72 results about "Reliability optimization" patented technology

The invention relates to the technical field of embedded system construction, in particular to a software dependency relationship automatic integration method and system based on meta packaging, and the method comprises the steps: S1, creating a meta packaging framework in an embedded construction system, and defining and constructing a stage hook through a Makefile; s2, automatically deploying the pre-compiled software package and the shared dependency library to a target directory of a construction system through a resource injection script; s3, analyzing a metadata file of the pre-compiled software package, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; and S4, inversely writing the global dependency relationship list into the dependency declaration configuration of the meta-packaging by utilizing the dependency aggregation script. By implementing the scheme, the problems of dependency sequence disorder and metadata auditing failure during embedded system integration are solved, the reliability of system integration is improved, the system construction efficiency is optimized, the safety of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention relates to the technical field of power battery echelon utilization, and discloses a decommissioned power battery system reliability optimization design method, which comprises the following steps: acquiring and preprocessing battery operation attenuation data; constructing an attenuation prediction model; performing dynamic clustering and grouping after multi-dimensional consistency evaluation; and generating a strategy and feeding back iterative optimization. The system comprises a data acquisition module, a preprocessing module, an attenuation prediction module, a consistency evaluation module, a dynamic grouping module and a strategy execution and feedback module. By adopting the scheme, the problems are solved, the grouping accuracy and stability are improved, the secondary utilization efficiency is improved, the cycle life is prolonged, the operation reliability is improved, and the maintenance cost is reduced.

The invention provides a composite beam reliability optimization method based on a failure probability function. The method comprises the following steps: 1) defining a random input variable, a design variable, a target function and a performance function; 2) calculating a failure sample and an extended failure probability based on an autonomous learning Kriging agent model and a Monte Carlo method according to the Bayesian theory; the method comprises the steps of (1) calculating a design parameter failure sample, (2) calculating a joint probability density implicit function on the basis of an adaptive K-nearest neighbor method and autonomous learning Kriging according to the calculated design parameter failure sample, and (3) calculating a failure probability implicit function on the basis of the Bayesian theorem.By means of the method, the problem that in the reliability optimization process, the calculation cost is difficult to bear due to reliability analysis is solved.

The invention provides an optimization method for predicting the long-term welding reliability of a wire harness terminal and a conductor, and the method comprises the steps: collecting temperature and time data in a welding process, processing the data through a thermal simulation model, and obtaining the change trend of conductor lattice arrangement; according to the change trend of conductor lattice arrangement, the initial thickness and component distribution of oxide layer formation are obtained, and microstructure features of an oxide layer group are determined; aiming at the microstructure characteristics of the oxide layer group, simulating a long-term use environment by adopting an accelerated aging test to obtain a prediction curve of resistance increase; according to the quantitative model of the aging behavior, a performance evolution rule in the actual use environment is obtained, and the incidence relation between welding parameters and long-term reliability is determined; for a process scheme for improving the stability of a conductor, the resistance change of the conductor in a high-temperature environment is simulated and verified, and the accuracy of quantitative correlation is judged.

The invention relates to the technical field of intelligent optimization design, in particular to a bridge prestressed steel beam intelligent optimization design method based on multi-factor coupling, and the method comprises the steps: carrying out the quantitative modeling of a plurality of parameters in the arrangement process of a bridge prestressed steel beam, achieving the precise expression of controllable variables, and improving the design precision of the bridge prestressed steel beam. The overall search capability of the arrangement scheme under multi-target tradeoff is enhanced, the rationality and stability of steel beam distribution are integrally improved, the structure local overload risk is reduced, an energy function sequence is constructed through indexes such as a midspan displacement value, a section stress ratio and the total length, the quality of a solution set is controlled by utilizing an energy difference value, intervention and screening of a residual value are introduced, and the stability of the structure is improved. The robustness of the design under the multivariable condition is enhanced, through logic judgment of displacement and counterforce, unqualified arrangement is eliminated, feasible arrangement output under structural consistency is achieved, structural efficiency and material use economy are enhanced, and design reliability, optimization solution efficiency and structural response control precision are remarkably improved.

The invention discloses a decoupling design method and system for reliability optimization of an angular contact ball bearing. The method comprises the following steps: determining initial design parameters of the angular contact ball bearing; constructing a reliability optimization design model which takes maximization of the rated dynamic load as a target and comprises probability reliability constraint processed by a penalty function; constructing a design variable and failure probability mapping agent model, specifically, identifying and quantifying uncertainty parameters, constructing a quasi-statics model and a limit state function, calculating the failure probability of a sample point through a probability reliability method, and training the agent model taking the design variable as input and the failure probability as output; and based on the proxy model, solving the reliability optimization design model by using an optimization algorithm, and outputting optimal bearing design parameters. According to the method, the reliability analysis process and the optimization iteration process are decoupled, so that the optimization efficiency is greatly improved while the design robustness is ensured.

The application provides an intelligent unmanned system decision reliability evaluation and optimization method based on reinforcement learning, and belongs to the technical field of intelligent unmanned systems. The application adopts a multi-layer operation network model based on a coloring graph theory to represent individual autonomy and functional diversity, overcomes the limitation of a single-layer network, and provides a basis for decision analysis. An intelligent unmanned system distributed decision model based on an enhanced actor-critic architecture is adopted to improve the strategy performance and learning stability of individual decision. An intelligent unmanned system decision reliability evaluation model oriented to an operation loop is adopted. The model integrates the proposed reliability indexes into the individual learning process to improve the effectiveness of evaluation. The model successfully quantifies the influence of individual decision on system reliability. A decision reliability optimization model based on a cooperative multi-agent deep deterministic policy gradient algorithm is adopted. The proposed cooperative reward is integrated into the policy gradient optimization process to optimize the decision reliability.

This invention discloses a PCBA (Printed Circuit Board Assembly) product screening method based on reliability requirements, comprising: assessing the early failure rate of the PCBA product; generating a reliability target value for the product based on the early failure rate and preset conditions; establishing a functional relationship between the reliability target value and the early failure rate, the stress intensity used for screening, and the screening time; and determining the optimal screening stress intensity and screening time in conjunction with the preset conditions. This method helps PCBA manufacturers to accurately determine screening results and costs; ensures that PCBA products maintain a reliable level while controlling screening costs; and helps PCBA manufacturers accurately screen products with potential failures, thereby improving product reliability and optimizing production efficiency.

The application discloses a wheel disc reliability optimization method considering multiple failure mode correlations, and belongs to the field of aerospace. The method comprises three steps: a pair-copula method is used to analyze correlations between failure modes; a reliability distribution method is used to distribute wheel disc reliability to the failure modes; and an improved multi-objective particle swarm optimization algorithm is used to realize wheel disc reliability optimization design. The application establishes a reliability analysis and distribution model based on a cost function from the perspective of system reliability, and realizes a low-cost reliability optimization method considering failure correlations.

The embodiment of the invention discloses a virtual power plant optimal scheduling method and related equipment considering the participation of a multi-type energy storage technology, and the method comprises the steps: building a virtual power plant system model and a multi-type energy storage characteristic analysis model for a virtual power plant, and carrying out the optimal scheduling of the virtual power plant based on the constraints of the virtual power plant system model and the multi-type energy storage characteristic analysis model. And establishing a virtual power plant optimization scheduling model, solving the virtual power plant optimization scheduling model by using an improved dogvessel colony algorithm, and scheduling the virtual power plant according to a solving result. According to the method, collaborative optimization scheduling of the energy resources in the virtual power plant is realized by constructing the optimization scheduling model considering the multi-type energy storage technology and combining the efficient solving algorithm, the defects of an existing virtual power plant scheduling method in the aspect of energy storage application can be overcome, the comprehensive utilization efficiency of the energy resources in the virtual power plant is improved, and the energy resource utilization rate of the virtual power plant is improved. The power supply stability and reliability are enhanced, and the economic benefits of the virtual power plant are optimized. The method is widely applied to the technical field of power system dispatching.

The invention discloses a time-varying reliability optimization design method and system for a helical gear. The method comprises the following steps: establishing a gear reliability optimization design model which takes gear volume minimization as an optimization target and takes full-life-cycle time-varying reliability as a constraint; constructing a gear contact fatigue time-varying limit state function coupling the time-varying load spectrum and the material strength degradation model; constructing a time-varying reliability analysis module based on an agent model and an active learning strategy so as to evaluate the time-varying limit state function; and an optimization algorithm is adopted to solve the gear reliability optimization design model, the time-varying failure probability of the candidate scheme is evaluated by calling the time-varying reliability analysis module, the time-varying reliability constraint is processed according to the time-varying failure probability, and optimal gear design parameters meeting the constraint are output. According to the method, efficient cooperation of time-varying reliability high-precision evaluation and lightweight optimization design is realized, and the problem that a time-varying factor cannot be considered in optimization due to too high analysis cost in a traditional method is solved.

The invention discloses a multi-chip module (MCM) interconnection reliability optimization method based on a WOA-GRNN. The method comprises the steps of MCM three-dimensional automatic modeling, load condition application, interconnection reliability WOA-GRNN modeling, and WOA-GRNN modeling result and analysis. According to the core of the method, an ANSYS parameterized language is adopted to construct a three-dimensional (3D) model and interconnection reliability analysis, a WOA-GRNN technology is adopted to quickly construct a nonlinear model of MCM input and output based on collected data, a reliability database is generated according to the nonlinear model, and key guidance is provided for MCM reliability design and analysis. The MCM interconnection reliability prediction method can greatly improve the MCM interconnection reliability prediction efficiency, further provides important reference for the layout design of the chip, and is an important measure for guaranteeing the MCM chip reliability.

This invention discloses a method and system for predicting the trajectory of electric vehicles at signalized intersections, relating to the field of intelligent transportation technology. The method and system provided by this invention improve the accuracy and efficiency of electric vehicle trajectory prediction in complex intersection scenarios by utilizing Mamba to capture long-term temporal dependencies in signalized intersection videos and incorporating kinematic physical constraints using PINN. By constructing counterfactual scenarios of trajectory feature parameters for counterfactual inference prediction, the causal quantification of key factors affecting trajectory prediction results is achieved, enhancing model interpretability. Furthermore, Bayesian methods are used to perform uncertainty analysis on the Mamba-PINN fusion model, quantifying the uncertainty of trajectory prediction and improving model reliability. The optimized model exhibits strong adaptability and can provide decision-making basis and technical support for traffic signal optimization and intelligent transportation systems.

The invention discloses a fiber reinforced composite material variable stiffness reliability multi-scale optimization design method, and belongs to the technical field of composite material structure reliability optimization design. The method comprises the following steps: constructing a composite material multi-scale reliability topological optimization model considering material and load uncertainty; an improved single-cycle single-vector chaos control (SLSV-MCC) method is provided, and probability constraints are efficiently and accurately processed by adopting a current design point gradient and a stable factor; and in combination with a normal distribution fiber optimization (NDFO) model, parameterizing a discrete fiber angle to realize synchronous optimization of a macroscopic topology and a microcosmic fiber path. According to the method, the problems of low calculation efficiency and unstable convergence when the existing method is faced with multi-scale and strong-nonlinearity problems are effectively solved, and a lightweight composite material design scheme with high reliability in an uncertain environment can be obtained.

The application discloses a kind of comprehensive flexible resource reliability optimization methods considering dynamic multi-energy demand response.Comprehensive flexible resource is divided into four kinds of multi-state comprehensive flexible resource, construct the multi-state model of comprehensive flexible resource considering time sequence characteristics, obtain the multi-energy load demand of each comprehensive flexible resource;Respectively, the multi-state model of comprehensive flexible resource reduction, transfer, alternative response considering time sequence characteristics is established, obtain the multi-energy load demand of comprehensive flexible resource after reduction, transfer, alternative response end in each state;Fusion three kinds of multi-state model, establish the multi-state model of total demand response, obtain the amount of demand side operation reserve provided in multiple states, and then calculate the reliability of the amount of demand side operation reserve provided by comprehensive flexible resource.The application adopts multi-state model to more accurately characterize the dynamic characteristics of comprehensive flexible resource, and accurately obtains the operation risk parameters of the amount of demand side operation reserve provided by comprehensive flexible resource.

The present application relates to the technical field of embedded system construction, and particularly relates to a software dependency automatic integration method and system based on meta packaging, wherein the method comprises the following steps: S1, creating a meta packaging framework in an embedded construction system, and defining a construction stage hook through a Makefile file; S2, automatically deploying pre-compiled software packages and shared dependent libraries to a target directory of the construction system through a resource injection script; S3, parsing a metadata file of the pre-compiled software packages, extracting dependency relationship information, and generating a global dependency relationship list based on a directed acyclic graph topological sorting algorithm; S4, using a dependency aggregation script to write the global dependency relationship list to a dependency declaration configuration of the meta packaging. Through implementation of the present application, the problems of dependency sequence disorder and metadata audit failure during integration of an embedded system are solved, and the reliability of system integration is improved, the system construction efficiency is optimized, the security of system construction is enhanced, and the maintainability and expansibility of system construction are improved.

The invention discloses a Voronoi type dot matrix reliability optimization method and device, and belongs to the field of structural lightweight design. The method comprises the steps of performing unit division on a to-be-optimized part, and determining the number of seed points in each unit according to a division result; sequentially inputting the number of the seed points in all the units into a pre-trained Voronoi type lattice structure optimization model, and outputting to obtain the optimal distribution of the seed points in each unit; wherein the structure optimization model is established according to unit quality, the number of seed points and a unit bearing failure probability, the unit bearing failure probability is determined according to a preset performance distribution agent model, and the performance distribution agent model is determined according to a finite element simulation result of a Voronoi type lattice geometric model; and according to the optimal distribution of the seed points in each unit, generating an optimal part meeting a preset requirement. According to the Voronoi type lattice structure, the reliability of the Voronoi type lattice structure can be effectively improved.

The application discloses a degradation fault output voltage ripple optimization method and system for a DC-DC converter, relates to data processing processes such as undersampling, feature extraction and feature selection, a fault diagnosis method based on an extreme learning machine and a reliability optimization method based on DC-DC converter model analysis, belongs to the field of power electronic system fault diagnosis, and comprises the following steps: undersampling technology for the output voltage of a DC-DC converter; time domain feature extraction technology for undersampling data of the output voltage of the DC-DC converter; recursive feature elimination technology based on a support vector machine for selecting the time domain features of the extracted undersampling data of the output voltage of the DC-DC converter; a support vector machine degradation fault diagnosis model based on a Gaussian kernel and a Bayesian optimization for learning multi-class degradation fault data and performing fault classification; and an analysis method based on a physical model of a power conversion device for optimizing the output voltage ripple of the power conversion device.

The invention provides a configuration reliability optimization method, device and equipment for an AC / DC hybrid power distribution network, and relates to the technical field of power distribution networks. The method comprises the following steps: carrying out load type division on loads in a target area, and determining photovoltaic output time sequence characteristics in the target area and load time sequence characteristics of each load type; obtaining an initial configuration scheme of the AC / DC hybrid power distribution network of the target area; taking the initial configuration scheme as an initial solution, and optimizing a preset to-be-optimized item in the initial configuration scheme based on a preset constraint condition to obtain a target configuration scheme of the AC / DC hybrid power distribution network of the target area; wherein in the optimization process, based on photovoltaic output time sequence characteristics and load time sequence characteristics of each load type, a sequential Monte Carlo method is adopted to simulate each configuration scheme and solve a reliability index, and the reliability index is used as a fitness function value. According to the invention, the planning quality and operation benefits of the AC-DC hybrid power distribution network can be improved.

The invention discloses a human-computer interaction task failure evolution path identification method based on a binary decision diagram, and aims to solve the problem that a task failure path in a complex human-computer interaction system is difficult to accurately identify. The method comprises the following steps: constructing a man-machine interaction task event tree and converting the man-machine interaction task event tree into BDD; determining a top event based on the event tree, constructing a fault tree and converting the fault tree into BDD; integrating the event tree BDD and the fault tree BDD to form a unified BDD corresponding to the ET-FT model; and traversing and identifying a failure evolution path based on the integrated BDD. Unified modeling of static logic and dynamic evolution is achieved through BDD, the causal relationship is quantified by combining Shannon decomposition and the ITE method, and the method has the advantages of being accurate in modeling, efficient in analysis and high in expansibility, can be widely applied to the fields of aerospace, medical equipment and the like and provides support for system reliability optimization.

A reliability design optimization method suitable for separating impact components of a shedding connector belongs to the technical field of engineering optimization. In order to solve the problems that traditional reliability optimization design is highly dependent on physical test, and the test cycle is long and the cost is high, the present application proposes a spatial self-adaptive sampling strategy for Kriging surrogate model, which combines model prediction uncertainty and gradient information, dynamically identifies active constraints, thereby effectively reducing misclassification and redundant sampling; in view of the different response characteristics of the objective function and the constraint function, an anisotropic sampling space is constructed for each function, guiding the sample to focus on the area with higher optimization potential; based on the maximum chord deviation criterion, the sampling points are placed in the nonlinear significant area, so as to improve the accuracy of the local surrogate model and accelerate the convergence speed, thereby realizing reliability design optimization.

The invention discloses an AC / DC hybrid power distribution network reliability evaluation and optimization method, a medium and equipment, and belongs to the technical field of power distribution, and the method comprises the steps: dividing a fault processing process into a fault isolation stage, a topology reconstruction stage and a system recovery stage, establishing a first optimization model by taking the minimum load reduction as the target to determine the disconnection combination of the circuit breaker and the voltage source converter, and establishing a second optimization model by taking the minimum load reduction as the target to determine the operation sequence of the interconnection switch, the standby line and the voltage source converter; and establishing a third optimization model to determine a network topology structure of the AC / DC hybrid power distribution network by taking minimization of load reduction as a target, establishing a power distribution network reliability optimization model to determine an optimal network topology structure of the AC / DC hybrid power distribution network, and performing reliability evaluation. The problems that a traditional Monte Carlo simulation method is low in calculation efficiency and an existing analysis method cannot effectively process the complex topological structure of the alternating-current and direct-current hybrid network are solved.

The invention discloses a power distribution network reliability optimization method based on vehicle-light cooperative scheduling. The method comprises the following steps: establishing an electric vehicle charging and discharging power model; establishing a distributed photovoltaic power generation power prediction model; based on the electric vehicle charging and discharging power model and the distributed photovoltaic power generation power prediction model, evaluating a reliability index, and defining a fault scene constraint according to the fault condition of the power distribution network; and based on the reliability index and the fault scene constraint, constructing an optimization model taking the charging and discharging power of the electric vehicle as a decision variable, and completing the reliability optimization of the power distribution network. Through cooperative control of the electric vehicle schedulable capacity and the distributed photovoltaic output, the power supply reliability during the fault period of the power distribution network is effectively improved, local consumption of renewable energy sources is promoted, and meanwhile, the operation stability of the power grid and the energy utilization efficiency are improved.

The invention discloses a turbine case fatigue reliability optimization design method, device and equipment and a medium, and the method comprises the steps: S1, building a turbine case axial symmetry parametric model based on a parametric design idea; s2, aiming at the service environment of the turbine case, applying a temperature field and a cyclic load, carrying out thermosetting coupling analysis, and calculating and obtaining stress-strain data of a dangerous point; s3, carrying out fatigue reliability analysis by adopting an AK-MCS method, and replacing the implicit performance function by the self-adaptive agent model in the fatigue reliability analysis process; and S4, taking the maximum fatigue life mean value under the reliability constraint as a target, carrying out optimization by adopting a nested SQP method based on a self-adaptive agent model, and outputting optimal design parameters of the turbine case. According to the method, the efficiency of the turbine case reliability analysis method is improved, the turbine case reliability analysis precision is greatly improved, and efficient and high-precision reliability optimization design of the turbine case is achieved.

The application relates to a power distribution network reliability optimization evaluation method based on vehicle-network interaction, which comprises an electric vehicle driving model, a vehicle-network coupling element reliability model, an electric vehicle charging and discharging incentive model and a fault recovery model with electric vehicle access, so as to solve the modeling problem of the influence of the travel characteristics and the charging and discharging characteristics of the electric vehicle under the guidance of the charging and discharging incentive model on reliability, thereby most effectively measuring the influence of the electric vehicle access to the power distribution network on the reliability index. The application can realize reliability optimization evaluation on the basis of considering vehicle-network interaction and has applicability when the operation scene changes, thereby making up for the problem of insufficient consideration of factors related to the electric vehicle access to the power distribution network in the existing method.

The application discloses a wind turbine tower reliability optimization method based on a probability checking safety coefficient, belongs to the field of tower reliability optimization design, and divides the safety coefficient checking into uncertain variable design values and characteristic values, constructs a multi-target reliability optimization design problem, constructs a mapping relationship between the uncertain variable design values and reliability, combines fixed quantile characteristic values, establishes a safety coefficient library available under different reliabilities, adopts a Wasserstein distance to evaluate the change degree of environmental load and guide safety coefficient selection in view of dynamic environmental load and different reliability design requirements, converts a complex reliability optimization problem into a deterministic optimization model for fast and efficient solution by matching the characteristic value quantile of the environmental parameters and the safety coefficient of the target reliability, obtains tower structure design variables, saves the calculation cost and design redundancy, and uses the design variables for the tower, which can improve the reliability of the tower under different service environments.

The invention discloses a rural power grid power supply device reliable optimization method based on a genetic algorithm. The method comprises the following steps: collecting and preprocessing operation data and system configuration data of a rural power grid power supply device; constructing a power distribution topological structure, and performing structured modeling and parametric calculation; defining a comprehensive reliability index set, and constructing a multi-objective optimization function; based on the multi-objective optimization function, carrying out global search and optimization by adopting an improved culture genetic algorithm, and outputting an optimal configuration scheme of the power supply device; performing performance analysis on the optimal configuration scheme to generate a reliability evaluation result; performing state diagnosis and optimization decision based on a reliability evaluation result to generate an operation maintenance scheme; and executing the operation maintenance scheme, collecting feedback data, and cooperatively updating the algorithm and the model to generate a reliability optimization result. According to the method, the improved culture genetic algorithm and the multi-objective optimization model are adopted, the reliability adaptive optimization of the rural power grid power supply device is realized, and the method has the advantages of high precision, strong robustness and excellent self-learning ability.

This invention provides a method, device, and equipment for optimizing the reliability of distribution networks based on full-component modeling, relating to the field of smart distribution technology. This invention addresses the assessment bias caused by incomplete consideration of fault sources by acquiring basic data of distribution network nodes and full parameters of reliability and cost for all components. It accurately characterizes the correlation between faults and power outages by analyzing the impact of single and multi-fault coupling on load nodes and constructing a fault correlation matrix for all components, reducing computational bias caused by multi-fault coupling. It replaces traditional massive sampling simulation methods with an explicit analytical calculation model based on matrix algebra operations, solving the problems of computational explosion and low efficiency in optimization iteration. Finally, by constructing a dual-objective optimization model and combining iterative solutions with the analytical model, it achieves coordinated optimization of distribution network reliability and economy, solving the problems of large bias and low efficiency in existing distribution network reliability calculation and optimization methods.

The invention discloses an accelerated life test method based on dynamic stress and a high-order interaction effect. The method comprises the following steps: firstly, acquiring CMAPSS multi-stress sensor data of NASA, and carrying out data preprocessing; secondly, on the basis of the generalized proportion advantage model, designing an acceleration test model, adding a dynamic stress function and a high-order stress interaction item into the model, and fitting parameters of the dynamic stress function by using preprocessed data to obtain a dynamic change rule of multiple stresses along with time; and finally, solving high-order stress interaction item parameters in the acceleration test model by adopting a maximum likelihood estimation method to obtain failure time distribution of the sensor under a multi-stress condition. Experiments prove that the method effectively solves the problems of dynamic stress change and multi-stress nonlinear interaction deficiency in an acceleration test, the sensor failure time prediction precision is improved, the prediction error of the method is lower than 10%, higher reliability and stability are shown under the multi-stress condition, and the method has good application prospects. The method can be widely applied to service life prediction and reliability optimization design of sensing equipment.

The embodiment of the invention discloses a virtual power plant optimal scheduling method based on an improved non-dominated sorting genetic algorithm and related equipment, and particularly relates to the virtual power plant optimal scheduling method based on the improved non-dominated sorting genetic algorithm. The defects of an existing virtual power plant scheduling method in the aspects of energy storage application, algorithm solution and multi-energy-storage-system cooperative control can be overcome, an optimal scheduling model considering the multi-type energy storage technology is constructed, an improved multi-target optimization algorithm is introduced, a differentiated multi-energy-storage-system cooperative mechanism is designed, and the optimal scheduling model of the multi-type energy storage technology is established. Efficient collaborative optimization scheduling of energy resources in the virtual power plant is realized, the energy utilization efficiency is improved, the operation cost is reduced, the power supply stability is enhanced, the comprehensive utilization efficiency of the energy resources in the virtual power plant is improved, the power supply stability and reliability are enhanced, and the economic benefits of the virtual power plant are optimized. The method is widely applied to the technical field of power system dispatching.