Joint operation and information data interaction processing method for multiple simulation devices

Abstract

The invention relates to the technical field of data processing, and discloses a joint operation and information data interaction processing method for multiple simulation devices. The method comprises the following steps: collecting electrical quantity data of boundary nodes of a plurality of simulation devices, calculating an electrical distance weight factor, and dividing the boundary nodes into strong coupling, medium coupling and weak coupling; predicting an electrical state change trend based on various node historical data; solving a data interaction strategy optimization solution set through predictive boundary state estimation; distributing timestamps for the boundary data and dynamically adjusting transmission priorities to generate a transmission queue; the devices perform data exchange according to queues and monitor synchronization precision and response performance. According to the invention, intelligent hierarchical management and dynamic priority scheduling of boundary data are realized, and the problems of inconsistency and insufficient real-time performance of boundary node data in the prior art are solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method for joint operation and information data interaction processing of multiple simulation devices. Background Technology

[0002] With the continuous expansion of power system scale and the rapid development of new power systems, large-scale power system simulation has become an important technical means for power system planning, operation, and control. In existing technologies, the joint operation of multiple simulation devices mainly adopts the method of static system partitioning and fixed task allocation. This involves dividing a complex power system model into multiple relatively independent subsystems according to geographical location or electrical connection characteristics, and deploying them on different digital real-time simulation devices for parallel computation. These methods typically establish inter-device connections based on Ethernet or dedicated communication buses, use periodic data exchange mechanisms to transmit boundary information, and ensure the convergence of the entire system through a master control device or distributed coordination algorithms. Regarding data interaction, existing technologies mainly rely on simple FIFO (First-In-First-Out) queue management and fixed priority scheduling strategies, performing synchronous computation and data exchange according to preset time steps. In addition, existing multi-device coordination methods also include global clock management based on time synchronization protocols, simple task scheduling based on load monitoring, and data synchronization mechanisms based on communication delay compensation.

[0003] However, existing technologies have significant shortcomings, primarily in the lack of a systematic solution for inconsistencies in boundary node data. Static system partitioning and task allocation strategies cannot adapt to the dynamic changes in power system operating conditions. When new energy output fluctuates, load changes abruptly, or equipment failures occur, the computational load of each simulation device becomes severely unbalanced, leading to some devices having idle computing resources while others are overloaded. More critically, existing technologies lack effective boundary data prediction capabilities and dynamic priority adjustment mechanisms. They cannot perform intelligent data interaction management based on the electrical coupling strength and data importance of boundary nodes. When handling electromagnetic transient simulations, data delays and synchronization errors frequently occur, severely affecting the real-time performance and accuracy of multi-device joint simulations. Summary of the Invention

[0004] This application provides a method for joint operation and information data interaction processing of multiple simulation devices, which enables intelligent hierarchical management and dynamic priority scheduling of boundary data, and solves the problems of inconsistency and insufficient real-time performance of boundary node data in the prior art.

[0005] This application provides a method for joint operation and information data interaction processing of multiple simulation devices, the method comprising:

[0006] Step S1: Collect the voltage amplitude, phase angle and current data of the boundary nodes of multiple simulation devices, calculate the electrical distance weighting factor to determine the coupling strength, and divide the boundary nodes into strongly coupled boundary nodes, moderately coupled boundary nodes and weakly coupled boundary nodes.

[0007] Step S2: Based on the historical electrical quantity data of the strongly coupled boundary node, the moderately coupled boundary node, and the weakly coupled boundary node, predict the trend of the electrical state change of the boundary node within the future time step.

[0008] Step S3: The trend of electrical state change of the boundary nodes is used to solve the boundary data interaction strategy optimization solution set through predictive boundary state estimation;

[0009] Step S4: Assign timestamps to the boundary data in the optimized solution set, and dynamically adjust the transmission priority using the electrical distance weighting factor to generate a boundary data transmission queue;

[0010] In step S5, each simulation device performs data exchange operations according to the boundary data transmission queue, and monitors the data synchronization accuracy and system response performance indicators.

[0011] The technical solution provided in this application addresses the lack of quantitative assessment of the importance of boundary nodes in existing technologies by collecting voltage amplitude, phase angle, and current data of boundary nodes from multiple simulation devices and calculating an electrical distance weighting factor to determine coupling strength. The electrical distance weighting factor comprehensively considers the equivalent impedance and physical distance parameters between boundary nodes, accurately reflecting the impact of different boundary nodes on the overall simulation accuracy. By classifying boundary nodes into three levels—strong coupling, medium coupling, and weak coupling—a hierarchical management and differentiated processing strategy for boundary data is achieved. Strongly coupled boundary nodes receive the highest data processing priority and the most stringent synchronization requirements, effectively ensuring the transmission quality and timeliness of critical boundary data. Furthermore, the technical feature of predicting the electrical state change trends of boundary nodes within future time steps based on historical electrical quantity data of boundary nodes with different coupling strengths overcomes the lack of boundary data prediction capabilities in existing technologies. By establishing an improved characteristic line equation parameter matrix and boundary condition correction terms, the voltage amplitude, phase angle, and current change patterns of each boundary node within multiple future time steps can be accurately predicted, providing a reliable theoretical basis for subsequent data interaction strategy formulation and significantly reducing synchronization errors and delays caused by data unpredictability. The technique of using predictive boundary state estimation to solve for the optimal solution set of boundary data interaction strategy addresses the lack of systematic optimization objectives in existing technologies. By constructing three objective functions—minimizing voltage deviation, minimizing phase angle synchronization error, and balancing device load—and employing the Pareto front analytical processing method to solve the multi-objective optimization problem, a comprehensive optimal solution set that balances simulation accuracy, synchronization performance, and computational efficiency is obtained, avoiding local optima and performance skew that may result from single-objective optimization. The technique of assigning timestamps to boundary data in the optimized solution set and dynamically adjusting transmission priorities using an electrical distance weighting factor to generate boundary data transmission queues solves the problem of lack of intelligent data transmission management in existing technologies. Nanosecond-level timestamps ensure precise timing relationships of data packets, and the dynamic priority adjustment mechanism calculates priority values ​​in real time based on the importance weighting coefficients and transmission urgency of the boundary data. Combined with a hierarchical caching architecture and intelligent discarding strategy, efficient queuing management and orderly transmission of boundary data are achieved, significantly improving the real-time performance and reliability of data exchange. Each simulation device performs data exchange operations according to the boundary data transmission queue and monitors the data synchronization accuracy and system response performance indicators. This solves the problem of the lack of dynamic performance monitoring and feedback adjustment mechanisms in the existing technology. By establishing a distributed coordination control architecture and a load redistribution strategy among devices, it realizes the coordinated joint operation of multiple devices, monitors key performance indicators such as data synchronization accuracy and system response time in real time, and provides timely and accurate feedback information for system optimization and fault diagnosis. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of one embodiment of the method for joint operation and information data interaction processing of multiple simulation devices in this application. Detailed Implementation

[0014] This application provides a method for joint operation and information data interaction processing of multiple simulation devices. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0015] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the method for joint operation and information data interaction processing of multiple simulation devices in this application includes:

[0016] Step S1: Collect the voltage amplitude, phase angle and current data of the boundary nodes of multiple simulation devices, calculate the electrical distance weighting factor to determine the coupling strength, and divide the boundary nodes into strongly coupled boundary nodes, moderately coupled boundary nodes and weakly coupled boundary nodes.

[0017] Step S2 predicts the trend of boundary node electrical state changes within the future time step based on historical electrical quantity data of strongly coupled, moderately coupled, and weakly coupled boundary nodes, respectively.

[0018] Step S3: Optimize the solution set by solving the boundary data interaction strategy by predictively estimating the changing trends of the electrical state of the boundary nodes.

[0019] Step S4: Assign timestamps to the boundary data in the optimized solution set, and dynamically adjust the transmission priority through the electrical distance weighting factor to generate a boundary data transmission queue;

[0020] In step S5, each simulation device performs data exchange operations according to the boundary data transmission queue, and monitors the data synchronization accuracy and system response performance indicators.

[0021] It is understood that the executing entity of this application can be a system for the joint operation and information data interaction processing of multiple simulation devices, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0022] Specifically, electrical quantity data is collected from the boundary nodes of each RT1000 and other digital real-time simulation devices to obtain raw datasets of voltage amplitude, phase angle, and current at the boundary nodes. These raw datasets contain real-time status information of the electrical connection points between the devices. Then, based on the raw datasets, the equivalent impedance and physical distance parameters between each boundary node are calculated. The equivalent impedance reflects the tightness of the electrical connection, and the physical distance parameter represents the spatial distribution relationship between the devices. The electrical distance weighting factor is obtained by multiplying the reciprocal of the equivalent impedance by the reciprocal of the physical distance coefficient. Next, the electrical distance weighting factor is compared and analyzed with a preset coupling strength threshold. When the weighting factor exceeds the high threshold, it is classified as strongly coupled; when it is between the high and low thresholds, it is classified as moderately coupled; and when it is below the low threshold, it is classified as weakly coupled. Boundary nodes are classified and labeled according to the coupling strength level to obtain classification results for strongly coupled, moderately coupled, and weakly coupled boundary nodes.

[0023] Historical electrical quantity data for strongly coupled, moderately coupled, and weakly coupled boundary nodes are processed into time series, arranging the historical data chronologically to form continuous voltage amplitude, phase angle, and current time series. Based on these time series, an improved characteristic line equation parameter matrix is ​​established. The characteristic line equation describes the propagation law of electromagnetic waves on the transmission line, and the parameter matrix includes key parameters such as wave propagation velocity and characteristic impedance. Then, the electrical state transition law parameters of the boundary nodes are calculated using boundary condition correction terms to perform electromagnetic transient wave propagation analysis. These boundary condition correction terms consider the mutual influence between multiple devices. Through wave propagation analysis, predicted voltage amplitude, phase angle, and current values ​​for each boundary node within future time steps are obtained. Finally, a boundary node state change vector is constructed based on these predicted values. This vector contains the trend information of voltage, phase angle, and current changes, forming the electrical state change trend of the boundary nodes.

[0024] The deviation between the electrical state change trend of the boundary nodes and the real-time voltage amplitude of each simulation device is calculated. By comparing the difference between the predicted and actual values ​​point by point, a boundary node voltage amplitude deviation matrix is ​​constructed, which reflects the voltage synchronization accuracy of each boundary point. Simultaneously, the phase angle difference between each device is calculated based on the boundary node electrical state change trend, and synchronization error accumulation is performed. The phase angle difference represents the degree of phase offset between devices. The accumulation operation superimposes the errors at multiple time points to obtain the cumulative phase angle synchronization error value. Furthermore, variance analysis is performed on the calculated load of each simulation device based on the boundary node electrical state change trend. By statistically analyzing the load dispersion of each device, a load imbalance metric between devices is calculated. The boundary node voltage amplitude deviation matrix, the cumulative phase angle synchronization error value, and the load imbalance metric between devices are then processed using Pareto front analysis with weighted coefficients. Pareto front analysis is a method for finding non-dominated solutions in multi-objective optimization. By balancing the relationship between the three objective functions, the optimal solution set for the boundary data interaction strategy is obtained.

[0025] Nanosecond-level timestamps are assigned to each boundary data in the optimized solution set of the boundary data interaction strategy. These timestamps ensure the temporal order of data packets, resulting in timestamp-marked boundary data packets. Then, importance weight coefficients for each boundary data are calculated based on the electrical distance weight factor, and priority calculations are performed considering the urgency of data transmission. The importance weight coefficient reflects the impact of the data on the overall simulation accuracy, while the urgency considers the timeliness requirements of the data. The dynamic priority value of the boundary data is calculated by combining these two factors. Next, the dynamic priority value of the boundary data is analyzed and processed in conjunction with the data packet size parameter for transmission scheduling. By comprehensively considering the network bandwidth usage of priority and data packet size, a transmission scheduling strategy is formulated, resulting in a boundary data transmission sorting strategy. Finally, according to the boundary data transmission sorting strategy, queues are constructed for the timestamp-marked boundary data packets, establishing ordered queues based on transmission priority and time order, resulting in a boundary data transmission queue.

[0026] A distributed coordination and control architecture is established based on the boundary data transmission queue, and the operational status of each simulation device is monitored. The distributed coordination and control architecture adopts a master-slave structure, with the master device responsible for global coordination and the slave devices responsible for specific execution. Real-time monitoring is used to obtain parameters such as device computational load rate, communication load rate, and boundary data synchronization error. Then, load balancing analysis is performed based on these parameters. By comparing the load conditions of each device, devices with excessively high or low loads are identified, and a load redistribution scheme is formulated, resulting in an inter-device load redistribution strategy. Next, the inter-device load redistribution strategy is coordinated and matched with the boundary data transmission queue to ensure consistency between data transmission and load distribution, generating data exchange execution instructions for each simulation device. Based on the data exchange execution instructions, data synchronization accuracy and system response time are monitored for each simulation device. By continuously monitoring data consistency and response latency between devices, data synchronization accuracy and system response performance indicators are obtained.

[0027] In one specific embodiment, step S1 includes:

[0028] Electrical quantity data are acquired and processed at the boundary nodes of each simulation device to obtain the raw datasets of voltage amplitude, phase angle and current at the boundary nodes;

[0029] Based on the original dataset, the equivalent impedance and physical distance parameters between each boundary node are calculated to obtain the electrical distance weighting factor.

[0030] The electrical distance weighting factor is compared and analyzed with the preset coupling strength threshold to obtain the coupling strength level of each boundary node;

[0031] Boundary nodes are classified and labeled according to their coupling strength level, resulting in classifications of strongly coupled, moderately coupled, and weakly coupled boundary nodes.

[0032] Specifically, during the electrical quantity data acquisition and processing of the boundary nodes of each simulation device, the boundary nodes are monitored in real time through the built-in data acquisition module of each simulation device. Boundary nodes refer to the electrical connection points connecting different simulation devices, and these nodes carry the function of transmitting electrical information between the devices. The data acquisition module continuously acquires the voltage amplitude, phase angle, and current of the boundary nodes according to a preset sampling frequency. The voltage amplitude reflects the magnitude of the voltage at the node, the phase angle represents the phase information of the voltage waveform, and the current represents the current intensity passing through the node. The acquisition process employs synchronous sampling technology to ensure that the data acquired by each device remains consistent in time. After analog-to-digital conversion and filtering, the acquired data forms a raw dataset containing timestamps, node identifiers, voltage amplitudes, phase angles, and current values. This raw dataset constitutes the basic data source for subsequent processing.

[0033] The process of calculating the equivalent impedance and physical distance parameters between boundary nodes based on the original dataset involves complex electrical parameter calculations. Equivalent impedance refers to the parameter that simplifies a complex electrical network to its equivalent impedance, calculated by analyzing the voltage and current relationships between boundary nodes. The calculation process extracts voltage and current data from adjacent boundary nodes in the original dataset, then applies an extended form of Ohm's law to calculate the impedance values ​​between nodes. Specifically, the resistance component is obtained by dividing the voltage difference by the current difference, and the reactance component is calculated using the phase angle difference. These two components are then combined to obtain the magnitude and phase angle of the equivalent impedance. The physical distance parameters are determined based on the physical layout and connection method of the simulation device, including the straight-line distance between devices, cable length, and signal propagation path length. Subsequently, the reciprocal of the equivalent impedance is multiplied by the reciprocal of the physical distance coefficient. The physical distance coefficient is a weighted coefficient set according to the distance; the closer the distance, the larger the coefficient. This calculation method yields the electrical distance weighting factor, which comprehensively reflects the electrical tightness and physical connection strength between boundary nodes.

[0034] The key step in classification is comparing the electrical distance weighting factor with preset coupling strength thresholds. These thresholds include two values: a strong coupling threshold and a weak coupling threshold, determined based on experience and theoretical analysis from power system simulation. The comparative analysis process compares the electrical distance weighting factor of each boundary node with these two thresholds. When the weighting factor is greater than the strong coupling threshold, it indicates a strong electrical interaction between the boundary node and its adjacent nodes. When the weighting factor is less than the weak coupling threshold, it indicates a weak electrical interaction. When the weighting factor is between the two thresholds, it indicates a moderate level of electrical interaction. Through this threshold comparison method, each boundary node is assigned a corresponding coupling strength level, which directly reflects the node's importance and the urgency of data interaction in multi-device joint simulation.

[0035] Classifying and labeling boundary nodes based on coupling strength levels is a crucial step in the entire classification process. This process converts the coupling strength levels obtained in the previous step into specific node category identifiers. The process establishes a node classification database containing fields such as node number, device identifier, coupling strength level, and classification result. Strongly coupled boundary nodes are marked as high-priority processing nodes, enjoying the highest transmission priority and the most stringent synchronization requirements during data interaction. Moderately coupled boundary nodes are marked as medium-priority processing nodes, and weakly coupled boundary nodes are marked as low-priority processing nodes. Classification labeling also includes color coding and graphic identifiers: strongly coupled nodes are marked in red, moderately coupled nodes in yellow, and weakly coupled nodes in green. This visual labeling facilitates intuitive identification of different categories of boundary nodes by operators. The resulting classification results contain detailed classification information for all boundary nodes, forming a hierarchical node management system.

[0036] In one specific embodiment, step S2 includes:

[0037] Historical electrical quantity data of strongly coupled, moderately coupled, and weakly coupled boundary nodes are processed to construct time series data, resulting in voltage amplitude time series, phase angle time series, and current time series for each type of boundary node.

[0038] An improved characteristic line equation parameter matrix is ​​established based on voltage amplitude time series, phase angle time series and current time series, and the electrical state transition law parameters of boundary nodes are obtained.

[0039] The electrical state transition law parameters of the boundary nodes are calculated using boundary condition correction terms and electromagnetic transient wave propagation analysis is performed to obtain the predicted voltage amplitude, phase angle and current values ​​of each boundary node in the future time step.

[0040] Based on the predicted voltage amplitude, phase angle, and current, a boundary node state change vector is constructed to obtain the trend of boundary node electrical state change.

[0041] Specifically, in the time series construction of historical electrical quantity data for strongly coupled, moderately coupled, and weakly coupled boundary nodes, electrical quantity records of boundary nodes with different coupling levels are extracted from the historical databases of each simulation device. This historical electrical quantity data includes continuous records of voltage amplitude, phase angle, and current of the boundary nodes over a past period. The time series construction process rearranges and organizes this historical data chronologically, forming an ordered data sequence with time as the horizontal axis and electrical quantity values ​​as the vertical axis. The construction process performs time synchronization correction on the historical data to eliminate clock deviations between different devices, and then resamples the data at uniform time intervals to ensure that each sequence has the same time resolution. For strongly coupled boundary nodes, the constructed time series has the highest sampling density and the longest historical span. The time series for moderately coupled and weakly coupled boundary nodes have correspondingly lower sampling densities, but maintain sufficient data length to meet prediction requirements. The resulting voltage amplitude time series reflects the voltage variation over time, the phase angle time series reflects the evolution trend of voltage phase, and the current time series shows the time-domain characteristics of current intensity. These three types of time series constitute the data foundation for subsequent parameter calculations.

[0042] The process of establishing an improved characteristic line equation parameter matrix based on voltage amplitude time series, phase angle time series, and current time series involves complex mathematical modeling. The characteristic line equation is a partial differential equation describing the propagation law of electromagnetic waves in the transmission medium. The improved characteristic line equation adds boundary condition corrections and the effects of multi-device coupling to the traditional equation. The parameter matrix establishment process analyzes the statistical characteristics of each time series, calculating the mean, variance, autocorrelation function, and cross-correlation function. These statistical parameters reflect the variation law and interrelationship of electrical quantities. Then, the least squares method is used to fit the time series data to determine the key parameters in the characteristic line equation, including wave propagation velocity, characteristic impedance, attenuation coefficient, and phase constant. The wave propagation velocity is calculated by analyzing the propagation delay of voltage and current waveforms between different boundary nodes. The characteristic impedance is determined by the ratio of voltage amplitude to current amplitude. The attenuation coefficient reflects the degree of energy loss of the signal during propagation, and the phase constant describes the phase characteristics of wave propagation. These parameters are combined to form a parameter matrix, where rows correspond to different boundary nodes and columns correspond to different characteristic line equation parameters. The obtained boundary node electrical state transition law parameters completely describe the electrical behavior mode of each boundary node.

[0043] The boundary condition correction term is a correction factor introduced to account for the mutual influence between multiple devices. The calculation of the correction term is based on the operating states and coupling strength of adjacent devices. The electromagnetic transient wave propagation analysis uses numerical calculation methods to solve the improved characteristic line equations. The calculation process uses the current boundary node state as the initial condition, and combines the boundary condition correction term and electrical state transition law parameters to iteratively calculate the evolution of the boundary node state within future time steps. The analysis considers wave reflection, refraction, and transmission phenomena. When electromagnetic waves propagate to the device boundary, some energy is reflected back to the original device, and some energy is transmitted to adjacent devices. The boundary condition correction term quantifies these complex boundary effects. The calculated voltage amplitude prediction values ​​describe the voltage amplitude changes of the boundary nodes at future times, the phase angle prediction values ​​give the expected evolution trajectory of the voltage phase, and the current prediction values ​​predict the trend of boundary current changes.

[0044] The state change vector of the boundary nodes, constructed based on predicted voltage amplitude, phase angle, and current values, is the output of the entire prediction process. This state change vector is a multi-dimensional vector containing multiple predicted electrical quantities, with each component corresponding to the predicted voltage amplitude, phase angle, and current. The construction process arranges the three types of predicted values ​​in chronological order to form a sequence of predicted values, and then combines these sequences into a multi-dimensional vector structure. Vector construction also includes the calculation of change trends. By analyzing the first and second differences of the predicted value sequences, the rate and acceleration of change of the electrical quantities are obtained. These derivative information reflects the dynamic characteristics of the boundary node states. The state change vector also contains uncertainty information; by calculating the confidence interval and error range of the predicted values, the reliability of the prediction results is quantified. The resulting trend of the electrical state changes of the boundary nodes is a comprehensive description encompassing predicted values, rates of change, accelerations, and uncertainty information. This trend accurately depicts the evolution of the electrical behavior of each boundary node over a future time period.

[0045] In one specific embodiment, step S3 includes:

[0046] The deviation between the electrical state change trend of the boundary node and the real-time voltage amplitude of each simulation device is calculated to obtain the boundary node voltage amplitude deviation matrix.

[0047] The phase angle difference between each device is calculated based on the trend of electrical state change of the boundary node, and the synchronization error accumulation is processed to obtain the phase angle synchronization error accumulation value.

[0048] Based on the trend of electrical state change at the boundary nodes, variance analysis is performed on the calculated load of each simulation device to obtain the load imbalance metric between devices.

[0049] The boundary node voltage magnitude deviation matrix, the cumulative value of phase angle synchronization error, and the load imbalance metric between devices are calculated using weighted coefficients and then subjected to Pareto front analysis to obtain the boundary data interaction strategy optimization solution set.

[0050] Specifically, in the deviation calculation process between the boundary node electrical state change trend and the real-time voltage amplitude of each simulation device, real-time voltage amplitude data at the current moment is obtained from each simulation device. The real-time voltage amplitude is the instantaneous voltage measurement value of the boundary node of each device, reflecting the current operating state of the device. The deviation calculation process compares the predicted voltage amplitude value in the boundary node electrical state change trend obtained in the previous step with the real-time measured voltage amplitude point by point. The calculation process uses two methods: absolute deviation and relative deviation. The absolute deviation is obtained by subtracting the measured value from the predicted value, and the relative deviation is obtained by dividing the absolute deviation by the measured value, which is expressed as a percentage of the deviation. The calculated deviation values ​​are arranged in a matrix according to the boundary node number and device identifier. The rows of the matrix correspond to different boundary nodes, and the columns correspond to different simulation devices. Each matrix element contains the voltage deviation value of that boundary node on the corresponding device. The boundary node voltage amplitude deviation matrix not only contains the numerical information of the deviation, but also records the temporal variation characteristics and spatial distribution pattern of the deviation, providing a quantitative indicator of voltage synchronization accuracy for subsequent multi-objective optimization.

[0051] Calculating the phase angle difference between devices based on the electrical state change trend of boundary nodes and performing synchronization error accumulation calculation is a key step in solving the phase angle synchronization problem. The phase angle difference reflects the phase difference of the voltage waveform between different devices, and the phase angle synchronization error directly affects the accuracy of multi-device joint simulation. The calculation process extracts the predicted phase angle values ​​of each device's boundary node from the electrical state change trend of the boundary nodes, and then calculates the phase angle difference between adjacent devices. The phase angle difference calculation needs to consider the periodicity of the phase angle, and angle normalization is required when the phase angle difference exceeds 180 degrees. The synchronization error accumulation calculation performs time integration on the phase angle difference values ​​at multiple time points. The integration process uses the trapezoidal integration method to numerically integrate the time series of phase angle difference values ​​to obtain the accumulated value of the phase angle synchronization error. The accumulation calculation also considers the error weighting distribution. The phase angle error of strongly coupled boundary nodes is given a higher weight coefficient, while the weight coefficients of moderately coupled and weakly coupled boundary nodes are correspondingly reduced. The cumulative phase angle synchronization error is a scalar value. The smaller the value, the higher the phase angle synchronization accuracy between devices; the larger the value, the more serious the synchronization error. This provides a quantitative basis for the phase angle synchronization objective function in multi-objective optimization.

[0052] Analysis of variance (ANOVA) of the computational load of each simulation device based on the changing trends of the electrical states of the boundary nodes is a crucial step in evaluating load balance. Computational load reflects the computational resources consumed by each simulation device when processing boundary data; load imbalance can lead to overload in some devices while other devices remain idle. ANOVA predicts the computational load demand of each device based on the changing trends of the electrical states of the boundary nodes, taking into account factors such as data processing complexity, communication overhead, and computation time. ANOVA is a statistical method for assessing the dispersion of data, quantifying the uniformity of load distribution by calculating the mean and variance of the computational load of each device. The calculation process involves calculating the arithmetic mean of the computational load of all devices, then calculating the squared deviation of each device's load from the mean, summing all squared deviations, and dividing by the number of devices to obtain the load variance. A smaller variance value indicates a more uniform load distribution across devices, while a larger variance value indicates a more severe load imbalance. The load imbalance metric between devices is a standardized result of the variance calculation, providing a quantitative evaluation standard for the load balancing objective function in subsequent multi-objective optimization.

[0053] The core algorithmic step in multi-objective optimization is the Pareto front analysis, which uses weighted coefficients to calculate the boundary node voltage magnitude deviation matrix, the cumulative phase synchronization error, and the load imbalance metric between devices. Pareto front analysis is a classic method for finding non-dominated solutions in multi-objective optimization. The weighted coefficient calculation standardizes and weights the three objective function values ​​with different dimensions. The weighting coefficients are determined based on the importance and priority of each objective function; the weighting coefficients for voltage deviation, phase synchronization, and load balancing reflect the emphasis on simulation accuracy, synchronization performance, and computational efficiency, respectively. The Pareto front analysis employs a non-dominated sorting algorithm to identify Pareto optimal solutions. Non-dominated solutions are those that cannot improve other objective function values ​​without worsening any of the other objective function values. The analytical process hierarchically sorts all candidate solutions according to their dominance relationships. The first layer contains all non-dominated solutions, the second layer contains solutions dominated by solutions in the first layer but not mutually dominated, and so on, forming a Pareto front hierarchy. The resulting boundary data interaction strategy optimization solution set contains multiple Pareto optimal solutions, each representing a different trade-off between the three objectives of voltage accuracy, phase synchronization, and load balancing.

[0054] In one specific embodiment, step S4 includes:

[0055] Nanosecond-level timestamp allocation is performed on each boundary data in the optimized solution set of the boundary data interaction strategy to obtain timestamp-marked boundary data packets;

[0056] The importance weight coefficient of each boundary data is calculated based on the electrical distance weight factor, and priority calculation is performed in combination with the urgency of data transmission to obtain the dynamic priority value of the boundary data.

[0057] By performing transmission scheduling analysis and processing on the dynamic priority values ​​of boundary data and the data packet size parameters, a boundary data transmission sorting strategy is obtained.

[0058] The boundary data transmission queue is obtained by constructing a queue for the timestamp-marked boundary data packets according to the boundary data transmission sorting strategy.

[0059] Specifically, in the nanosecond-level timestamp allocation process for each boundary data in the boundary data interaction strategy optimization solution set, each boundary data is extracted from the boundary data interaction strategy optimization solution set obtained in the previous steps. The boundary data includes voltage, phase angle, and current information of different boundary nodes, as well as corresponding transmission strategy parameters. The nanosecond-level timestamp allocation process uses a high-precision clock source to generate timestamps. The timestamp accuracy reaches the nanosecond level to meet the strict time synchronization requirements of electromagnetic transient simulation. The time step of electromagnetic transient simulation is usually at the microsecond level, so the timestamp accuracy of the data packets must be at least at the nanosecond level to ensure timing accuracy. The allocation process assigns a unique timestamp identifier to each boundary data. The timestamp contains absolute time information and relative timing information. The absolute time information records the specific moment the data was generated, and the relative timing information records the relative position of the data in the entire transmission sequence. The timestamp also contains the lifetime information of the data packet. Data packets that have exceeded their lifetime will be automatically discarded to avoid expired data affecting simulation accuracy. The resulting timestamp-marked boundary data packets contain the original boundary data and precise timestamp information.

[0060] The core of dynamic priority management lies in calculating the importance weight coefficients of each boundary data based on the electrical distance weight factor and performing priority calculations in conjunction with the data transmission urgency. The importance weight coefficients reflect the impact of boundary data on the overall simulation accuracy, while the transmission urgency reflects the timeliness requirements of the data. The calculation process extracts the calculated electrical distance weight factor; a larger electrical distance weight factor value indicates stronger coupling at the corresponding boundary node, and thus higher data importance. The importance weight coefficients are obtained by normalizing the electrical distance weight factor. Normalization divides the weight factors of all boundary nodes by the maximum value, resulting in an importance weight coefficient distribution between 0 and 1. The calculation of data transmission urgency is based on the timestamp information of the data packet and the time difference between the current moment and the data packet. A smaller time difference indicates fresher data and higher transmission urgency, while a larger time difference indicates older data and lower transmission urgency. The priority calculation process involves a weighted sum of the importance weight coefficients and the transmission urgency. The weighting coefficients are determined according to the specific needs of the simulation application; applications with high real-time requirements have a higher weight for transmission urgency, while applications with high accuracy requirements have a higher weight for the importance weight coefficients. The obtained dynamic priority value of the boundary data is a value between 0 and 1. The larger the value, the higher the transmission priority of the boundary data.

[0061] The key step in transmission strategy formulation is to analyze and process the dynamic priority value of boundary data and the packet size parameter for transmission scheduling. The packet size parameter reflects the transmission overhead of boundary data, and transmission scheduling analysis needs to balance priority and transmission efficiency. The packet size parameter is obtained by counting the number of bytes in the boundary data, including the total number of bytes of data header, payload data, and checksum information. The larger the packet, the more network bandwidth it consumes and the longer the transmission time. The transmission scheduling analysis uses a bandwidth efficiency evaluation algorithm, which divides the dynamic priority value of the boundary data by the packet size parameter to obtain a transmission efficiency index, which reflects the priority gain per unit of transmission overhead. The analysis process also considers network congestion and available bandwidth resources. When the network is congested, high-priority small packets are transmitted first, and when the network is idle, large packets can be transmitted to improve bandwidth utilization. Transmission scheduling analysis also includes packet merging and fragmentation strategies. Merging multiple small packets can reduce transmission overhead, while fragmenting large packets can avoid occupying network resources for a long time. The resulting boundary data transmission sorting strategy includes the transmission order, timing, and method of each boundary packet. This strategy comprehensively considers multiple factors such as priority, transmission efficiency, and network conditions.

[0062] The queue construction process for timestamped boundary data packets, based on the boundary data transmission sorting strategy, is a crucial part of data management. This queue construction process organizes discrete boundary data packets into an ordered transmission queue. The queue structure includes data packet storage, sorting, and scheduling functions. The construction process determines the position of each data packet in the queue according to the boundary data transmission sorting strategy, which includes multiple dimensions such as priority sorting, time sorting, and size sorting. A multi-level queue structure is used: high-priority data packets enter the fast queue, medium-priority data packets enter the standard queue, and low-priority data packets enter the slow queue. Different scheduling algorithms and service strategies are used for different queue levels. Queue management also includes a flow control mechanism. When the queue length exceeds a threshold, flow control is activated, suspending the enqueueing of low-priority data packets and prioritizing the processing of high-priority data packets. The queue construction process also implements dynamic adjustment functionality, dynamically adjusting queue parameters and scheduling strategies based on network conditions and simulation requirements to ensure the queue's adaptability and robustness. The resulting boundary data transmission queue is a dynamically managed data structure containing all timestamped boundary data packets to be transmitted and their transmission control information.

[0063] In one specific embodiment, the process of constructing a queue for timestamped boundary data packets according to a boundary data transmission sorting strategy may specifically include the following steps:

[0064] A hierarchical caching architecture is established based on the boundary data transmission sorting strategy, and the cache level allocation processing is performed on the timestamp-marked boundary data packets to obtain high-speed cache boundary data packets and regular cache boundary data packets.

[0065] The timestamps of the cache boundary data packets and the regular cache boundary data packets are processed by clock skew correction to obtain the corrected timestamp boundary data packets.

[0066] The corrected timestamp boundary data packets are sorted according to the order of their timestamps to obtain a temporally arranged boundary data packet sequence.

[0067] A queue overflow protection mechanism is established based on the temporal arrangement of boundary data packet sequences, and an intelligent discarding strategy is configured to obtain the boundary data transmission queue.

[0068] Specifically, in establishing a hierarchical caching architecture based on boundary data transmission sorting strategies and allocating cache levels for timestamp-marked boundary data packets, the hierarchical caching architecture is a multi-level storage management structure containing cache levels with different access speeds and capacities. High-speed caches have faster access speeds but smaller capacities, while regular caches have larger capacities but relatively slower access speeds. The establishment process first analyzes the transmission priority and access frequency of each timestamp-marked boundary data packet based on the boundary data transmission sorting strategy obtained in previous steps. High-priority data packets require fast access, while frequently accessed data packets need to reside in the cache for longer periods. The cache level allocation process uses a threshold judgment method, comparing dynamic priority values ​​with preset high-speed cache thresholds. When the priority value exceeds the high-speed cache threshold, the corresponding timestamp-marked boundary data packet is allocated to the high-speed cache level; when the priority value is below the threshold, it is allocated to the regular cache level. The allocation process also considers data packet size limitations; excessively large data packets, even with high priority, can only be allocated to the regular cache to avoid wasting high-speed cache resources. High-speed cache boundary data packets mainly contain real-time electrical quantity data and emergency control signals from strongly coupled boundary nodes, while regular cache boundary data packets mainly contain historical data and status information from moderately and weakly coupled boundary nodes. The allocation results form a hierarchical cache management system, with different management strategies and access mechanisms adopted for different levels of cache.

[0069] Clock skew correction of timestamps for both cached and regular cached boundary data packets is a crucial step in resolving multi-device clock synchronization issues. Clock skew refers to the difference in clock references between different simulation devices, which leads to inaccurate timestamps in boundary data packets and affects data timing. The clock skew correction process first calculates the time deviation between each simulation device and a standard clock source, typically a Network Time Protocol (NTP) server or GPS clock. Deviation calculation involves periodically sending clock synchronization messages to measure the clock offset of each device, taking into account factors such as network transmission latency and clock drift. The correction operation uses linear interpolation to correct the timestamps; the correction formula adds the corresponding device's clock skew value to the original timestamp to obtain the corrected timestamp. The correction process corrects the timestamps of both cached and regular cached boundary data packets separately. Because cached data packets have stricter timeliness requirements, their clock correction accuracy is higher. After correction, the timestamps of the boundary data packets are unified to the standard clock reference, eliminating clock differences between different devices and ensuring the consistency and accuracy of boundary data packets in the time dimension.

[0070] The sorting algorithm for corrected timestamp boundary data packets according to their timestamp order is a crucial step in establishing a correct data time sequence. The sorting algorithm rearranges the discrete boundary data packets according to their timestamp order, forming a temporally continuous data sequence. The sorting algorithm uses quicksort, an efficient comparison sorting algorithm. Its basic idea is to select a pivot element, divide the array into two parts (less than the pivot and greater than the pivot), and then recursively sort the two parts. In this invention, the comparison standard for sorting is the corrected timestamp value of the boundary data packets; packets with smaller timestamps are sorted first, and packets with larger timestamps are sorted later. The sorting process also considers the handling of packets with the same timestamp. When multiple packets have the same timestamp, a secondary sort is performed according to the packet priority, with higher priority packets appearing first. The sorting process sorts the boundary data packets in both the cache and the regular cache. The packets in the cache are fewer in number, so the sorting speed is faster, while the packets in the regular cache are more numerous, but the sorting speed requirement is relatively lower. The resulting temporally arranged boundary data packet sequence is a data set strictly arranged in chronological order, accurately reflecting the evolution of the boundary data in the time dimension.

[0071] Establishing a queue overflow protection mechanism based on the temporal arrangement of boundary data packet sequences and configuring an intelligent discarding strategy are crucial for queue management security. The queue overflow protection mechanism is a protective measure taken when the buffer queue capacity approaches saturation, preventing new data packets from causing queue overflow and data loss. The protection mechanism includes capacity monitoring, threshold setting, and trigger condition configuration. Capacity monitoring tracks queue usage in real time, threshold setting determines the critical point for triggering protection measures, and trigger condition configuration defines the specific conditions for activating the protection mechanism. The intelligent discarding strategy is the data management strategy when the queue overflows. This strategy intelligently selects which data packets to discard based on factors such as importance, timeliness, and size. The discarding strategy employs a multi-level discarding algorithm: first discarding timed-out data packets, then discarding the lowest priority data packets, and finally discarding the data packets occupying the most space. The strategy configuration also includes a data recovery mechanism; for important discarded data packets, their identification information is recorded, and attempts are made to re-acquire them when conditions permit. The configuration parameters of the intelligent discarding strategy are differentiated according to different types of boundary data packets. Data packets at strongly coupled boundary nodes have a stricter protection level, while data packets at weakly coupled boundary nodes are more easily discarded. The resulting boundary data transmission queue is a data structure with a robust protection mechanism and intelligent management functions. This queue can ensure the security and integrity of critical data under various abnormal circumstances.

[0072] In one specific embodiment, step S5 includes:

[0073] A distributed coordination and control architecture is established based on the boundary data transmission queue, and the operating status of each simulation device is monitored and processed to obtain the device's computational load rate, communication load rate, and boundary data synchronization error parameters.

[0074] Based on the device load rate, communication load rate and boundary data synchronization error parameters, load balancing analysis and processing are performed to obtain the load redistribution strategy between devices.

[0075] The inter-device load redistribution strategy and the boundary data transmission queue are coordinated and matched to obtain the data exchange execution instructions for each simulation device.

[0076] Based on the data exchange execution instructions, the data synchronization accuracy and system response time of each simulation device are monitored and processed to obtain the data synchronization accuracy and system response performance indicators.

[0077] Specifically, in establishing a distributed coordination and control architecture based on the boundary data transmission queue and monitoring the operational status of each simulation device, the distributed coordination and control architecture is a multi-level control and management structure, comprising a hierarchical organization of master and slave nodes. The master node is responsible for global decision-making and coordination management, while the slave nodes are responsible for specific execution and status feedback. The establishment process first analyzes the data exchange needs and processing capabilities of each simulation device based on the boundary data transmission queue obtained in the previous steps. The number, size, and priority distribution of data packets in the queue reflect the workload intensity of each device. The distributed coordination and control architecture adopts a master-slave topology, selecting the simulation device with the strongest computing power and the most stable network connection as the master node, and the remaining devices as slave nodes. A control communication link is established between the master node and each slave node. Operational status monitoring is achieved by periodically collecting the operating parameters of each simulation device. These parameters include key indicators such as CPU utilization, memory usage, network bandwidth usage, and data processing latency. The device's computing load rate is calculated by dividing the current CPU utilization by the maximum CPU processing capacity, reflecting the device's computing resource utilization. The communication load rate is calculated by dividing the current network bandwidth usage by the maximum available bandwidth, reflecting the device's communication resource utilization. The boundary data synchronization error parameter is calculated by comparing the deviation of the boundary data received by each device with the standard reference value. The larger the deviation value, the more serious the synchronization error, and the smaller the deviation value, the higher the synchronization accuracy.

[0078] Load balancing analysis based on device load rate, communication load rate, and boundary data synchronization error parameters is the core algorithm of dynamic load management. Load balancing analysis aims to identify overloaded and underloaded devices and formulate reasonable load redistribution schemes. The analysis process first standardizes and normalizes the load parameters of each device, converting load indicators of different dimensions into standardized values ​​between 0 and 1. The standardization process uses a maximum-minimum value normalization method, subtracting the minimum value from the original load value and dividing by the difference between the maximum and minimum values. Load balance is assessed by calculating the variance of the load for each device. A smaller variance indicates a more uniform load distribution, while a larger variance indicates a more severe load imbalance. The imbalance metric is calculated by first calculating the arithmetic mean of the loads of all devices, then calculating the squared difference between each device's load and the mean, summing all squared differences, and dividing by the number of devices to obtain the load variance. The load redistribution strategy uses a load transfer algorithm. This algorithm identifies high-load devices with load rates exceeding an upper threshold and low-load devices with load rates below a lower threshold, and then calculates the amount of work that needs to be transferred from high-load devices to low-load devices. The task transfer calculation takes into account task divisibility, transfer cost, and inter-device communication latency, prioritizing the transfer of divisible computing tasks with lower transfer costs. The final inter-device load redistribution strategy includes specific task transfer schemes, transfer timing, and transfer paths, ensuring a balanced load distribution across devices.

[0079] Coordinating and matching the inter-device load redistribution strategy with the boundary data transmission queue is a crucial step in unified scheduling management. This coordination and matching process must simultaneously consider the needs of load redistribution and boundary data transmission, avoiding conflicts and interference between the two. The process first analyzes the task transfer plan in the load redistribution strategy, identifying which tasks need to be transferred from which device to which device at which time. Then, it analyzes the data transmission plan in the boundary data transmission queue, determining the transmission time and path for each data packet. The matching algorithm employs a time window scheduling method, dividing time into multiple consecutive time windows, and uniformly scheduling task transfers and data transmission activities within each window. A conflict detection mechanism identifies situations where task transfers and data transmissions occur simultaneously within the same time window. When a conflict is detected, a priority scheduling principle is used for coordination, with higher-priority boundary data transmissions executed first, and lower-priority task transfers postponed. The coordination and matching also considers network bandwidth resource allocation, reserving corresponding bandwidth resources for task transfers and data transmissions respectively, avoiding performance degradation caused by competition for the same network resources. The final data exchange execution instruction is a comprehensive scheduling scheme containing detailed time arrangements and resource allocation, which uniformly coordinates the execution process of load redistribution and data transmission.

[0080] Monitoring the data synchronization accuracy and system response time of each simulation device according to the data exchange execution instructions is a crucial step in performance evaluation and feedback adjustment. This monitoring process verifies the effectiveness of coordinated control by tracking the data synchronization status and response performance of each device in real time. Data synchronization accuracy monitoring is achieved by comparing the consistency of the boundary data received and transmitted by each device. Consistency checks include numerical accuracy checks and timestamp accuracy checks. Numerical accuracy checks calculate the numerical deviation between received and transmitted data, while timestamp accuracy checks calculate the data transmission time delay. System response time monitoring is achieved by measuring the time interval from the issuance of the data exchange instruction to its completion. Response time includes three components: instruction transmission time, data processing time, and result feedback time. The monitoring process employs a sliding window statistical method, collecting statistical characteristics of synchronization accuracy and response time within a fixed-length time window, including indicators such as mean, standard deviation, maximum, and minimum values. The performance indicator calculation process involves statistical analysis of the monitored raw data. The data synchronization accuracy indicator is derived by calculating the root mean square value of the synchronization error, and the response time performance indicator is derived by calculating the percentile of the response time. The monitoring results also include performance trend analysis, which assesses the effectiveness and stability of the coordinated control strategy by comparing the trends of performance indicators over different time periods.

[0081] In one specific embodiment, the process of performing load balancing analysis and processing based on the device's calculation of load rate, communication load rate, and boundary data synchronization error parameters can specifically include the following steps:

[0082] The device's computational load rate, communication load rate, and boundary data synchronization error parameters are standardized and normalized to obtain the device's comprehensive load evaluation index.

[0083] The load difference between each simulation device is calculated based on the device load comprehensive evaluation index, and threshold comparison analysis is performed to obtain the identification result of the unbalanced load device.

[0084] Based on the identification results of the unbalanced load device, the computing tasks of the high-load device are decomposed and split to obtain reassignable computing task units.

[0085] The load redistribution strategy between devices is obtained by matching and allocating the reassignable computing task units with the remaining computing power of low-load devices.

[0086] Specifically, in the standardization and normalization of parameters such as device computational load rate, communication load rate, and boundary data synchronization error, the standardization and normalization process transforms parameters with different dimensions and numerical ranges into a unified standard data processing method, eliminating weight bias caused by numerical differences between parameters. The standardization process first statistically analyzes the numerical ranges of the three types of parameters, calculating the maximum and minimum values ​​for each type. The numerical range of device computational load rate is typically between 0 and 1, as is the communication load rate. The numerical range of the boundary data synchronization error parameter depends on the specific application scenario. The normalization process uses a maximum-minimum normalization method, subtracting the minimum value of the corresponding parameter type from each original parameter value, and then dividing by the difference between the maximum and minimum values ​​to obtain a standardized value between 0 and 1. The process also includes weight allocation calculation. Based on the performance requirements of multi-simulation device joint operation, the importance weights of the three types of parameters are determined. Computational load typically has the highest weight because it directly affects simulation speed; communication load has a medium weight because it affects data exchange efficiency; and synchronization error has a relatively low but not negligible weight because it affects simulation accuracy. The device load comprehensive evaluation index is calculated by weighting three standardized parameters according to their corresponding weights. This index comprehensively reflects the overall load level and operating status of each simulation device.

[0087] The core algorithm for identifying load imbalance is to calculate the load difference between simulation devices based on the comprehensive load evaluation index and perform threshold comparison analysis. Load difference measures the unevenness of load distribution among different devices, and threshold comparison determines the range of devices requiring load adjustment. The load difference calculation uses analysis of variance. First, the arithmetic mean of the comprehensive load evaluation index of all devices is calculated. Then, the squared difference between the evaluation index of each device and the mean is calculated. The sum of all squared differences is divided by the number of devices to obtain the load variance. The square root of the variance is the load difference. A larger difference value indicates a more uneven load distribution among devices, while a smaller difference value indicates a more balanced load distribution. Threshold comparison analysis sets two criteria: a high load threshold and a low load threshold. The high load threshold is typically set to 1.2 to 1.5 times the average load, and the low load threshold is set to 0.5 to 0.8 times the average load. Comparative analysis compares the comprehensive evaluation index of each device with two thresholds. Devices with an evaluation index exceeding the high-load threshold are identified as high-load devices, while those with an index below the low-load threshold are identified as low-load devices. Devices with an index between the two thresholds are considered to have normal load. The results of the unbalanced load device identification include a list of high-load devices, a list of low-load devices, and the degree to which each device exceeds its load limit, providing precise target location for subsequent task reallocation.

[0088] Decomposing and splitting the computational tasks of high-load devices based on the identification results of unbalanced load devices is a crucial step in task reorganization. This decomposition breaks down complex simulation computations into multiple relatively independent sub-task units, allowing some sub-tasks to be transferred to other devices for execution. The decomposition process first analyzes the type and structure of the computational tasks currently being executed by the high-load devices. These tasks typically include different types such as electromagnetic transient calculations, power flow calculations, stability analysis, and protection action simulations. The task decomposition employs a functional modular approach, breaking down large computational tasks into multiple independent computational modules according to functional boundaries. Each module has clearly defined input / output interfaces and independent computational logic. The splitting process also considers inter-task dependencies, identifying which sub-tasks have data or temporal dependencies, ensuring that the split task units maintain both relative independence and necessary interrelationships. The splitting algorithm prioritizes separating computationally intensive tasks with fewer dependencies, as these tasks have the least impact on the original device after transfer and are the easiest to integrate into the new device. The reassignable computational task units are computational modules with standardized interfaces and clearly defined functions, formed after decomposition and splitting. These modules contain complete information such as the task's computational logic, data interfaces, resource requirements, and execution time.

[0089] The matching and allocation of reassignable computing tasks with the remaining computing power of low-load devices is the implementation step of load redistribution. This matching and allocation process needs to comprehensively consider the compatibility between task requirements and device capabilities to ensure the rationality and feasibility of task allocation. The matching and allocation process first assesses the remaining computing power of each low-load device. Remaining computing power is calculated by subtracting the current computing load from the device's maximum computing power, including resource indicators such as remaining CPU processing power, available memory space, and idle network bandwidth. The task-device matching degree calculation considers multiple matching factors, including computing resource matching degree, technical compatibility matching degree, and communication efficiency matching degree. Computing resource matching degree is calculated by comparing the task's resource requirements with the device's remaining resources; a higher matching degree is achieved when the resource requirements are less than the remaining resources. Technical compatibility matching degree assesses the fit between the task's computing type and the device's area of ​​expertise; similar types of computing tasks are executed more efficiently on similar devices. Communication efficiency matching degree considers the data exchange cost with the original device after task transfer; devices with closer physical distances and better network connection quality have higher communication efficiency matching degrees. The allocation operation employs the Hungarian algorithm to find the optimal matching scheme between tasks and devices. This algorithm seeks the allocation combination that maximizes the overall matching degree under multiple constraints. The final inter-device load redistribution strategy specifies in detail the target device, transfer time, interface protocol, and monitoring scheme for each redistributable computing task unit.

[0090] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for joint operation and information data interaction processing of multiple simulation devices, characterized in that, The method includes: Step S1: Collect the voltage amplitude, phase angle and current data of the boundary nodes of multiple simulation devices, calculate the electrical distance weighting factor to determine the coupling strength, and divide the boundary nodes into strongly coupled boundary nodes, moderately coupled boundary nodes and weakly coupled boundary nodes. Step S2: Based on the historical electrical quantity data of the strongly coupled boundary node, the moderately coupled boundary node, and the weakly coupled boundary node, predict the trend of the electrical state change of the boundary node within the future time step. Step S3: The trend of electrical state change of the boundary nodes is used to solve the boundary data interaction strategy optimization solution set through predictive boundary state estimation; Step S4: Assign timestamps to the boundary data in the optimized solution set, and dynamically adjust the transmission priority using the electrical distance weighting factor to generate a boundary data transmission queue; In step S5, each simulation device performs data exchange operations according to the boundary data transmission queue, and monitors the data synchronization accuracy and system response performance indicators.

2. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 1, characterized in that, Step S1 includes: Electrical quantity data are acquired and processed at the boundary nodes of each simulation device to obtain the raw datasets of voltage amplitude, phase angle and current at the boundary nodes; Based on the original dataset, the equivalent impedance and physical distance parameters between each boundary node are calculated to obtain the electrical distance weighting factor; The electrical distance weighting factor is compared and analyzed with a preset coupling strength threshold to obtain the coupling strength level of each boundary node; The boundary nodes are classified and labeled according to the coupling strength level to obtain the classification results of strongly coupled boundary nodes, moderately coupled boundary nodes, and weakly coupled boundary nodes.

3. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 1, characterized in that, Step S2 includes: The historical electrical quantity data of the strongly coupled boundary nodes, moderately coupled boundary nodes, and weakly coupled boundary nodes are processed by time series construction to obtain the voltage amplitude time series, phase angle time series, and current time series of each type of boundary node. Based on the voltage amplitude time series, phase angle time series and current time series, an improved characteristic line equation parameter matrix is ​​established to obtain the electrical state transition law parameters of the boundary node. The electrical state transition law parameters of the boundary nodes are calculated using boundary condition correction terms and electromagnetic transient wave propagation analysis is performed to obtain the predicted voltage amplitude, phase angle and current values ​​of each boundary node in the future time step. Based on the predicted voltage amplitude, phase angle, and current values, a boundary node state change vector is constructed to obtain the trend of boundary node electrical state change.

4. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 1, characterized in that, Step S3 includes: The deviation between the electrical state change trend of the boundary node and the real-time voltage amplitude of each simulation device is calculated to obtain the boundary node voltage amplitude deviation matrix. Based on the trend of electrical state change of the boundary node, the phase angle difference between each device is calculated and the synchronization error accumulation is processed to obtain the phase angle synchronization error accumulation value. Based on the trend of electrical state change of the boundary nodes, variance analysis is performed on the calculated load of each simulation device to obtain the load imbalance metric between devices. The boundary node voltage amplitude deviation matrix, phase angle synchronization error cumulative value, and inter-device load imbalance metric value are calculated using weighted coefficients and then subjected to Pareto front analysis to obtain the boundary data interaction strategy optimization solution set.

5. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 1, characterized in that, Step S4 includes: Nanosecond-level timestamp allocation processing is performed on each boundary data in the optimized solution set of the boundary data interaction strategy to obtain timestamp-marked boundary data packets; Based on the electrical distance weighting factor, the importance weighting coefficient of each boundary data is calculated, and priority calculation is performed in combination with the urgency of data transmission to obtain the dynamic priority value of the boundary data. The dynamic priority values ​​of the boundary data and the data packet size parameters are subjected to transmission scheduling analysis and processing to obtain the boundary data transmission sorting strategy. According to the boundary data transmission sorting strategy, the timestamp-marked boundary data packets are processed to form a queue, thus obtaining the boundary data transmission queue.

6. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 5, characterized in that, The step of constructing a queue for the timestamped boundary data packets according to the boundary data transmission sorting strategy to obtain a boundary data transmission queue includes: A hierarchical caching architecture is established based on the boundary data transmission sorting strategy, and the timestamp-marked boundary data packets are processed for cache level allocation to obtain high-speed cache boundary data packets and regular cache boundary data packets. The timestamps of the high-speed cache boundary data packets and the regular cache boundary data packets are processed by clock skew correction to obtain corrected timestamp boundary data packets; The corrected timestamp boundary data packets are processed by a sorting algorithm according to the order of their timestamps to obtain a time-ordered boundary data packet sequence; A queue overflow protection mechanism is established based on the time-ordered boundary data packet sequence, and an intelligent discarding strategy is configured to obtain the boundary data transmission queue.

7. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 1, characterized in that, Step S5 includes: A distributed coordination and control architecture is established based on the boundary data transmission queue, and the operating status of each simulation device is monitored and processed to obtain the device's computational load rate, communication load rate, and boundary data synchronization error parameters. Based on the load rate, communication load rate and boundary data synchronization error parameters of the device, load balancing analysis and processing are performed to obtain the load redistribution strategy between devices. The inter-device load redistribution strategy is coordinated and matched with the boundary data transmission queue to obtain the data exchange execution instructions for each simulation device. The data synchronization accuracy and system response time of each simulation device are monitored and processed according to the data exchange execution instructions to obtain the data synchronization accuracy and system response performance indicators.

8. The method for joint operation and information data interaction processing of multiple simulation devices according to claim 7, characterized in that, The load balancing analysis and processing based on the device's calculated load rate, communication load rate, and boundary data synchronization error parameters yields an inter-device load redistribution strategy, including: The device's load rate, communication load rate, and boundary data synchronization error parameters are standardized and normalized to obtain a comprehensive load evaluation index for the device. Based on the comprehensive load evaluation index of the device, the load difference between each simulation device is calculated and threshold comparison analysis is performed to obtain the identification result of the unbalanced load device. Based on the load imbalance device identification results, the computing tasks of the high-load device are decomposed and split to obtain reassignable computing task units. The reassignable computing task unit is matched and allocated with the remaining computing power of the low-load device to obtain the inter-device load redistribution strategy.

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