316 results about "System model" patented technology

The invention discloses a conversion method and system based on SysML and Modelica model semantic mapping. The method comprises the following steps that S1, a Modelica standard model library is imported into a SysML modeling tool; s2, completing the definition of behavior elements, and forming an SysML model instance; s3, exporting a Modelica model which can be executed by the Modelica simulator; s4, establishing a mapping relation table between SysML model elements and Modelica model elements; s5, when a change event occurs in the SysML model, updating the content of the corresponding Modelica model; s6, the Modelica model is imported into the SysML model, and a corresponding SysML model instance is generated; s7, simulation result mapping and behavior modeling generation are executed, a state transition diagram structure is formed, and structure updating of internal block diagrams in the system model is completed. According to the method, bidirectional conversion and joint simulation optimization between models are realized, and the method is suitable for cross-platform modeling, collaborative design and functional verification scenes of a complex system.

The invention relates to a quantitative evaluation method for robust stability of a hybrid multi-infeed system constructed with a network. The quantitative evaluation method comprises the following steps: S1, constructing a hybrid multi-infeed system model; s2, enabling the series-parallel multi-infeed system to be equivalent to an equivalent network construction subsystem and an equivalent network following subsystem, and respectively calculating a generalized short-circuit ratio as a power grid strength quantitative index; s3, selecting a nominal system model, and determining a critical value of small interference stability of a nominal system through the dominant feature root locus; s4, drawing a frequency domain characteristic curve of the uncertainty perturbation singular value, and determining an uncertainty perturbation upper bound; and S5, drawing a complementary sensitivity peak curve when the decoupled nominal system is connected to alternating current networks with different power grid intensities, and quantifying a critical value and a margin of robust stability under a power grid intensity perspective by combining an uncertainty perturbation upper bound. Compared with the prior art, the robust small-interference stability quantitative evaluation method is creatively provided, and technical support can be provided for coordinated optimization configuration and safe and stable operation of the high-proportion new energy grid-connected system.

The invention relates to the technical field of new energy power systems, and discloses a new energy consumption software model construction method based on a simulation platform, and the method comprises the steps: firstly dividing new energy consumption system units, determining the topological structure and operation parameters of the new energy consumption system units, and constructing a multi-physics field coupling simulation model; then, carrying out distributed parallel solution on the model, defining a global optimization target, constructing a joint optimization and network reconstruction model, respectively adopting a deep reinforcement learning algorithm and graph neural network optimization, and generating an optimization scheme through multi-agent collaboration; whether the optimization target converges is verified, if yes, control parameters are output, and if not, optimization variables are updated to continue optimization; and finally, the optimized parameters are imported into the digital twin platform calibration model integrated with the block chain technology, and hyper-parameters are adjusted. The method improves the accuracy of a new energy consumption system model, optimizes the system operation, enhances the cooperative stability, and promotes the efficient utilization of new energy and the sustainable development of a power system.

The invention relates to an unmanned aerial vehicle group-oriented sensing communication integrated network multi-resource joint scheduling method, and belongs to the technical field of wireless communication, and the method comprises the steps: building a system model of multiple UAV-ISAC tasks, and defining a joint optimization problem; extracting spatio-temporal features from the dynamic heterogeneous graph in which the unmanned aerial vehicle, the user and the sensing target are abstracted as nodes and the relationship is abstracted as edges; taking the features as input, and adopting a layered multi-agent reinforcement learning architecture to solve the joint optimization problem on line; in the architecture, resource allocation and trajectory planning actions are generated through cooperation of a central Actor and all unmanned aerial vehicle Actors, and system performance is evaluated by a central Critic; constructing a multi-target weighted reward function, stabilizing a training process by combining experience playback and a Mini-batch sampling mechanism, and updating network parameters in parallel; and obtaining an optimal resource allocation and unmanned aerial vehicle trajectory strategy through training. The sensing performance is improved, the communication quality is guaranteed, and the defects in the aspect of dynamic resource scheduling in the prior art are overcome.

The invention provides a fault diagnosis method and system for a spindle box of a high-speed gear milling machine based on fault mechanism simulation and data fusion. The method mainly comprises the following steps: S1, calculating a tooth profile wear curve of a gear based on an Archard wear principle; s2, three-dimensional modeling software is used for building system models in different wear states, and simulation data sets under different wear degrees are obtained with the help of dynamics simulation software; s3, establishing a data acquisition system to obtain an experimental vibration data set under the actual working condition of the spindle box; s4, constructing a spindle box fault diagnosis model by using the analogue simulation data set obtained in the step S2; and S5, performing fault diagnosis on the experimental vibration data set obtained in the step S3 by using the spindle box fault diagnosis model constructed in the step S4, and identifying the health condition of the spindle box system. Important data support is provided for a main axle box fault diagnosis and health management system, and the diagnosis accuracy and reliability are improved.

The invention discloses an MBSE-based aircraft undercarriage system model integration method, and belongs to the technical field of system engineering. The method comprises the following steps: constructing an aircraft undercarriage system architecture, a classification mapping of simulation design information and a GOPPRR-E element model, and a meta-model library; constructing an architecture model according to a demand-function-logic-physical process, and generating a Simulink simulation model according to the architecture; a simulation model semantic mapping rule is constructed according to an OSLC specification, and design information is analyzed to establish an integrated data model; and a Simulink simulation tool integration adapter is constructed, and a tool interoperation service based on Web is provided. According to the method, a multidisciplinary model can be accurately constructed, uniform expression of architecture and simulation models and semantic consistency of heterogeneous models are realized, two-way transmission of cross-domain information and interoperation between tools are supported, the problem of interoperation between architecture modeling and simulation modeling tools in aircraft undercarriage system design is effectively solved, and the design efficiency is improved.

The invention relates to the technical field of satellite internet, and discloses a low-orbit satellite phased array multi-beam interference modeling and suppression method and system, and the method comprises the steps: selecting a Kaiser window as a core filtering method, and achieving the optimization of beam characteristics through the dynamic adjustment of a shape parameter beta; generating an initial beam directional diagram based on a digital phase matching method, and multiplying the Kaiser window function coefficient by the excitation weight of the 64-array-element linear array element by element to realize spatial domain weighted filtering; the method comprises the following steps: constructing a training data set containing multi-scene interference characteristics, calculating a corresponding covariance matrix and an accurate inverse matrix thereof to form a sample pair, designing a deep neural network architecture, inputting a flattened covariance matrix vector, and learning a complex nonlinear mapping relation from the covariance matrix to the inverse matrix through a multi-layer full-connection structure; a mean square error is used as a loss function to constrain network output precision, and a multi-beam interference system model is constructed; according to the invention, stable and efficient communication of the low-orbit satellite system in a complex electromagnetic environment and under rapid channel change is ensured.

The invention belongs to the technical field of mass concrete construction, and discloses a temperature control system model, a construction method and application. A water cooling technology is mostly adopted in traditional mass concrete construction, but water is difficult to take in special construction sites, a large number of water resources need to be wasted, temperature control measures of the traditional water cooling technology are lagged, and the crack probability is remarkably increased. According to the temperature control system model, the construction method and the application, a second-order response surface is used for fitting a multi-parameter temperature control system model, and the temperature control system model is corrected and optimized according to measured data acquired by a temperature control acquisition unit, so that a closed-loop control system is formed. The concrete grid units and the corresponding air guide pipes serve as minimum temperature control units, construction, temperature control and management are carried out, the management and control granularity is finer and more accurate, and a new research and development direction is provided for the temperature control technology of mass concrete construction.

The invention discloses an edge computing network task unloading and resource allocation method based on optimal service quality, which comprises the following steps of: constructing a system architecture comprising a cloud server, a plurality of edge servers and mobile equipment, and establishing a multi-dimensional system model covering task characteristics, service cache, communication transmission, computing resources, service cost and service quality; constructing an optimization problem aiming at maximizing the long-term service quality of all mobile equipment, and forming a mixed integer nonlinear programming model under the constraints of mobile equipment cost constraint, storage capacity limitation, bandwidth, computing resources and the like; a double-time-slot hierarchical decision-making mechanism is designed, and multi-dimensional joint optimization of service caching, task unloading and resource allocation is realized through a collaborative mechanism that short-term resource allocation is constrained through a long-term caching decision and short-term performance feedback optimizes long-term caching. According to the method, the overall quality of service (QoS) can be effectively improved and the task processing delay and energy consumption can be reduced under the condition that the cost of the mobile equipment and the system resource limitation are met.

The invention discloses an anti-robustness test method and system for an end-to-end automatic driving system, electronic equipment and a storage medium. The method comprises the following steps: acquiring image data of a surrounding environment in a driving process of an automatic driving vehicle; when the current frame is the first frame, the initial disturbance is random Gaussian noise; except the first frame, the initial disturbance of the subsequent frame is the weighted disturbance generated by the previous n frames; based on the image data, multi-view BEV semantic features are obtained, semantic perception consistency loss is constructed, and semantic feature disturbance is optimized through an optimizer; the semantic feature disturbance is used as an initial value, trajectory planning confrontation loss is constructed, and final planning-level confrontation disturbance is generated based on multi-step iteration of gradient symbols; and adding the final planning-level adversarial disturbance to the acquired image data for adversarial testing. According to the invention, the super-resolution effect of the video in the complex motion scene is improved, and the quality and visual effect of the video are enhanced. According to the method, the robustness of the automatic driving system model can be improved.

The invention belongs to the technical field of power plant thermodynamic and control, and particularly relates to a fractional order model of an ultra-supercritical coal-fired unit coordination control system and an identification method. The fractional order model comprises a coal pulverizing system, a boiler steam-water system and a steam turbine power generation system; the modeling method comprises the following steps: S1, acquiring and storing data of an ultra-supercritical coal-fired unit coordinated control system DCS; s2, constructing a fractional order model of the coordination control system; and S3, fitting a to-be-solved function through acquired data, and identifying dynamic parameters of the fractional order model offline by adopting a segmented time-sharing adaptive particle swarm method. According to the method, fractional order modeling is carried out on the coordinated control system of the coal-fired unit, the dynamic parameters of the system are identified by adopting a segmented time-sharing adaptive particle swarm identification method, the optimal model parameters are obtained through iterative calculation, the precision of the unit coordinated control system model under the wide load is greatly improved, and the system performance is improved. And implementation of a control strategy based on a system model is facilitated.

The invention discloses a margin analysis method based on an MBSE spaceflight equipment model, and the method comprises the following steps: S1, building an MBSE model of spaceflight equipment, and converting design parameters, function demands and system constraints into a unified system model; s2, analyzing and determining coupling design parameters through a system model; s3, margin representation data are formed according to the coupling design parameters; s4, generating a margin distribution result based on the margin representation data; s5, based on the margin distribution result, generating a visual margin distribution result; s6, calculating a safety margin boundary based on a visual margin distribution result; s7, generating an over-limit alarm and verification report based on the safety margin boundary; and S8, monitoring the change of the design parameters in real time according to the over-limit alarm and verification report, and recording the over-limit condition of the design parameters. According to the method, the MBSE concept is introduced, and a more systematic and accurate view angle is provided for margin analysis by establishing a formalized model of the system.

The invention relates to a disturbance source positioning method based on dynamic mode decomposition, which belongs to the technical field of power system operation and analysis and comprises the following steps of: accurately extracting oscillation mode characteristics of a system by performing matrix decomposition on actually measured data; furthermore, a participation factor analysis model fusing time-space characteristics is constructed, and the spatial distribution characteristics and the time evolution characteristics of the modal are comprehensively considered, so that accurate positioning of the oscillation source is realized. Compared with a traditional method, the method has the advantages of no dependence on a detailed system model, high positioning precision and the like, is realized completely based on measurement data calculation, avoids complex system modeling, and has relatively high practical application value and popularization potential.

The invention relates to the technical field of building safety, and discloses a safety transformation monitoring system for building construction and a method thereof. The system comprises a data acquisition and standardization module, a dynamic security entity relationship map construction module, an organization capability and cognitive load modeling module, a hybrid risk identification and conduction calculation engine, a self-adaptive security transformation strategy generation engine and a closed-loop self-calibration module. According to the method, a dynamic security entity relation graph is constructed and updated in real time, the cognitive load of management personnel is quantified, and the cognitive load is used as a dynamic adjustment factor of the conduction probability of risks on the graph; when a safety transformation strategy is generated, comprehensively evaluating a risk reduction effect, resource cost and additional cognitive load cost to output an optimal adaptive strategy; and finally, performing closed-loop self-calibration on the system model by utilizing execution feedback. According to the method, dynamic prospective risk prediction, self-adaptive strategy generation and continuous self-optimization are realized.

The invention relates to the technical field of automatic control, in particular to a switching positive system identification method and system and a storage medium, and aims to solve the problems that in the prior art, a model violates non-negative physical constraints, hyper-parameter estimation is prone to local optimum, calculation efficiency is low, and switching path identification precision is insufficient. The method comprises the following steps: firstly, determining non-negative priori of a system model and pulse response truncated Gaussian distribution through a preparation module; constructing a Bayesian network, and iteratively optimizing a switching path by adopting Gibbs sampling; and finally, based on regularization optimization and Gibbs sampling in combination with Monte Carlo approximation, accurately estimating the pulse response of the subsystem. According to the method, the non-negative characteristics are embedded into the whole identification process, global parameter optimization is achieved, high-dimensional integral calculation is simplified, identification precision and stability are improved, and the method is suitable for multi-scene engineering application.

The invention discloses a ship pipeline system model generation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a pipeline schematic diagram and a specification configuration file after receiving a modeling instruction for a pipeline system, determining an element model of a part forming the pipeline system from a model library according to the pipeline schematic diagram, according to the pipeline schematic diagram and the specification configuration file, the primitive model is adopted to generate an initial pipeline system model, and the initial pipeline system model is adjusted to obtain a target pipeline system model when a model adjustment instruction is received. The initial pipeline system model is generated by triggering the modeling instruction, and the target pipeline system model is obtained by triggering the adjustment instruction to adjust the initial pipeline system model, so that a designer does not need to manually model and adjust the model for multiple times according to a pipeline schematic diagram, and the modeling efficiency of the pipeline system model is improved; the design progress of the pipeline system is accelerated; and the design quality is improved.

The invention relates to the field of intelligent water affairs, and discloses a precise flow control system for intelligent water affairs, which comprises the steps of collecting local water flow change data in real time, extracting flow velocity disturbance characteristics and pipe network transient pressure response, and judging whether unexpected disturbance trigger exists or not; constructing a local disturbance label library based on disturbance trigger source features and a historical operation model, and identifying disturbance sources and flow direction tendentiousness by using fuzzy discrimination and variable weight filtering algorithms; according to the pipe section hydraulic characteristics corresponding to the mismatching behavior, the flow of the target pipe section is corrected through a high-resolution electric proportional valve set and a two-way feedback control unit; constructing a cross-section response chain through the intelligent edge control node, and generating a multivariable adjustment gradient according to the multi-period callback simulation data; flow state data and peripheral environment variables before and after the current round of regulation and control intervention are collected, and system model parameters are dynamically corrected through the integrated evolutionary prediction network. The stability of the intelligent water affair system is improved.

The invention provides an automatic generation method supporting an MBSE system model and a design document. MBSE system model project information is read, and design document basic information is automatically generated; system design flow information of a methodological model constructed in an MBSE system model project is read, corresponding document chapters are generated according to flow design content C, the sequence of the document chapters is determined according to the design flow sequence A, and the outline level of each chapter in the design document is determined according to the hierarchical relation in the design flow hierarchy B; on the basis of the generated design document structure and the read design process corresponding model D, automatically filling each chapter with model content; and dynamically updating the content of the design document. The problems that in the prior art, a design document generation method based on templates and rules is poor in flexibility, and the document and the model are difficult to dynamically synchronize can be solved, and the consistency of the system model and the design document is guaranteed.

The model assembly room comprises a model assembly area, the side face of the model assembly area is communicated with a model preparation area, a model storage area and a control area, and a hoisting hole is formed in the top of the model assembly area; a model assembly upper supporting frame used for supporting the upper portion of the model assembly and a model assembly lower supporting steel column used for supporting the bottom of the model assembly are arranged in the model assembly area. The model assembly room is suitable for stably supporting a large model assembly, debugging and assembling the model assembly in the supporting process, and transferring the model assembly out of the model assembly room from the top of the model assembly room through the model hoisting device after assembly is completed; the technical blank of lack of a model assembly room suitable for stably supporting the large model assembly in the prior art is filled, so that a worker can complete efficient and safe assembly of the large model assembly without entering a test section, and the test efficiency and safety are improved.

The invention relates to the technical field of comprehensive energy systems, and particularly provides a low-carbon building-oriented electro-hydrogen-heat comprehensive energy system modeling method, which comprises the following steps of determining different physical fields involved in a low-carbon building-oriented electro-hydrogen-heat comprehensive energy system and mechanism models under each physical field, determining a model interface of each mechanism model; establishing a dynamic model of each component device according to each mechanism model; based on the dynamic model of each component device, establishing each subsystem model; and combining the sub-system models according to the model interfaces in the physical fields to obtain the low-carbon building-oriented electro-hydrogen-heat comprehensive energy system model. Therefore, on the basis of a modularized modeling thought, the modeling models are divided according to different fields, the coupling relation and the dynamic characteristics in the electro-hydrogen-heat comprehensive energy system can be accurately reflected, then the maintainability and the simulation efficiency of the models can be more effectively improved, and pre-judgment of dynamic changes of the system under different load conditions is helped.

The invention relates to the field of bulk grain pneumatic conveying, and provides a bulk grain pneumatic conveying system model order reduction method and device in order to improve the accuracy of an order reduction model, when a function library is constructed, a traditional function is introduced, and a function for reflecting nonlinear and chaotic characteristics generated by interaction of particles and airflow is also introduced; during data processing, a dynamic sliding window is adopted for data collection, and when data changes drastically, the window is enlarged properly to obtain more comprehensive information; when the data change is small, the window is correspondingly reduced, and the real-time response capability is improved; model optimization is carried out based on chaos indexes, so that the model accuracy is improved; by regularly verifying the accuracy of the model, the model can continuously and accurately describe the dynamic characteristics of the system.

The invention provides a building power supply and distribution electrical system model construction method based on an analogy analysis method, and belongs to the technical field of power distribution electrical system models. And carrying out load identification and capacity prediction by using the load capacity adaptive matching model. And then constructing a high-voltage side capacity model block by adopting a graph theory algorithm, and realizing capacity adaptation through recursion degree calculation and model change gain degree evaluation. Establishing a multi-physics field coupling effect sub-model, and describing the stability characteristics of the system by using a current stability matrix; and analyzing and evaluating a nonlinear load and a distributed power supply through big data, and establishing a load prediction and power evaluation sub-model. And then a capacity gain matrix is calculated by adopting comprehensive dynamic model adaptation simulation, parameter backstepping optimization is realized, and finally a final power supply and distribution electrical system model is generated.

The invention discloses an intelligent optimization control method and system for a compressed air energy storage power station, and relates to the technical field of power grids, and the method comprises the steps: S1, building a system model corresponding to the compressed air energy storage power station, and collecting data; s2, training a reinforcement learning agent, and learning to obtain an optimal control strategy capable of adapting to the operating environment of the compressed air energy storage power station; s3, using the prediction model to predict the future operation state of the compressed air energy storage power station, and optimizing the control sequence; and S4, executing real-time control operation, adjusting a control strategy according to feedback data, and performing dynamic optimization control. According to the method, reinforcement learning and model prediction control are organically combined, a reinforcement learning agent is introduced, a control strategy is continuously learned and optimized, and adaptive adjustment is quickly made according to factors in multiple aspects such as the current generation power, the power load, the energy storage state and the environment in the face of real-time fluctuation of the new energy generation power.

The invention relates to the technical field of artificial intelligence and control, in particular to a non-linear multi-agent system fixed time consistency control method and device and a medium. According to the control method, a backstepping recursion method is used as a control design framework, a self-adaptive fixed time consistency controller based on an auxiliary compensation system is provided, and the problem of non-linear multi-self-body system consistency control under time-varying input delay is solved. By providing and changing a control input signal by a person, the motion trajectory of the multi-agent system can be modified as required. Time-varying input time delay and an unknown nonlinear function are considered in a system model, so that the system is more general. Wherein an unknown time-varying delay function is processed by constructing an auxiliary compensation system, and an unknown nonlinear function is processed by using radial basis function neural network approximation. According to a fixed time control correlation lemma, an actual fixed time adaptive consistency control method is provided to ensure that the consistency error of the multi-agent system converges to a neighborhood near an original point in fixed time.

The invention relates to the field of temperature acquisition systems, and discloses a high-precision sampling processing method based on multi-model temperature sensors, and the method comprises the steps: obtaining an original ADC data block of a temperature sensor, and extracting a dynamic feature fingerprint; analyzing the dynamic feature fingerprints to determine a model probability vector characterizing a known working mode and a novelty score characterizing an unknown working mode; according to the model probability vector, the estimation results of the multiple extended Kalman filters are fused through an interactive multi-model algorithm, a temperature value is obtained, and a residual sequence is generated; and updating the system model online according to the novelty score or the residual sequence. The system comprises a data acquisition and feature extraction module, a sensor state sensing module, a self-adaptive temperature estimation module and a system online evolution module. According to the method, a closed-loop processing flow with state sensing and online evolution capabilities is constructed, so that the problem that the precision of a traditional fixed model is reduced under a complex working condition is solved.

The invention relates to the technical field of building construction control, and particularly discloses a hydraulic synchronous control method based on model prediction, which comprises the following steps: constructing a state space model for a silo group hydraulic lifting system; state changes at future moments are predicted through the system model; solving a control problem on a finite time domain through an optimizer; the actual lifting height of the hydraulic jack is fed back to the control system, the system state at the next moment is predicted again, and control input is adjusted; a rolling optimization strategy is adopted, only the optimal control input of the current moment is executed at each control moment, and the future control input is recalculated according to the feedback value of the next moment. By establishing an accurate system model and optimizing control input, the problem that the sliding speed and height of each jack are inconsistent due to traditional manual operation can be effectively avoided, the geometric accuracy of silo group sliding formwork construction is ensured, and the construction quality is improved.

The invention discloses a whole-process project management system and method based on a large language model, relates to the technical field of construction engineering management, and solves the problem that full-life-cycle data of a construction project is difficult to analyze through a constructed GPT system model. And a three-party benefit balance model is difficult to construct to optimize three-party benefit conflicts in construction engineering. Comprising the following modules: a data acquisition and preprocessing module used for collecting project data; the intelligent engineering decision module is used for extracting a six-tuple entity from the unstructured data and identifying key milestone turning through semantic similarity analysis; the construction energy efficiency evaluation module is used for calculating an innovation fusion coefficient and a project fusion coefficient; the three-party collaborative revenue calculation module is used for defining a three-party joint revenue function; the dynamic equilibrium risk quantification module is used for locally calculating gradients in three parties and outputting an optimal combination; and the risk coping and monitoring integrated module is used for generating a coping strategy through the GPT system model.

The invention provides a SysML model construction method, a SysML model driven design model construction method and a SysML model driven design model construction system. The SysML model driven design model construction method comprises the following steps: obtaining a first system model, a first equipment model and a process model; the first system model, the first device model and the process model are described based on a meta-model extended from SysML semantics; constructing a design model according to a conversion rule, the first system model, the first equipment model and the process model; wherein the meta model extended from SysML semantics can be used for identifying information used for constructing the design model; the first system model is used for describing a system for constructing the design model; the first equipment model is used for describing equipment for constructing the design model; and the flow model is used for constructing the design model comprising a pipeline and instrument flow chart.

The invention discloses a modeling method for an electromagnetic transient model of a new energy hydrogen production system. The modeling method comprises the following steps: step 1, planning and designing an overall framework of a model; 2, building a wind electronic system model; step 3, building a photovoltaic subsystem model; 4, building an energy storage subsystem model; 5, building a hydrogen energy subsystem model; according to the method, a foundation is laid for research in different aspects such as further simulation condition setting, system steady-state / transient-state operation characteristic extraction, system parameter and control strategy design, engineering demonstration and the like.

The invention discloses a polar region island micro-grid optimal scheduling method considering frequency constraint, and relates to the technical field of island micro-grid optimal scheduling, and the micro-grid optimal scheduling method comprises the following steps: building a system model; constructing and solving a multi-time scale joint optimization architecture; embedding a frequency transient constraint as a minute layer decision constraint in a linearization or convex approximation form; and according to a solving result, issuing a hierarchical instruction to various distributed resources and implementing online monitoring and safety guarantee measures. According to the method, through explicit embedding and linear convex approximation of frequency transient constraint, the defect that only steady-state frequency is considered or an empirical threshold value is adopted in traditional scheduling is overcome, uncontrollable frequency transient caused by short-time high-power disturbance is avoided, minute-level frequency safety guarantee is achieved, the frequency peak value and the minimum point are controlled, and the scheduling efficiency is improved. And load removal or equipment protection actions caused by transient overrun are reduced, so that the power supply continuity and the system reliability are improved.