step S103: Determine the initial lifetime and storage type of the storage node based on the remaining available resources and the status.

[0130] It should be understood that the more available resources remain, the longer the storage node can continue to operate normally. When setting the basic lifetime of the storage node, it is necessary to determine based on the remaining available resources of the current storage node.

[0131] Understandably, different types of storage nodes are suited to different storage types. If a storage node has a large amount of available storage space but relatively slow read / write speeds, it is suitable for large-capacity data storage with low read / write speed requirements, such as tape storage or ordinary hard disk storage, suitable for scenarios like data backup. If the remaining resources indicate that the storage node has high read / write speeds but relatively small storage space, it is more suitable for flash storage, used for critical data storage with high read / write speed requirements, such as cache data storage for databases.

[0132] In one example, the remaining available resources can be met by future storage demand S. remaining The status is indicated by a health score, determined by failure rate and response time. Different storage types are categorized using different logics: high-performance storage offers low latency and high IOPS, suitable for frequently accessed data, such as SSDs. Balanced storage balances performance and cost. Archive storage offers high capacity and low cost, suitable for infrequently accessed data, such as object storage. The formula for calculating the initial time to live (Base_TTL) is as follows:

[0133]

[0134] Among them, P health For health scoring, T max This is the preset maximum survival time. W space and W health These are the preset weights for remaining available resources and health score, initially set at 0.6 and 0.4 respectively, and can be adjusted based on the adaptation of the previous initial survival time.

[0135] In special cases, the initial time to live (TTL) may need to be dynamically adjusted. For example, when the CPU or memory load is greater than 70%, the Base_TTL may be shortened by 20%; when the bandwidth is less than 50%, the Base_TTL may be extended by 15%.

[0136] Step S104: Initialize the storage node according to the storage type.

[0137] Understandably, storage nodes are initialized based on a predetermined storage type. Storage types can be diverse, such as local disk storage, distributed file system storage, and cloud storage, each with different characteristics and requirements. In this step, the system performs a series of initialization tasks on the storage node according to the specified storage type. This may include creating necessary storage directories, configuring storage parameters, and establishing connections with storage services. In this way, the storage node can be correctly configured and prepared according to the established storage type, thus laying the foundation for subsequent data storage and management.

[0138] In the third embodiment, step S20 includes:

[0139] Step S201: Obtain the data sensitivity of each data object in the power plant data.

[0140] It is important to understand that data sensitivity refers to the degree of sensitivity of the information contained in each data object within the power plant's data. It is determined by the type of each data object, and the sensitivity of each data object is preset by management personnel. For example, Top Secret 5: Core control system parameters, which directly affect power grid security; Confidential 4: Detailed electricity consumption records and contract electricity prices for users, involving user privacy or trade secrets; Internal 3: Business operation data such as equipment maintenance records and fuel procurement quantities; Restricted to Public 2: Non-sensitive data that requires authorized access, such as power generation statistical reports; Public 1: Data that can be released to the public, such as environmental monitoring summaries.

[0141] Step S202: Determine the first degree of correlation between the data object and the production business and the second degree of correlation between the data object and the business system.

[0142] It should be noted that the first degree of correlation refers to the closeness between the data object and the power plant's production operations. For example, the real-time operating parameters of a generator unit can be determined by assessing its role and impact on the production process. The second degree of correlation refers to the degree of correlation between the data object and the power plant's business systems, including the power generation management system, equipment monitoring system, and financial management system. These data are frequently used and interacted with in multiple business systems. By analyzing the usage of the data object in various business systems, including data input, output, interaction, and sharing, the second degree of correlation of the data object can be determined.

[0143] Step S203: Determine the scope of influence of the data object based on a preset evaluation process.

[0144] It should be noted that the pre-defined assessment process refers to a series of standardized and regulated operating steps and methodologies that are established in advance before assessing the impact scope of the power plant's data objects. This process can be designed based on the power plant's business characteristics, management needs, and relevant industry standards and best practices.

[0145] Understandably, for data objects in a power plant, the scope of impact refers to the degree of influence on various aspects of the power plant when problems occur with the data object (such as data loss, errors, or leaks).

[0146] Step S204: Classify each data object according to the data sensitivity, the first correlation, the second correlation, and the scope of influence to obtain the data level of each data object.

[0147] It is understandable that when classifying the data objects, different weights can be set for data sensitivity, first degree of relevance, second degree of relevance and scope of influence based on the classification rules determined by historical data, and each factor can be scored according to the specific situation. Then, the total score is calculated and the data level is determined based on the total score.

[0148] In one example, the data sensitivity is F. The first correlation is the production correlation F1, which is related to production operations, determined by the number of productions I and the production time α of the data object. T Determined, i.e., F1 = I × log 10 (α T +1). The first degree of relevance is the business relevance F2, which can be measured by the number of system calls U and the number of cross-system references C within a unit of time (hour). ref Calculate, i.e., F2 = U + C ref Scope of influence F dThis is expressed as the product of data sensitivity and the number of system calls U. Finally, based on the average of data sensitivity, the first correlation, the second correlation, and the influence range, and rounded up, the graded data is obtained, and the data grade corresponding to the graded data is selected within the statistical grade interval.

[0149] In this embodiment, the remaining available resources and status of storage nodes are determined by analyzing the data compression results of the previous time period. This allows for the initialization of storage nodes, their lifetimes, and storage types. Based on actual resource and status information, the initialization of storage nodes is more scientific and reasonable, reducing performance issues or resource waste caused by improper initialization. Simultaneously, by comprehensively considering data sensitivity, relevance to production and business systems, and the scope of impact, data objects are classified. This enables a comprehensive and accurate assessment of data importance, achieving differentiated data storage management and enhancing the storage system's ability to protect data of varying importance.

[0150] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the data storage method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0151] This application also provides a data storage device; please refer to... Figure 4 The data storage device includes:

[0152] The node initialization module 10 is used to obtain the power plant data generated in the current time period and initialize the storage node and the lifetime of the storage node based on the data compression result of the previous time period.

[0153] The grade determination module 20 is used to determine the data grade of each data object in the power plant data;

[0154] The data storage module 30 is used to store the data object into the storage node according to the data level, and activate the lifetime to obtain an initial storage set;

[0155] The storage adjustment module 40 is used to adjust the initial storage set based on the monitored changes in the lifetime when the lifetime begins to shorten, so as to obtain a target storage set.

[0156] The data storage device provided in this application, employing the data storage method described in the above embodiments, can solve the technical problem that existing storage nodes cannot dynamically adjust according to changes in data importance or system load fluctuations, resulting in the premature elimination of critical data or the long-term retention of redundant data, affecting the storage of new data and overall storage performance. Compared with the prior art, the beneficial effects of the data storage device provided in this application are the same as those of the data storage method provided in the above embodiments, and other technical features in the data storage device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0157] This application provides a data storage device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the data storage method in Embodiment 1 above.

[0158] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a data storage device suitable for implementing embodiments of this application. The data storage device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The data storage device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0159] like Figure 5As shown, the data storage device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in the read-only memory 1002 or a program loaded from the storage device 1003 into the random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the data storage device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the data storage device to communicate wirelessly or wiredly with other devices to exchange data. Although the diagram shows data storage devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0160] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0161] The data storage device provided in this application, employing the data storage method described in the above embodiments, can solve the technical problem that existing storage nodes cannot dynamically adjust according to changes in data importance or system load fluctuations, resulting in the premature obsolescence of critical data or the long-term retention of redundant data, affecting the storage of new data and overall storage performance. Compared with the prior art, the beneficial effects of the data storage device provided in this application are the same as those of the data storage method provided in the above embodiments, and other technical features of this data storage device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0162] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0163] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0164] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the data storage method in the above embodiments.

[0165] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0166] The aforementioned computer-readable storage medium may be included in a data storage device; or it may exist independently and not assembled into a data storage device.

[0167] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the data storage device, cause the data storage device to perform the data storage method described above.

[0168] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0169] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0170] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0171] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described data storage method. This solves the technical problem that existing storage nodes cannot dynamically adjust to changes in data importance or system load fluctuations, resulting in the premature obsolescence of critical data or the long-term retention of redundant data, thus affecting the storage of new data and overall storage performance. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the data storage method provided in the above embodiments, and will not be repeated here.

[0172] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the data storage method described above.

[0173] The computer program product provided in this application can solve the technical problem that existing storage nodes cannot dynamically adjust according to changes in data importance or system load fluctuations, resulting in the premature obsolescence of critical data or the long-term retention of redundant data, affecting the storage of new data and overall storage performance. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the data storage method provided in the above embodiments, and will not be repeated here.

[0174] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A data storage method, characterized in that, The data storage method includes: Acquire the power plant data generated within the current time period, and initialize the storage node and the corresponding lifetime of the storage node based on the data compression result of the previous time period; Determine the data level of each data object in the power plant data; The data object is stored in the storage node according to the data level, and the lifetime is activated to obtain the initial storage set; When the lifespan begins to shorten, the initial storage set is adjusted based on the monitored changes in the lifespan to obtain the target storage set; The initial storage set includes core nodes, buffer nodes, and eviction nodes; The step of adjusting the initial storage set based on the monitored changes in the lifetime when the lifetime begins to shorten, to obtain the target storage set, includes: When the survival time begins to shorten, the changes in the survival time are monitored in real time. If the change is that the first lifespan of the eliminated node decreases to the first safety threshold, then the first data object in the eliminated node is migrated to the buffer node, and the eliminated node is reclaimed to obtain the first adjustment result; If the change is that the second lifetime of the core node decreases to the second safety threshold, then the second data object of the core node is migrated to the eviction node to obtain the second adjustment result; The initial storage set is adjusted based on the first adjustment result and / or the second adjustment result to obtain the target storage set; After the step of migrating the first data object in the eliminated node to the buffer node and reclaiming the eliminated node to obtain the first adjustment result if the change is that the first lifetime of the eliminated node decreases to the first safety threshold, the method further includes: Determine whether the fluctuation in the current lifetime of the buffer node exceeds a preset safety range; When the fluctuation in the lifetime of the buffer node exceeds a preset safety range, the Shannon entropy value is calculated based on the distribution of the third data object in the buffer node. Based on the Shannon entropy value, the buffer node is detected to see if it meets the preset reallocation conditions, and the entropy value detection result is obtained. If the entropy detection result indicates that the buffer node meets the preset reallocation conditions, and the Shannon entropy value is greater than the high entropy alarm threshold, then the anti-entropy storage optimization algorithm is activated to reallocate the buffer node. If the entropy detection result indicates that the buffer node meets the preset redistribution conditions, and the Shannon entropy value is less than the low entropy optimization threshold, then the buffer node is redistributed according to the data aggregation degree of the buffer node.

2. The data storage method as described in claim 1, characterized in that, The step of migrating the second data object of the core node to the eviction node to obtain the second adjustment result if the change is that the second lifetime of the core node decreases to the second safety threshold, includes: If the change is that the second lifetime of the core node drops to the second safety threshold, a migration queue is generated based on the data level of the second data object stored in the core node; A third survival time is set for the migration queue based on the second survival time; Based on the configured migration queue, the second data object is migrated to the elimination node, and the first lifetime of the elimination node after the migration is completed is adjusted according to the third lifetime to obtain the second adjustment result.

3. The data storage method as described in claim 1 or 2, characterized in that, The step of acquiring power plant data generated within the current time period and initializing the storage node and its corresponding lifetime based on the data compression result of the previous time period includes: The power plant data for the current time period is obtained from the business system through the data acquisition interface; Analyze the data compression results of the previous time period to determine the remaining available resources and status of the storage nodes; The initial lifetime and storage type of the storage node are determined based on the remaining available resources and the status. Initialize the storage node according to the storage type.

4. The data storage method as described in claim 1 or 2, characterized in that, The step of determining the data level of each data object in the power plant data includes: Obtain the data sensitivity of each data object in the power plant data; Determine the first degree of association between the data object and the production business, and the second degree of association between the data object and the business system; The scope of influence of the data object is determined based on a preset evaluation process; The data objects are classified according to the data sensitivity, the first correlation, the second correlation, and the scope of influence to obtain the data level of each data object.

5. A data storage device, characterized in that, The device includes: The node initialization module is used to obtain the power plant data generated in the current time period and initialize the storage node and the corresponding lifetime of the storage node based on the data compression result of the previous time period. The grade determination module is used to determine the data grade of each data object in the power plant data; The data storage module is used to store the data object into the storage node according to the data level, and activate the lifetime to obtain an initial storage set, wherein the initial storage set includes core nodes, buffer nodes and eviction nodes; A storage adjustment module is used to adjust the initial storage set based on the monitored changes in the lifetime when the lifetime begins to shorten, so as to obtain a target storage set; The storage adjustment module is further configured to monitor the changes in the lifetime in real time when the lifetime begins to shorten; if the change is that the first lifetime of the evicted node drops to a first safety threshold, then the first data object in the evicted node is migrated to the buffer node, and the evicted node is reclaimed, resulting in a first adjustment result; if the change is that the second lifetime of the core node drops to a second safety threshold, then the second data object of the core node is migrated to the evicted node, resulting in a second adjustment result; and adjust the initial storage set based on the first adjustment result and / or the second adjustment result to obtain a target storage set. The storage adjustment module is further configured to determine whether the fluctuation of the current lifetime of the buffer node exceeds a preset safety range; when the fluctuation of the lifetime of the buffer node exceeds the preset safety range, calculate the Shannon entropy value based on the distribution of the third data object in the buffer node; detect whether the buffer node meets a preset redistribution condition based on the Shannon entropy value, and obtain an entropy detection result; if the entropy detection result indicates that the buffer node meets the preset redistribution condition and the Shannon entropy value is greater than the high entropy alarm threshold, then start the anti-entropy storage optimization algorithm to redistribute the buffer node; if the entropy detection result indicates that the buffer node meets the preset redistribution condition and the Shannon entropy value is less than the low entropy optimization threshold, then redistribute the buffer node according to the data aggregation degree of the buffer node.

6. A data storage device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the data storage method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the data storage method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the data storage method as described in any one of claims 1 to 4.

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