A method and system for interval replication based on SCD model

Abstract

The application discloses an interval replication configuration method and system based on an SCD model, and the method comprises the following steps: collecting multi-dimensional system state parameters and preprocessing the multi-dimensional system state parameters to obtain preprocessed system state parameters; inputting the preprocessed system state parameters into a system state evaluation function to determine the system stability degree and the configuration change intensity, and then dynamically calculating a replication interval adjustment amount based on a multi-factor weighting formula to obtain a set replication interval; and performing interval replication configuration of the SCD model according to the set replication interval to obtain an interval replication result. The application is suitable for intelligent substation reconstruction and expansion, distributed database master-slave synchronization and other high-reliability scenarios, and has outstanding engineering application value.

Description

Technical Field

[0001] This invention relates to an interval replication configuration method and system based on the SCD model, belonging to the fields of intelligent substation configuration management, IEC 61850 protocol adaptation, distributed system data synchronization and high-reliability cluster operation and maintenance technology. Background Technology

[0002] In the upgrading and expansion of intelligent substation automation systems and the master-slave synchronization of distributed database clusters, SCD interval replication and data synchronization are core technologies for improving project deployment efficiency, achieving rapid capacity expansion, ensuring high system availability, and reducing the total lifecycle maintenance cost. Existing interval replication and data synchronization technologies suffer from four major technical defects, which have become bottlenecks restricting the large-scale construction of high-reliability projects:

[0003] 1. The replication strategy is rigid and lacks dynamic adaptation capabilities.

[0004] Existing technologies generally employ a one-way execution mode with fixed time intervals and fixed trigger cycles, completely decoupling the replication strategy from the real-time operating status of the system. Frequent replication during periods of high system load and network congestion exacerbates resource consumption, leading to data congestion, transmission timeouts, and synchronization failures. During periods of low system load and sparse data updates, fixed-interval replication generates a large number of invalid operations, resulting in a severe waste of computing, storage, and bandwidth resources, and overall low operating efficiency.

[0005] 2. Lack of multi-dimensional decision-making and insufficient ability to balance.

[0006] Existing replication algorithms focus solely on data replication needs, lacking a multi-dimensional state awareness and comprehensive decision-making mechanism. They fail to comprehensively consider key operational metrics such as node load, network transmission latency, data update frequency, and configuration change rate. This single-dimensional decision-making can easily lead to a mismatch between replication strategies and actual needs, making it impossible to achieve an optimal balance between data consistency, system performance, and resource consumption.

[0007] 3. Lacks closed-loop feedback and self-optimization, relying on human experience.

[0008] Traditional replication processes operate on a one-way execution model, lacking quantitative evaluation and closed-loop feedback mechanisms for replication effectiveness, data consistency, and system stability. Core configurations such as replication intervals and weight parameters heavily rely on manual experience and preset settings, making it impossible to iteratively optimize based on actual operational results. Over long-term operation, strategy deviations accumulate, easily leading to problems such as synchronization lag, decreased consistency, and inefficient resource utilization, making it difficult to meet the requirements of adaptive and self-optimizing system development.

[0009] 4. Insufficient compatibility with IEC 61850 standard makes project implementation difficult.

[0010] The lack of deep adaptation to the SCD model of smart substations leads to naming conflicts, duplicate numbers, and cross-interval reference errors when copying core objects such as IED, LN, DOI, and DA. The GOOSE / SV subscription and publish relationship cannot be automatically synchronized and corrected, and configuration consistency verification relies heavily on manual completion, resulting in a high configuration error rate, long debugging cycle, and high operation and maintenance difficulty, which directly affects the deployment quality and operational safety of the substation's secondary system.

[0011] Therefore, there is an urgent need for an adaptive, highly consistent, low-resource-consumption, and strongly specification-compatible method that can meet the requirements of high-reliability scenarios such as smart substations and distributed databases. Summary of the Invention

[0012] The purpose of this invention is to provide an interval replication configuration method and system based on the SCD model. By deeply integrating the dynamic configuration capability of the IEC61850 SCD model with the adaptive load balancing mechanism of interval replication, a fully closed-loop adaptive operation system of "state monitoring - system evaluation - dynamic calculation - intelligent execution - feedback optimization" is constructed. This fundamentally solves the core technical pain points of traditional interval replication algorithms, such as fixed intervals, weak adaptability, lack of multi-factor decision-making, low configuration efficiency, and insufficient consistency guarantee.

[0013] To achieve the above objectives, the present invention is implemented using the following technical solution.

[0014] In a first aspect, the present invention provides an interval replication configuration method based on the SCD model, comprising:

[0015] Collect multi-dimensional system state parameters and preprocess them to obtain preprocessed system state parameters;

[0016] The preprocessed system state parameters are input into the system state evaluation function to determine the system stability and the intensity of configuration changes. Then, the replication interval adjustment is dynamically calculated based on the multi-factor weighted formula to obtain the set replication interval.

[0017] Based on the set replication interval, the interval replication configuration of the SCD model is executed to obtain the interval replication result.

[0018] Furthermore, the status parameters of the substation automation system or distributed system are collected in real time at a fixed frequency. The system status parameters include node load information, network status information, data update information, and operating performance indicators.

[0019] The node load information includes node CPU utilization, memory usage, and disk I / O; the network status information includes average inter-node transmission latency, maximum latency, and jitter; the data update information includes the number of data updates and the amount of data per unit time; and the operational performance indicators include the number of system configuration changes, replication success rate, and synchronization latency.

[0020] The preprocessing refers to performing anomaly removal, filtering and noise reduction, and normalization on the system state parameters to generate node load factors. Network latency factor Data update frequency factor Configuration change rate and system performance indicators All factor values ​​are normalized to the interval [0, 1].

[0021] Furthermore, the system state evaluation function expression is as follows:

[0022] ;

[0023] in, for The system state evaluation value at time t, with a range of [0, +∞), A larger value indicates a more unstable system state and more frequent configuration changes; for The system configuration change rate at any given time, expressed in times / second, represents the number of times the system configuration changes per unit of time. for The system performance metrics are calculated from parameters such as replication success rate and data synchronization latency, with values ​​ranging from [0, 1]. The closer the value is to 1, the better the system performance. , This is a weighting coefficient, with a value range of [0, 1], used to improve stability when prioritizing configuration. Values ​​that improve replication performance when prioritizing copy performance. Values.

[0024] After receiving the preprocessed system state parameters, the method of this invention strictly follows the order of "first system state assessment, then replication interval optimization calculation," sequentially completing macroscopic state judgment and microscopic interval optimization. The basis for the macroscopic state judgment is: when... A value that is too high indicates system instability and frequent configuration changes, triggering a conservative replication strategy to extend the replication interval and ensure system security; when... When the value is relatively small, it indicates that the system is running smoothly with few configuration changes, and a conventional dynamic strategy is adopted to shorten the replication interval and improve the real-time synchronization.

[0025] Furthermore, the dynamic calculation of the replication interval adjustment amount The expression is:

[0026] ;

[0027] in, This is the adjustment amount for the replication interval, in seconds. To extend the replication interval, To shorten the replication interval.

[0028] When the system is under high load, high latency, or frequent data updates, the calculation yields a positive result. It automatically extends the replication interval; when system resources are sufficient, network is smooth, and data updates are sparse, a negative result is calculated. The replication interval should be shortened appropriately.

[0029] The final expression for the replication interval is:

[0030] ;

[0031] The constraints are:

[0032] ;

[0033] in, For the weighting coefficients, satisfying ; , is the node load factor, with a value range of [0, 1], which is obtained by normalizing the node's CPU utilization, memory usage, etc.; is the network delay factor, with a value range of [0, 1], obtained by normalizing the transmission delay between nodes; The data update frequency factor has a value range of [0, 1] and is obtained by normalizing the number of data updates per unit time. This is the initial reference interval; As a safe lower limit for the replication interval, This is the upper limit for the safe replication interval.

[0034] The method of this invention solves the problems of resource waste, network congestion and synchronization lag caused by fixed-interval replication by determining the set replication interval and combining it with constraints to make the replication strategy accurately match the real-time operating status of the system.

[0035] Furthermore, the dynamically calculated replication interval is used to initiate the replication process. In the smart substation scenario, the interval replication is strictly performed in accordance with the IEC 61850 specification: First, the original SCD file is parsed to extract the complete configuration and reference relationships of IED, LN, DOI, and DA in the source interval; then, a unique name and instance number for the target interval are automatically generated, configuration information is copied in batches and differential corrections are completed; communication relationships such as GOOSE subscription and SV sampling are updated synchronously, and cross-interval reference conflicts are automatically repaired; finally, configuration conflict detection, specification compatibility verification, and functional verification are performed to generate a compliant and usable new SCD file and import it into the system to achieve fast, accurate, and conflict-free replication of interval configuration.

[0036] Furthermore, real-time data collection of execution performance data such as replication success rate, data synchronization latency, CPU and bandwidth resource consumption, and configuration error rate is used to calculate the data consistency level between nodes using a consistency metric formula.

[0037] The system status assessment results, replication performance indicators, and consistency scores are fed back into the SCD model. The model automatically iterates and optimizes the weight coefficients based on the above data, and dynamically corrects the replication interval calculation strategy and replication execution logic.

[0038] The method of this invention runs the above process in sync with status monitoring, dynamic adjustment, and replication execution, continuously improving the algorithm's adaptability, accuracy, and stability, achieving self-optimization without human intervention, and comprehensively improving replication efficiency, data consistency, and system resource utilization.

[0039] Furthermore, the consistency metric formula is expressed as follows:

[0040] ;

[0041] in, This is a data consistency metric, with a value range of [0,1]. This means that the data on all nodes is completely consistent. This indicates that the data across all nodes is completely inconsistent. The value of the same data item on different nodes; This refers to the total number of data items participating in the consistency measurement; the more data items, the more accurate the measurement result. This defines the range of values ​​for data items, used to eliminate the influence of dimensions and ensure the objectivity and consistency of evaluation results.

[0042] Secondly, the present invention provides an interval replication configuration system based on the SCD model, comprising:

[0043] The status parameter acquisition module is used to collect multi-dimensional system status parameters and preprocess them to obtain preprocessed system status parameters.

[0044] The replication interval calculation module is used to input the preprocessed system state parameters into the system state evaluation function to determine the system stability and the intensity of configuration changes, and then dynamically calculate the replication interval adjustment based on the multi-factor weighted formula to obtain the set replication interval.

[0045] The interval replication configuration module is used to execute the interval replication configuration of the SCD model according to the set replication interval, and obtain the interval replication result.

[0046] Thirdly, the present invention provides a computer-readable storage medium having a computer program / instructions stored thereon, which, when executed by a processor, implements the steps of the interval replication configuration method based on the SCD model as described in any of the first aspects.

[0047] Fourthly, the present invention provides a computer device, comprising:

[0048] Memory, used to store computer programs / instructions;

[0049] A processor for executing the computer program / instructions to implement the steps of the interval replication configuration method based on the SCD model as described in any of the first aspects.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0051] 1. The interval replication configuration method based on the SCD model provided by this invention integrates multi-dimensional system state parameters such as node load, network latency, and data update frequency, combined with a consistency quantification measurement formula and a closed-loop feedback mechanism, to achieve monitoring, control, and optimization of data synchronization quality; relying on the SCD model, it realizes dynamic adaptive adjustment of the replication interval, so that the replication strategy is accurately matched with the real-time system state: automatically reducing the replication frequency in high-load, high-latency, and network congestion scenarios to alleviate network pressure and system resource occupation; and automatically increasing the replication frequency in low-load, low-latency, and lightweight update scenarios to fully release system resources;

[0052] The improved SCD replication interval algorithm used in this invention fully complies with the IEC 61850 standard and supports multiple distributed architectures. It can be quickly deployed without large-scale modifications to existing systems. The algorithm is simple to implement, has low hardware dependency, and low deployment threshold. It can be widely used in fields such as power automation, distributed data centers, and industrial internet, and has extremely high engineering practicality and promotion value.

[0053] 2. The computer-readable storage medium and computer device provided by the present invention can execute the steps of the interval replication configuration method based on the SCD model provided by the present invention. Attached Figure Description

[0054] Figure 1 This is an overall flowchart of the interval replication configuration method based on the SCD model provided in an embodiment of the present invention. Detailed Implementation

[0055] It should be noted that:

[0056] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0057] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0058] Example 1

[0059] like Figure 1 As shown, this embodiment introduces an interval replication configuration method based on the SCD model, including:

[0060] Collect multi-dimensional system state parameters and preprocess them to obtain preprocessed system state parameters;

[0061] The preprocessed system state parameters are input into the system state evaluation function to determine the system stability and the intensity of configuration changes. Then, the replication interval adjustment is dynamically calculated based on the multi-factor weighted formula to obtain the set replication interval.

[0062] Based on the set replication interval, the interval replication configuration of the SCD model is executed to obtain the interval replication result.

[0063] In summary, this embodiment collects and preprocesses multi-dimensional system state parameters, combines them with system state evaluation functions and multi-factor weighted formulas, dynamically calculates and sets the optimal replication interval, and completes the interval replication configuration of the SCD model. This method achieves accurate matching between the replication strategy and the real-time system state. The core is the adoption of an improved interval replication algorithm based on the SCD model, which constructs a closed-loop adaptive mechanism of "state monitoring - system evaluation - dynamic calculation - intelligent execution - feedback optimization". It can automatically sense load fluctuations, network quality changes and data update intensity, and autonomously complete the replication interval optimization and model parameter iteration without manual intervention. In addition, the algorithm supports compatible parsing of multiple types of SCD models, can flexibly adapt to different architectures and different business scenarios, and has strong versatility and adaptability.

[0064] Preferably, this embodiment uses a 220kV intelligent substation automation system as an engineering scenario for introduction. The system contains 10 similar circuit breaker bays, and the business operations have extremely high requirements for configuration consistency, IEC 61850 protocol compatibility, and deployment efficiency. The specific implementation process is as follows:

[0065] After the algorithm starts, it first enters the initialization phase, which completes the basic parameters and model configuration required for the entire process, providing a stable and reliable operating benchmark for the subsequent operation.

[0066] Considering the high stability and real-time requirements of intelligent substations, this embodiment sets a baseline replication interval. The valid interval is 20s–40s; the replication interval is configured with dynamically adjusted weights, where node load α=0.4, network latency β=0.4, and data update frequency γ=0.2, thereby strengthening the impact of node load and network stability; system status evaluation weights are set. Highlighting configuration changes, To enhance system performance, the status monitoring sampling frequency was set to 5 seconds / time, and the consistency verification threshold was set to 0.99. The IEC 61850 protocol rules, SCD file parsing library, and interval naming and numbering rules were loaded to complete algorithm initialization and environment verification.

[0067] Real-time data collection is performed on parameters such as substation monitoring and control, protection node loads, station control layer network latency, interval configuration change frequency, replication success rate, and communication link quality. After filtering, noise reduction, anomaly removal, normalization, and validity verification, node load factors are generated. Network latency factor Data update frequency factor Configuration change rate and system performance indicators All factor values ​​are standardized within Within the range, it provides reliable data support for dynamic decision-making.

[0068] When calculating the set replication interval, the SCD model calculation in this embodiment is first based on the system state evaluation function. The system's operational stability and the intensity of configuration changes are comprehensively assessed; the system state evaluation function expression is as follows:

[0069] ;

[0070] in, for The system state evaluation value at time t, with a range of [0, +∞), A larger value indicates a more unstable system state and more frequent configuration changes; for The system configuration change rate at any given time, expressed in times / second, represents the number of times the system configuration changes per unit of time. for The system performance metrics are calculated from parameters such as replication success rate and data synchronization latency, with values ​​ranging from [0, 1]. The closer the value is to 1, the better the system performance. , is the weighting coefficient, with a value range of [0, 1].

[0071] Then, the interval adjustment amount is calculated using the dynamic adjustment formula for the replication interval. The dynamic calculation of the replication interval adjustment amount The expression is:

[0072] ;

[0073] in, This is the adjustment amount for the replication interval, in seconds. To extend the replication interval, To shorten the replication interval.

[0074] Final Press Determine the optimal replication interval and constrain it within a safe range. Internally: In this embodiment, when the system is in the interval expansion and configuration debugging stage, the configuration change rate is high, and the algorithm automatically extends the replication interval to 40s to avoid configuration conflicts caused by frequent replication; when the system enters the stable operation period, the configuration is unchanged and the node load is low, the algorithm automatically shortens the replication interval to 20s to improve synchronization efficiency and real-time performance.

[0075] This embodiment initiates the replication process by setting the replication interval as described above, including: selecting one well-configured and verified circuit breaker bay as the source bay; extracting the IED, LN, DOI, DA, GOOSE, and SV subscription relationships completely using an SCD parsing tool; creating nine target bays in batches, automatically generating unique IED names, LN instance numbers, and addresses, and completing full configuration replication; automatically correcting cross-bay reference relationships, calibrating the GOOSE sending and subscription matching relationship, and the SV sampling link mapping relationship; and after completing conflict detection, protocol compatibility verification, and functional verification, generating a compliant new SCD file and importing it into the system for operation.

[0076] After the replication process is completed, this embodiment performs feedback optimization on the interval replication results: by collecting performance data such as replication execution time, number of configuration errors, verification pass rate, and debugging time, and using a consistency metric formula to calculate configuration consistency; the expression of the consistency metric formula is:

[0077] ;

[0078] in, This is a data consistency metric, with a value range of [0,1]. This means that the data on all nodes is completely consistent. This indicates that the data across all nodes is completely inconsistent. The value of the same data item on different nodes; This refers to the total number of data items participating in the consistency measurement; the more data items, the more accurate the measurement result. This defines the range of values ​​for data items, used to eliminate the influence of dimensions and ensure the objectivity and consistency of evaluation results.

[0079] The performance data and system status evaluation results are then fed back to the SCD model to continuously optimize the weight coefficients, naming rules, reference calibration logic, and replication interval strategy, so as to improve the accuracy and efficiency of subsequent batch replication and achieve full-process self-optimization operation.

[0080] The results show that in this 220kV smart substation project, the configuration time for 10 similar bays was reduced from 8 hours to 1.5 hours, improving configuration efficiency by 81.25%; the configuration error rate was reduced by 85%, and the configuration consistency reached 100%; the overall system commissioning time was reduced by 60%; the entire process complied with the requirements of IEC 61850, reducing manual intervention by 90%, significantly reducing operation and maintenance costs, and demonstrating outstanding engineering application value.

[0081] Preferably, this embodiment also uses a MySQL 1-master-4-slave cluster as a typical distributed database synchronization scenario to achieve dynamic adaptation and closed-loop optimization of master-slave data replication. The specific implementation process is as follows:

[0082] After the algorithm starts, it first enters the initialization phase, which completes the basic parameters and model configuration required for the entire process, providing a stable and reliable operating benchmark for the subsequent operation.

[0083] Based on the high concurrency and high frequency update characteristics of the database business, this embodiment sets the baseline replication interval as follows: The legal replication interval range is 5s–20s; the replication interval is dynamically adjusted with weights, where node load α=0.2, network latency β=0.3, and data update frequency γ=0.5, to strengthen the impact of data update frequency on the replication strategy; the system status evaluation weights are set as w1=0.4 to highlight configuration changes and w2=0.6 to highlight system performance; the status monitoring sampling frequency is set to 2s / time, and data normalization rules, anomaly filtering thresholds, and consistency verification standards are configured to complete the loading of the SCD model and the initialization of the runtime environment.

[0084] The system collects real-time system status parameters such as CPU utilization, memory usage, disk I / O, network round-trip latency, jitter, transaction commit frequency, and data update volume of the master node and four slave nodes. After filtering, noise reduction, anomaly removal, and normalization, a node load factor is generated. Network latency factor Data update frequency factor Configuration change rate and system performance indicators And upload in real time.

[0085] In calculating the set replication interval, the SCD model calculation in this embodiment is first based on the system state evaluation function. The system's operational stability and the intensity of configuration changes are comprehensively assessed; the system state evaluation function expression is as follows:

[0086] ;

[0087] in, for The system state evaluation value at time t, with a range of [0, +∞), A larger value indicates a more unstable system state and more frequent configuration changes; for The system configuration change rate at any given time, expressed in times / second, represents the number of times the system configuration changes per unit of time. for The system performance metrics are calculated from parameters such as replication success rate and data synchronization latency, with values ​​ranging from [0, 1]. The closer the value is to 1, the better the system performance. , is the weighting coefficient, with a value range of [0, 1].

[0088] Then, the interval adjustment amount is calculated using the dynamic adjustment formula for the replication interval. The dynamic calculation of the replication interval adjustment amount The expression is:

[0089] ;

[0090] in, This is the adjustment amount for the replication interval, in seconds. To extend the replication interval, To shorten the replication interval.

[0091] final Define and set the replication interval, and constrain it within a safe range. Internal: In this embodiment, when the MySQL cluster is in peak business period, i.e., CPU utilization is higher than 80% and transaction and data update frequency increases sharply, the replication interval is adjusted. If the value is positive, the algorithm automatically extends the replication interval to 15-20 seconds to avoid exacerbating system congestion; when the cluster is in a low-traffic period, i.e., CPU utilization is below 30% and data updates are sparse, the replication interval adjustment amount is [not specified]. If the value is negative, the algorithm automatically shortens the replication interval to 5s–8s to improve real-time synchronization.

[0092] Based on the dynamically calculated set replication interval, the master node is controlled to asynchronously batch synchronize the Binlog logs to the four slave nodes to complete data replication and playback. During the replication process, execution effect data such as replication start and end time, master-slave synchronization delay, replication success rate, packet loss rate, and resource utilization rate are recorded in real time.

[0093] After the replication process is completed, the data consistency score of the master and slave nodes is calculated using a consistency metric formula, and metrics such as replication success rate, synchronization latency, CPU and bandwidth consumption are collected; the system status assessment value is then... Consistency level and operational performance data are fed back to the SCD model in real time. The model automatically iterates and optimizes the weight coefficients and replication interval benchmarks to continuously improve replication efficiency and system stability, thereby achieving full-process self-optimization operation.

[0094] The results show that, after deploying this algorithm in a MySQL cluster, compared with the traditional fixed-interval replication algorithm, the master-slave synchronization latency is reduced by 45%, the fault recovery time is shortened by 40%, the system CPU and bandwidth resource consumption is reduced by 32%, the cluster QPS is increased by 20%, and the data consistency is stable at over 99.95%, with a significant improvement in overall performance and reliability.

[0095] Example 2

[0096] Based on the interval replication configuration method based on the SCD model described in Embodiment 1, this embodiment introduces an interval replication configuration system based on the SCD model, including:

[0097] The status parameter acquisition module is used to collect multi-dimensional system status parameters and preprocess them to obtain preprocessed system status parameters.

[0098] The replication interval calculation module is used to input the preprocessed system state parameters into the system state evaluation function to determine the system stability and the intensity of configuration changes, and then dynamically calculate the replication interval adjustment based on the multi-factor weighted formula to obtain the set replication interval.

[0099] The interval replication configuration module is used to execute the interval replication configuration of the SCD model according to the set replication interval, and obtain the interval replication result.

[0100] Example 3

[0101] Based on the SCD model-based interval replication configuration method described in Embodiment 1, this embodiment introduces a computer-readable storage medium storing a computer program / instruction. When the computer program / instruction is executed by a processor, it implements the steps of the SCD model-based interval replication configuration method as described in any of Embodiment 1.

[0102] Example 4

[0103] Based on the interval replication configuration method based on the SCD model described in Embodiment 1, this embodiment provides a computer device, including:

[0104] Memory, used to store computer programs / instructions;

[0105] A processor for executing the computer program / instructions to implement the steps of the interval replication configuration method based on the SCD model as described in any one of Embodiments 1.

[0106] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0108] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0109] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0110] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for configuring interval replication based on the SCD model, characterized in that, include: Collect multi-dimensional system state parameters and preprocess them to obtain preprocessed system state parameters; The preprocessed system state parameters are input into the system state evaluation function to determine the system stability and the intensity of configuration changes. Then, the replication interval adjustment is dynamically calculated based on the multi-factor weighted formula to obtain the set replication interval. Based on the set replication interval, the interval replication configuration of the SCD model is executed to obtain the interval replication result.

2. The interval replication configuration method based on the SCD model according to claim 1, characterized in that, The system status parameters include node load information, network status information, data update information, and operating performance indicators; The node load information includes node CPU utilization, memory usage, and disk I / O. The network status information includes the average transmission delay between nodes, the maximum delay, and the jitter value. The data update information includes the number of data updates and the amount of data per unit time. The operational performance metrics include the number of system configuration changes, replication success rate, and synchronization latency. The preprocessing refers to the process of removing anomalies, filtering and reducing noise, and normalizing the system state parameters to generate node load factor, network latency factor, data update frequency factor, configuration change rate, and system performance indicators.

3. The interval replication configuration method based on the SCD model according to claim 1, characterized in that, The system state evaluation function expression is as follows: ； in, for System state assessment value at any given time; for Rate of change of system configuration at any time; for Real-time system performance metrics; , These are the weighting coefficients.

4. The interval replication configuration method based on the SCD model according to claim 1, characterized in that, The dynamic calculation of the replication interval adjustment amount The expression is: ； The final expression for the replication interval is: ； The constraints are: ； in, These are the weighting coefficients; Node load factor; Network latency factor; Update the frequency factor for the data; This is the initial reference interval; As a safe lower limit for the replication interval, This is the upper limit for the replication interval.

5. The interval replication configuration method based on the SCD model according to claim 1, characterized in that, The interval replication configuration conforms to the IEC 61850 standard, and the process includes: Parse the original SCD file and extract the complete configuration and reference relationships of IED, LN, DOI, and DA in the source interval; Automatically generate unique names and instance numbers for target intervals, batch copy configuration information and perform differential corrections; Synchronously update the communication relationship between GOOSE subscription and SV sampling, and automatically resolve cross-interval reference conflicts; Perform configuration conflict detection, specification compatibility verification, and functional verification, generate a compliant new SCD file, and import it into the system.

6. The interval replication configuration method based on the SCD model according to claim 1, characterized in that, Also includes: Collect execution performance data and use a consistency metric formula to calculate the data consistency level between nodes; The execution performance data and consistency score are fed back to the SCD model, which iteratively optimizes the weight coefficients and replication strategy based on the data.

7. The interval replication configuration method based on the SCD model according to claim 6, characterized in that, The formula for the consistency metric is expressed as follows: ； in, This is a measure of data consistency. The value of the same data item on different nodes; The total number of data items participating in the consistency measure; This represents the range of values ​​that a data item can take.

8. An interval replication configuration system based on the SCD model, characterized in that, include: The status parameter acquisition module is used to collect multi-dimensional system status parameters and preprocess them to obtain preprocessed system status parameters. The replication interval calculation module is used to input the preprocessed system state parameters into the system state evaluation function to determine the system stability and the intensity of configuration changes, and then dynamically calculate the replication interval adjustment based on the multi-factor weighted formula to obtain the set replication interval. The interval replication configuration module is used to execute the interval replication configuration of the SCD model according to the set replication interval, and obtain the interval replication result.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instruction is executed by the processor, it implements the steps of the interval replication configuration method based on the SCD model as described in any one of claims 1 to 7.

10. A computer device, characterized in that, include: Memory, used to store computer programs / instructions; A processor for executing the computer program / instructions to implement the steps of the interval replication configuration method based on the SCD model as described in any one of claims 1 to 7.

Images