355 results about "Load model" patented technology

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides an optical storage and charging cooperative control method and system based on multi-energy complementation. The method comprises the following sub-steps: respectively establishing a photovoltaic power generation model, an energy storage system model and a charging load model; acquiring historical illumination information, charging information and electricity price information, constructing a prediction model based on a neural network, and predicting and outputting illumination intensity, charging load demand power and electricity price in a future time period; constructing a target optimization function by taking the annual net cost and the power deviation rate as optimization targets; according to the method, the cooperative optimization control of the photovoltaic power generation, energy storage and charging system is realized, the annual net cost is reduced, the power deviation is reduced, the energy storage and charging cooperative control strategy is established, the target optimization function is solved by adopting the improved whale optimization algorithm, and the charging and discharging power sequence is generated according to the optimal solution obtained by solving and is input to the system for execution. And the operation efficiency and reliability of the whole system are improved.

The invention discloses an electric power marketing management information platform daily electric power fitting method and related equipment, and relates to the technical field of electric power data management, and the method comprises the steps: obtaining historical load data, external environment data and equipment operation state data of a target region; constructing an initial load fitting model based on the historical load data; extracting a date characteristic factor according to a preset time classification rule, and dynamically correcting the initial load fitting model based on the date characteristic factor to generate a corrected load model; generating a multi-source fusion feature based on the external environment data and the equipment operation state data; and inputting the multi-source fusion features into the corrected load model, and outputting a target load prediction result.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

The invention discloses a virtual power plant intelligent aggregation optimization control method for multi-type flexible resources, and the method comprises the steps: constructing a dynamic characteristic model of distributed resources, wherein the dynamic characteristic model comprises a photovoltaic output probability prediction model, an energy storage SOC-life coupling model, an electric vehicle behavior chain model, an adjustable load constraint model, and an industrial interruptible load model; an edge agent node calculates an adjustable potential interval of a resource cluster in real time and uploads the adjustable potential interval to a cloud end, a global optimization target is solved on the cloud end based on an improved sparrow search algorithm (ISSA), after a scheduling instruction is generated, model parameters are corrected in a rolling mode according to actual output deviation calculated in real time, and a scheduling result is obtained. And triggering a resource fault emergency strategy for prediction deviation and resource fault problems occurring in the operation process of the virtual power plant. Through an edge-cloud collaborative architecture and a multi-stage optimization strategy, accurate modeling, optimization aggregation and intelligent scheduling of distributed resources are realized.

The invention relates to the technical field of intelligent distribution, in particular to a power grid load distribution system and method based on intelligent control, and the system comprises a data collection and processing module which carries out the real-time collection and data processing of all node data and meteorological data of a power grid, and obtains the actual power grid load data; the communication network module is used for transmitting the actual power grid load data; the intelligent regulation and control module is used for constructing a rural power grid load model to obtain a load prediction value; the monitoring alarm module is used for monitoring the actual rural electricity consumption condition in real time according to the load prediction value; the system verification module is used for verifying the stability of the power grid after regulation and control configuration, verifying the resource utilization efficiency of the power grid according to the load rate of the transformer, and adjusting the regulation and control configuration; and the execution feedback module is used for performing control strategy command execution on the power grid equipment. The power supply and demand balance of the rural power grid is realized, and the utilization efficiency of power grid resources is improved.

The invention discloses a battery capacity performance testing method and device and a storage medium, and relates to the technical field of battery capacity performance testing. By constructing a dynamic load model and a multi-load test protocol and combining a high-precision data acquisition and filtering algorithm, adaptive data analysis and a machine learning regression model, the problems of difficulty in balancing precision and efficiency, large data acquisition error, fixed test protocol and the like in the traditional battery test are solved; by dynamically adjusting test parameters and environmental conditions, complex working conditions of the battery in actual use are accurately simulated, meanwhile, dynamic updating and optimization of a test protocol are achieved, the test precision, the data reliability and the test efficiency are remarkably improved, and a comprehensive, efficient and high-adaptability solution is provided for battery performance evaluation and health management.

The invention provides a computer hardware performance evaluation method and system based on an analogue simulation model, and relates to the technical field of computer performance evaluation. According to the method, hardware component models such as a processor, a memory and I / O are constructed by analyzing a system structure description file, semantic analysis is performed on an instruction stream or a parallel computing graph of a to-be-evaluated application, and a task load model is formed. The model is input into an event-driven scheduling module, simulation is executed on the hardware component model, task-resource mapping is generated, and task time delay, communication traffic and power consumption are recorded. And calculating average response time delay, data bandwidth and unit energy consumption according to the simulation data, and outputting an evaluation report compared with the performance baseline. According to the method, real load-oriented multi-dimensional performance prediction can be realized, and the method can be used for architecture optimization and scheme selection.

The invention relates to the technical field of intelligent temperature control, in particular to an industrial mainboard computer heat dissipation optimization method and system based on AI intelligent regulation and control, and the method comprises the steps: obtaining mainboard real-time temperature data, carrying out time sequence preprocessing, and generating distributed time sequence data; extracting thermal features and classifying and identifying the thermal features to obtain coordinate distribution of the high-energy-consumption area; inputting a classification model to output a classification result, and screening areas exceeding a preset threshold to determine a priority regulation label; performing spatial interpolation based on the label to generate a heat map, and extracting heat distribution boundary data; analyzing thermal resistance according to the boundary data and identifying a bottleneck region to obtain thermal resistance distribution; performing fan parameter optimization based on the thermal resistance to obtain an optimized heat dissipation strategy; inputting a load model, and outputting dynamic load adaptation regulation and control parameters; and real-time regulation and control and energy efficiency evaluation are executed according to the regulation and control parameters, and final heat dissipation operation parameters are screened and solidified. The method can solve the problem of heat dissipation control lag.

The invention provides a virtual power plant scheduling method and system based on distributed resource operation domain description, and relates to the technical field of virtual power plant optimization scheduling, and the method comprises the steps: determining a virtual power plant distributed resource operation domain; considering the influence of demand response load active power and reactive power adjustment on the virtual power plant output model, and constructing a comprehensive load model; dividing a feasible region of a variable in a bilinear term in the comprehensive load model into several non-intersecting regions by adopting a segmented McCormick relaxation method, and converting the regions into a mixed integer linear programming form; the operation constraint condition of the virtual power plant is considered, a mixed integer linear programming form is combined, boundary points of the operation domain of the virtual power plant are solved, and the range of the distributed resource operation domain of the virtual power plant is depicted; and in the distributed resource operation domain range of the virtual power plant, constructing a virtual power plant optimization scheduling model by taking the sum of the total operation cost and the demand response compensation as a target, and applying the obtained optimal operation scheduling strategy to the virtual power plant for execution.

The invention relates to the technical field of source network load storage intelligent scheduling, and discloses a source network load storage AI intelligent scheduling method and system, and the system comprises a data collection module, a fluctuation analysis module, a constraint calculation module, a load modeling module, an energy storage analysis module, a decision engine module, and a safety correction module. Output data of a photovoltaic power station, a wind power plant and a traditional power plant are collected in real time through an Internet of Things sensor, and a multi-source heterogeneous energy data pool is constructed; aligning the corrected source load data with the energy storage state and the power grid operation parameters according to a time sequence to generate a four-dimensional optimization combination matrix; matching an optimal scheduling algorithm through a bionic search strategy, and analyzing the data matrix through the deep reinforcement learning model to generate a three-section scheduling instruction; and finally, the power grid control system executes power generation adjustment, load regulation and control and energy storage charging and discharging instructions. According to the system, the edge computing gateway is adopted to realize data acquisition, the new energy consumption capability and the power grid stability are improved, and the risk of source-grid load-storage collaborative failure is reduced.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention relates to a dual-resource constraint flexible job shop scheduling method for reducing worker load, which comprises the following steps: S1, construction of a worker load model: dividing the worker load model into four conditions of light work, moderate work, micro-severe work and rest according to daily work arrangement of workers, and setting the maximum working time length for each worker, the calculation module is used for calculating extra workloads; in the scheduling method provided by the invention, three different initialization strategies are combined, and the proportion of the initialization strategies in a population is set, so that the diversity and quality of an initial solution are ensured, specifically, the used initialization strategies comprise random initialization, initialization according to the process completion time and initialization according to the process remaining time; under the random initialization strategy, the initial solution of the population has great diversity, which is helpful for avoiding the trouble of a local optimal solution.

The invention relates to a system for a user-side energy storage sharing framework. A power energy scheduling optimization method comprises: establishing a user load model, an energy storage battery model and a user-to-user capacity sharing model; with a minimum electricity cost of a whole community as an objective optimization function, resolving optimal scheduling based on the function using a MILP algorithm as an optimization technique for power energy scheduling of a user-side energy storage sharing framework; and performing power energy scheduling for users according to a resolved result. The invention determines a user cost model under the user-side energy storage sharing framework, establishes an electricity model of non-transferable loads and transferable loads and corresponding constraints, and obtains optimal values of adjustable variables to control electricity consumed by electric loads by resolving an optimal value of an objective function under the precondition of reducing user costs, thus further reducing electricity costs.

The invention discloses a load management system based on a load model in the plastic product industry. The load management system comprises a load time sequence model building module which classifies and marks equipment according to operation behaviors of the equipment to obtain a load time sequence model; the data acquisition and fusion module acquires power, current, voltage and start-stop state data of equipment in real time through a monitoring point position, and generates a real-time load data set in combination with real-time production state data; the load prediction and adjustability evaluation module generates a load prediction curve by using a long short-term memory network, generates an adjustability score by adopting a support vector machine model, and determines an adjustable process list in combination with real-time production state data; and the scheduling execution module determines candidate adjustment equipment according to the load prediction curve, the adjustability score and the adjustable process list, constructs a multi-objective optimization function through a multi-objective optimization algorithm, and generates a load adjustment strategy by using a particle swarm optimization algorithm to execute scheduling on the equipment. According to the invention, the refinement level of load management is improved.

Disclosed are a risk assessment method for a distributed power distribution network considering a line fault and a device thereof, belonging to the field of risk assessment of faults of a power distribution network. The method includes the following steps: establishing a wind generating set output model, a photovoltaic module output model and a load model, and generating a wind generating set output data sample, a photovoltaic module output data sample and a load sample; establishing a short-term fault rate model of a wind generating set, a photovoltaic module and a line, and generating a system state sample; establishing an objective function and a constraint condition, and reconstructing the power distribution network to obtain the reconstructed power distribution network topology; carrying out the risk assessment and an entropy weight method.

The invention discloses a method and a system for identifying a power imbalance state of a power system under climate and weather changes. The method comprises the following steps: determining climate and weather environment data of a target region; determining a current climate and meteorological environment event of the target region; if the climate and meteorological environment event meets the preset type, triggering an electric power imbalance state identification process of a target electric power system in the target region; constructing a source side output model matched with the power supply side, and constructing a load model matched with the load side; constructing a source-load coupling prediction model of the target power system; inputting real-time power operation data of the target power system into the source-load coupling prediction model to obtain power gap data; obtaining an identification result of the current state of the target power system; and generating an imbalance state identification log of the target power system. According to the method and the system for identifying the electric power unbalance state of the electric power system under the climate and weather change, the electric power unbalance state is comprehensively evaluated from the source side and the load side.

The invention discloses a micro-grid scheduling optimization method and system, a terminal and a storage medium. A user model of each user node in a micro-grid is established in advance; the user model comprises a plurality of energy models: a heating ventilation and air conditioning system model, a load model, a clean energy model, an energy storage system model, a user-power grid energy transaction model and an inter-user energy transaction model; determining a balance relation between energy loss and output according to the user model, and determining constraint conditions; establishing a user cost function of each user node according to the cost function of each energy model; and establishing an optimization problem according to the user cost function of each user node, obtaining an optimal solution according to the constraint condition and the optimization problem through an alternating direction multiplier algorithm, and determining an optimal scheduling scheme of the microgrid. All users are designed as peer-to-peer nodes, and a distributed scheduling algorithm is adopted, so that the autonomy of the system can be enhanced, the communication burden can be reduced, and meanwhile, the efficient privacy protection level can be improved.

The invention provides an optimized scheduling method for an integrated energy system of an electrolytic aluminum park. The optimized scheduling method comprises the following steps: constructing a refined electrolytic aluminum load model, and constructing a demand response model and a carbon market model in which electrolytic aluminum load participates; an improved CSP-CCHP unit model is constructed, and an original self-contained power plant of the electrolytic aluminum park is replaced with the improved CSP-CCHP unit; an electrolytic aluminum park comprehensive energy system optimization scheduling model is constructed and solved, optimization scheduling results are obtained, and the optimization scheduling results comprise park operation total cost, park operation carbon emission, various unit output conditions and various load optimization scheduling results. Compared with a model which only considers the simple characteristic of the load in the prior art, the optimal scheduling method for the integrated energy system of the electrolytic aluminum park, provided by the invention, has the advantages that the model is more refined and practical, and accurate scheduling support is provided for participation of the load of the electrolytic aluminum park in the power market and demand response.

The invention discloses a permanent magnet synchronous motor joint simulation method, which comprises the following steps of: firstly, establishing a permanent magnet synchronous motor finite element model in Maxwell, and defining material nonlinear characteristics and motion boundary conditions; a motor drive control circuit is constructed in Simplorer, the finite element model is imported through a Twinbuild tool, and an electromagnetic-circuit joint simulation environment is established; creating a motor multi-body dynamic model and an external load model in Adams, and respectively exporting the motor multi-body dynamic model and the external load model into an FMU format and an Adamssub format; and finally, establishing an electromagnetic-circuit-mechanism joint simulation platform in the Simulink, and executing joint simulation. Compared with traditional joint simulation, according to the method, through electromagnetic-circuit-mechanism three-field real-time interaction, the motor can sense the load sudden change working condition, then the torque and current of the motor are rapidly adjusted through the PI controller, the problems that in engineering practice, the external load of the motor is not real, and the abnormal load is detected are solved, and the reliability of the motor is improved. And the crossing from an ideal load to an actual load is realized.

The invention discloses a distributed power supply load model establishment method and system, and relates to the technical field of load model establishment, after an establishment system obtains power system information through an API interface, a topological graph model of a power system is established, each component in the power system is abstracted as a node and an edge in the topological graph model, and the topological graph model is established; after simulation software is used for conducting simulation detection on the topological graph model, a target value of the topological graph model is calculated through an objective function, an optimal path between nodes is updated through a path algorithm, current distribution of edges is adjusted through a graph cut algorithm, power loss is reduced, overload is avoided, the steps are repeated for multiple iterations, and when convergence conditions are met, the optimal path is obtained. And outputting the final topological graph model as a comprehensive load model. The system can optimize the targets at the same time through a graph optimization algorithm, improves the overall benefits of the system, and overcomes the defect that the complexity of the power system cannot be comprehensively considered in single target optimization.

The invention relates to the technical field of industrial robot fault diagnosis, and discloses a fault early warning and remote diagnosis method for an industrial robot electrical control system, and the method comprises the steps: obtaining a mechanical load parameter through a potential load model, an inertia load model and a basic dynamics model, obtaining an electrical state parameter through an electrical efficiency model, and carrying out the fault early warning and remote diagnosis. A correction factor is generated through a correction model in combination with the environment temperature, a dynamic current threshold value is calculated through a threshold value model based on the correction factor and a basic current threshold value, and finally health scores and fault early warning information are output through a diagnosis model in combination with the vibration acceleration and servo ring following errors. According to the method, mechanical, electrical and environmental multi-source parameters are fused, dynamic threshold adjustment and multi-dimensional health assessment are realized, normal working conditions and abnormal states are effectively distinguished, the fault early warning accuracy is remarkably improved, the false alarm rate is reduced, and a reliable remote diagnosis scheme is provided for the industrial robot.

The invention belongs to the field of Internet of Vehicles, and discloses an unmanned aerial vehicle Internet of Vehicles performance optimization method combining trajectory and unloading and related equipment, and the method comprises the steps: firstly constructing a communication and load model; constructing a multi-objective optimization function in combination with multiple calculation unloading modes, and setting constraint conditions such as an unloading decision, time delay, energy consumption and a flight area by taking minimization of total time delay and total energy consumption as an objective; and the function is solved by alternately iteratively updating the flight path and calculating the unloading mode until the maximum number of iterations is reached, and an optimal result is output. According to the method, the communication and load characteristics and multiple calculation unloading modes of the unmanned aerial vehicle auxiliary vehicle networking system are comprehensively considered, and comprehensive optimization of the system performance is realized through optimization functions and constraint conditions. According to the method, the problems of track optimization and quick response processing of calculation unloading in a complex scene can be effectively solved, the service quality of vehicle users is remarkably improved, the total time delay and the total energy consumption are reduced, and an effective solution is provided for performance improvement of an unmanned aerial vehicle networking system.

A multi-hydraulic support load model training method and a multi-hydraulic support load prediction method. The multi-hydraulic support load model training method comprises: acquiring leg pressure data, top beam pitch angle data, top beam roll angle data, coal mining machine cutting position data, coal mining machine traction speed data, and an initial hydraulic support pressure regression prediction model; preprocessing the data to generate a dataset; generating a training dataset and a verification dataset on the basis of the dataset; and training the initial hydraulic support pressure regression prediction model on the basis of the training dataset and the verification dataset, so as to generate a target hydraulic support pressure regression prediction model of a target fully-mechanized coal mining face hydraulic support. By taking into account four dimensions of the top beam pitch angle data, the top beam roll angle data, the coal mining machine cutting position data, and the coal mining machine traction speed data, the target hydraulic support pressure regression prediction model is established and generated by training, so that the support pressure prediction effect can be improved; in addition, by means of model prediction, the prediction efficiency can be improved, and the prediction costs can be reduced.

The invention discloses a photovoltaic energy storage and charging integrated power station capacity optimization design method and system, and relates to the technical field of power system distributed photovoltaic energy storage planning, and the method comprises the steps: building an electric vehicle queue charging load model based on a preset parameter system describing user charging behaviors and probability distribution fitting; generating a photovoltaic charging load uncertainty scene of the target power station based on the generative adversarial network, the electric vehicle queue charging load model and the photovoltaic output power of the target power station; based on the photovoltaic charging load uncertainty scene, constructing a target robust optimization model; solving the target robust optimization model to obtain an optimal capacity planning result of the target power station; and based on the optimal capacity planning results of different target power stations, utilizing Kriging interpolation to obtain the coupling relationship between the charging behavior and the optimal capacity. The technical problem that the integration capability of the photovoltaic and battery energy storage system cannot be accurately determined in the prior art is relieved.

The invention discloses a power grid dispatching method in an energy storage participation scene. The method comprises the following steps: establishing a master-slave game system taking an energy storage end of a demand side of a power distribution network as an upper layer and an electrical load end of the demand side as a lower layer, wherein the master-slave game system comprises an upper-layer energy storage model and a lower-layer electrical load model; inputting the scheduling capacity and the scheduling ratio of the energy storage end into the upper-layer model, outputting electric quantity transmission cost parameters of the energy storage end and the electric load end, inputting the electric quantity transmission cost parameters into the lower-layer model, outputting the transmission electric quantity of the energy storage end and the electric load end after processing, and inputting the transmission electric quantity into the upper-layer model for circulation until a variable reaches a steady-state value, and obtaining the scheduling amount to realize the scheduling of the power grid. According to the method, dynamic collaborative optimization of energy storage and power utilization load side resources can be realized, the problems of low resource utilization rate and insufficient model precision in the prior art are solved, the overall optimization and scheduling flexibility of an energy system is remarkably improved, and the technical blank in the field of collaborative optimization and dynamic interaction in the prior art is filled.

The invention relates to the technical field of engineering monitoring, in particular to a bridge structure state monitoring method and system based on digital twinning, and the method comprises the steps: building a multi-source monitoring data matrix based on a data timestamp; establishing a geometric model, a material attribute model and a load model of a bridge structure based on the multi-source monitoring data matrix, and fusing to obtain a bridge digital twin initial model; and carrying out structural mechanical analysis based on the bridge digital twin initial model to obtain stress distribution data of each region of the bridge. Through real-time acquisition and analysis of multi-source monitoring data, the method can dynamically evaluate the structural state of the bridge and timely identify potential safety hazards, so that the safety of the bridge is improved; and through calibration of the initial model, the obtained digital twin precise model reflects the real state of the bridge, and a more accurate data basis is provided for subsequent structural analysis.

The invention discloses a demand side aggregation load cooperative control method and system, and the method comprises the steps: obtaining user side electricity price, market electricity price fluctuation and user baseline load prediction data, and carrying out the analysis to obtain a demand response target; selecting corresponding price type and excitation type demand response modes based on the demand response target in combination with user characteristics, and constructing a corresponding demand side aggregation load model; wherein a price-type demand side resource aggregation load model is constructed based on a price-type demand response mode; based on the excitation type demand response mode, constructing an excitation type demand side aggregation load model; according to the demand response mode and the demand side aggregation load model, a regulation and control strategy of the aggregation load is determined, and the regulation and control strategy comprises a regulation type strategy, a control type strategy and a mixed type strategy; and classifying the regulation and control strategies according to an applicable scene to obtain a regulation and control strategy classification result. According to the method, the new energy consumption capability can be improved, the system operation cost is reduced, and the system operation economy is improved.

The invention discloses a power distribution network operation parameter optimization identification method based on optimal power flow calculation, and relates to the technical field of power distribution systems, and the method comprises the steps: collecting historical operation parameters of each node of a power distribution network, screening to obtain a data collection priority of an optimal power flow output index, and reasonably distributing node sampling frequencies according to the data collection priority; measuring and collecting operation parameters of the power distribution network according to the distributed sampling frequency, carrying out pre-detection on the collected data, and meanwhile, carrying out adaptive anomaly judgment and sampling frequency dynamic adjustment and carrying out multi-scale decomposition and sensitivity analysis on node residual errors, so as to realize optimal power flow calculation time window adjustment driven by dynamic parameters; and the load model parameters are verified to ensure the accuracy and reliability of the parameter identification result. Through combination of historical data, real-time measurement and residual sensitivity information, transient disturbance, medium-term fluctuation and long-term trend can be considered in the operation parameter identification of the power distribution network, and the online precision of a load model and line parameters is improved.

The invention discloses a source load impact and system bearing life evaluation method and system, equipment and a medium, and relates to the technical field of traction power supply, and the method comprises the steps: collecting data to recognize impact, building a source load model, analyzing an impact diffusion path, evaluating system bearing, fusing multi-dimensional damage, calculating the residual life of the equipment, and automatically updating parameters. And generating a closed-loop regulation and control strategy. The system comprises a data analysis and dynamic modeling module, a bearing evaluation and conduction analysis module, a damage coupling and life prediction module and a parameter correction and strategy generation module. The traction impact time domain-frequency domain-topology integrated quantization is realized, and the problem of impact time-space diffusion assessment is solved. A life model is constructed, and multi-dimensional stress damage is accurately mapped. The micro-simulation and on-line filtering technology is utilized to ensure that model parameters are adaptively updated along with working conditions, and the problem of disjunction between evaluation and actual conditions is solved. The method supports side cloud cooperation and closed-loop pre-control, effectively improves the safety margin of the power grid, and delays the aging of equipment.