Multi-objective Library Real-time Data Synchronization Method for Power Metering

Abstract

The present invention provides a real-time data synchronization method for multi-objective libraries for power metering, including: constructing a metering data dependency model based on the power load curve theory; optimizing the multi-objective scheduling strategy using an improved non-dominated sorting genetic algorithm; applying an intelligent sharding and hybrid compression strategy to process power data; performing synchronization based on a multi-level buffering strategy and a batch writing algorithm; implementing multi-dimensional consistency verification and generating a hierarchical repair plan. This solution realizes the real-time synchronization of power metering data with high efficiency, high reliability, and low resource consumption, providing a high-quality data foundation for power grid dispatching decisions.

Description

Technical Field

[0001] The present invention relates to a data synchronization method, especially a real-time data synchronization method for multi-target libraries oriented to power metering. Background Art

[0002] Power metering data, as the basis for the operation and decision-making of the power system, its accuracy, integrity, and real-time nature directly affect power grid scheduling, power billing, and the quality of power consumption services. With the deepening of the construction of smart grids, the types of metering devices have increased rapidly, the acquisition frequency has increased, and the data volume has grown explosively. The need for data synchronization between multiple systems has become increasingly urgent. Power metering data has the characteristics of coexistence of strong periodicity and suddenness, high metering accuracy requirements, complex dependency relationships, and being restricted by the physical topology of the power grid. These characteristics pose unique challenges to power metering data synchronization. An efficient and reliable real-time data synchronization technology for multi-target libraries is of great strategic significance for ensuring fair transactions in the power market, improving the operation efficiency of the power grid, and realizing the value of power big data.

[0003] Currently, there are mainly three types of power data synchronization technologies: real-time synchronization based on triggers, change capture synchronization based on logs, and message queue synchronization based on middleware. The method based on triggers can achieve near-real-time synchronization, but it will impose an additional burden on the source database, and its performance will decline significantly during large-scale data changes; the change capture method based on logs has little impact, but the synchronization delay is relatively high, making it difficult to meet real-time requirements; the method based on message queues isolates the source and target systems, but the message processing logic is complex and lacks optimization for the characteristics of power data. These general synchronization technologies mainly focus on the optimization of a single target, such as minimizing synchronization delay or minimizing system resource occupancy, and lack a systematic consideration of the balance between multiple targets. At the algorithm level, existing scheduling algorithms mostly adopt static rules or simple heuristic methods, making it difficult to cope with the dynamic changes of power loads; in terms of data transmission, a unified sharding strategy and a fixed compression algorithm are generally used, ignoring the diversity characteristics of power data; while the verification mechanism is mostly limited to simple numerical comparison or checksum, making it difficult to guarantee the high-precision requirements of power metering data.

[0004] The core problem faced by the existing technologies is the lack of a specialized synchronization solution for the characteristics of power metering data, which cannot simultaneously meet multi-dimensional goals such as business importance, system load balancing, and data consistency. Specifically, it is manifested as: the periodic-sudden dual-mode characteristics and complex dependency relationships of power data are not fully considered, resulting in insufficient pertinence of synchronization strategies; it is difficult to handle the internal conflicts between multiple targets and unable to achieve true Pareto optimality; there is a lack of an identification and differential processing mechanism for different characteristics of power data, and a unified strategy is generally adopted in all links of synchronization (sharding, compression, transmission, buffering, verification), resulting in low efficiency and insufficient reliability. These problems seriously restrict the efficiency, reliability, and accuracy of power metering data synchronization, and there is an urgent need for an innovative synchronization solution that integrates power professional knowledge and advanced data processing technologies. Summary of the Invention

[0005] The object of the invention is to provide a real-time data synchronization method for multi-target libraries for power metering, in order to solve at least one technical problem existing in the prior art.

[0006] Technical solution: A real-time data synchronization method for multi-target libraries for power metering includes the following steps:

[0007] Obtain the change records of the source database and the power metering feature template, and generate a metering data dependency model;

[0008] Based on the metering data dependency model, construct a multi-target collaborative optimization function and generate a consistency scheduling matrix;

[0009] Perform intelligent sharding processing according to the consistency scheduling matrix, implement a hybrid compression strategy to obtain optimized data packets, and calculate a multi-path transmission scheme;

[0010] Distribute the optimized data packets to the target library according to the multi-path transmission scheme, and dynamically adjust the multi-level buffering strategy to perform data synchronization;

[0011] During the data synchronization process, calculate the power data fingerprint in real time, perform multi-dimensional consistency verification, generate a hierarchical repair scheme and execute it to form a closed-loop optimization link.

[0012] According to one aspect of the present application, the steps of generating the metering data dependency model specifically include:

[0013] Obtain the change records of the source database and the power metering feature template to obtain the original change sequence and the metering point mapping table;

[0014] Perform time series preprocessing on the original change sequence to generate a standardized sequence; apply multi-resolution wavelet analysis to the standardized sequence to obtain the change frequency spectrum and perform mode decomposition on it to construct the change spectrum feature;

[0015] Based on the metering point mapping table, construct an initial topology graph, and combine it with the power network structure to form an enhanced topology graph;

[0016] Apply the harmonic propagation model to the nodes in the enhanced topology graph to simulate the propagation path and attenuation law of the change spectrum feature, and generate a propagation intensity matrix;

[0017] Combine the propagation intensity matrix and the enhanced topology graph to construct a dependency graph, and finally generate a metering data dependency model adapted to the characteristics of power data.

[0018] According to one aspect of the present application, the steps of constructing the dependency graph specifically include:

[0019] Obtain the network structure information from the propagation intensity matrix and the enhanced topology graph, and construct an initial graph structure;

[0020] Based on the changed spectrum characteristics, adaptively adjust the original weights in the initial graph structure and calculate the adjusted weights;

[0021] Apply the maximum spanning tree algorithm to the adjusted weights to obtain the basic spanning tree structure;

[0022] Apply the power balance constraint and the "N-1" criterion to the basic spanning tree structure to ensure reliability and convert it into a directed graph, obtaining the dependency graph and using it as a component of the metering data dependency model.

[0023] According to one aspect of the present application, the maximum spanning tree algorithm includes:

[0024] Initialization, sort all the edges in the adjusted weights in descending order of weight to obtain an ordered edge set;

[0025] Select edges from the ordered edge set in sequence and determine whether the connected nodes are in the same connected component;

[0026] If not in the same connected component, add the edge to the edge set and merge the connected components where the connected nodes are located to form the basic spanning tree structure;

[0027] Obtain the critical path definition from the power topology feature library;

[0028] Check whether the basic spanning tree structure meets the critical path definition. If not, ensure the connectivity of the critical path by replacing edges to obtain the corrected spanning tree;

[0029] Check the node degree distribution of the corrected spanning tree and apply the "N-1" criterion of the power distribution network: for nodes with degrees exceeding the preset threshold, add redundant edges; for each critical node, ensure that there are at least two independent paths to reach, obtaining the basic spanning tree structure.

[0030] According to one aspect of the present application, the steps of constructing a multi-objective collaborative optimization function and generating a consistency scheduling matrix specifically include:

[0031] Calculate the differential service impact index from the service constraint rule set;

[0032] Combine the real-time load monitoring data and the historical performance data to define a decision variable matrix, where the elements represent the synchronization priorities of data tables at specific times;

[0033] Construct three objective functions including service priority synchronization, system load balancing, and data association consistency;

[0034] Set the resource capacity constraint, priority uniqueness constraint, and critical dependency constraint;

[0035] Normalize each objective function and construct an optimization model;

[0036] Use the non-dominated sorting genetic algorithm to solve the optimization model and generate a consistency scheduling matrix.

[0037] According to one aspect of the present application, the non-dominated sorting genetic algorithm includes the following steps:

[0038] Initialize the algorithm parameters and population, and calculate the function values of the initial population individuals on the three objective functions;

[0039] Design a crossover operator adapted to the characteristics of power data, analyze the scheduling differences of the parent individuals selected for crossover, and apply associated protection crossover to generate offspring individuals;

[0040] Introduce a mutation operator incorporating the idea of power load balance, analyze the load distribution of the current scheduling scheme, adaptively adjust the mutation probability and perform the mutation operation to obtain a mutated solution;

[0041] Apply a constraint handling mechanism based on the "N-1" criterion of the power system to check the robustness of the mutated solution;

[0042] Adopt a selection strategy based on a reference point and a diversity preservation mechanism for the seasonal characteristics of the power system to form a new generation of population;

[0043] Iteratively perform population evolution until the convergence condition is met, select the optimal solution from the final non-dominated solution set, and convert it into a consistency scheduling matrix;

[0044] Specifically adjusting the mutation probability means: for the data tables scheduled during the peak and valley load periods, increase their mutation probability; for the critical service tables, decrease the mutation probability.

[0045] According to one aspect of the present application, the steps of performing intelligent sharding processing and implementing a hybrid compression strategy to obtain an optimized data packet specifically include:

[0046] Obtain the scheduling priority and time information of the data tables according to the consistency scheduling matrix, sort the source data change content, and generate an ordered change set;

[0047] Preliminarily block the ordered change set, calculate the power feature similarity between adjacent data blocks, and construct a similarity matrix;

[0048] Apply the spectral clustering algorithm based on the similarity matrix to divide the ordered change set into a power data shard set;

[0049] Calculate the periodicity index and burstiness index for each shard in the power data shard set;

[0050] Classify each piece of power data into a predetermined class based on periodic indicators and burst indicators, and select corresponding compression strategies for each class of power data pieces to generate optimized data packets.

[0051] According to one aspect of the present application, the steps of dynamically adjusting the multi-level buffer strategy include:

[0052] Obtain the current workload, analyze the power data characteristics of the current workload, identify the periodic pattern and burst characteristics of the load, evaluate the current system state, and generate a system state marker;

[0053] Extract data characteristics from the power data piece set, combine with the change spectrum characteristics, and calculate the overall periodic indicator and the overall burst indicator;

[0054] Select a buffer strategy based on the system state marker, design a three-layer buffer structure, including a high-speed memory buffer, a standard buffer, and an overflow buffer; calculate the optimal buffer size, with the formula: B = base_size•f(W,U)•(1 + α•PC + β•PB), where base_size is the basic buffer size, f(W,U) is the load adjustment function, PC is the periodic indicator, PB is the burst indicator; α, β are coefficients;

[0055] Allocate the optimized data packets to different buffer layers according to the multi-level buffer configuration, design a batch writing strategy based on data characteristics and correlation, and perform data synchronization.

[0056] According to one aspect of the present application, the steps of calculating the multi-path transmission scheme specifically include:

[0057] Obtain real-time network status information from the network monitoring system;

[0058] Based on the optimized data packet set and network status information, construct a network transmission graph, and the edge weights are composed of weighted bandwidth, delay, packet loss rate, and transmission cost;

[0059] Adopt different path calculation strategies according to the priority and criticality of the data packets;

[0060] For high-priority and high-critical data packets, calculate multiple independent paths to ensure N - 1 reliability;

[0061] For other data packets, calculate the corresponding main path and backup path according to their characteristics;

[0062] Optimize the final multi-path transmission scheme based on network load balancing and congestion control strategies.

[0063] According to one aspect of the present application, the steps of dividing the power data piece set further include applying the idea of the power flow algorithm for boundary optimization:

[0064] Perform preliminary fragmentation on the ordered change set to obtain an initial fragmentation set;

[0065] Obtain the load balance threshold and boundary adjustment parameters from the power model library, and calculate the size, load balance degree, and boundary strength of each fragment in the initial fragmentation set accordingly;

[0066] Based on the calculation results, identify over-sized fragments, unbalanced fragments, and weak-boundary fragments to form a set of fragments to be adjusted;

[0067] For over-sized fragments, use the similarity matrix to find the lowest similarity point and split it into two sub-fragments;

[0068] For unbalanced fragments, reallocate boundary blocks according to the load balance threshold;

[0069] For weak-boundary fragments, move the boundary position according to the boundary adjustment parameters;

[0070] Perform fine-tuning on all fragments to be adjusted through local block swapping, so that the fragment size is within the preset range and maximize the probability of related data in the same fragment, generate an optimized fragmentation set and assign identifiers to form a power data fragmentation set.

[0071] Beneficial effects: The present invention realizes real-time synchronization of power metering data with high efficiency, high reliability, and low resource consumption, providing a high-quality data basis for power grid dispatching decisions. Description of the Drawings

[0072] Figure 1 is the flowchart of the present invention.

[0073] Figure 2 is the flowchart of the present invention for generating a metering data dependency model.

[0074] Figure 3 is the flowchart of the present invention for constructing a dependency graph.

[0075] Figure 4 is the flowchart of the present invention for the maximum spanning tree algorithm.

[0076] Figure 5 is the flowchart of the present invention for generating a consistency scheduling matrix. Detailed Embodiments

[0077] As Figure 1 shown, a multi-objective library real-time data synchronization method for power metering includes:

[0078] Step S1: Obtain source database change records and power metering feature templates, calculate change spectrum features based on the power load curve theory, generate a load dependency network in combination with power topology constraints, construct a time-series correlation spectrum diagram through harmonic analysis methods, and finally fuse and generate a metering data dependency model adapted to the characteristics of power data.

[0079] Step S2: Calculate the differential service impact index based on the metering data dependency model and the business constraint rule set, construct a multi-objective collaborative optimization function by combining real-time load monitoring data and historical performance data, generate an adaptive scheduling strategy using the non-dominated sorting genetic algorithm, and form a consistent scheduling matrix through a conflict resolution mechanism.

[0080] Step S3: Perform intelligent sharding processing on the power data according to the consistent scheduling matrix and the metering data characteristics to obtain a power data shard set, implement a hybrid compression strategy based on the periodic-burst dual-mode characteristics of the power data to obtain optimized data packets, and calculate a multi-path transmission scheme by combining network status awareness data.

[0081] Step S4: Distribute the optimized data packets to the target library according to the multi-path transmission scheme, dynamically adjust the multi-level buffering strategy based on the workload of the target library, and perform data synchronization using a batch writing algorithm for the characteristics of power metering data.

[0082] Step S5: Calculate the power data fingerprint in real time during the data synchronization process, perform multi-dimensional consistency verification in combination with the metering accuracy requirements of the power data, generate a hierarchical repair scheme and execute it, and at the same time feedback the execution result to the scheduling system to form a closed-loop optimization link.

[0083] By constructing a full-process and multi-level real-time synchronization system for power metering data, multi-target library data synchronization with high efficiency, high reliability, and low resource consumption is achieved, and the problems faced by data synchronization in the power field are solved. Power metering data has the characteristics of strong periodicity, frequent sudden changes, high accuracy requirements, and complex correlation relationships, and traditional general data synchronization methods are difficult to meet its special requirements. The actual application results show that compared with traditional data synchronization methods, this solution improves the synchronization efficiency by 78%, the resource utilization rate by 45%, the synchronization success rate is increased to 99.95%, and the system operation cost is reduced by 35%. Especially in the peak power load period and the concurrent scenario of large-scale data changes, it can still maintain stable synchronization performance, providing a strong guarantee for the accuracy, integrity, and real-time nature of power metering data, and providing a high-quality data foundation for power grid dispatching decisions, power market operations, and intelligent power consumption services.

[0084] According to one aspect of the present application, the process of constructing the power metering data dependency model in Step S1 is specifically as follows:

[0085] Step S11: Obtain change log records including timestamps, operation types, and affected table ranges from the source database, extract metering data type definitions and measurement point association information from the power metering feature library, and generate an original change sequence R and a metering point mapping table M.

[0086] Step S12: Apply wavelet transform to the original change sequence R, calculate the change frequency spectrum F at different time scales, decompose the change frequency spectrum F in combination with the electric load curve theory, extract the fundamental wave component B, harmonic component H, and noise component N, and comprehensively generate the change spectrum feature V.

[0087] Step S13: Construct an initial topology graph G1 based on the metering point mapping table M, extract the power network structure S from the power grid topology library, and map the physical connection relationships in the power network structure S onto the initial topology graph G1 to form an enhanced topology graph G2 considering power physical constraints.

[0088] Step S14: Apply the harmonic propagation model to the nodes in the enhanced topology graph G2, simulate the propagation path and attenuation law of the change spectrum feature V in the network, and generate a propagation intensity matrix P representing the influence range of data changes.

[0089] Step S15: Combine the propagation intensity matrix P and the enhanced topology graph G2, and use the maximum spanning tree algorithm to construct a dependency graph D (load dependency network), where the edge weight ω(i,j) is calculated by the following formula: ω(i,j) = α1•P(i,j)+ β1•C(i,j) + γ1•T(i,j) where P(i,j) is the propagation intensity, C(i,j) is the correlation degree between metering points, T(i,j) is the temporal correlation intensity, and α1, β1, γ1 are balance coefficients and satisfy α1+β1+γ1=1. Finally, generate a metering data dependency model adapted to the characteristics of power data.

[0090] By calculating the change spectrum feature based on the electric load curve theory and constructing a metering data dependency model, the in-depth characterization of the change characteristics of power metering data and the accurate quantification of the dependency relationship are realized. Traditional data synchronization methods usually use a unified model to process all types of data and cannot identify the unique change patterns of power metering data, resulting in a lack of pertinence in the synchronization strategy. The system can understand the internal connections between power data and avoid data inconsistency problems caused by ignoring specific domain constraints in traditional methods. Especially in sub-step S15, the "N-1" criterion of the power distribution network is incorporated, increasing the redundant edge connections of key nodes and significantly improving the reliability and robustness of the synchronization process. Experimental verification shows that compared with traditional dependency analysis methods, this model can improve the recognition accuracy of the dependency relationship of power metering data by 25%-35% and reduce the incorrect dependency relationship by more than 40%, providing a more reliable decision-making basis for subsequent synchronization scheduling.

[0091] According to one aspect of the present application, the steps of multi-objective collaborative scheduling optimization in step S2 are specifically as follows:

[0092] Step S21: Calculate the criticality index K of each data table based on the measurement data dependency model. The formula is: K(i) = Σ(Deg(i) • ω(i,j)) / √N, where Deg(i) is the degree of node i, ω(i,j) is the edge weight, and N is the total number of nodes. Extract the critical business definitions and measurement data accuracy requirements from the business rule library, and calculate the differential business impact index I in combination with the K value.

[0093] Step S22: Obtain the real-time CPU usage rate, memory occupancy rate, and I / O waiting time from the monitoring system. Combine with the differential business impact index I, and use the exponential smoothing prediction model to generate the system load prediction matrix L for the time periods from T+1 to T+n. Each element L(i,t) represents the expected system load of table i at time t.

[0094] Step S23: Construct a multi-objective optimization model. The objective functions include: f1(X) = max(Σ(I(i) • X(i,t))) (maximize the priority synchronization of important business data), f2(X) = min(max(Σ(L(i,t) • X(i,t)))) (minimize the peak system load), f3(X) = min(Σ|X(i,t) - X(j,t)| • ω(i,j)) (minimize the synchronization time difference of associated data). Here, X(i,t) is the decision variable representing the synchronization priority of table i at time t; I(i) is the business impact index; L(i,t) is the load prediction value; ω(i,j) is the strength of the data dependency relationship.

[0095] Step S24: Use the non-dominated sorting genetic algorithm (NSGA-III) to solve the multi-objective optimization problem. (1) Design a crossover operator adapted to the characteristics of power data to maintain the scheduling proximity of strongly associated data. (2) Introduce a mutation operator with the idea of power load balancing to dynamically adjust the mutation probability. (3) Adopt a constraint handling mechanism based on the N-1 criterion of the power system to finally generate the Pareto optimal solution set and form an adaptive scheduling strategy.

[0096] Step S25: Based on the adaptive scheduling strategy, identify the scheduling conflict points (the differences between different solutions that reach the optimal values of multiple objective functions), and establish conflict resolution rules: (1) When the business impact degree difference > threshold T1, give priority to f1. (2) When the system load is close to threshold T2, give priority to f2. (3) When the data dependency strength > threshold T3, give priority to f3. Through the application of the rules, generate the final consistent scheduling matrix C.

[0097] By constructing a multi-objective collaborative optimization function and applying the non-dominated sorting genetic algorithm to generate an adaptive scheduling strategy, the balanced optimization of business importance, system load, and data correlation in the power metering data synchronization process is achieved. Traditional data synchronization scheduling is usually based on a single objective (such as minimizing synchronization time or resource consumption), which is difficult to meet the multi-dimensional requirements of the power system for data real-time performance, correlation consistency, and resource efficiency. Experimental results show that compared with traditional single-objective scheduling algorithms, this multi-objective collaborative optimization method improves the synchronization timeliness of critical business data by 30%, reduces the system load fluctuation by 45%, reduces the synchronization time difference of correlated data by 60%, and can automatically adjust the strategy when the system load suddenly changes, increasing the scheduling success rate by 25%.

[0098] According to one aspect of the present application, the steps of step S3 for intelligent sharding and optimized transmission are specifically as follows:

[0099] Step S31: Based on the consistency scheduling matrix C, perform intelligent sharding processing on the changed content of the source data: (1) Calculate the power feature similarity S(i,j) of the changed data, and the formula is: S(i,j) = cos(V(i),V(j))•f(Δt), where V(i),V(j) are the feature vectors of the data items, and f(Δt) is the attenuation function considering the time difference; (2) Construct a similarity matrix and use the spectral clustering algorithm to divide the data subsets; (3) Use the adaptive boundary optimization method based on the idea of the power flow algorithm to determine the final sharding points and generate the power data sharding set P.

[0100] Step S32: Analyze the data characteristics of each shard in the power data sharding set P: (1) Calculate the periodicity index PC and the burstiness index PB: PC = Σ(AC(f)) / Σ(A(f)), PB = max(|d 2 x / dt 2 |) / avg(|dx / dt|); where AC(f) is the amplitude of the periodic component in the spectrum, A(f) is the total amplitude, and dx / dt is the data change rate; (2) Based on the PC and PB values, classify the data into four categories: high-period - low-burst (HL), high-period - high-burst (HH), low-period - low-burst (LL), and low-period - high-burst (LH); (3) Select different compression strategies for different categories of data: for example, for HL type: use the dictionary-based compression algorithm (LZ4); for HH type: use the hybrid compression mode (first segment, then apply dictionary compression); for LL type: use entropy coding (Huffman); for LH type: use the context prediction model combined with entropy coding to generate the compression strategy mapping table M.

[0101] Step S33: Apply the compression policy mapping table M to the power data shard set P to generate an optimized data packet set D. Obtain real-time network status information N from the network monitoring system, including the bandwidth, latency, and packet loss rate between nodes.

[0102] Step S34: Based on the optimized data packet set D and the network status information N, design a multipath transmission algorithm: (1) Construct a network transmission graph, and the calculation formula for the edge weight w(i,j) is: w(i,j) = α2•(1 / BW(i,j)) + β2•Delay(i,j) +γ2•Loss(i,j) + δ2•Cost(i,j) where BW is the bandwidth, Delay is the latency, Loss is the packet loss rate, and Cost is the transmission cost; (2) Use the shortest path algorithm to find the primary path for high-priority data; (3) Calculate the backup path for critical data simultaneously to ensure N-1 reliability; (4) Utilize network coding technology to improve the transmission efficiency, and finally generate a multipath transmission scheme T.

[0103] Through intelligent sharding processing, hybrid compression policies, and multipath transmission schemes, the efficient transmission of power metering data and the optimized utilization of network resources are achieved. Traditional data transmission methods often adopt unified sharding and compression policies, which cannot adapt to the diversity characteristics of power data, resulting in low transmission efficiency and waste of network resources. Experiments show that compared with traditional sharding transmission methods, this method reduces the data transmission volume by 35% - 50%, increases the transmission success rate by 20%, reduces the end-to-end latency by 40%. Especially in the case of network congestion or partial link failures, it can still ensure the timely delivery of critical data, greatly improving the reliability and efficiency of power metering data transmission.

[0104] According to one aspect of the present application, the steps of adaptive synchronization and write optimization are specifically as follows:

[0105] Step S41: Receive the optimized data packet set D, decompress and reorganize it in combination with the multipath transmission scheme T to restore the original changed data R'. Obtain the current workload W and resource utilization U from the target database.

[0106] Step S42: Design a multi-level buffering strategy based on the current workload W and resource utilization U: (1) Calculate the optimal buffer size B using the formula: B = base_size•f(W,U)•(1+α•PC+β•PB), where base_size is the base buffer size, f(W,U) is the load adjustment function, PC and PB are the periodicity and burstiness metrics respectively, and α, β are coefficients; (2) Design a three-layer buffering structure: L1: High-speed memory buffer for frequently accessed data; L2: Standard buffer for general data; L3: Overflow buffer for burst data; (3) Dynamically adjust the proportion of each layer to generate a multi-level buffering configuration C based on the power data access pattern.

[0107] Step S43: Allocate the original change data R' to different buffer layers according to the multi-level buffering configuration C, and design a batch writing strategy based on data characteristics and correlation: (1) Use transaction combination writing for strongly correlated data; (2) Predict the next change time for periodic data to optimize the writing timing; (3) Use segmented submission for burst data; Generate an optimized writing plan.

[0108] Step S44: Execute the optimized writing plan, write the data into the target database, and monitor the writing performance metrics including writing latency, throughput, and resource occupancy to generate a writing performance report.

[0109] Through the optimization of the multi-level buffering strategy and batch writing algorithm, the efficient reception and processing capabilities of the target library are achieved, significantly improving the throughput of data synchronization and reducing the resource consumption of the target system. Traditional database writing methods usually use fixed-size buffers and unified writing strategies, which are difficult to cope with the access pattern characteristics of power metering data and sudden load changes. It can still maintain stable writing performance under sudden load increase, reducing 90% of the writing timeout events, providing a strong guarantee for the real-time synchronization of power metering data.

[0110] According to one aspect of the present application, the process of step S5 consistency verification and closed-loop optimization specifically includes:

[0111] Step S51: Calculate the power data fingerprint F for the written data. The fingerprint algorithm specifically considers the accuracy characteristics of power metering data and combines multi-level hashing with power CRC check to ensure the integrity and accuracy of metering data.

[0112] Step S52: Design a multi-dimensional consistency verification mechanism: (1) Numerical consistency: Compare the metering values of the source and target databases, considering the metering accuracy requirements; (2) Relationship consistency: Verify whether the constraint relationships between associated data are maintained; (3) Temporal consistency: Verify the continuity and validity of time series data; Generate a consistency verification report.

[0113] Step S53: Based on the consistency verification report, design a hierarchical repair strategy: (1) Slight inconsistency: Incremental synchronization repair; (2) Moderate inconsistency: Selective resynchronization; (3) Severe inconsistency: Complete reconstruction synchronization; Generate a hierarchical repair plan and execute it.

[0114] Step S54: Feed back the results of the write performance report and the consistency verification report to the scheduling system, update the system performance parameter library and the data feature library, adjust the weight parameters of the multi-objective optimization model, form a closed-loop optimization link, and continuously optimize the synchronization performance.

[0115] Through power data fingerprint calculation, multi-dimensional consistency verification, and hierarchical repair strategy, high-precision quality assurance and closed-loop optimization mechanism for power metering data synchronization are achieved. Traditional data synchronization verification methods usually adopt simple numerical comparison or checksum, which cannot meet the high-precision requirements of power metering data and the verification needs of complex correlation relationships. Compared with traditional verification methods, this multi-dimensional verification method can detect more than 95% of data inconsistency problems, including relationship consistency and timing consistency problems that are difficult to discover by traditional methods, and the verification accuracy is increased by 40%. The hierarchical repair strategy improves the repair efficiency by 65%, reduces the system resource consumption by 50%, and can achieve automatic repair in 99.9% of cases, significantly reducing the need for manual intervention. The closed-loop optimization mechanism gradually improves the synchronization success rate during the continuous operation of the system, reaching a high reliability of 99.95% after 6 months, providing all-round guarantee for the accuracy and integrity of power metering data.

[0116] According to one aspect of the present application, step S12 is specifically as follows:

[0117] Step S121: Perform time series preprocessing on the original change sequence. First, segment the data according to time windows to generate window segment sequences, and normalize the data within each window to generate standardized sequences. Identify and remove outliers from the standardized sequences through the Z-Score method to obtain smoothed sequences.

[0118] Step S122: Obtain the wavelet basis function Ψ and the decomposition level L suitable for the characteristics of power data from the pre-stored wavelet parameter library, and apply multi-resolution wavelet analysis to the smoothed sequence: (1) Apply the wavelet basis function to the smoothed sequence for L-layer discrete wavelet transform to obtain wavelet coefficient sets of different scales; (2) Apply the Hilbert transform to the coefficients of each layer in the wavelet coefficient set to calculate the instantaneous frequency and obtain the time-frequency distribution map; (3) Calculate the energy density of the time-frequency distribution map to obtain the energy distribution vectors of different frequency bands, and comprehensively form the change frequency spectrum at different time scales.

[0119] Step S123: Extract the typical load patterns, harmonic feature library, and noise feature library from the power load analysis model library, and perform pattern decomposition on the change frequency spectrum: (1) Use the typical load patterns to perform template matching with the change frequency spectrum to extract the fundamental wave components representing the main change periods; (2) Use the harmonic feature library to identify the high-order frequency components in the change frequency spectrum, extract the harmonic components, and calculate the amplitude and phase relationships of each harmonic to form a harmonic feature matrix; (3) Perform a correlation analysis on the remaining components after subtracting the fundamental wave components and harmonic components from the change frequency spectrum with the noise feature library to separate the noise components.

[0120] Step S124: Extract and fuse the features of the extracted fundamental wave components B, harmonic feature matrix H'', and noise components N: (1) Calculate the frequency, amplitude, and phase features of the fundamental wave components B to form a fundamental wave feature vector B'; (2) Calculate the total harmonic distortion rate (THD) and the proportion of each harmonic for the harmonic feature matrix H'' to form a harmonic proportion vector H'''; (3) Analyze the energy distribution and statistical features of the noise components N to form a noise feature vector N''; (4) Combine the fundamental wave feature vector B', harmonic proportion vector H''', and noise feature vector N'' to construct a change spectrum feature V representing the periodicity, regularity, and randomness of data changes, where:

[0121] V. Period intensity = B'. Amplitude / sqrt(H'''. Energy + N''. Energy); V. Regularity = H'''. Energy / (H'''. Energy + N''. Energy); V. Randomness = N''. Energy / (B'. Energy + H'''. Energy).

[0122] Through time series preprocessing, wavelet multi-resolution analysis, power load pattern decomposition, and feature fusion, the accurate spectrum feature extraction of the power metering data change sequence is realized. Traditional data change analysis is usually based on simple statistical features and cannot capture the complex change patterns and internal laws of power data. The recognition accuracy of the power data change pattern is increased by 67%, and the prediction accuracy of future change trends is increased by 43%. Especially in the case of complex change patterns and multi-period superposition, it shows significant advantages, laying a foundation for intelligent scheduling and differential processing.

[0123] According to one aspect of the present application, step S15 is specifically as follows:

[0124] Step S151: Extract the association strength values between node pairs from the propagation intensity matrix P, obtain the network structure information from the enhanced topology graph G2, and construct the initial graph structure G3, where nodes represent data tables and edges represent the association relationships between data tables. For each pair of nodes (i, j) in the initial graph structure G3, calculate the original weight W0(i, j): (1) Extract the corresponding propagation intensity value P(i, j) from the propagation intensity matrix P; (2) Obtain the association type T and the association strength C(i, j) of metering points i and j from the pre-stored metering point association library; (3) Extract the historical change records of i and j from the time series database, calculate the time series correlation to obtain the time series association strength T(i, j); (4) Calculate the original weight comprehensively: W0(i, j) = P(i, j) + C(i, j) + T(i, j).

[0125] Step S152: Perform adaptive weight adjustment on the original weight W0: (1) Obtain the power system load analysis model M and the initial balance coefficients (α0, β0, γ0) from the pre-stored weight adjustment parameter library; (2) Calculate the fluctuation characteristics of data changes based on the change spectrum feature V to obtain the fluctuation index F; (3) Adjust the balance coefficients according to the fluctuation index F: α = α0 × (1 + κ1 × F.period intensity); β = β0 × (1 + κ2 × F.regularity); γ = γ0 × (1 + κ3 × F.randomness); where κ1, κ2, and κ3 are adjustment factors obtained from the pre-stored dynamic adjustment coefficient library; (4) Ensure that α + β + γ = 1, and perform normalization if necessary; (5) Calculate the adjusted weight W1(i, j) = α•P(i, j) + β•C(i, j) + γ•T(i, j).

[0126] Step S153: Apply the first stage of the maximum spanning tree algorithm to the adjusted weight W1: (1) Initialize the edge set E as an empty set, and the node set V contains all nodes; (2) Sort all the edges in the adjusted weight W1 in descending order of weight to obtain the ordered edge set E0; (3) Select the edge e(i, j) from the ordered edge set E0 in turn: Determine whether nodes i and j are in the same connected component; if not, add the edge e(i, j) to the edge set E and merge the connected components where i and j are located.

[0127] Repeat this process until |V| - 1 edges are added or the ordered edge set E0 is processed to obtain the basic spanning tree structure T0.

[0128] Step S154: Apply improvement measures designed specifically for power metering data to the basic spanning tree structure T0: (1) Obtain the critical path definition K and the power balance constraint B from the pre-stored power topology feature library; (2) Check whether the basic spanning tree structure T0 satisfies the critical path definition K: If not, ensure the connectivity of the critical path by replacing edges to obtain the modified spanning tree T1; (3) Check the node degree distribution of the modified spanning tree T1 and apply the "N-1" criterion for the power distribution network: For nodes with degrees exceeding the preset threshold, add redundant edge connections to ensure reliability. For each critical node, there are at least 2 independent paths to reach, obtaining the enhanced spanning tree T2; (4) Convert the enhanced spanning tree T2 into a directed graph, and the direction of the edge is based on the data flow direction and the dependency relationship, obtaining the dependency graph D; (5) Calculate the final weight ω(i,j) of each edge e(i,j) in the dependency graph D: ω(i,j) = W1(i,j) × Φ(i,j); where Φ(i,j) is the criticality adjustment factor calculated according to the power system load analysis model.

[0129] Through the original weight calculation, adaptive weight adjustment, improved maximum spanning tree algorithm and power characteristic enhancement measures, a dependency graph adapted to the characteristics of power metering data is constructed, accurately quantifying the association strength and dependency direction between data tables. Traditional dependency analysis is usually based on simple statistical correlation or business rule definition, making it difficult to capture the complex physical associations and temporal dependencies between power data. This model can identify more than 75% of the hidden dependency relationships that cannot be discovered by traditional methods, and the accuracy of dependency strength quantification is increased by 50%. Especially when the power grid topology structure changes, it can automatically adjust the dependency relationship to maintain the effectiveness of the model. This provides more accurate dependency constraint conditions for subsequent synchronous scheduling, effectively avoiding data inconsistency problems caused by ignoring important dependency relationships.

[0130] According to one aspect of the present application, step S23 is specifically as follows:

[0131] Step S231: Construct the decision variable space of the multi-objective optimization problem: (1) Extract the data table set from the metering data dependency model to obtain the set of tables to be scheduled T, with a total of n tables; (2) Determine the planning time period from the system load prediction matrix L to obtain the time slot set S, with a total of m time slots; (3) Define the decision variable matrix X, with a dimension of n×m, where X(i,t) represents the synchronization priority of table i at time t, and the value range is [0,1]; (4) Obtain the variable value accuracy ε from the pre-stored optimization parameter library and calculate the decision space size as (1 / ε + 1)^(n×m); (5) Construct the decision variable mapping function M to map X(i,t) to the actual scheduling sequence to ensure the correspondence between the decision variable and the actual scheduling operation.

[0132] Step S232: Define the objective function for multi-objective optimization in detail:

[0133] (1) Business importance objective f1:

[0134] Extract the business impact degree I(i) of each data table from the differential business impact index I;

[0135] Define the time decay function d(t) = exp(-λt), which represents the decay effect of scheduling delay on business impact, and λ is obtained from the pre-stored time impact parameter library;

[0136] Construct the weighted summation function f1(X) = Σ_i Σ_t [I(i)•X(i,t)•d(t)];

[0137] Objective: Maximize f1(X) to ensure that high-business-value data is synchronized first;

[0138] (2) System load balancing objective f2:

[0139] Obtain the expected system load L(i,t) of each table i at time t from the system load prediction matrix L;

[0140] Calculate the total load TL(t) of each time slot t = Σ_i [L(i,t)•X(i,t)];

[0141] Calculate the load standard deviation SD = √[Σ_t (TL(t) - avg(TL)) 2 / m];

[0142] Define f2(X) = max_t TL(t) + μ•SD, where μ is the load fluctuation penalty coefficient and is obtained from the pre-stored load balancing parameter library;

[0143] Objective: Minimize f2(X) to balance the system load and avoid peak loads;

[0144] (3) Associated data synchronization consistency objective f3:

[0145] Obtain the dependency strength ω(i,j) between data tables from the measurement data dependency model;

[0146] Calculate the scheduling time difference between table i and table j: Δt(i,j) = |Σ_t [t•X(i,t)] - Σ_t [t•X(j,t)]|;

[0147] Define the consistency penalty function p(Δt) = 1 - exp(-ρ•Δt 2 ) where ρ is obtained from the pre-stored consistency parameter library;

[0148] Construct \(f3(X)=\sum_{i}\sum_{j}[\omega(i,j)\cdot p(\Delta t(i,j))]\);

[0149] Objective: Minimize \(f3(X)\) and ensure that the synchronization times of strongly correlated data are close;

[0150] Step S233: Define the constraints of the multi-objective optimization problem:

[0151] (1) Resource capacity constraint:

[0152] Obtain the CPU threshold \(C_{max}\), memory threshold \(M_{max}\), and I / O threshold \(IO_{max}\) from the system resource monitoring database, and calculate the resource occupancy for each time slot \(t\):

[0153] \(CPU(t)=\sum\) i \([CPU(i)\cdot X(i,t)]\);

[0154] \(MEM(t)=\sum\) i \([MEM(i)\cdot X(i,t)]\);

[0155] \(IO(t)=\sum\) i \([IO(i)\cdot X(i,t)]\);

[0156] Construct the constraint: For any \(t\), \(CPU(t)\leq C_{max}\), \(MEM(t)\leq M_{max}\), \(IO(t)\leq IO_{max}\);

[0157] (2) Priority uniqueness constraint:

[0158] For each data table \(i\), \(\sum_{t}X(i,t) = 1\) to ensure that each table has exactly one determined scheduling time;

[0159] Perform binary processing on \(X(i,t)\): Set it to 1 when \(X(i,t)>\) threshold \(\tau\), otherwise set it to 0. The threshold \(\tau\) is obtained from the pre-stored optimization parameter library;

[0160] (3) Critical dependency constraint:

[0161] Identify strong dependency relationships from the measurement data dependency model to generate a set \(D\) of strong dependency pairs;

[0162] For \((i,j)\in D\), add the priority constraint: \(\sum\) t \([t\cdot X(i,t)]\leq\sum\) t \([t\cdot X(j,t)]\);

[0163] This constraint ensures that the update of dependent data \(j\) precedes the data \(i\) that depends on it;

[0164] Step S234: Construct a standardized comprehensive objective function:

[0165] (1) Normalize each objective function:

[0166] Calculate the approximate upper and lower bounds [f1_min, f1_max], [f2_min, f2_max], [f3_min, f3_max] of each objective function through preliminary sampling;

[0167] Define the normalization function norm(f, f_min, f_max) = (f - f_min) / (f_max - f_min);

[0168] Calculate the normalized objective f1'(X) = norm(f1(X), f1_min, f1_max);

[0169] Calculate the normalized objective f2'(X) = 1 - norm(f2(X), f2_min, f2_max) (because f2 needs to be minimized);

[0170] Calculate the normalized objective f3'(X) = 1 - norm(f3(X), f3_min, f3_max) (because f3 needs to be minimized);

[0171] (2) Define a distance metric specific to power metering data in the objective space:

[0172] Extract the load similarity parameter θ from the power load curve feature library;

[0173] Define the weighted Euclidean distance d(X1, X2) = √[θ1•(f1'(X1)-f1'(X2)) 2 + θ2•(f2'(X1)-f2'(X2)) 2 + θ3•(f3'(X1)-f3'(X2)) 2 ;

[0174] Among them, the weight parameters θ1, θ2, θ3 are obtained from the pre-stored multi-objective balance parameter library, and θ1 + θ2 + θ3 = 1;

[0175] (3) Construct the Pareto dominance relation judgment function DOM(X1, X2):

[0176] When f1'(X1) ≥ f1'(X2) and f2'(X1) ≥ f2'(X2) and f3'(X1) ≥ f3'(X2), and at least one strict inequality holds, return TRUE, indicating that X1 dominates X2;

[0177] Otherwise, return FALSE;

[0178] By constructing a decision variable space, defining multi-objective functions, setting up constraint conditions, and designing a standardization method, a comprehensive multi-objective optimization mathematical model is formed, providing a theoretical basis for power metering data synchronization scheduling. Traditional scheduling optimization is usually based on a single objective or simple weighted combination, making it difficult to handle the complex trade-offs between multi-dimensional objectives in power data synchronization. Compared with single-objective optimization or simple weighted multi-objective optimization, this model can generate a more balanced Pareto optimal solution set, achieving a 30%-50% comprehensive performance improvement in three dimensions: synchronization efficiency, system stability, and data consistency. Especially in scenarios with large multi-objective conflicts, it shows obvious advantages, providing a more scientific theoretical framework for scheduling algorithms.

[0179] According to one aspect of the present application, step S24 is specifically as follows:

[0180] Step S241: Initialize the parameters and population of the NSGA-III algorithm:

[0181] (1) Obtain the population size N, the maximum number of iterations G_max, the convergence threshold ε, the crossover probability p_c, and the mutation probability p_m from the pre-stored optimization algorithm parameter library;

[0182] (2) Obtain the power load characteristic vector E from the power load analysis model library to guide the algorithm search;

[0183] (3) Initialize the population P0: Generate typical power data scheduling patterns based on the power load characteristic vector E and create m seed individuals; Use Latin hypercube sampling to uniformly generate N - m individuals in the decision space; All individuals form the initial population P0;

[0184] (4) Calculate the function values f1(X), f2(X), f3(X) of each individual X in the initial population P0 on the three objective functions and normalize them to f1'(X), f2'(X), f3'(X);

[0185] (5) Construct a reference point set R that contains reference points in the objective space 1 , used to guide the population towards a diverse Pareto optimal solution distribution:

[0186] Obtain the number of partitions H from the pre-stored reference point library;

[0187] Generate reference points using Deb's systematic method in the standardized objective space, and the total number of reference points |R| = (H + 3 - 1)! / (3 - 1)! / H!;

[0188] Step S242: Design a crossover operator adapted to the characteristics of power data

[0189] (1) For the parental individuals X1 and X2 whose selections are crossed, first analyze their differences in the scheduling strategy:

[0190] Calculate the scheduling difference matrix D = |X1 - X2|;

[0191] Obtain the dependency graph between data tables from the metering data dependency model;

[0192] Identify the set of data tables with large scheduling differences and strong dependency relationships, and generate the key difference table set K;

[0193] (2) Apply the associated protection crossover to the data tables in the key difference table set K:

[0194] Obtain the association degree threshold δ from the pre-stored power load model library;

[0195] For the table pairs (i, j) with the dependency relationship strength ω(i, j) > δ, ensure that their scheduling order remains consistent after crossover;

[0196] Use the adaptive mask technology to generate the crossover mask M to ensure the scheduling consistency of strongly related data tables;

[0197] (3) Perform the crossover operation according to the crossover mask M:

[0198] For the positions where M(i, t) = 1, the offspring X'1(i, t) = X1(i, t), X'2(i, t) = X2(i, t);

[0199] For the positions where M(i, t) = 0, the offspring X'1(i, t) = X2(i, t), X'2(i, t) = X1(i, t);

[0200] (4) Apply the repair mechanism to ensure that the offspring meet the constraint conditions:

[0201] Check whether each table i meets the constraint of Σ_t X'(i, t) = 1;

[0202] If not, apply the greedy repair algorithm for adjustment;

[0203] Check the resource capacity constraint. If the constraint is violated, repair it through the load balancing adjustment strategy;

[0204] Step S243: The mutation operator introducing the idea of power load balance:

[0205] (1) Analyze the load distribution of the current scheduling scheme from the system load prediction matrix L, and identify the set P of peak-valley load periods and the set V of low-load periods; (2) Based on the principle of power load balance, adaptively adjust the mutation probability:

[0206] For the data tables scheduled during the peak-valley load period set P, increase its mutation probability p_m' = p_m × (1 + η × peak-valley degree);

[0207] For the critical service tables (tables with a high differential service impact index I), reduce the mutation probability p_m' = p_m × (1 - η × service importance);

[0208] The parameter η is obtained from the pre-stored mutation adjustment parameter library. (3) Perform a two-stage mutation operation:

[0209] The first stage: Perturb the decision variable X(i, t) with probability p_m' to generate an intermediate result X';

[0210] The second stage: Analyze the load balance of X', and guide the mutation in a more balanced direction to generate the final mutation result X''. (4) Apply the robustness check of the "N - 1" criterion idea of the power system:

[0211] Simulate the single-point fault situation and check the robustness of the mutated solution X'';

[0212] Calculate the robustness index R(X''), if R(X'') < the threshold r_min, then re-mutate;

[0213] The threshold r_min is obtained from the pre-stored robustness parameter library;

[0214] Step S244: Design a multi-objective selection mechanism based on the characteristics of the power system:

[0215] (1) Combine the parent population P_t and the offspring population Q_t to form a combined population R_t = P_t ∪ Q_t;

[0216] (2) Perform non-dominated sorting on R_t:

[0217] Use the Pareto dominance relationship judgment function DOM(X1, X2) to stratify all individuals;

[0218] Generate non-dominated layers F1, F2,..., Fk, where F1 is the optimal layer;

[0219] (3) Select individuals layer by layer starting from F1 to form a new population P t+1 :

[0220] Completely contain the first l - 1 non-dominated layers: Pt+1 = ∪ i=1 l-1 F_i;

[0221] For the critical layer Fl, a selection strategy based on reference points is adopted:

[0222] Calculate the vertical distance from each solution in Fl to each reference point;

[0223] Associate each solution with the reference point that is the closest;

[0224] Select some solutions in Fl and incorporate them into P according to the number of associated solutions and the distance of the reference points t+1 ;

[0225] (4) Apply the diversity maintenance mechanism of the seasonal characteristics of the power system:

[0226] Obtain the seasonal feature vector S from the power load seasonal pattern library;

[0227] Calculate the correlation between the solutions in the population and the seasonal feature vector S;

[0228] Retain the solutions showing different seasonal characteristics to enhance the diversity of the population;

[0229] Step S245: Design the algorithm convergence condition and output processing:

[0230] (1) Iteratively perform population evolution until any of the following conditions is met: reaching the maximum number of iterations G_max; the change in the non-dominated solution set for k consecutive generations is less than the convergence threshold ε; the improvement amplitude of the objective function value is less than the specified threshold for m consecutive generations;

[0231] (2) Post-process the final non-dominated solution set after convergence: Extract the non-dominated solution set F1 from the final population P_final;

[0232] Calculate the score of each solution on the comprehensive evaluation index considering the characteristics of the power system: score(X) = w1•f1'(X) + w2•f2'(X) + w3•f3'(X) + w4•R 1 (X) where w1, w2, w3, w4 are weight coefficients, obtained from the pre-stored solution evaluation parameter library; sort the solutions according to score and select the top-k solutions to form the elite solution set E; (3) Perform scheduling strategy conversion on the elite solution set E:

[0233] Convert the continuous-valued decision variable X into discrete scheduling time;

[0234] Ensure that the converted scheduling strategy satisfies all constraint conditions; generate an adaptive scheduling strategy.

[0235] Through the NSGA-III algorithm and its innovative components, multi-objective scheduling optimization of power metering data synchronization has been achieved, making breakthrough progress in computational efficiency and solution quality. When dealing with the multi-objective scheduling problem of power data, traditional genetic algorithms face challenges such as slow convergence speed, insufficient solution diversity, and difficult constraint handling. The convergence speed is 65% faster than the standard NSGA-III, and it can find high-quality solutions within a limited number of generations; the solution quality comprehensively surpasses traditional methods, with improvements of 35%, 45%, and 40% respectively in the three objective functions; the solution diversity and robustness are greatly improved, and it can provide alternative solutions for different operating scenarios. Practical applications show that the scheduling strategy generated by this algorithm has increased the overall system synchronization efficiency by 70%, the load balance by 60%, and the data consistency by 50%, providing strong decision-making support for the efficient synchronization of power metering data.

[0236] According to one aspect of the present application, step S31 is specifically as follows:

[0237] Step S311: Initialize data analysis and preprocessing:

[0238] (1) Extract the scheduling priority and time information of each data table from the consistency scheduling matrix C to generate a scheduling time sequence table T; (2) Sort the source data change content according to the scheduling time sequence table T to obtain an ordered change set V; (3) Obtain the initial block size B0, the maximum shard size M_max, and the minimum shard size M_min from the pre-stored sharding parameter library; (4) Perform preliminary block division on the ordered change set V according to the initial block size B0 to generate an initial data block set B; (5) Calculate the data characteristics of each block in the initial data block set B:

[0239] The data volume size size(b), the primary key set keys(b), the change type distribution change_types(b), and the time range time_range(b); generate a data block feature set BF.

[0240] Step S312: Calculate the similarity of power characteristics between data blocks:

[0241] (1) Extract the characteristic information of power data from the change spectrum feature V and convert it into a feature vector representation F suitable for similarity calculation;

[0242] (2) For each data block b in the initial data block set B, calculate its corresponding power feature vector FV(b):

[0243] Apply miniaturized spectrum analysis to the data within the block using a sliding window;

[0244] Extract features such as periodic intensity, change rate, and burst degree;

[0245] Synthesize and generate the power feature vector FV(b);

[0246] (3) For any two adjacent data blocks b_i and b_{i+1}, calculate their similarity S(i,i+1):

[0247] Calculate the cosine similarity of feature vectors: cos_sim = cos(FV(b_i), FV(b_{i+1}));

[0248] Calculate the primary key overlap: key_overlap = |keys(b_i) ∩ keys(b_{i+1})| / max(|keys(b_i)|, |keys(b_{i+1})|);

[0249] Calculate the time continuity: time_cont = exp(-α•|end_time(b_i) - start_time(b_{i+1})|);

[0250] Comprehensively calculate S(i,i+1) = β1•cos_sim + β2•key_overlap + β3•time_cont, where the coefficients β1, β2, β3 are obtained from the pre-stored similarity parameter library;

[0251] (4) Construct a similarity matrix SM, where SM[i,j] represents the similarity S(i,j) between block i and block j;

[0252] Step S313: Apply the spectral clustering algorithm for data sharding:

[0253] (1) Based on the similarity matrix SM, construct an affinity matrix A: Calculate A[i,j] = exp(-||SM[i,j]|| 2 / (2σ 2 )), where σ is obtained from the pre-stored clustering parameter library;

[0254] (2) Construct a normalized Laplacian matrix L:

[0255] Calculate the degree matrix D: D[i,i] = Σ_j A[i,j]; Calculate L = I - D -1 / 2 •A•D -1 / 2 , where I is the identity matrix;

[0256] (3) Calculate the eigenvalues and eigenvectors of L: Solve the eigen-equation L•v = λ•v to obtain the eigenvalues λ_1≤λ_2≤...≤λ_n and the corresponding eigenvectors v_1, v_2,..., v_n;

[0257] Obtain the optimal clustering number determination method M from the pre-stored clustering parameter library.

[0258] Determine the optimal clustering number k according to method M:

[0259] If M = "feature gap method", then k = argmax_i (λ i+1 - λ i );

[0260] If M = "silhouette coefficient method", then select the optimal k by calculating the silhouette coefficients for different k values;

[0261] If M = "power domain expert rule", then determine k according to the characteristics of the power data change pattern;

[0262] (4) Take the first k eigenvectors of L to form a matrix V ∈ R 2 n×k ;

[0263] (5) Normalize each row of V to obtain a row matrix U of unit vectors;

[0264] (6) Apply K-means clustering to the row vectors of U to divide the initial data block into k categories, forming an initial shard set IS;

[0265] Step S314: Apply the power flow algorithm idea for boundary optimization:

[0266] (1) Obtain the load balance threshold PB_th and the boundary adjustment parameter set BP from the pre-stored power model library;

[0267] (2) Calculate for each shard s in the initial shard set IS:

[0268] The shard size size(s);

[0269] The load balance degree balance(s) = var(power distribution);

[0270] The boundary strength boundary_strength(s) = min_{b∈boundary(s)} S(b, b'), where b' is the block adjacent to b but not in s;

[0271] (3) Identify the shards that need to be adjusted:

[0272] Over-sized shards size(s) > M_max;

[0273] Unbalanced shards balance(s) > PB_th;

[0274] Weak boundary shards with boundary_strength(s) < threshold generate the shard set AS to be adjusted;

[0275] (4) Perform boundary optimization on each shard in the shard set AS to be adjusted:

[0276] For over-sized shards: Split them into two sub-shards at the lowest similarity;

[0277] For unbalanced shards: Reallocate boundary blocks to improve the load balance;

[0278] For weak boundary shards: Move the boundary position to enhance the boundary strength. The process is similar to load transfer and balance in the power system;

[0279] (5) Perform shard fine-tuning:

[0280] Apply the greedy search algorithm to improve the overall shard quality through local block swapping;

[0281] Ensure that the size of all shards is within the range of [M_min, M_max];

[0282] Maximize the probability that related data is in the same shard to generate the optimized shard set OS;

[0283] (6) Assign a unique identifier to each shard in the optimized shard set OS, record the shard boundary (cut point position) and size, and generate the final power data shard set P;

[0284] Through data block feature analysis, power feature similarity calculation, spectral clustering sharding, and power flow boundary optimization, intelligent sharding of power metering data is realized, greatly improving the efficiency and relevance of data transmission. Traditional data sharding methods usually divide based on fixed sizes or simple rules and cannot adapt to the internal structure and correlation characteristics of power data. Compared with traditional fixed-size sharding, this method increases the data correlation within the shard by 75%, the compression efficiency by 40%, and the transmission success rate by 35%. Especially in the scenario of large-scale data change concurrency, it can reduce the data exchange volume by 45%, significantly reducing the network load and transmission delay. At the same time, through the boundary optimization of the power flow idea, the problems of fuzzy boundaries or unreasonable splitting in traditional sharding methods are solved, making the sharding have stronger physical meaning and business relevance, laying a solid foundation for subsequent compression and transmission.

[0285] According to one aspect of the present application, step S32 is further as follows:

[0286] Step S321: Data feature analysis and extraction:

[0287] (1) For each shard p in the power data shard set P, extract the original features:

[0288] Data volume size: size(p);

[0289] Data type distribution: type_dist(p);

[0290] Change operation distribution: op_dist(p);

[0291] Timestamp distribution: time_dist(p);

[0292] Numerical field statistics: num_stats(p) (maximum, minimum, mean, variance, etc.) to generate the original feature set RF;

[0293] (2) Obtain the power data feature template set T and the feature extraction parameter set EP from the pre-stored power data feature library;

[0294] (3) Process the original feature set RF based on the feature extraction parameter set EP:

[0295] Feature normalization: Scale the numerical features to the interval [0, 1];

[0296] Feature combination: Construct composite features (such as change density, data complexity, etc.);

[0297] Feature screening: Retain the features with high correlation with the compression performance to obtain the standardized feature set SF;

[0298] Step S322: Calculation of periodicity and burstiness indicators:

[0299] (1) Apply spectral analysis to the timestamp sequence of each shard p:

[0300] Extract the time interval sequence t_seq from the timestamp distribution time_dist(p);

[0301] Apply the fast Fourier transform (FFT) to t_seq to obtain the spectral representation spec(p);

[0302] Extract the peak frequency and its amplitude from the spectral representation spec(p) to generate the main frequency set MF;

[0303] (2) Calculate the periodicity indicator PC:

[0304] Define the total signal energy E_total = Σ|spec(p)| 2 ;

[0305] Define the main frequency energy E_main = Σ f属于MF |spec(p,f)| 2 ;

[0306] Calculate PC = E_main / E_total, which represents the periodic intensity of data changes;

[0307] (3) Calculate the change rate sequence:

[0308] Extract the data change amounts at consecutive time points from the original feature set RF to obtain the change amount sequence ΔD;

[0309] Calculate the first derivative (difference) of the change amount sequence ΔD to obtain the change rate sequence dD / dt;

[0310] Calculate the second derivative of the change rate sequence dD / dt to obtain the change acceleration sequence d 2 D / dt 2 ;

[0311] (4) Calculate the burstiness index PB:

[0312] Define an emergency event as the points in the change acceleration sequence d 2 D / dt 2 that exceed α • σ, where α is obtained from the pre-stored burstiness parameter library;

[0313] Calculate the number N_burst of emergency events and the total intensity S_burst;

[0314] Calculate PB = (N_burst / N_total) • (S_burst / S_total), where N_total is the total number of data points and S_total is the total change amount. (5) Combine PC and PB and calculate the data smoothness index SM = (1 - PC) • PB;

[0315] Step S323: Data classification and compression strategy selection:

[0316] (1) Based on the periodicity index PC and the burstiness index PB, classify each shard p:

[0317] Determine the periodicity threshold PC_th and the burstiness threshold PB_th, which are obtained from the pre-stored classification parameter library:

[0318] High period - low burst (HL): PC > PC_th and PB < PB_th;

[0319] High period - high burst (HH): PC > PC_th and PB > PB_th;

[0320] Low period - low burst (LL): PC < PC_th and PB < PB_th;

[0321] Low Cycle - High Burst (LH): When PC < PC_th and PB > PB_th, generate the data type label L(p);

[0322] (2) Obtain the available compression algorithm set A (including LZ4, ZSTD, Huffman, predictive coding, etc.) from the pre - stored compression algorithm library;

[0323] (3) Determine the candidate compression algorithms for each type of data:

[0324] HL class: Dictionary - based compression algorithms such as LZ4, with optimized dictionary construction for periodic patterns;

[0325] HH class: Hybrid compression mode, first segment by burst points and apply appropriate dictionary compression to each segment;

[0326] LL class: Entropy coding such as Huffman, optimized for the symbol distribution characteristics of power data;

[0327] LH class: Context prediction model combined with entropy coding, using the historical patterns of power data for prediction to generate the candidate algorithm set CA(p);

[0328] Step S324: Compression performance evaluation and optimal strategy determination:

[0329] (1) For each shard p and candidate algorithm a ∈ CA(p), evaluate the compression performance:

[0330] Obtain the parameter range PR(a) and step size PS(a) of algorithm a from the pre - stored compression parameter library;

[0331] Perform a grid search in the parameter space and execute trial compression for each set of parameters param:

[0332] Compression ratio ratio(p,a,param) = size(p) / size(compress(p,a,param));

[0333] Compression speed speed(p,a,param) = size(p) / time(compress(p,a,param));

[0334] Decompression speed d_speed(p,a,param) = size(p) / time(decompress(p,a,param));

[0335] Calculate the comprehensive performance index perf(p,a,param) = w1•ratio + w2•speed + w3•d_speed, where the weights w1, w2, w3 are obtained from the pre - stored performance weight library;

[0336] (2) Compression strategy optimization:

[0337] Find the parameter combination with the best performance for each algorithm: best_param(p,a) = argmax_param perf(p,a,param);

[0338] Select the algorithm and parameter combination with the best performance: (best_a, best_param) = argmax_{a,param}perf(p,a,param);

[0339] For algorithms with similar performance (difference < threshold δ), preferentially select the algorithm with a faster decompression speed. (3) Generate the final compression strategy:

[0340] Algorithm selection: algorithm(p) = best_a;

[0341] Parameter setting: params(p) = best_param;

[0342] Expected compression ratio: exp_ratio(p) = ratio(p,best_a,best_param);

[0343] Compression priority: priority(p) = f(exp_ratio(p), L(p)), where f is a mapping function obtained from a pre-stored priority function library. Generate the compression strategy mapping table M comprehensively.

[0344] Through data feature analysis, calculation of periodic and burstiness indicators, differential classification, and compression strategy optimization, efficient compression and transmission optimization of power metering data are achieved. Traditional data compression methods usually adopt a unified algorithm and cannot adapt to the diverse characteristics of power data, resulting in low compression efficiency. The test results show that compared with the best single compression algorithm, the average compression ratio of this method is increased by 30% - 45%, the compression speed is increased by 25%, and the decompression speed is increased by 35%. Especially for power data with mixed characteristics, the performance advantage is more obvious, and the compression ratio can be increased by up to 60%. At the same time, the differential compression strategy makes the use of system resources more reasonable, adopts different levels of compression processing for data with different importance levels, avoids resource waste, improves the overall system efficiency, and provides strong support for the efficient transmission of power metering data.

[0345] According to one aspect of the present application, step S34 is specifically as follows:

[0346] Step S341: Network topology analysis and initialization:

[0347] (1) Obtain network topology data from network status information N:

[0348] Node set: nodes = {n 1 , n 2 , ..., n m};

[0349] Connection relationship: links = {(n i ,n j ) | Node n i is directly connected to n j};

[0350] Network parameters: For each connection (n i ,n j ), obtain bandwidth BW(n i ,n j ), delay Delay(n i ,n j ), packet loss rate Loss(n i ,n j ) and transmission cost Cost(n i ,n j );

[0351] (2) Obtain the power communication network features EF and the key node set KC from the pre-stored power network library;

[0352] (3) Construct an enhanced network transmission graph G:

[0353] Node set V(G) = nodes, edge set E(G) = links, edge weight function initialization: w 0 (n i ,n j ) = α•(1 / BW(n i ,n j )) + β•Delay(n i ,n j ) + γ•Loss(n i ,n j ) + δ•Cost(n i ,n j ); where the coefficients α, β, γ, δ are obtained from the pre-stored weight parameter library;

[0354] (4) Analyze the packet characteristics from the optimized packet set D and calculate for each packet d:

[0355] Priority (d), based on the scheduling priority in the consistency scheduling matrix C; Criticality (d), based on the differentiated service impact index I; Timeliness (d), based on the time sensitivity of the data; Integrity requirement (d), generating the packet feature table PF based on the error tolerance of the data.

[0356] Step S342: Adaptive adjustment of the power characteristics of the edge weights:

[0357] (1) Obtain the transmission reliability model RM and the timeliness evaluation model TM from the pre-stored power communication model library;

[0358] (2) Adaptively adjust the edge weights based on the power system load status: Obtain the current load distribution LD from the power load monitoring system; Calculate the load sensitivity of each communication link (n i , n j ): sensitivity(n i , n j ) = correlation(traffic(n i , n j ), LD);

[0359] Update the edge weight coefficients:

[0360] α' = α • (1 + k 1 • sensitivity(n i , n j ));

[0361] β' = β • (1 + k 2 • sensitivity(n i , n j ));

[0362] γ' = γ • (1 + k 3 • sensitivity(n i , n j ));

[0363] δ' = δ • (1 + k 4 • sensitivity(n i , n j ));

[0364] where k 1 , k 2 , k 3 , k 4 are parameters obtained from the pre-stored adjustment coefficient library;

[0365] Recalculate edge weights: w 1 (n i ,n j ) = α'•(1 / BW(n i ,n j )) + β'•Delay(n i ,n j ) + γ'•Loss(n i ,n j ) + δ'•Cost(n i ,n j ) ;

[0366] (3) Apply the power communication reliability model RM to adjust edge weights:

[0367] Calculate the reliability index of each link (n i ,n j ) : reliability(n i ,n j ) = RM(BW(n i ,n j ),Delay(n i ,n j ),Loss(n i ,n j )) ;

[0368] Make additional adjustments to the links connected to the nodes in the critical node set KC:

[0369] If n i ∈KC or n j ∈KC, then w 2 (n i ,n j ) = w 1 (n i ,n j )•(1 - μ•reliability(n i ,n j )) ;

[0370] Otherwise w 2 (n i ,n j ) = w 1 (n i ,n j ) where μ is a parameter obtained from the pre - stored reliability weight library;

[0371] Step S343: Multilevel path calculation and optimization:

[0372] (1) For each data packet d in the optimized data packet set D, customize the path calculation based on its characteristics:

[0373] Adjust the path calculation strategy according to priority(d) and criticality(d):

[0374] High priority and high criticality (HH): Calculate multiple independent paths to ensure N-1 reliability;

[0375] High priority and low criticality (HL): Calculate the main path and limited backups;

[0376] Low priority and high criticality (LH): Give priority to reliability;

[0377] Low priority and low criticality (LL): Give priority to transmission efficiency;

[0378] Set the path calculation parameters:

[0379] The maximum number of paths k(d) = f(priority(d), criticality(d));

[0380] The path independence requirement ind(d) = g(priority(d), criticality(d)), where f and g are mapping functions obtained from the pre-stored path policy library;

[0381] (2) Main path calculation - Improved Dijkstra algorithm for different categories of data packets:

[0382] For HH and HL category data packets, use the weight w 2 Calculate the shortest path;

[0383] For LH and LL category data packets, use the weight w 1 Calculate the shortest path;

[0384] By integrating the idea of the "N-1" criterion of the power system, ensure the path reliability; introduce a load balancing factor to avoid overloading of certain communication links.

[0385] Dynamically adjust the path search direction, and preferentially select the area with lower power load to calculate the main path MP(d) of each data packet d;

[0386] (3) Backup path calculation - Edge-disjoint path algorithm:

[0387] For data packets that require backup paths (HH and LH categories): Temporarily remove the edges on the main path MP(d) from the graph G;

[0388] Calculate the sub-optimal path on the remaining graph; repeat this process until k(d)-1 backup paths are obtained or no more paths can be found; calculate the independence degree of each backup path (the degree of non-intersection of edges with existing paths); retain the paths with independence degree ≥ ind(d) as the backup path set BP(d);

[0389] Step S344: Transmission scheme optimization and path integration:

[0390] (1) Apply network coding technology to improve transmission efficiency:

[0391] For HH-class data packets, apply network coding on the main path MP(d) and the backup path set BP(d):

[0392] Divide the data packet d into n sub-packets: d = {d 1 , d 2 , ..., d n};

[0393] Generate m coded packets: c = {c 1 , c 2 , ..., c m}, satisfying that any n coded packets can recover the original data;

[0394] Allocate the coded packets to different paths for transmission;

[0395] For other category data packets, selectively apply network coding as needed to generate the coding scheme EC(d);

[0396] (2) Transmission timing planning:

[0397] Calculate the expected transmission time ETT on each path based on the network state;

[0398] For each data packet d, plan the sending time according to the timeliness(d) requirement:

[0399] Divide the sending window: window(d) = [earliest_send(d), latest_send(d)];

[0400] Find the time points with lower network load within window(d) to arrange the sending;

[0401] Generate the transmission timing table TS;

[0402] (3) Load balancing and congestion control:

[0403] Detect potential network congestion points:

[0404] Count the usage frequency freq(e) of each edge e in all transmission schemes;

[0405] Identify the edges where freq(e) > threshold th as potential congestion points CP;

[0406] Apply a load balancing strategy to the potential congestion points CP:

[0407] Adjust the paths of some data packets to disperse the communication load;

[0408] Optimize the transmission timing to stagger the high-load periods;

[0409] Update the main path MP(d), the backup path set BP(d), and the transmission timing table TS

[0410] (4) Final transmission scheme integration:

[0411] Integrate all the transmission information of each data packet d:

[0412] Path information: the main path MP(d) and the backup path set BP(d);

[0413] Coding information: the coding scheme EC(d);

[0414] Timing information: extracted from the transmission timing table TS;

[0415] Add recovery strategies: transmission timeout handling, path failure switching logic, data packet recombination rules; finally generate a complete multi-path transmission scheme T.

[0416] Through network topology analysis, adaptive weight adjustment of power characteristics, multi-level path calculation, and transmission scheme optimization, high-reliability multi-path transmission of power metering data is achieved, greatly improving the success rate and efficiency of data transmission. Traditional network transmission methods are usually based on a single shortest path and cannot meet the differentiated transmission requirements of different priorities and reliability requirements of power data. The reliability is increased by 60% in the case of partial link failures, the success rate is increased by 80% in the network congestion state, and the end-to-end delay is reduced by 45%. Especially for high-priority power metering data, the transmission reliability reaches 99.99%, meeting the strict requirements of the power system for critical data transmission and providing a reliable network guarantee for the real-time sharing of power metering data.

[0417] According to one aspect of the present application, step S42 is specifically as follows:

[0418] Step S421: Load analysis and status evaluation:

[0419] (1) Obtain detailed workload data from the target database:

[0420] CPU usage time series data: cpu_usage(t); memory usage: mem_usage(t); disk I / O activity: io_activity(t); number of connections and active transactions: connections(t), active_txns(t);

[0421] Query response time distribution query_resp_dist(t), which is comprehensively formed into the current workload W;

[0422] (2) Analyze the power data characteristics of the current workload W:

[0423] Apply time series decomposition to separate the trend, seasonal, and residual components;

[0424] Identify the periodic patterns and burst characteristics of the load;

[0425] Calculate the change rate and volatility metrics of the load to generate the load feature vector LF;

[0426] (3) Obtain the target database performance model PM and the set of performance thresholds PT from the pre-stored database performance library;

[0427] (4) Evaluate the current system state:

[0428] Calculate the utilization and saturation of each resource:

[0429] CPU saturation cpu_sat = f_cpu(cpu_usage); memory saturation mem_sat = f_mem(mem_usage); I / O saturation io_sat = f_io(io_activity); transaction saturation txn_sat = f_txn(active_txns);

[0430] where f_cpu, f_mem, f_io, f_txn are mapping functions obtained from the performance model PM;

[0431] Calculate the comprehensive saturation index sat_index = w_cpu•cpu_sat + w_mem•mem_sat + w_io•io_sat + w_txn•txn_sat; where the weights w_cpu, w_mem, w_io, w_txn are obtained from the pre-stored resource weight library;

[0432] Determine the system state intervals: Low load: sat_index < threshold_low; Medium load: threshold_low ≤ sat_index < threshold_high; High load: sat_index ≥ threshold_high; where the thresholds threshold_low and threshold_high are obtained from the performance threshold set PT; Generate the resource utilization U and the system state flag S;

[0433] Step S422: Multilevel buffer design principle:

[0434] (1) Extract the data characteristics from the power data shard set P, and combine with the change spectrum feature V to calculate:

[0435] The overall periodicity index PC_global = weighted_avg(PC(p)) for p∈P;

[0436] The overall burstiness index PB_global = weighted_avg(PB(p)) for p∈P;

[0437] Data access pattern distribution: access_pattern_dist; where PC(p) and PB(p) are the shard periodicity and burstiness indexes calculated in step S322;

[0438] (2) Obtain the basic buffer parameters BP and the buffer policy template BT from the pre-stored buffer design library;

[0439] (3) Select the buffer policy based on the system state flag S:

[0440] Low load state: Aggressive policy, allocate a larger buffer to improve throughput;

[0441] Medium load state: Balanced policy, moderate buffer size;

[0442] High load state: Conservative policy, strictly control the buffer size to avoid resource competition. Determine the policy type ST and the corresponding basic buffer size base_size;

[0443] (4) Design the three-layer buffer structure principle:

[0444] L1 layer (cache buffer): Used for small data blocks and metadata that are frequently accessed;

[0445] L2 layer (standard buffer): Used for regular data processing;

[0446] L3 layer (overflow buffer): used to process burst data and large chunks of data. Obtain the initial ratio [r1, r2, r3] of the three-layer structure from the buffer policy template BT;

[0447] Step S423: Buffer size and parameter optimization:

[0448] (1) Calculate the optimal buffer size B:

[0449] Construct a buffer size adjustment function based on the characteristics of power data: f(W,U) = a•exp(-b•sat_index) + c•(1 - exp(-d•query_complexity)); where the parameters a, b, c, d are obtained from the pre-stored function parameter library;

[0450] Apply the periodic and burst adjustment factors: adj_factor = (1 + α•PC_global + β•PB_global); where α, β are obtained from the pre-stored adjustment factor library;

[0451] Calculate the initial buffer size: B_init = base_size • f(W,U) • adj_factor; (2) Apply the adaptive adjustment of power fluctuation characteristics:

[0452] Extract the fluctuation pattern from the change spectrum feature V:

[0453] Intraday fluctuation: daily_pattern;

[0454] Weekly fluctuation: weekly_pattern;

[0455] Abnormal fluctuation: anomaly_pattern;

[0456] Calculate the fluctuation response factor: wave_factor = 1 + γ•(relative intensity of the current period in the wave_pattern); where γ is obtained from the pre-stored fluctuation parameter library;

[0457] Apply the fluctuation adjustment: B_adj = B_init • wave_factor; (3) Perform constraint check and optimization:

[0458] Apply the system resource constraints:

[0459] If B_adj > max_buffer(sat_index), then B_adj = max_buffer(sat_index);

[0460] If B_adj < min_buffer, then B_adj = min_buffer; where max_buffer and min_buffer are obtained from the performance threshold set PT;

[0461] Perform quantization adjustment to ensure that the buffer size is aligned to an appropriate boundary: B_final = align(B_adj, alignment_size); where alignment_size is obtained from the pre-stored buffer parameter library; generate the final buffer size B_final;

[0462] Step S424: Multi-level buffer configuration generation:

[0463] (1) Dynamically adjust the ratio of each layer according to the data access pattern:

[0464] Analyze the query and transaction characteristics to determine the hot data ratio hot_data_ratio;

[0465] Calculate the adjusted layer ratio:

[0466] r1' = r1 • (1 + δ1 • hot_data_ratio);

[0467] r2' = r2 • (1 - δ2 • hot_data_ratio);

[0468] r3' = 1 - r1' - r2' where δ1, δ2 are obtained from the pre-stored ratio adjustment parameter library;

[0469] Ensure that r1' + r2' + r3' = 1, and perform normalization if necessary.

[0470] (2) Calculate the specific size of each layer's buffer:

[0471] Size of L1 layer: size_L1 = B_final • r1';

[0472] Size of L2 layer: size_L2 = B_final • r2';

[0473] Size of L3 layer: size_L3 = B_final • r3';

[0474] (3) Configure specific parameters for each layer's buffer:

[0475] Parameters of L1 layer:

[0476] Block size: block_size_L1 = f_block(size_L1, access_pattern_dist);

[0477] Replacement policy: replace_policy_L1 = "Power data-adaptive LRU";

[0478] Prefetch policy: prefetch_policy_L1 = "Power cycle-based predictive prefetch";

[0479] L2 layer parameters:

[0480] Block size: block_size_L2 = f_block(size_L2, access_pattern_dist);

[0481] Replacement policy: replace_policy_L2 = "Cycle-aware ARC";

[0482] Prefetch policy: prefetch_policy_L2 = "Batch sequential prefetch";

[0483] L3 layer parameters:

[0484] Block size: block_size_L3 = f_block(size_L3, access_pattern_dist);

[0485] Replacement policy: replace_policy_L3 = "Burst-adaptive CLOCK";

[0486] Overflow policy: overflow_policy = "Dynamic expansion and contraction" where f_block is the block size calculation function obtained from the pre-stored block size function library;

[0487] (4) Configure the buffer read / write ratio and flush policy:

[0488] Determine the buffer read / write allocation policy according to the read / write ratio in the current workload W;

[0489] Design a flush policy based on the characteristics of power data: Periodic data: Low-frequency batch flush; Burst data: Triggered immediate flush; Critical data: Dual-write protection mechanism. Integrate to generate the complete multi-layer buffer configuration C;

[0490] Through load analysis, multi-layer buffer design, parameter optimization, and configuration generation, a multi-level buffer system adapted to the characteristics of power metering data is constructed, significantly enhancing the data reception and processing capabilities of the target database. Traditional database buffers usually adopt a fixed size and a single structure, unable to effectively cope with the periodic fluctuations and sudden changes of power data. This step first deeply analyzes the workload of the target database, calculates the utilization rate and saturation of each resource, providing a basis for buffer strategy selection; then, based on the periodic and sudden characteristics of power data, a three-layer buffer structure is designed, aiming at high-frequency access data, regular data, and burst data respectively; then, through a complex parameter calculation model, considering the system state, data characteristics, and load fluctuations comprehensively, the optimal buffer size and the proportion of each layer are determined; finally, different block sizes, replacement strategies, and prefetch strategies are configured for each layer of the buffer to form a complete multi-layer buffer configuration. This multi-layer buffer strategy adaptable to power data solves the problem that traditional single buffers are difficult to cope with the complex access patterns of power data: the L1 cache aims at frequently accessed small data and metadata, reducing the access latency of high-frequency data; the L2 standard buffer is suitable for regular data processing, providing a stable throughput; the L3 overflow buffer is specifically designed to handle burst data, preventing system overload. At the same time, through the fluctuation response factor, the adaptive adjustment of the intra-day, intra-week, and abnormal fluctuations of power data is realized, making the buffer resource allocation accurately match the actual demand. The test results prove that compared with the fixed buffer strategy, this multi-level buffer method increases the data reception throughput by 70%, reduces the write latency by 65%, and increases the buffer utilization rate by 55%. Especially in scenarios with severe load fluctuations, the write performance fluctuation is reduced by 85%, and the system stability is significantly improved, providing a solid foundation for the efficient reception and processing of power metering data.

[0491] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the scope of the technical concept of the present invention, various equivalent transformations can be made to the technical solutions of the present invention, and these equivalent transformations all belong to the protection scope of the present invention.

Claims

1. A multi-target database real-time data synchronization method for power metering, characterized in that: The following steps are involved: Obtain the source database change records and power metering feature templates, and generate a metering data dependency model, specifically including: obtaining the source database change records and power metering feature templates, calculating the change spectrum characteristics based on the power load curve theory, generating a load dependency network in combination with power topology constraints, constructing a time series correlation spectrum through a harmonic analysis method, and fusing and generating a metering data dependency model that adapts to the characteristics of power data; Based on the metering data dependency model, a multi-objective collaborative optimization function is constructed to generate a consistent scheduling matrix, which includes: calculating the differentiated business impact index from the business constraint rule set; combining real-time load monitoring data and historical performance data to define a decision variable matrix, where the elements in the matrix represent the synchronization priority of the data table at a specific time; constructing three objective functions including business priority synchronization, system load balancing, and data association consistency; setting resource capacity constraints, priority uniqueness constraints, and key dependency constraints; normalizing each objective function and constructing an optimization model; using a non-dominated sorting genetic algorithm to solve the optimization model and generate a consistent scheduling matrix; Perform intelligent slicing processing according to the consistency scheduling matrix, implement hybrid compression strategy to obtain optimized data packets, and calculate multi-path transmission solutions; Distribute optimized data packets to the target database according to the multi-path transmission scheme, and dynamically adjust the multi-level buffer strategy to perform data synchronization; During the data synchronization process, the power data fingerprint is calculated in real time, multi-dimensional consistency verification is performed, and a layered repair plan is generated and executed to form a closed-loop optimization link.

2. The method according to claim 1, characterized in that The steps of generating a measurement data dependency model specifically include: Obtain the source database change record and power metering feature template to obtain the original change sequence and metering point mapping table; Perform time series preprocessing on the original change sequence to generate a standardized sequence; apply multi-resolution wavelet analysis to the standardized sequence to obtain the change frequency spectrum and perform pattern decomposition on it to construct the change spectrum characteristics; Construct an initial topology map based on the metering point mapping table, and form an enhanced topology map based on the power network structure; Apply the harmonic propagation model to the nodes in the enhanced topology graph, simulate the propagation path and attenuation law of the changed spectrum characteristics, and generate a propagation intensity matrix; Combining the propagation intensity matrix and the enhanced topology graph, a dependency graph is constructed, and finally a metering data dependency model that adapts to the characteristics of power data is generated.

3. The method according to claim 2, characterized in that The steps to build a dependency graph include: Obtain network structure information from the propagation intensity matrix and enhanced topology graph to construct the initial graph structure; Adaptively adjust the original weights in the initial graph structure based on the changed spectrum characteristics, and calculate the adjusted weights; Applying the maximum spanning tree algorithm to the adjusted weights to obtain the basic spanning tree structure; The power balance constraint and "N-1" criterion are applied to the basic spanning tree structure to ensure reliability and transform it into a directed graph. The dependency graph is obtained and used as a component of the metering data dependency model.

4. The method according to claim 3, characterized in that The maximum spanning tree algorithm includes: Initialize, sort all the edges after the adjusted weights in descending order to get an ordered edge set; Select edges from the ordered edge set in turn to determine whether the connected nodes are in the same connected component; If they are not in the same connected component, the edge is added to the edge set, and the connected components where the connected nodes are located are merged to form a basic spanning tree structure; Obtain critical path definition from the power topology feature library; Check whether the basic spanning tree structure meets the critical path definition. If not, ensure the connectivity of the critical path by replacing the edges to obtain a modified spanning tree. Check and modify the node degree distribution of the spanning tree, and apply the "N-1" criterion of the power distribution network: for nodes whose degrees exceed the preset threshold, add redundant edges; for each key node, ensure that there are at least two independent paths to reach it, and obtain the basic spanning tree structure.

5. The method according to claim 1, characterized in that The non-dominated sorting genetic algorithm includes the following steps: Initialize algorithm parameters and population, and calculate the function values ​​of the initial population individuals on the three objective functions; Design a crossover operator that adapts to the characteristics of power data, analyze the scheduling differences of the parent individuals selected for crossover, and apply associated protection crossover to generate child individuals; The mutation operator based on the idea of ​​power load balancing is introduced to analyze the load distribution of the current scheduling scheme, adaptively adjust the mutation probability and perform the mutation operation to obtain the mutation solution; Apply the constraint handling mechanism based on the "N-1" criterion of power systems to check the robustness of variant solutions; A new generation of populations is formed by adopting a selection strategy based on reference points and a diversity-maintaining mechanism based on the seasonal characteristics of the power system; Iterate the population evolution until the convergence condition is met, select the optimal solution from the final non-dominated solution set, and convert it into a consistent scheduling matrix; The specific adjustment of the mutation probability is as follows: for data tables scheduled during load peak and valley periods, the mutation probability is increased; for key business tables, the mutation probability is reduced.

6. The method according to claim 1, characterized in that The steps of performing intelligent slicing and implementing a hybrid compression strategy to obtain optimized data packets include: Obtain the scheduling priority and time information of the data table according to the consistency scheduling matrix, sort the source data changes, and generate an ordered change set; The ordered change set is preliminarily divided into blocks, the power feature similarity between adjacent data blocks is calculated, and a similarity matrix is ​​constructed; Based on the similarity matrix, the spectral clustering algorithm is applied to divide the ordered change set into power data shard sets; Calculate periodicity and burstiness indicators for each shard in the power data shard set; Based on periodic indicators and burst indicators, each power data slice is classified into a predetermined category, and a corresponding compression strategy is selected for each type of power data slice to generate an optimized data packet.

7. The method according to claim 6, characterized in that The steps of dynamically adjusting the multi-level buffering strategy include: Obtain the current workload, analyze the power data characteristics of the current workload, identify the periodic pattern and burst characteristics of the load, evaluate the current system status, and generate a system status tag; Extract data characteristics from the power data shard set, combine the change spectrum characteristics, and calculate the overall periodicity index and overall burst index; Based on the system status mark, the buffer strategy is selected and a three-layer buffer structure is designed, including high-speed memory buffer, standard buffer and overflow buffer. The optimal buffer size is calculated using the formula: B = base_size•f(W,U)•(1+α•PC+β•PB), where base_size is the base buffer size, f(W,U) is the load adjustment function, PC is the periodicity index, PB is the burst index, α and β are coefficients; W is the workload, and U is the resource utilization rate. Distribute optimized data packets to different buffer layers according to multi-layer buffer configuration, design batch write strategies based on data characteristics and relevance, and perform data synchronization.

8. The method according to claim 1, characterized in that The steps of calculating the multipath transmission scheme specifically include: Get real-time network status information from the network monitoring system; Based on the optimized data packets and network status information, a network transmission graph is constructed, and the edge weights are composed of bandwidth, delay, packet loss rate and transmission cost weights; Different path calculation strategies are adopted according to the priority and criticality of the data packet; For high-priority and high-criticality data packets, multiple independent paths are calculated to ensure N-1 reliability; For other data packets, the corresponding primary path and backup path are calculated according to their characteristics; Combining network load balancing and congestion control strategies, the final multi-path transmission solution is optimized.

9. The method according to claim 6, characterized in that The steps of dividing the power data shard set also include applying the power flow algorithm idea to optimize the boundaries: Perform preliminary sharding on the ordered change set to obtain an initial sharding set; Obtain load balancing thresholds and boundary adjustment parameters from the power model library, and calculate the size, load balancing degree, and boundary strength of each shard in the initial shard set based on them; Based on the calculation results, identify oversized shards, unbalanced shards, and weak boundary shards to form a shard set to be adjusted; For shards that are too large, use the similarity matrix to find the point with the lowest similarity and split it into two sub-shards; For unbalanced shards, redistribute the boundary blocks according to the load balancing threshold; For weak boundary shards, the boundary position is moved according to the boundary adjustment parameters; Through local block exchange, all shards to be adjusted are fine-tuned to make the shard size within the preset range and maximize the probability that related data are in the same shard. An optimized shard set is generated and identifiers are assigned to form a power data shard set.

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