A decentralized adaptive distributed co-simulation scheduling system

Abstract

A decentralized adaptive distributed co-simulation scheduling system includes multiple local scheduling domains. The local scheduling domains communicate with each other through an "inter-domain interaction interface". Each local scheduling domain contains one local master node and several local slave nodes that make up the subsystem. The local master node communicates with the local slave nodes to automatically perform dynamic partitioning of local master and slave nodes, intelligent model allocation based on computational complexity, time-series scheduling with adaptive step size, step size optimization and data interaction based on least common multiple, and prediction of subsequent simulation data based on fault breakpoints.

Description

Technical Field

[0001] This invention relates to simulation scheduling systems, and more particularly to a decentralized adaptive distributed joint simulation scheduling system, belonging to the field of simulation scheduling. Background Technology

[0002] For joint simulation systems of large-scale equipment (such as aerospace equipment, large ships and equipment), in core application scenarios characterized by "large model scale, significant differences in simulation scale, and complex subsystem coupling," existing centralized scheduling schemes and simple distributed scheduling schemes cannot coordinate the technical pain points of "high-precision timing synchronization, high-performance simulation advancement, and high system reliability." Specifically, centralized scheduling suffers from a sharp drop in simulation efficiency due to overload of the global central master node, and the fixed step size cannot adapt to the needs of multi-scale models. At the same time, the failure of the central node can easily lead to system interruption. Simple distributed scheduling, on the other hand, suffers from node load imbalance due to static pre-allocation, waiting and blocking caused by fixed data interaction nodes, and a high task failure rate due to the lack of a dynamic fault takeover mechanism. Ultimately, both schemes fall into the inherent contradiction of "sacrificing efficiency to ensure synchronization" or "disrupting synchronization to pursue efficiency," failing to meet the comprehensive requirements of "efficient advancement, accurate synchronization, and stable reliability" for joint simulation systems in the development of large-scale equipment, thus restricting the efficiency of equipment development and testing accuracy.

[0003] Furthermore, both schemes handle faults differently. The centralized distributed master node monitors the status of computing nodes via "heartbeat packets" (sent every 100ms), but lacks redundancy. If the master node fails (e.g., hardware crash, software failure), the system is directly interrupted, requiring manual restart of the master node and reloading of simulation data; the recovery process relies on manual operation. The simple distributed scheme employs a stateless monitoring and dynamic takeover mechanism between nodes. If a node fails (e.g., network disconnection, computational anomaly), the modules it is responsible for immediately stop, and related nodes remain blocked due to the inability to receive data, ultimately leading to simulation task failure. Therefore, how to handle faults and achieve seamless fault continuity is another pressing technical problem that needs to be solved. Summary of the Invention

[0004] 1. The core technology of this invention First, the decentralized architecture distributes computing and communication loads. Second, dynamic step size adaptation of multi-scale simulation models. Third, by combining the dynamic changes in the model's computational complexity with the coupling strength of the subsystems, distributed node load balancing is achieved, coordinating the timing synchronization and efficient advancement of distributed nodes. Fourth, simulate the fault node for the faulty module, and embed the simulation results into the above-mentioned decentralized system in real time until the fault repair time node.

[0005] 2. Specific technical solutions To achieve the core of the above technical solution and address the dilemmas of existing technologies, this invention provides a decentralized adaptive distributed joint simulation scheduling system, comprising multiple local scheduling domains. These local scheduling domains communicate with each other via an "inter-domain interaction interface." Each local scheduling domain includes one local master node and several local slave nodes that make up the subsystem. The local master node communicates with the local slave nodes to automatically perform dynamic partitioning of local master and slave nodes, intelligent model allocation based on computational complexity, time-series scheduling with adaptive step size, step size optimization and data interaction based on the least common multiple, and prediction of subsequent simulation data based on fault breakpoints.

[0006] Optionally, the method of dynamically dividing local master-slave nodes is based on the coupling strength between the models of the system and subsystems and the difference in simulation scale, and dynamically divides local master-slave nodes, specifically including coupling relationship determination and scale adaptation division.

[0007] Optionally, the coupling relationship determination includes: quantifying the degree of coupling between models through a "coupling strength calculation model", with input parameters including the frequency of data interaction between models. Unit: times / second, data dependency The value ranges from 0 to 1, and is determined by the correlation between the model's input and output, and the amount of interactive data. Unit: MB / time, the coupling strength calculation formula is as follows:

[0008] Where α, β, and γ are weighting coefficients.

[0009] Optionally, scale adaptation partitioning: Define the simulation scale matching degree within the same local scheduling domain. (k is the node number), the calculation formula is as follows:

[0010] in, This represents the total number of models within the local scheduling domain. Let k be the set of models that node k can support. The simulation scale of model m, in seconds.

[0011] Optionally, the intelligent model allocation based on computational complexity includes Model computational complexity assessment: Establish a "quantitative index system for model computational complexity," and calculate the complexity weight of each model from three dimensions: "computational load," "memory requirements," and "time sensitivity," using the Analytic Hierarchy Process (AHP).

[0012]

[0013] in, , , Weights determined for AHP Node resource status awareness: Each distributed node reports its current resource utilization (CPU utilization) to its local master node in real time. Memory usage Network bandwidth utilization The formula for calculating resource redundancy is as follows:

[0014] Intelligent allocation algorithm execution: Local master nodes are allocated based on the "complexity-resource matching principle" model. like (Lightweight model) and If node resource redundancy meets the requirements, then the model will be assigned to a single node; like (Heavyweight model) or The "module splitting algorithm" is then used to split the model into K sub-modules, based on the internal functional boundaries of the model, and assigned to different local slave nodes.

[0015] The timing scheduling method with adaptive propagation step size specifically includes: Dynamic time-series progression table establishment: Based on the coupling relationship between models in the domain, the local master node generates a "dynamic time-series progression table" to identify data interaction points between models (i.e., the time points when the output of one model is the input of another model) and defines the interaction strength. ,when At that time, mark the time node as a "keyframe node". ”; The timing progression table records the requirements for issuing progression commands for key frame nodes, including... The timestamps, the model pairs that need to be interacted with, and the data interaction format are all determined by the local slave nodes. The advancement of non-critical frame nodes is autonomously controlled by the local slave nodes. Master-slave node collaborative advancement: including local master nodes: in "Data exchange and advance instructions" are constantly issued to local slave nodes. These instructions include... The time base and interactive data verification rules are established. After receiving instructions from the slave node, the current simulation results are uploaded to the master node. The master node verifies the data validity through the "data consistency verification algorithm." After the verification is passed, the next round of keyframe nodes is confirmed. , Model interaction requirements are dynamically determined, including local slave nodes: and Between, according to the simulation scale of its own model Choose step size independently , Must meet and , To advance the number of simulations, multi-step continuous simulations are performed; at the same time, the multiple models that a node is responsible for are sorted according to time sensitivity priority.

[0016] Preferably, priority Prioritize A high-precision model ensures the simulation accuracy of a highly sensitive model.

[0017] Inter-domain timing coordination: Different local scheduling domains achieve timing synchronization through an "inter-domain keyframe alignment" mechanism, defining the inter-domain synchronization degree. (a and b are adjacent domains):

[0018] when At that time, the inter-domain interaction coordination node (determined through negotiation between the two domain master nodes) adjusts the keyframe node so that... At the same time, ensure the consistency of data interaction timing between domains, whereby, It is the greatest common divisor.

[0019] Distinguishing between intra-node and inter-node interactions: If m1 and m2 are within the same local slave node, then the slave node is ordered as follows: It can complete data interaction on its own, and the interaction process is realized through "intra-node shared memory". There is no need to report to the local master node, and the communication latency is reduced to less than 1ms. If m1 and m2 are on different nodes, then the local master node will determine the order of their operation. Data interaction commands are triggered at all times, and the commands are transmitted through a "low-latency communication protocol" to ensure that the interaction latency is ≤10ms.

[0020] Optionally, methods for predicting subsequent simulation data based on the fault breakpoint include: The S1 search module retrieves the second historical data from the first data point at a given time point under the same operating parameters until the fault recovery time point. S2 divides all data between the first and second historical data into multiple time nodes according to the time-series scheduling of the adaptive advancement step size, acquires the data at the multiple time nodes, and finally performs step size optimization based on the least common multiple to train the long short-term memory model. Based on the trained long short-term memory model, subsequent simulation data predicted after the first historical data are used for data interaction.

[0021] The operating parameter environment refers to the operating data environment of modules in the simulation system other than the fault module.

[0022] 3. Beneficial effects Solving the bottlenecks of traditional centralized scheduling centers and the difficulty of synchronization in simple distributed systems: This invention features a decentralized local master-slave architecture. By dynamically dividing local scheduling domains (based on the matching degree between model coupling strength and simulation scale), master nodes within a domain coordinate scheduling and inter-domain collaborative interaction. This not only distributes the computing and communication load (avoiding the risk of overload in centralized global centers) but also ensures synchronization within the domain through local master nodes (solving the problem of difficulty in synchronization in simple distributed systems with complete dispersion).

[0023] Improving the load imbalance problem of simple distributed static allocation: The multi-dimensional intelligent model allocation mechanism of this invention quantifies the model complexity based on "computational load - memory requirements - time sensitivity", combines the real-time resource status allocation model of nodes, and also supports dynamic migration when the load is too high, avoiding the uneven node load caused by simple distributed static pre-allocation and improving resource utilization.

[0024] Overcoming the adaptability defects of existing fixed step size / synchronization point solutions: The key frame driven adaptive timing scheduling of this invention identifies key frame nodes through interaction intensity (the master node only controls synchronization for key frames) and autonomously selects the step size from non-key frames of the node. This not only adapts to the step size requirements of multi-scale models (solving the accuracy / resource waste problem of centralized fixed step size), but also avoids node waiting and blocking caused by simple distributed fixed synchronization points.

[0025] Optimize the problems of one-size-fits-all interaction step size and communication latency in existing technologies: The LCM step size optimization and differentiated interaction mechanism of this invention determines the exchange step size according to the least common multiple of the autonomous step size of the interaction model, and uses shared memory within nodes and low-latency protocols between nodes, which not only ensures the timing matching of data interaction, but also reduces communication latency and improves synchronization accuracy.

[0026] Resolving the inherent contradiction between "synchronization and efficiency": This invention integrates the above-mentioned architecture, allocation, scheduling, and interaction mechanisms. The synergistic effect of multiple mechanisms breaks through the common problems of existing technologies that "sacrifice efficiency for synchronization" or "destroy synchronization for efficiency", and achieves a synergistic improvement in simulation efficiency and timing synchronization. Attached Figure Description

[0027] Figure 1 Adaptive region partitioning diagram, Figure 2 A schematic diagram illustrating a method for predicting subsequent simulation data based on fault breakpoints. Detailed Implementation

[0028] Figure 1 The paper presents a decentralized adaptive distributed co-simulation scheduling system with different regional distribution states before and after adaptive regional division. Taking region A in the figure as an example, it has a local master node and other local child nodes to form a subsystem.

[0029] Multiple local scheduling domains communicate with each other through an "inter-domain interaction interface". Each local scheduling domain contains one local master node and several local slave nodes that make up the subsystem. The local master node communicates with the local slave nodes to automatically perform dynamic partitioning of local master and slave nodes, intelligent model allocation based on computational complexity, time-series scheduling with adaptive step size, step size optimization and data interaction based on least common multiple, and prediction of subsequent simulation data based on fault breakpoints.

[0030] The method of dynamically partitioning local master-slave nodes is based on the coupling strength between the models of the system and subsystems and the difference in simulation scale. It dynamically partitions local master-slave nodes, specifically including coupling relationship determination and scale adaptation partitioning.

[0031] The coupling relationship determination includes: quantifying the degree of coupling between models through a "coupling strength calculation model", with input parameters including the frequency of data interaction between models. Unit: times / second, data dependency The value ranges from 0 to 1, and is determined by the correlation between the model's input and output, and the amount of interactive data. Unit: MB / time, the coupling strength calculation formula is as follows:

[0032] Where α, β, and γ are weight coefficients, α + β + γ = 1, determined by the analytic hierarchy process (AHP), with α = 0.4, β = 0.3, and γ = 0.3; max(F) is the maximum interaction frequency of all model pairs, and max(V) is the maximum amount of interaction data of all model pairs. When the value is ≥0.7, model i and model j are classified into the same local scheduling domain.

[0033] Optionally, scale adaptation partitioning: Define the simulation scale matching degree within the same local scheduling domain. (k is the node number), the calculation formula is as follows:

[0034] in, This represents the total number of models within the local scheduling domain. Let k be the set of models that node k can support. The simulation scale of model m, in seconds. (Select) And calculate resource redundancy ( The node is designated as the local master node, and the remaining nodes are designated as local slave nodes. During the simulation, if the coupling relationship of the subsystem or the model scale changes (e.g., ...), the node is designated as the local master node, and the remaining nodes are designated as local slave nodes. Drop to below 0.5 or If the value drops below 0.6, the local scheduling domain and master-slave nodes will be automatically re-divided.

[0035] The computationally complex-based intelligent model allocation mechanism includes: Model computational complexity assessment: Establish a "quantitative index system for model computational complexity," and calculate the complexity weight of each model from three dimensions: "computational load," "memory requirements," and "time sensitivity," using the Analytic Hierarchy Process (AHP).

[0036] in, , , For the weights determined by AHP, take , , , .

[0037] a. Computational workload indicators Measured by the number of instructions executed in a single simulation (unit: million instructions, MIPS), the standardized formula is: ; b. Memory requirement indicators Measured by the memory space required for model execution (unit: GB), the standardized formula is: ; c. Time sensitivity index Measured as the reciprocal of the maximum allowable simulation delay (in milliseconds), the standardized formula is: .

[0038] Node resource status awareness: Each distributed node reports its current resource utilization (CPU utilization) to its local master node in real time. Memory usage Network bandwidth utilization The formula for calculating resource redundancy is as follows:

[0039] Intelligent allocation algorithm execution: Local master nodes are allocated based on the "complexity-resource matching principle" model. like (Lightweight model) and If node resource redundancy meets the requirements, then the model will be assigned to a single node; like (Heavyweight model) or Then, the "module splitting algorithm" is used to split the model into K sub-modules. , The average resource redundancy of slave nodes within the domain is determined by splitting the submodules based on the functional boundaries within the model, ensuring the coupling strength between submodules. And assign them to different local slave nodes.

[0040] During the simulation, the local master node is recalculated every 5 minutes (configurable). and ,right or Nodes with excessive load will be dynamically migrated using the model. The target node to be migrated must meet certain requirements. And between submodules To achieve resource balance.

[0041] The timing scheduling method with adaptive propagation step size specifically includes: Dynamic time-series progression table establishment: Based on the coupling relationship between models in the domain, the local master node generates a "dynamic time-series progression table" to identify data interaction points between models (i.e., the time points when the output of one model is the input of another model) and defines the interaction strength. ,when At that time, mark the time node as a "keyframe node". ”; The timing progression table records the requirements for issuing progression commands for key frame nodes, including... The timestamps, the model pairs that need to be interacted with, and the data interaction format are all determined by the local slave nodes. The advancement of non-critical frame nodes is autonomously controlled by the local slave nodes. Master-slave node collaborative advancement: including local master nodes: in "Data exchange and advance instructions" are constantly issued to local slave nodes. These instructions include... The time base and interactive data verification rules are established. After receiving instructions from the slave node, the current simulation results are uploaded to the master node. The master node verifies the data validity through the "data consistency verification algorithm." After the verification is passed, the next round of keyframe nodes is confirmed. , Model interaction requirements are dynamically determined, including local slave nodes: and Between, according to the simulation scale of its own model Choose step size independently , Must meet and , To advance the number of simulations, multi-step continuous simulations are performed; at the same time, the multiple models that a node is responsible for are sorted according to time sensitivity priority.

[0042] Priority Prioritize A high-precision model ensures the simulation accuracy of a highly sensitive model.

[0043] Inter-domain timing coordination: Different local scheduling domains achieve timing synchronization through an "inter-domain keyframe alignment" mechanism, defining the inter-domain synchronization degree. (a and b are adjacent domains):

[0044] when At that time, the inter-domain interaction coordination node (determined through negotiation between the two domain master nodes) adjusts the keyframe node so that... At the same time, ensure that the timing of data interaction between domains is consistent.

[0045] Step size optimization and data interaction based on least common multiple include: Interaction step size calculation: For any two models m1 and m2 that have data interaction, let their autonomous advancement step sizes be respectively and The local master node calculates the least common multiple of the two. Use it as a data exchange frame node Right now:

[0046] in, The greatest common divisor; Distinguishing between intra-node and inter-node interactions: If m1 and m2 are within the same local slave node, then the slave node is ordered as follows: It can complete data interaction on its own, and the interaction process is realized through "intra-node shared memory". There is no need to report to the local master node, and the communication latency is reduced to less than 1ms. If m1 and m2 are on different nodes, then the local master node will determine the order of their operation. Data interaction commands are triggered at all times, and the commands are transmitted through a "low-latency communication protocol" to ensure that the interaction latency is ≤10ms.

[0047] like Figure 2 Methods for predicting subsequent simulation data based on fault breakpoints include: S1 searches (on the timeline) the fault module for the second historical data after the first data point at a given time point under the same operating parameters, up to the fault recovery time point. S2 divides all data between the first and second historical data into multiple time nodes according to the time-series scheduling of the adaptive advancement step size, acquires the data at the multiple time nodes, and finally performs step size optimization based on the least common multiple to train the long short-term memory model. Based on the trained long short-term memory model, subsequent simulation data predicted after the first historical data are used for data interaction.

Claims

1. A decentralized adaptive distributed co-simulation scheduling system, characterized in that, It includes multiple local scheduling domains, which communicate with each other through an inter-domain interaction interface. Each local scheduling domain contains one local master node and several local slave nodes that make up the subsystem. The communication between the local master node and the local slave nodes is used to: dynamically divide the local master and slave nodes based on the coupling strength and simulation scale differences between the simulation models of the system and subsystems. Intelligent model allocation based on computational complexity: Based on the computational complexity weight of each simulation model and the resource redundancy of each local slave node, the simulation model is allocated to the local slave node. Adaptive timing scheduling of advancement step size: A dynamic timing advancement table is generated based on the coupling relationship between the simulation models of the master and slave nodes in the domain. The simulation time nodes where the interaction intensity reaches the set value are identified as key frame nodes. At the key frame nodes, the local master node performs global synchronization control, and at the non-key frame nodes, the local slave nodes autonomously select the step size for simulation according to the simulation scale of their own models. Step size optimization and data interaction based on least common multiple: For two simulation models that have data interaction, the least common multiple of their autonomous advancement step size is calculated as the data exchange frame node. If the two models are in the same local slave node, the data interaction is completed through shared memory. If they are in different nodes, the local master node triggers the data interaction through a low-latency communication protocol. Predicting subsequent simulation data based on fault breakpoints: When a simulation module fails, search for the first historical data at the fault time point under the same operating parameters and the second historical data at the fault recovery time point. Divide all data between the first and second historical data into multiple time points according to the time-series scheduling of adaptive advancement step size and obtain the data at each time point. Perform step size optimization based on least common multiple and train a long short-term memory model to predict simulation data during the fault period for data interaction.

2. The system according to claim 1, characterized in that, Local master-slave node dynamic partitioning includes coupling relationship determination and scale-adaptive partitioning.

3. The system according to claim 2, characterized in that, The coupling relationship determination includes: quantifying the degree of coupling between models by calculating the coupling strength, with input parameters including the frequency of data interaction between models. Unit: times / second, data dependency The value ranges from 0 to 1, and is determined by the correlation between the model's input and output, and the amount of interactive data. Unit: MB / time, the coupling strength calculation formula is as follows: ； Where α, β, and γ are weight coefficients, α + β + γ = 1, determined by the analytic hierarchy process, with α = 0.4, β = 0.3, and γ = 0.

3. max(F) is the maximum interaction frequency of all model pairs, max(V) is the maximum interaction data volume of all model pairs, and i and j are model numbers. When the value is ≥0.7, model i and model j are classified into the same local scheduling domain.

4. The system according to claim 3, characterized in that, Scale adaptation partitioning: Define the simulation scale matching degree within the same local scheduling domain. k is the node number, and the calculation formula is as follows: ； in, This represents the total number of models within the local scheduling domain. Let k be the set of models that node k can support. Simulation scale of model m, unit: seconds; selection And calculate resource redundancy , The node is designated as the local master node and the remaining nodes are designated as local slave nodes. During the simulation, if the coupling relationship of the subsystem or the model scale changes, the local scheduling domain and master and slave nodes are automatically re-divided.

5. The system according to claim 3 or 4, characterized in that, The computationally complex-based intelligent model allocation mechanism includes: Model computational complexity assessment: Establish a quantifiable index system for model computational complexity, and calculate the complexity weight of each model from three dimensions: "computational load," "memory requirements," and "time sensitivity," using the Analytic Hierarchy Process (AHP). ； in, , , For the weights determined by AHP, take , , , ; a. Computational quantity indicators Measured by the number of instructions executed in a single simulation, the unit of instruction count is millions of instructions, or MIPS; the standardized formula is... ; b. Memory requirement indicators : The memory space required to run the model, measured in GB, using the standardized formula as follows. ; c. Time sensitivity index The normalized formula is the reciprocal of the maximum simulation delay allowed by the model, measured in milliseconds. ; Wherein, min(C) and max(C) are the minimum and maximum values ​​of the computational load index, min(M) and max(M) are the minimum and maximum values ​​of the memory requirement index, and min(T) and max(T) are the minimum and maximum values ​​of the time sensitivity index. Node resource status awareness: Each distributed node reports its "current resource utilization rate," i.e., CPU utilization rate, to its local master node in real time. Memory usage Network bandwidth utilization The formula for calculating resource redundancy is as follows: ； Among them, R node This refers to the resource redundancy of a node. Intelligent allocation algorithm execution: Local master nodes allocate resources based on complexity-resource matching principles. like and If so, the model is assigned to a single node; like or Then, the "module splitting algorithm" is used to split the model into K sub-modules. , The average resource redundancy of slave nodes within the domain is determined by splitting the submodules based on the functional boundaries within the model, ensuring the coupling strength between submodules. And assign them to different local slave nodes.

6. The system according to claim 5, characterized in that, During the simulation, the local master node is recalculated every 5 minutes. and ,right or Nodes with excessive load will be dynamically migrated using the model. The target node to be migrated must meet certain requirements. And between submodules To achieve resource balance.

7. The system according to claim 6, characterized in that, The timing scheduling method for adaptive propagation step size specifically includes: Dynamic time-series progression table establishment: Based on the coupling and association relationships between the master and slave nodes within the domain, the local master node generates a "dynamic time-series progression table" to identify data interaction points between models and define the interaction strength. ,when When this condition is met, the time point in time when the data interaction occurs is marked as a keyframe node. ; The timing progression table records the requirements for issuing progression commands for key frame nodes, including... The timestamps, the model pairs that need to be interacted with, and the data interaction format are all determined by the local slave nodes. The advancement of non-critical frame nodes is autonomously controlled by the local slave nodes. Master-slave node collaborative advancement: including local master nodes: in "Data exchange and advance instructions" are constantly issued to local slave nodes. These instructions include... The time base and interactive data verification rules are established. After receiving instructions from the slave node, the current simulation results are uploaded to the master node. The master node verifies the data validity through the "data consistency verification algorithm." After the verification is passed, the next round of keyframe nodes is confirmed. , Model interaction requirements are dynamically determined, including local slave nodes: and Between, according to the simulation scale of its own model Choose step size independently , Must meet and , To advance the simulation count, multiple consecutive simulations are performed; simultaneously, the multiple models that a node is responsible for are prioritized according to their time sensitivity. Prioritize A high-precision model ensures the simulation accuracy of a highly sensitive model; Inter-domain timing coordination: Different local scheduling domains achieve timing synchronization through an "inter-domain keyframe alignment" mechanism, defining the inter-domain synchronization degree. Let a and b be adjacent domains: ； when At that time, the inter-domain interaction coordination node adjusts the keyframe node, so that... At the same time, ensure that the timing of data interaction between domains is consistent.

8. The system according to claim 6 or 7, characterized in that, Step size optimization and data interaction based on least common multiple include: Interaction step size calculation: For any two models m1 and m2 that have data interaction, let their autonomous advancement step sizes be respectively and The local master node calculates the least common multiple of the two. Use it as a data exchange frame node Right now: ； in, The greatest common divisor; Distinguishing between intra-node and inter-node interactions: If m1 and m2 are within the same local slave node, then the slave node is ordered as follows: It can complete data interaction on its own, and the interaction process is realized through "intra-node shared memory". There is no need to report to the local master node, and the communication latency is reduced to less than 1ms. If m1 and m2 are on different nodes, then the local master node will determine the order of their operation. Data interaction commands are triggered at all times, and the commands are transmitted through a "low-latency communication protocol" to ensure that the interaction latency is ≤10ms.

9. The system according to claim 8, characterized in that, Methods for predicting subsequent simulation data based on fault breakpoints include: The S1 search module retrieves the second historical data from the first data point at a given time point under the same operating parameters until the fault recovery time point. S2 divides all data between the first and second historical data into multiple time nodes according to the time-series scheduling of the adaptive advancement step size, acquires the data at the multiple time nodes, and finally performs step size optimization based on the least common multiple to train the long short-term memory model. Based on the trained long short-term memory model, it performs data interaction with the subsequent simulation data predicted after the first historical data.

10. The system according to claim 9, characterized in that, The operating parameter environment refers to the operating data environment of modules in the simulation system other than the fault module.

Images