445 results about "Dynamic load balancing" patented technology

The invention discloses a dynamic load balancing method and system for an AI model intelligent machine, and relates to the technical field of computer science, and the method comprises the steps: constructing an agent behavior strategy model through combining deep reinforcement learning and a multi-agent system MAS; constructing an intelligent scheduling model based on user request data and resource data, inputting the data into the model, performing resource demand prediction and task characteristic analysis, and outputting an optimal resource allocation scheme; constructing a dynamic load balancing function based on the optimal resource allocation scheme, and automatically adjusting the dynamic load balancing function when the network topology and the load mode change; collecting user feedback data, and performing deep analysis on the user feedback data to obtain a user satisfaction score and a service quality score as a basis for constructing a user feedback evaluation function; constructing a resource scheduling optimization model based on the intelligent scheduling model, the dynamic load balancing function and the user feedback evaluation function, and predicting a user experience strategy; and according to a prediction result, carrying out strategy adjustment and resource redistribution decision making.

The invention belongs to the technical field of server cluster scheduling, and particularly relates to a dynamic load balancing-based server cluster scheduling method, which comprises the following steps of: acquiring load data of each server in a server cluster in real time; performing quantitative evaluation on the acquired load data through a preset load evaluation model to obtain a real-time load value and a load stability score of each server; receiving an external task to be processed, and analyzing resource demand parameters and task type characteristics of the task; determining a target server of the task based on the server state level, the load stability score, the task resource demand parameter and the task type feature; and updating the load evaluation model and the scheduling strategy in real time based on the historical scheduling data, the task operation feedback data and the industry scene characteristic parameters. According to the method, through multi-dimensional load evaluation, accurate matching of tasks and servers and dynamic strategy optimization, the resource utilization rate and task processing efficiency of the server cluster are effectively improved, and the requirements of different industry scenes are met.

The invention relates to the technical field of network routing, and discloses a dynamic load balancing method and system for network routing, and the method comprises the steps: carrying out the periodic sampling and feature extraction of a processing load of a network node and a transmission load of a network link, and obtaining a load state vector and a network load degree; constructing a load balancing decision matrix, and performing comparison operation on the load balancing decision matrix and the multi-stage load threshold to obtain a load optimization instruction sequence; identifying a high-load source node set and a low-load target node set based on the load optimization instruction sequence, and performing load prediction modeling to obtain a task migration scheme; performing path optimization of reinforcement learning based on the task migration scheme to obtain a multi-routing path set; and carrying out load partition clustering and routing strategy distribution on the multi-routing path set to obtain a regional routing mapping table, and the method reduces the network delay, improves the resource utilization rate and realizes dynamic load balance.

The invention relates to the technical field of distributed computing, and discloses a distributed computing power scheduling method and system based on dynamic load balancing. The method comprises the following steps: acquiring real-time resource state data of a plurality of nodes in the distributed system and demand characteristics of a task to be allocated, generating a dynamic task allocation strategy, allocating the task to a target node for execution, monitoring a resource state of the target node in real time, and triggering task reallocation when the resource state deviates from a preset threshold value. The system comprises a monitoring acquisition module, a task analysis module, a strategy generation module, a scheduling execution module and a resource abstraction module. According to the method, tasks and node resources can be accurately matched, the resource utilization rate is improved, the real-time performance and reliability of the tasks are guaranteed, the overall performance of a distributed system is optimized through heterogeneous resource unified scheduling and a resource reserving and recycling mechanism, and the method is suitable for various distributed computing scenes.

The invention relates to the technical field of data processing, and discloses a heterogeneous computing acceleration method and system based on a deep learning framework network. The method comprises the following steps: acquiring performance parameters of heterogeneous computing equipment to obtain an equipment characteristic data set; receiving a calculation task and analyzing the calculation task into an operation sequence; the operation sequence is coded into a gene sequence, task decomposition is optimized through a genetic recombination algorithm, and a subtask set marked with acceleration characteristics is obtained; performing matching analysis on the sub-task set and the equipment characteristic data set to obtain a task allocation scheme; deploying the subtasks to corresponding equipment according to the allocation scheme to obtain a distributed execution framework; and monitoring the running state of the framework in real time, and dynamically adjusting resource allocation to obtain a calculation result of speed-up ratio improvement. According to the method, a self-adaptive task decomposition and resource allocation and dynamic load balancing mechanism can be realized, and the execution efficiency and the resource utilization rate of the deep learning task are improved.

The invention relates to the technical field of communication networks, and discloses a distributed network resource optimization scheduling method and system under a load balancing strategy. The method comprises the following steps: if first service request information sent by a client is received, determining a health state weight, a response time weight and a bandwidth weight; determining a first candidate node; if the number of the first candidate nodes is at least two, calculating a health detection score of each first candidate node; and calculating the expected revenue of each first candidate node, taking the first candidate node with the highest expected revenue as a first target node, and distributing the first service request information to the first target node. According to the scheme, the weight is dynamically adjusted according to the current load and resource conditions, the allocation strategy can be automatically adjusted in the face of different load conditions, dynamic load balancing is achieved, and the overload problem is avoided. And the node most suitable for the current load is preferentially selected, so that the service quality and the user satisfaction can be improved.

The invention discloses a block chain multi-node secure communication method for an industrial internet, and the method comprises the steps: constructing a multi-chain cooperation mechanism, and obtaining an inter-chain interaction path table; determining a task partition optimization scheme and inter-chain interaction path update data based on the inter-chain interaction path table; matching an adaptive node resource for each task according to a task partition optimization scheme; determining an encryption mechanism between a cross-chain protocol and a block chain according to the security requirement of the task; constructing a multi-level block chain node hierarchical architecture and an intra-chain and inter-chain communication architecture, and formulating a parallel communication scheduling scheme; formulating a dynamic load balancing strategy based on the node resource use condition and the task execution state; and obtaining the task execution progress and the resource use condition of each block chain node, and adjusting the task scheduling and cross-chain communication path. Through the multi-dimensional collaborative design, the execution efficiency, the resource utilization rate and the communication security of the industrial internet are remarkably improved, and the stability and the reliability in a complex multi-chain collaborative scene are ensured.

The invention discloses a charging pile system management method based on dynamic load balancing, and particularly relates to the technical field of charging pile management, and the method comprises the steps: collecting time domain scheduling data of a charging pile side, a power grid side and a distributed renewable energy output device side, carrying out the fusion filtering, and obtaining a station-level state vector; generating a station-level prediction sequence of the charging pile in the next control period based on the station-level state vector and a rolling prediction model; an inner-layer controller reads the station-level state vector and the station-level prediction sequence, establishes a power prediction optimization model and solves the model, and outputs an expected station-level power trajectory and station-level power redundancy; a result output by the inner-layer controller is received, and a station-level power envelope curve and an energy storage charging and discharging set value are comprehensively solved and transmitted; the slope upper limit of the station-level power envelope curve is contracted based on the oscillation amplitude, the new slope upper limit is substituted into the next solution, the subsequent power change rate is limited, and the problem of unstable power exchange between the charging station and the superior power grid is effectively solved.

The invention discloses a dynamic load balancing optimization method for AI intelligent computing system server equipment, which relates to the technical field of load balancing optimization, and comprises the following steps: acquiring equipment performance data, and preprocessing the acquired equipment performance data; based on the preprocessed equipment performance data, extracting equipment state features through time sequence analysis and periodic fluctuation detection; predicting the future load change of the equipment based on the state characteristics of the equipment, dividing the priority of the equipment according to the prediction result, and analyzing the resource demand; generating a resource allocation strategy by applying a resource matching algorithm according to the device priority and the resource demand; and executing the resource allocation strategy, receiving feedback information in real time in the execution process, and optimizing the resource allocation strategy according to the feedback information. According to the method, the response speed and the service quality of the server equipment are remarkably improved, and the resource utilization efficiency is maximized.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention discloses an edge computing node dynamic load balancing method based on a multi-agent system, and relates to the technical field of node load balancing. According to the edge computing node dynamic load balancing method based on the multi-agent system, the current computing power communication data and the operation environment data of each edge computing node are collected, the operation load index of each node is analyzed, and whether an overload node exists or not is judged; if an overload node exists, a plurality of nodes adjacent to the overload node are obtained, a task migration candidate set is screened out, finally, a target node is determined from the candidate set, and task migration operation is executed; the operation load index of the edge computing node is dynamically quantified by collecting and comprehensively analyzing multiple parameters such as the CPU utilization rate, the memory occupancy rate, the communication delay, the temperature rise rate, the I / O blocking rate, the power consumption fluctuation rate, the cache write-in waiting time and the vibration disturbance amplitude in real time. Compared with a traditional single index or fixed threshold mode, the method can identify the node overload state more accurately and timely.

The invention discloses a charging pile dynamic load balancing scheduling system based on reinforcement learning, and the system comprises a data collection module which comprises 5G communication units installed at the charging piles, and is used for obtaining the power of the charging piles, vehicle battery parameters and power grid load data in real time; the reinforcement learning decision module maps the data in the data acquisition module into a state vector through a feature extraction network, and generates a power distribution strategy by adopting a PPO + DQN hybrid algorithm; the communication protocol module realizes state synchronization and control instruction transmission between the charging piles based on an OCPP2.0 standard; the execution control module converts the control instruction into a PWM control signal, and adjusts the output power of each charging pile in real time; through the reinforcement learning decision module, the system can analyze the charging pile power, the vehicle battery parameters and the power grid load data in real time, generate an accurate power distribution strategy, ensure accurate matching of the charging pile power and the electric vehicle demand power, and thus significantly improve the charging efficiency.

The invention provides an all-weather autonomous inspection method and system based on cluster task dynamic load balancing, and relates to the technical field of inspection monitoring, and the method comprises the steps: collecting the remaining electric quantity, the positioning position, the task queue length and the sensor health state of an unmanned aerial vehicle cluster in real time through an airborne terminal; fusing the meteorological data and the multi-source environment sensing data, and constructing a dynamic obstacle map and a meteorological influence model; dividing an inspection area into a plurality of sub-areas based on an electronic fence, dynamically adjusting the inspection priority of each sub-area, setting task weights of a water taking head and a raw water pipeline facility, and distributing inspection tasks according to the priorities; based on the remaining power of the unmanned aerial vehicle, the task priority, the current load and the meteorological data, a bipartite graph matching model of the unmanned aerial vehicle and the task is constructed, the matching weight is dynamically calculated, the optimal matching distribution of the unmanned aerial vehicle and the inspection task is completed, and the efficient inspection operation of the unmanned aerial vehicle cluster in the complex environment is realized.

The invention discloses a charging pile intelligent distribution system based on dynamic load balancing and a charging pile, and relates to the technical field of charging piles, a distribution module is used for receiving an operation feature group of each charging pile at a first response moment, generating a feature weight set, and simulating importance judgment logic in the feature weight set according to a feature priority, dynamic load distribution of the charging piles is realized; the process module is used for receiving the data of each source in real time in the dynamic load distribution process, performing multi-source analysis, checking the fault burstiness of the charging pile, analyzing the communication interruption, predicting the charging demand performance and the power supply performance based on the data of each source at a second response moment, and constructing a comprehensive risk index in combination with an evidence theory; and the updating module is used for generating a judgment signal according to the comprehensive risk index value, and the judgment signal comprises an updating instruction and an iteration result.

The invention relates to the technical field of wireless communication and edge computing fusion, in particular to a wireless network resource allocation method based on edge intelligence, which comprises the following steps: S1, collecting a multi-dimensional parameter set of network edge nodes in real time; s2, converting node position coordinates into a network topological structure map, and mapping service type labels and bandwidth requirements into service requirement feature vectors; s3, generating a domain division matrix based on weight distribution of the network topological structure atlas; s4, outputting a resource block configuration tensor according to the time-frequency feature of the business demand feature vector; s5, generating a resource allocation mapping relation table; and S6, generating and executing a node-level resource scheduling instruction set. According to the invention, by constructing a resource scheduling mechanism fusing a topological structure and service features, precise resource allocation and dynamic load balancing of a cooperative domain level and a node level are realized, and the efficiency and the resource utilization rate of multi-task concurrent processing in an edge computing environment are improved.

The invention relates to the technical field of computer architecture and parallel computing, and discloses a parallel task scheduling algorithm for a heterogeneous multi-core processor, which comprises the steps of task modeling, resource mapping, dynamic load balancing, communication optimization, task scheduling decision and execution monitoring. Task allocation is adjusted in real time through dynamic load balancing, cross-core communication delay is reduced in combination with communication optimization, and an efficient task allocation sequence is generated by using an improved genetic algorithm. According to the method, the resource utilization rate and the task execution efficiency of the heterogeneous multi-core processor in a high-performance computing scene can be improved, meanwhile, the robustness and adaptability of an algorithm are enhanced, and the task allocation problem in a complex computing scene is effectively solved.

The invention relates to the technical field of computing task balancing, and discloses a dynamic load balancing method and system for a multi-core heterogeneous ASIC computing mainboard, which comprises the following steps: collecting performance data of each computing core on the multi-core heterogeneous mainboard, generating a computing core information set, analyzing the load adaptability of the computing cores based on the performance data, and obtaining load adaptation feature distribution; the load condition of the calculation core is monitored in real time to obtain a load change trend, the future load condition is predicted, load adaptive feature distribution and load prediction are combined, balance optimization is carried out, a load balancing scheme is generated to adjust load distribution of the calculation core, dynamic load balancing is achieved, the utilization rate of calculation resources is improved, and overload or idling of the calculation core is avoided. The system performance and the response speed are improved, different load requirements are met, load changes are dynamically adapted, task scheduling and energy efficiency management are optimized, and the problem that in the prior art, a task allocation scheme is dynamically and continuously changed in a balanced mode in real time, and consequently the execution effect of a mainboard is poor is solved.

The invention discloses an intelligent management and control system for machine room equipment, and relates to the field of machine room equipment management and control, the system comprises an energy consumption management and control module, a predictive maintenance module and a security situation awareness module, the energy consumption management and control module is based on the synergistic effect of a dynamic load balancing module, a phase change heat dissipation control module and a block chain energy consumption account book module; and the energy consumption cost of machine room equipment is reduced. According to the intelligent management and control system for the machine room equipment, power supply strategies can be switched according to the actual state of the machine room equipment through the dynamic load balancing module, so that the energy consumption of the machine room equipment can be effectively reduced; according to the invention, corresponding measures can be taken at different stages when the equipment has faults, the influence of the equipment faults on services is reduced to the greatest extent, the personnel movement track, the operation instruction and the environmental parameters can be fused through the arranged security situation awareness module, and the illegal intrusion detection accuracy is improved.

According to the big data management-based data labeling method and system provided by the invention, efficient, accurate and intelligent data labeling is realized by integrating multi-virtual machine collaborative labeling, multi-model fusion labeling, adaptive data preprocessing and hierarchical cleaning and a multi-dimensional task scheduling and dynamic load balancing mechanism. The method comprises the steps that self-adaptive preprocessing and layered cleaning are conducted on original big data, noise is removed, missing values are filled up, and format conversion is completed; dividing the preprocessed data into a plurality of subtasks according to data characteristics; distributing the sub-tasks to different virtual machine instances for parallel processing by utilizing a multi-virtual machine technology; labeling the same data fragment on each virtual machine by adopting a multi-model labeling mechanism; adopting a weighted fusion or voting mechanism to generate a comprehensive annotation result for the output results of the multiple models in each virtual machine; and summarizing the labeling results of the virtual machines to form a global data labeling result.

A system and method are provided that use metadata encoded in a data flow to determine security actions to perform at a policy-enforcement point based on the security-chain context for the data flow that is provided by metadata (e.g., the security-chain context can include which security operations have been performed upstream on which data packets). The policy-enforcement point receives the data flow and the metadata, including attestations of the security operations that have previously (e.g., upstream) been applied to the data flow. Based on the attested to security operations, the policy-enforcement point selects what security actions to apply next to the data flow, e.g., additional security operations to apply, allow the data flow into a workload or trust zone, drop the workload, perform dynamic load balancing.

The invention relates to the technical field of data processing, discloses a novel spatio-temporal stream data distributed computing load balancing method and system, a terminal and a storage medium, and aims at dynamically adjusting task distribution of distributed computing nodes through distribution prediction of spatio-temporal stream data, effectively relieving the problem of data skew in a distributed system and improving the distributed computing efficiency. The method mainly comprises the following steps: 1, space-time stream data adaptive network division and distributed node mapping; 2, carrying out deep modeling and distribution prediction on spatio-temporal flow data features; and 3, deducing distributed computing resource demands and realizing load balancing. According to the method, future laws can be effectively mined from the space-time stream data, dynamic load balancing of the computing nodes is achieved, the data skew phenomenon is effectively relieved, and the space-time stream data computing processing efficiency is improved.

The invention discloses a hybrid cloud architecture-oriented heterogeneous resource automatic discovery and integrated configuration method, and relates to the technical field of integrated configuration. Comprising the following steps: automatically identifying and dynamically adjusting a resource detection frequency to capture new resources through an adaptive learning and dynamic optimization mechanism; a deep learning model is utilized to predict a resource state change trend, detection is initiated in advance, and real-time monitoring and active response are realized; a dynamic API adaptation strategy and a self-learning mechanism are introduced, and cross-platform API intelligent adaptation and calling success rate optimization are achieved; a self-adaptive event-driven detection and multi-modal feature fusion method is adopted, and the detection frequency is dynamically adjusted based on anomaly detection; through a multi-modal attribute fusion and attribute mapping and conversion method, a unified resource description model is constructed, and standardization and consistency of resource attributes are carried out. According to the method, the operation is ensured to accord with laws and regulations such as GDPR, HIPAA and the like, and the validity of the operation and the consistency of the international standard are ensured while efficient collaboration and dynamic load balancing of cross-cloud resources are realized.

The invention relates to the technical field of power distribution management, in particular to an intelligent UPS power distribution management method and system based on dynamic load balance, and the method comprises the steps: monitoring the energy consumption parameters of each load in real time, and generating a multi-dimensional energy consumption data set based on the energy consumption parameters; inputting the multi-dimensional energy consumption data set into a preset power prediction model, and predicting a power demand prediction result of each load in future time; dynamically generating a load priority queue based on the power demand prediction result of each load and the real-time capacity state of the UPS, and adjusting the power distribution strategy of the UPS to each load in real time; and acquiring working data of the UPS system under the current power distribution strategy, acquiring abnormal data based on the working data of the UPS system, and updating the load power distribution strategy according to the abnormal data. According to the invention, the energy efficiency of the UPS power distribution system is improved, the stability of the system is enhanced, and the output power distribution of the UPS is dynamically adjusted.

The invention discloses a steel rolling equipment optimization system capable of reducing energy consumption, and relates to the technical field of steel rolling, and the optimization system comprises a load monitoring module, a dynamic load balancing module, a load adjusting module and an intelligent control module. Load prediction is performed in combination with an advanced machine learning model, and dynamic load balance adjustment is performed based on the load prediction, so that overload of part of rolling mill stations and light-load idle states of other rolling mill stations can be effectively avoided, the whole rolling equipment always runs in an optimal load state, energy waste is greatly reduced, the energy utilization efficiency is remarkably improved, and the economic benefit is increased. Moreover, the intelligent control module adjusts the frequency and precision of load monitoring according to the production plan and the real-time load condition, and further optimizes the energy management.

The invention relates to the technical field of cloud edge collaborative computing power scheduling, and discloses a heterogeneous computing power scheduling optimization method based on a cloud edge collaborative architecture. The method comprises the following steps: acquiring real-time computing power state data of all available computing nodes in the cloud edge collaborative architecture; performing heterogeneous type division on the computing nodes according to the real-time computing power state data to generate a three-layer computing power resource pool containing cloud computing nodes, edge computing nodes and terminal computing nodes; extracting task calculation features for the current to-be-scheduled task set, wherein the features comprise calculation intensity, data dependence and real-time requirements; constructing an initial task allocation scheme based on the matching relationship between the task calculation features and the three-layer computing power resource pool; iteratively optimizing the initial scheme by adopting a dynamic load balancing strategy to generate a final task scheduling instruction; the instructions are distributed to the corresponding computing nodes to be executed, and computing power state changes in the task execution process are continuously monitored.

The invention relates to the technical field of AGV intelligent scheduling, and discloses an intelligent scheduling and resource allocation method and system for a flexible AGV, and the method comprises the steps: carrying out the grid processing of an AGV operation region, and obtaining a dynamic scheduling network; modeling an AGV state space in the dynamic scheduling network to obtain a multi-agent scheduling model; calculating a task priority score of the AGV based on a multi-agent scheduling model, discretizing an operation time axis into time slices, and obtaining a time slice resource allocation result; according to the time slice resource allocation result, the AGV scheduling decision is optimized to obtain a distributed scheduling strategy; dividing the distributed scheduling strategy to obtain a master control AGV and a slave AGV, and generating a cluster collaborative decision result; and resource allocation and dynamic load balancing are carried out based on a cluster collaborative decision result to obtain a scheduling instruction of the AGV cluster, collaborative decision of the AGV cluster is realized, and consistency of global task decomposition and local resource allocation is ensured.

In a GPU design, “launching a worker” is de-coupled from “assigning a work item” in a work distributor, and new handshake mechanisms between a worker and the work-distributor is provided for work assignment, in order to provide persistent kernel functionality. In example embodiments, software specifies the work that has to be done, hardware selects a variable number of workers based on available resources, and a hardware scheduler handshaking with the executing workers assigns more work as previously assigned work is completed and / or more resources become available.

The invention provides a charging pile power distribution method and system based on dynamic load balancing, and the method comprises the following steps: obtaining the local state information of each charging pile, the state information at least comprises the charging queuing length, the current vehicle charge state, the current load power, the battery health state, the predicted residence time, the electricity price information and the power grid load state; based on the historical charging behavior, the electricity price fluctuation and the traffic flow data, the vehicle access frequency, the electricity price trend and the power demand of each charging pile in the future time period are predicted through a time sequence neural network; a prediction model is constructed; according to the method, forward-looking power planning is realized by entering a multi-dimensional prediction mechanism; the power distribution efficiency is improved by adopting a local collaborative game; the self-adaption and generalization ability is realized through reinforcement learning, and the robustness of a scheduling strategy in multiple scenes is enhanced; through a multi-target scheduling index, the electricity price, the battery life, the waiting time and the power grid load are optimized at the same time.

The invention relates to an efficient deployment method and system for a privatized large language model ecological service. The system comprises an evaluation module, an index collector, a monitoring module, a vLLM framework, a model cluster service module and a request distribution module. The evaluation module performs quantitative evaluation on the performance of the system under different hardware platforms; the index collector collects server cluster performance indexes, cluster resource states and load information in real time; the monitoring module is used for monitoring the server cluster; the vLLM framework dynamically allocates computing resources to realize management of a multi-modal server cluster, and performs unified management and scheduling through PM2 to realize multi-model efficient parallel task processing; a vLLM framework is started by the model cluster service module, and different large model instances are packaged in a Docker container; the request distribution module distributes external requests in a self-adaptive mode according to cluster resource states and load information fed back by the index collector in real time. Intelligent request distribution and dynamic load balancing are realized, and integration and efficient management of a multi-modal large language model are realized.

The invention provides a distributed rendering task scheduling method based on dynamic load balancing, which relates to the technical field of virtual reality simulation, and comprises the following steps: analyzing and preprocessing input physical simulation data through a multi-source model, and converting the format of the processed data into a manageable virtual set data format; loading the texture data to a video memory as required in a virtual mapping mode; dynamic memory loading and coloring are carried out based on the virtual set, and LOD generation is completed; dynamic task fragmentation and scheduling are carried out on the virtual set rendering task and the heterogeneous multi-source rendering task, real-time monitoring is carried out on GPU / CPU / memory loads of heterogeneous resources, and task scheduling is carried out according to a monitoring result; through a distributed cluster mode, a high-resolution rendering task is divided into a plurality of low-resolution rendering tasks, and the low-resolution rendering tasks are rendered on different nodes. According to the invention, high-fidelity, high-response and high-reliability three-dimensional scene simulation capability is provided for high-complexity scenes such as a hybrid system.