43 results about "Kernel extreme learning machine" patented technology

The invention belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

The invention relates to the technical field of power prediction, in particular to a photovoltaic power generation power prediction method, device and equipment and a computer storage medium. According to the photovoltaic power generation power prediction method provided by the invention, in order to make up the defects of a heuristic algorithm, a VMD-KELM photovoltaic power generation power prediction model based on BWO optimization is provided; the model combines the advantages of VMD (variational mode decomposition), KELM (kernel extreme learning machine) and BWO (white whale optimization algorithm), and the photovoltaic power environment can be quickly and accurately predicted according to the nonlinear data mining requirement and the high-uncertainty photovoltaic power environment.

The invention belongs to the technical field of power transmission line monitoring and dynamic capacity increasing. A power transmission line dynamic current-carrying capacity prediction and capacity increase method based on double-model adaptive fusion comprises the following steps: data acquisition and preprocessing: acquiring historical weather and current-carrying capacity data, and cleaning and standardizing the historical weather and current-carrying capacity data; weather-driven prediction: optimizing a kernel extreme learning machine through an artificial egret optimization algorithm to predict future meteorological parameters, and substituting the future meteorological parameters into a heat balance equation to calculate a first current-carrying capacity predicted value; performing data-driven prediction, optimizing variational mode decomposition by adopting a dynamic perception and accurate capture algorithm, and directly predicting a second current-carrying capacity predicted value in combination with a long-short-term memory network; in the fusion step, weights are dynamically distributed according to errors of the two models on the verification set, and self-adaptive weighted fusion is carried out to obtain a final point prediction value; and a probability modeling step: fitting probability distribution based on historical errors, and constructing a confidence interval. According to the invention, the precision and reliability of current-carrying capacity prediction are improved, and dynamic capacity increasing and safe operation of the power transmission line are realized.

The invention discloses a fault diagnosis and health management prediction method based on a laser gyroscope, and belongs to the technical field of inertial navigation equipment health management. The method comprises the following steps: acquiring and preprocessing a time sequence data set of operation core parameters of the laser gyroscope; performing wavelet packet decomposition on the time sequence data to extract high-frequency fault features, extracting time sequence statistical features by a sliding window method, and combining principal component analysis to perform dimensionality reduction and fusion to obtain a multi-dimensional feature vector; inputting the feature vectors into an improved kernel extreme learning machine model, optimizing parameters through a particle swarm optimization algorithm, and then outputting fault types and grades; on the basis of a fault diagnosis result, an analytic hierarchy process is adopted to endow parameter weights, and a health factor calculation model is constructed to quantify a health state; taking the health factor sequential sequence and the key parameter degradation trend as input, and combining a bidirectional long-short-term memory neural network with health factor sequence constraint to predict residual life and a confidence interval; new data are regularly brought in, and model parameters are updated through transfer learning to realize dynamic iteration. The method solves the problems of early fault recognition lag and low life prediction precision of a traditional method, is suitable for the fields of aerospace, precision navigation and the like, and has remarkable engineering application value.

The invention discloses an SVG voltage ride-through control parameter identification method based on a kernel extreme learning machine in a double-solution space, and the method comprises the following steps: S1, dividing SVG fault stages, building an SVG steady-state control model and a fault control model, and according to the difference of the influence of control parameters on steady-state characteristics and fault characteristics, carrying out the recognition of SVG voltage ride-through control parameters; dividing a solution space of SVG control parameters into a double-solution space composed of a steady-state parameter solution space and a dynamic parameter solution space; s2, extracting a corresponding observed quantity characteristic value, constructing an input-output sample, and establishing a kernel extreme learning machine model in a double-solution space; s3, training a kernel extreme learning machine by using the input-output samples, and inputting RT-LAB hardware in-loop test data into the trained kernel extreme learning machine to identify SVG control parameters; according to the method, the adaptability and the accuracy of identification are improved, so that accurate modeling of the SVG is realized.

The invention provides a resin composition for a halogen-free flame-retardant cable sheath and a preparation method of the resin composition, and belongs to the technical field of polymer composites for cables, the preparation method comprises the following steps: S1, raw material grading pretreatment and core parameter accurate determination; s2, gradient pre-dispersion and uniformity feedback regulation and control; s3, step-by-step grafting compatibility and grafting rate feedback regulation and control; s4, carrying out dynamic flame-retardant regulation and control melt blending; and S5, gradient cooling granulation and finished product performance verification. In two key links of gradient pre-dispersion and dynamic melt blending, an improved fruit fly optimization algorithm-adaptive fuzzy neural network algorithm and an improved sparrow search algorithm-kernel extreme learning machine algorithm are respectively embedded, and the two algorithms are bidirectionally interactively fused, so that the problems that traditional process parameters are set according to experience and the performance fluctuation is large are solved, and the dynamic melt blending method is suitable for industrial production. The synergistic improvement of the uniformity, the flame retardant property and the mechanical property of the resin composition is realized.

The invention discloses a lithium battery health state estimation method and system, electronic equipment and a storage medium, and belongs to the field of battery state prediction.The method comprises the steps that probability modeling is conducted on a small amount of lithium battery charging and discharging time sequence data through a variational self-encoding network, an expansion sample with degradation consistency is generated, and an expansion data set is formed; key health indexes reflecting battery aging characteristics in the extended data set are constructed into a characteristic matrix as input; a kernel extreme learning machine is adopted as a nonlinear regression prediction model, global optimization is carried out on kernel function parameters and regularization coefficients of the nonlinear regression prediction model through an artificial bee colony optimization algorithm, and finally an SOH prediction model which is obtained through training and represents the relation between the battery health state and the characteristics is obtained; and outputting a battery health state estimation result based on the SOH prediction model. According to the method, the health state prediction precision and efficiency can be improved.

This application provides a method, apparatus, and electronic device for detecting outliers in satellite telemetry data. The outlier detection model for telemetry data is trained using a kernel extreme learning machine, which can effectively detect outliers in satellite telemetry data, greatly improving the efficiency and accuracy of outlier detection.

The application relates to a fan blade icing fault diagnosis method based on a GWO-REKELM, and the method comprises the following steps: 1) pre-processing and normalizing all fan operation data; 2) using a random forest to perform feature screening on all fan operation data; 3) using an oversampling technology to balance the data set of the screened fan operation data; and 4) using a grey wolf optimization kernel extreme learning machine model with an improved objective function to train and test the data set, and completing fan blade icing fault diagnosis. Compared with the prior art, the application has the advantages of improving the correctness of fan blade icing fault diagnosis and the like.

The invention provides a pumped storage unit vibration trend prediction method and system based on a combined model, and belongs to the technical field of pumped storage unit detection. Comprising the following steps: acquiring an original vibration signal and performing variational mode decomposition by adopting a self-adaptive signal decomposition method to generate a plurality of subsequences; a singular spectrum analysis method is adopted to extract dominant components of all the subsequences, and superposition reconstruction is carried out on residual components; the processed subsequences are converted into a training set and a test set of a prediction model through phase-space reconstruction, and the training set and the test set serve as prediction model input for model training; and on the basis of a kernel extreme learning machine model and an IBiGRU-KAN model, predicting the high-frequency sub-sequence and the low-frequency sub-sequence in each sub-sequence, and superposing the prediction results of each sub-sequence to determine the vibration trend of the pumped storage unit. According to the method, complex data processing and model calculation tasks can be efficiently executed, and the vibration trend of the pumped storage unit is accurately predicted.

The present application relates to a kind of short-term load prediction method based on ISSA-HKELM, the method includes: first, in view of the defect of kernel extreme learning machine KELM, combined with Gaussian kernel function and polynomial kernel function, construct the hybrid kernel extreme learning machine HKELM with stronger generalization ability;Second, in view of the problem that sparrow search algorithm is easy to fall into local extremum, adaptive t distribution strategy and dynamic adaptive weight are introduced to improve sparrow search algorithm;Third, the improved sparrow search algorithm ISSA is used to optimize the parameter of hybrid kernel extreme learning machine HKELM and establish ISSA-HKELM prediction model;Finally, short-term load prediction is carried out using the established ISSA-HKELM model.Compared with prior art, the present application has good prediction accuracy and robustness and the like advantages.

The present invention discloses a temperature extraction method for a Brillouin optical time-domain analysis system (BOTDA) based on a nuclear extreme learning machine, comprising: using the Brillouin optical time-domain analysis system to collect Brillouin gain spectrum parameters of a test optical fiber; using the nuclear extreme learning machine to analyze the parameters collected by the Brillouin optical time-domain analysis system; using the obtained real matrix as training data for the nuclear extreme learning machine; training the nuclear extreme learning machine using the training data to extract accurate temperature information; and utilizing a faster processing speed to improve system performance. The present invention introduces a nuclear extreme learning machine algorithm to improve the temperature extraction accuracy and efficiency of the Brillouin optical time-domain analysis system, which is beneficial to the application of the Brillouin optical time-domain analysis system in actual detection.

The application discloses a street lamp line ground fault intelligent diagnosis system and method, belongs to the technical field of power equipment operation monitoring, and comprises a data acquisition and preprocessing unit, a feature extraction unit and a fault diagnosis unit. The data acquisition and preprocessing unit collects original residual current data of a street lamp line through a residual current transformer and an oscilloscope. The application adopts a parrot optimization algorithm to adaptively determine optimal decomposition parameters of VMD, overcomes the limitations of artificial parameter setting, ensures the quality of data decomposition, lays a foundation for extracting high-quality fault features, solves the problems of lack of reliability of existing fault protection, great influence of human factors, and low accuracy of existing street lamp ground fault diagnosis systems and methods, calculates sample entropy values, constructs a multi-dimensional feature vector set representing line states, inputs the multi-dimensional feature vector set into a fault diagnosis unit, and the fault diagnosis unit trains a kernel extreme learning machine classifier by using the feature vector set.

The invention discloses a porous scaffold SLM preparation method and device based on machine learning, and belongs to the technical field of additive manufacturing. The method comprises the following steps: firstly, designing a composite lattice porous structure consisting of inner-layer simple cubic cells and outer-layer body-centered cubic cells, wherein the inner-layer simple cubic cells and the outer-layer body-centered cubic cells are directly connected through nodes; then, a Latin hypercube sampling design test scheme is adopted, and initial small sample data of the selective laser melting process are obtained; then, a regression synthesis minority class oversampling technology is introduced to carry out enhancement processing on the data set; on the basis, establishing a kernel extreme learning machine prediction model optimized by a swarm intelligence algorithm; and finally, reversely solving optimal process parameters based on the target performance and completing preparation. Through the composite structure design, the stress shielding effect is effectively relieved, the problems of high experiment cost, less sample data and low prediction precision in traditional process optimization are solved, and efficient and accurate customized manufacturing of the high-performance medical CoCrMo implant is achieved.

The invention discloses a target identification method for a nonlinear aperture inverse imaging simulation dynamic intersection process. The method comprises the following steps: S1, generating a knowledge base containing nonlinear aperture inverse imaging simulation data; s2, performing multimode feature extraction and data fusion based on the knowledge base to obtain a fusion feature vector; s3, further fusing the fusion feature vectors to obtain depth features; and S4, training a kernel extreme learning machine by using the depth features after dimension reduction, and performing target recognition by taking the trained kernel extreme learning machine as a kernel classifier. According to the method, the dynamic rendezvous imaging data knowledge base can be established to the greatest extent through analog simulation under the condition that actual measurement data is deficient, early-stage training data is provided for an identification algorithm, and the algorithm debugging efficiency and accuracy during actual measurement application are improved.

The invention discloses a distributed power supply output interval pseudo measurement generation method considering uncertainty, comprising the following steps: for a wind generating set, constructing and training a double-layer kernel extreme learning machine model, the first layer of the model is used for correcting the predicted wind speed, and the second layer of the model is used for correcting the predicted wind speed; the second layer is used for predicting uncertainty upper and lower limit intervals of fan output under the corrected wind speed; for a photovoltaic generator set, a dual-output feedforward neural network model is constructed and trained, and the model directly outputs an output upper and lower limit interval of a photovoltaic system under a predicted meteorological condition; and taking the predicted output upper and lower limit intervals as interval pseudo-measurement data of the corresponding distributed power supply nodes, and applying the interval pseudo-measurement data to state estimation or operation optimization of the power distribution network. According to the method, the output fluctuation range of wind power and photovoltaic power at the future moment can be effectively described, reliable interval pseudo measurement data is generated, and the defect of insufficient monitoring of distributed power supply nodes is overcome.

The present disclosure provides a method for determining the phreatic line of a stepped reservoir bank slope group, a program product and an electronic device, and relates to the technical field of engineering data processing. The method comprises: obtaining first monitoring data of a target slope and second monitoring data of a related associated slope; fusing the first monitoring data and the second monitoring data according to a time-varying weight of hydraulic association to obtain a hydraulic association fusion feature; processing the hydraulic association fusion feature of the target slope and the hydraulic association fusion feature of a plurality of sample data by using a kernel extreme learning machine to obtain a phreatic line elevation inversion value; constructing a hydraulic association strength field based on the distance between slopes and the fluctuation rate of the reservoir water level; fusing the phreatic line elevation inversion value and the key parameters in the first monitoring data to obtain a group perception fusion feature; and obtaining a phreatic line elevation optimization value according to the group perception fusion feature, the hydraulic association strength field and the phreatic line elevation inversion value. The present disclosure improves the accuracy and stability of determining the phreatic line of the stepped reservoir bank slope group.

The present invention provides an effective wind speed soft measurement method, comprising a wind turbine SCADA data acquisition module, an offline optimization calculation module based on an improved gray wolf optimized IGWO algorithm, an effective wind speed soft measurement modeling module based on a kernel extreme learning machine (KELM) algorithm, and the like. The signal output end of the wind turbine SCADA data acquisition module sequentially passes through a SCADA data preprocessing and normalization module, an offline optimization calculation module based on an improved gray wolf optimized IGWO algorithm, an effective wind speed soft measurement modeling module based on a kernel extreme learning machine (KELM) algorithm, an effective wind speed soft measurement model parameter optimization module based on the IGWO algorithm, and an effective wind speed soft measurement model performance evaluation module, and finally outputs the effective value of the wind speed. The present invention establishes an effective wind speed soft measurement model, which does not require wind tunnel experiments or virtual simulations to obtain the data required for effective wind speed soft measurement modeling, but only requires SCADA data of the wind turbines actually operating in the wind farm. The model has the characteristics of low cost, high prediction accuracy, and ease of implementation.

The application provides an abnormal load identification method and system of a subway direct-current power supply network and a readable storage medium, belongs to the technical field of urban rail transit direct-current traction power supply systems, obtains feeder line current data based on a single-end traction substation feeder line current collection current loop, and performs sampling window management; performs variational mode decomposition on the same window feeder line current, calculates energy weight, energy entropy and sample entropy, and constructs a multi-dimensional entropy feature matrix; then generates state identification through energy entropy first-level discrimination and correlation dimension second-level screening, and inputs the state identification into a kernel extreme learning machine diagnosis model after fusion with the entropy features, and outputs normal load, system oscillation, locomotive oscillation or short-circuit fault classification results to generate corresponding protection disposal.

The present application proposes a vocal singing practice quality grade evaluation method of fractional order spectrogram deep learning, including the following steps; Step S1, collect the audio signal of the singer's vocal vowel singing practice, and according to the singing index, label the corresponding quality grade, construct the sample data set, and use it for model training, testing and verification; Step S2, convert the audio signal into a series of fractional order spectrograms; Step S3, construct the fractional order spectrogram deep feature extraction network based on DenseNet and channel attention mechanism in the model, input the extracted fractional order spectrogram deep features into the BiLSTM network, and extract the time sequence features of the singing practice signal; Step S4, the quantum fireworks algorithm is used to optimize the hyperparameters of the kernel extreme learning machine of the model, and the extracted time sequence features are mapped to a high-dimensional space for quality grade decision, forming the evaluation result; Step S5, train the model; The present application can better adapt to the characteristics of non-stationary signals and provide more accurate spectrum analysis.

The invention relates to the technical field of cable detection, and discloses a cable insulation state diagnosis model construction method, a cable insulation state diagnosis method and a cable insulation state diagnosis device. Combining a plurality of base learners including a gradient boosting decision tree algorithm model, a lightweight gradient elevator algorithm model, an extreme gradient boosting tree algorithm model and a support vector machine algorithm model to construct a target base learner combination model, and using an improved frost ice optimization algorithm to optimize a mixed kernel extreme learning machine as a meta learner; therefore, the finally constructed cable insulation state diagnosis model can give consideration to different feature selection logics and model advantages, the diagnosis precision and generalization ability are effectively improved, information redundancy is reduced, and the problem of performance limitation of a single model is solved.

A kind of intelligent prediction and control method of blasting setback distance based on kernel extreme learning machine, the blasting design parameters and explosive parameters affecting the blasting setback distance are collected as input characteristics, at the same time, the actual measurement of blasting setback distance is taken as output characteristics;The feature parameters in the original data are verified and screened to obtain the final data and construct the training set and test set;The kernel extreme learning machine model based on sand cat group optimization is trained using the training set to obtain the kernel extreme learning machine prediction model;The performance of the kernel extreme learning machine prediction model is tested using the test set;The prediction value of the blasting setback distance is directly obtained by inputting the feature parameters into the kernel extreme learning machine prediction model, and finally the input feature parameter value is adjusted according to the actual production needs to control the blasting setback distance. This method can realize the accurate prediction and effective control of the blasting setback distance in open-pit mine, which has important practical value and scientific significance for reducing the blasting hazards in open-pit mine and ensuring the mining process.

The application provides a noise-based operation insulator contamination grade evaluation method, comprising the following steps: S1. obtaining noise signals of sample insulators under different environmental temperatures, different humidities and different contamination grades; S2. performing filtering processing on the noise signals; S3. extracting time domain characteristic parameters from the filtered noise signals; S4. performing wavelet transform on the filtered noise signals, and extracting wavelet characteristic parameters in a set frequency band; S5. inputting the time domain characteristic parameters, the wavelet characteristic parameters, the environmental temperature and the humidity into a kernel extreme learning machine to perform training; and S6. obtaining insulator operation information in actual working conditions in real time, and inputting the operation information into the trained kernel extreme learning machine to obtain the contamination grade of the insulator, wherein the operation information comprises the environmental temperature, the humidity and the noise signal of the insulator.

A method and system for evaluating the carrying capacity of a distribution network for large-scale automobile charging loads. First, by combining a variational autoencoder and a polynomial fitting method, efficient data expansion is achieved, effectively increasing the number of training samples and reducing the risk of overfitting. Then, an adaptive biological influence optimization method is used to improve stability in complex training environments, improve the accuracy of feature extraction and training efficiency. Then, an autoencoder neural network with adaptive feature refinement is used to finely capture and reconstruct data, optimize information flow, reduce dimensionality while retaining important information. Then, an adaptive error compensation mechanism of a kernel extreme learning machine is used to adjust the error learning rate and weight update in real time, thereby improving the accuracy and robustness of the model. Through the above optimization training, the model can fully grasp the characteristics of the charging load data and realize accurate evaluation of the carrying capacity of the distribution network through the final category of the output. Therefore, the accuracy of the present invention is relatively high.

The application discloses an acceleration sensor fault diagnosis method based on IEMLLE-WKELM, which comprises the following steps: feature extraction, using a time-frequency analysis method to extract feature information of the acceleration sensor under different fault states; data dimension reduction, performing dimension reduction processing on high-dimensional feature data; model construction and parameter optimization; fault diagnosis, outputting the fault diagnosis result of the acceleration sensor; the acceleration sensor fault diagnosis method can reduce the features of the acceleration sensor data through the local linear embedding (LLE) algorithm based on information entropy measurement (IEM), can solve the influence of the non-aligned sample position difference, and can optimize the penalty factor and the kernel parameter of the weighted kernel extreme learning machine through the chaotic particle swarm optimization (CPSO) algorithm, so that the method can avoid falling into local optimization, the accuracy of the fault diagnosis is 99.375%, and the diagnosis accuracy of the acceleration sensor is higher than that of other methods.

The invention discloses a mechanical fault diagnosis method for a switch cabinet, belongs to the field of switch cabinet maintenance, and solves the problem of inaccurate fault diagnosis in the prior art, and the technical scheme comprises the steps: S1, collecting a vibration signal of the switch cabinet, and carrying out the noise reduction preprocessing of the vibration signal; s2, performing adaptive variational mode decomposition on the vibration signal after noise reduction preprocessing, and performing decomposition according to a decomposition layer number K determined in an adaptive manner to obtain K intrinsic mode function components; s3, constructing an initial feature vector based on the K intrinsic mode function components, and extracting a diagnosis feature vector through singular value decomposition and a differential spectrum thereof; s4, constructing a data set by utilizing the diagnosis feature vector, optimizing key parameters of a kernel extreme learning machine by adopting an improved chaos sparrow optimization algorithm, and establishing a mechanical fault diagnosis model of the switch cabinet; and S5, the actually collected switch cabinet vibration signals are sequentially processed in S1 to S3 and then are input into the mechanical fault diagnosis model, and corresponding fault types are output. According to the invention, fault diagnosis is more accurate.

The application relates to the technical field of power grids and discloses a power prediction method, device, equipment, medium and product for photovoltaic power generation. The method comprises the following steps: acquiring a multimodal historical data set corresponding to a photovoltaic station, adopting a multivariate variational modal decomposition method to perform modal decomposition on the multimodal historical data set, and acquiring a plurality of first modal components; acquiring modal prediction results corresponding to the plurality of first modal components according to the plurality of first modal components through a kernel extreme learning machine prediction model optimized by a multi-target white shark optimization algorithm; finding prediction weight values corresponding to the plurality of first modal components, and acquiring a power prediction result corresponding to the photovoltaic station according to the modal prediction results corresponding to the plurality of first modal components and the prediction weight values. The scheme of the embodiment can realize multimodal data feature mining and efficient prediction of the photovoltaic station, and can improve the accuracy of power prediction for photovoltaic power generation.

This invention relates to the field of fault identification technology, and particularly to a method for identifying leakage current in photovoltaic-connected distribution networks based on NCA and SSA-KELM. The steps include: collecting residual current data under different conditions of photovoltaic-connected distribution networks; extracting features from the collected residual current data; preprocessing the extracted features; filtering the preprocessed features using the nearest neighbor component analysis (NCA) method; training a kernel extreme learning machine (KELM) model using the filtered features while simultaneously optimizing the parameters of the KELM model using the sparrow search algorithm (SSA), thereby obtaining an SSA-KELM leakage current identification model; inputting the residual current feature sample to be tested into the SSA-KELM model for output category identification, thus obtaining the leakage current type of the sample to be tested. This invention can accurately identify the leakage current fault type in photovoltaic-connected distribution networks.

The invention discloses a mounting device fault diagnosis method based on a GSWOA-KELM model, and belongs to the technical field of fault diagnosis, and the method comprises the steps: obtaining a vibration signal of a mounting device, the vibration signal comprising vibration sub-signals of different parts of the mounting device; dividing each vibration sub-signal into a plurality of data segments, and generating a first fusion data set by calculating a plurality of time domain characteristic indexes of each data segment; and inputting the first fusion data set into a fault diagnosis model to obtain fault type diagnosis results of different parts in the mounting device, the fault diagnosis model being a pre-trained kernel extreme learning machine, so that more fault features of the mounting device can be obtained, and the problems of single information collection by a single sensor, insufficient fault features and the like are solved. In addition, the regularization coefficient and kernel parameters of the kernel extreme learning machine are determined by the global search whale optimization algorithm, and the accuracy of fault diagnosis of the mounting device is improved.

The invention discloses a park electric vehicle charging and discharging control method and system based on reinforcement learning and a storage medium, and the method comprises the following steps: S1, obtaining related historical data, and constructing a historical environment state; s2, predicting the total load of a plurality of time steps of the park in the future according to the historical data of the power grid load and the external environment factors by using a joint model of a long-short-term memory neural network and a deep kernel extreme learning machine; s3, inputting the historical environment state and the predicted total load of a plurality of time steps of the future park into DDPG, wherein the DDPG takes minimization of the peak-valley difference of the power grid load, maximization of the economic benefit of the load aggregator and maximization of the demand satisfaction degree of the electric vehicle user as a reward function; and S4, the operation state of the park is monitored in real time, real-time environment state input of the DDPG is constructed, and the DDPG outputs an electric vehicle charging and discharging power instruction based on the dynamic state. The charging and discharging behaviors of the electric vehicle can be regulated and controlled in real time in a complex dynamic environment so as to stabilize a power grid.