76 results about "Probabilistic neural network" patented technology

The invention relates to the technical field of artificial intelligence and data processing, in particular to a mining explosion-proof high-voltage power distribution device-oriented all-insulation state intelligent monitoring method, which specifically comprises the following steps of: acquiring training data to construct a data set; marking the data in the data set; preprocessing the data in the data set; constructing an intelligent monitoring model for the full-insulation state of the mining explosion-proof high-voltage power distribution device based on a deep probabilistic neural network, inputting the preprocessed data set into the model for training, and repeating the iterative training process until a preset iteration stopping condition is met to obtain a trained model; and inputting the preprocessed real-time data into the trained model, generating probability distributions of normal operation, slight degradation and serious degradation and corresponding uncertainty quantized values by an output layer, and obtaining a final diagnosis result according to a preset discrimination condition. According to the invention, the accuracy and anti-interference capability of insulation state diagnosis are significantly improved.

The invention relates to the technical field of automobile automatic transmission, and discloses an AMT bearing operation state detection system and method under an active and passive switching working condition, and the method comprises the steps: collecting real-time data through a vibration acceleration sensor, a current sensor, a temperature sensor and a rotating speed encoder, carrying out the preprocessing of envelope demodulation, Kalman filtering and the like, and carrying out the detection of the operation state of an AMT bearing; and generating a time-frequency characteristic matrix by using variational mode decomposition and Hilbert transform. And inputting the matrix into a probabilistic neural network model adopting a sliding time window mechanism, and outputting a bearing health state probability value. A multi-parameter state evaluation model optimized by a quantum genetic algorithm is constructed, an optimal feature combination is obtained, and early warning levels and maintenance suggestions are output through a belief rule base inference device in combination with a hierarchical diagnosis control model (including an acquisition layer, an analysis layer and an execution layer). The system is further provided with a signal verification module to ensure data reliability. According to the invention, multi-source data fusion and dynamic adaptive diagnosis are realized, and the state detection precision and real-time performance of the AMT bearing under complex working conditions are improved.

The invention discloses an intelligent identification method for a rock tension-shear rupture mechanism based on acoustic emission RA-AF parameters, and belongs to the technical field of intelligent identification. The method comprises the following steps: carrying out indirect tension test and variable-angle shear test on rock, acquiring RA-AF data of the rock under tension and shear loads by adopting an acoustic emission technology, and respectively constructing RA-AF standard databases corresponding to tension and shear fracture mechanisms based on a DBSCAN clustering algorithm; wherein 4 / 5 is used as training set data, and 1 / 5 is used as test set data. A probabilistic neural network is used for learning RA-AF data feature information of a pulling and shearing fracture mechanism, an intelligent classification model of the pulling and shearing fracture mechanism is established, the accuracy rate of the model is detected, and the model which reaches the detection standard through adjustment is applied to quantitative recognition of the fracture mechanism of the same rock under different static loads. According to the method, the data processing workload of workers can be reduced, and the classification efficiency is improved.

The invention relates to the technical field of fault diagnosis, and discloses a transformer adaptive fault diagnosis method, system and equipment based on a sensor, and the method comprises the steps: collecting an operation signal of a transformer through a sensing device, carrying out the feature extraction of the operation signal, and obtaining a plurality of signal feature data; each signal feature data is input into each corresponding signal fault diagnosis model to obtain a plurality of fault diagnosis sub-results, and each signal fault diagnosis model is constructed by adopting a probabilistic neural network model; according to a preset weight value, weighted summation is carried out on the fault diagnosis sub-results, a fault diagnosis result of the transformer is obtained, and the weight value is obtained through calculation based on the diagnosis accuracy of each signal fault diagnosis model. According to the invention, comprehensive data processing and diagnostic analysis are carried out on various sensor signals, so that efficient and accurate diagnosis of the transformer fault can be realized, and thus a powerful guarantee is provided for stable operation of a power system.

A ship steam turbine fault diagnosis method relates to the field of ship steam turbine fault diagnosis. The invention aims to solve the problem that smoothing factors are difficult to determine when ship steam turbine fault diagnosis is carried out by utilizing a probabilistic neural network. According to the invention, the fault feature data of the steam turbine is collected, and the fault feature data is input into the probabilistic neural network fault diagnosis model to obtain a fault identification result. The steam turbine data is processed by adopting a Min-Max standardization method, so that the distribution and relative relation of the original data can be reserved, the scale consistency of different characteristics can be ensured, and the reliability of diagnostic data can be effectively ensured. The probabilistic neural network is optimized through a salat swarm intelligent optimization algorithm, an optimal smoothing factor can be obtained, and the problem of smoothing factor selection is solved. The method has the advantages of being easy to train, rapid in convergence, high in accuracy and the like, and faults can be rapidly and effectively recognized.

The application discloses a battery thermal runaway early warning method, device and equipment of energy storage power station and medium, using neural network to predict the battery thermal runaway risk of energy storage power station, early warning before the battery thermal runaway of energy storage power station, improve the safety.In addition, first train the probabilistic neural network with a small amount of training samples, and after the convergence of the probabilistic neural network, train the back propagation neural network with the soft label output by the probabilistic neural network, solve the problem of slow learning speed and insufficient training samples in the initial training of the back propagation neural network, and improve the model training efficiency.At the same time, the soft label can solve the problem that the back propagation neural network is easy to fall into local minimum value in the training process.

The invention discloses an electro-hydraulic actuating mechanism fault diagnosis method, and relates to the field of electro-hydraulic actuating mechanism fault diagnosis. The invention aims to solve the problem that the traditional parameter optimization method is easy to fall into local optimum and high in calculation cost due to high dependence on selection of smooth factors in the existing fault diagnosis method for the electro-hydraulic actuating mechanism based on the probabilistic neural network. According to the fault diagnosis method for the electro-hydraulic actuating mechanism, the fault feature data of the electro-hydraulic actuating mechanism are collected and input into the probabilistic neural network fault diagnosis model, and a fault recognition result is obtained. The probabilistic neural network is optimized through an index-trigonometric function optimization algorithm, the optimal smoothing factor can be obtained, and the smoothing factor selection problem can be effectively solved. The method has the advantages of being easy to train, rapid in convergence, high in accuracy and the like, and faults can be rapidly and effectively recognized.

The invention provides a method for predicting the state of a wind driven generator bearing, and relates to the technical field of electrical equipment fault diagnos.The method comprises the steps that firstly, digital twin models of the wind driven generator bearing under various working conditions are established; the vibration acceleration of each measuring point of the wind driven generator is obtained through electromagnetic-structural field coupling simulation, and a feature database is constructed through obtained signal data; decomposing the signal under the fault condition in the feature database, calculating the fitness of the signal, selecting the optimal signal, and obtaining an optimal modal signal; performing time domain feature extraction on the optimal modal signal; inputting the signal feature vectors measured in different bearing states into the optimized probabilistic neural network, and finally obtaining a model capable of accurately judging the running state of the wind driven generator bearing; vibration acceleration data measured by a detection probe of the wind driven generator in practical application are input into the neural network, the current running state of the wind driven generator bearing can be distinguished, and then monitoring and prediction of the state of the wind driven generator bearing are completed.

According to the geomagnetic matching integrated navigation method based on the improved probabilistic neural network, the improved probabilistic neural network is utilized to indicate a trajectory for inertial navigation, geomagnetic actual measurement vectors are recognized at intervals, the method is short in training time, fixed in structure, simple in learning process and minimum in expected risk of misclassification, a Bayesian posterior probability output result can be obtained, and the method is suitable for popularization and application. The method has the advantages of strong non-linear recognition capability, good real-time performance, high matching rate and high precision, and can overcome the influence of geomagnetic measurement errors; smooth factors of the improved probabilistic neural network method change adaptively according to different pattern types, the relevance between feature vectors and pattern states is better represented, the actual effect of the input feature vectors on correct classification results is reflected, the improved probabilistic neural network can effectively improve the accuracy of geomagnetic matching positioning, and the accuracy of geomagnetic matching positioning is improved. Therefore, the matching result is more accurate and reliable, and the accuracy of the geomagnetic matching rate and the positioning precision is effectively improved especially in a geomagnetic feature weak region.

This invention discloses a photovoltaic system islanding detection method and system based on Santlet transform and Ridglet probabilistic neural network. The method includes: collecting historical voltage data of the photovoltaic inverter, preprocessing it, and then sequentially acquiring the data to be analyzed using a sliding window; performing time-frequency analysis using Santlet continuous wavelet transform to obtain the corresponding time-frequency spectrum; extracting multi-dimensional time-frequency feature vectors and inputting them into a trained Ridglet probabilistic neural network classification model to obtain the corresponding probability values; if the probability value is greater than a probability threshold, it is determined that the photovoltaic system has experienced islanding, and a trip signal is generated and sent to the grid-connected circuit breaker to disconnect it. This invention significantly improves the accuracy, speed, and reliability of islanding detection, effectively reduces the detection blind zone, and has good adaptability to complex power grid environments.

The invention relates to the technical field of fault diagnosis, in particular to a rapier loom fault diagnosis system. According to the system, fault tree analysis based on whale algorithm optimization and a probabilistic neural network are fused; the fault diagnosis method comprises the following steps: step 1, establishing a rapier loom fault tree diagnosis model; step 2, optimizing parameters of the fault tree model by using a whale algorithm; step 3, constructing a sample data set and carrying out normalization processing; and step 4, carrying out probabilistic neural network training and fault diagnosis. According to the diagnosis model based on fusion optimization of the probabilistic neural network and the fault tree algorithm, on the basis of a rapier loom fault tree diagnosis model, real-time newly-added state monitoring data of a monitoring system is introduced; and converting the data set into a sample feature vector, performing normalization processing, importing the sample feature vector into a probabilistic neural network algorithm model for training, and calculating and outputting a fault symptom probability, thereby realizing real-time, complete and rapid fault identification and positioning of the rapier loom fault diagnosis system.

The invention belongs to the field of oil and gas field exploration and development, and discloses a numerical reservoir model optimization and history fitting method and related device.The method comprises the steps that firstly, geological exploration data are utilized, a spatial clustering algorithm is adopted to automatically determine the local updating range of a structural model, and structural layers and stratums are optimized through spatial interpolation; outputting an optimized construction model and uncertainty measurement; based on the model and logging lithofacies attribute data, adaptively identifying a lithofacies updating key area and variation function parameters by applying a machine learning algorithm, and performing lithofacies simulation to output an optimized lithofacies model; combining the porosity, permeability and saturation attribute data, adaptively determining an attribute optimization range and variation function parameters by adopting a probabilistic neural network, and outputting an optimized attribute model through phase control attribute modeling; a three-dimensional oil-water two-phase black oil seepage model is constructed, a global optimization algorithm is adopted to automatically adjust parameters based on a target function, simulation data is calculated until convergence, optimal parameters are output to complete historical fitting, and efficient and reliable intelligent support is provided for complex oil reservoir development.

This invention discloses a method and system for transformer partial discharge pattern recognition based on an optimized probabilistic neural network. The method first acquires the transformer partial discharge signal; then, it establishes a two-dimensional spectrum of the partial discharge phase distribution pattern based on the partial discharge signal and extracts discharge statistical features from this spectrum; finally, it inputs the discharge statistical features into the optimized probabilistic neural network for pattern recognition to obtain the partial discharge pattern. The optimized probabilistic neural network uses a pollination algorithm to optimize the smoothing factor, and the switching probability in the pollination algorithm is a nonlinear function that decreases with the number of iterations. This invention can more accurately and efficiently classify and identify different partial discharge patterns of transformers, providing a data foundation for transformer fault diagnosis and resolution.

This invention discloses a bolt loosening location method and system based on environmental vibration and multi-scale PNN. The location method includes the following steps: S1: Establishing a refined finite element simulation model of the transmission tower and dividing it into multi-scale hierarchical substructures; S2: Simulating bolt loosening conditions in the finite element simulation model and extracting operational modal analysis results; S3: Calculating the frequency change ratio and modal compliance residual matrix norm, and constructing a multi-source damage-sensitive feature vector after weighted fusion; S4: Constructing and training a probabilistic neural network with an adaptive smoothing factor adjustment mechanism; S5: Acquiring on-site environmental vibration data and identifying modal parameters; S6: Extracting and calculating the measured data of the tower using the random subspace identification method, and inputting it into the trained probabilistic neural network to obtain the location results of the main bolt loosening area. This method achieves accurate bolt loosening location through scientific substructure division and multi-source fusion strategies, providing a reference for the safe operation and maintenance of tower bolts.

This invention provides an intelligent evaluation method for rockburst hazard at working faces based on principal component analysis-probabilistic neural networks, belonging to the field of rockburst hazard evaluation technology. An evaluation index system is established and data is collected by combining rockburst hazard influencing factors, drill cuttings monitoring, critical stress index monitoring, and actual field conditions. Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a training set and a test set. A probabilistic neural network (PNN) is used to train the evaluation model on the data in the training set, and then the performance of the evaluation model is tested using data from the test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's smoothing factor needs to be modified and retrained until the accuracy of the evaluation result meets the set value.

The application discloses an acceleration-based personalized virtual reality black insertion method for the disabled, which comprises the following steps: step 1, constructing an acceleration-based and personalized black insertion duration dataset based on the types of the disabled users; wherein the acceleration is divided into T types according to the size, and the disabled users have N types; step 2, obtaining the acceleration of the user, and calculating the time domain features and the frequency domain features of the acceleration; step 3, constructing a probability neural network based on the time domain and frequency domain features of the acceleration, inputting the time domain features and the frequency domain features of the acceleration into the probability neural network, and classifying the acceleration; and step 4, judging the type of the disabled user, combining the acceleration classification obtained in step 3, and obtaining the personalized black insertion duration of the user through the personalized black insertion duration dataset. The probability neural network based on the time domain and frequency domain features of the acceleration can well reflect the differences between different accelerations, reduce the influence of abnormal values on the network accuracy, and improve the differences between different features.

The present invention discloses a method for hierarchical classification of Internet of Vehicles (IoV) data based on probabilistic neural network and reinforcement learning, belonging to the field of hierarchical classification of IoV data, which includes the following steps: S1, obtaining data samples in the IoV scenario and establishing a sample data set; S2, calculating the road condition vectors of each roadside according to the established sample data set; S3, deploying agents in each roadside, and each agent makes decisions based on its own and the road condition vectors of adjacent road sides; S4, using a probabilistic neural network to sort the data in the sample data set; S5, uploading the sorted data according to the decisions of the agents to achieve hierarchical classification of IoV data. By adopting the above method for hierarchical classification of IoV data based on probabilistic neural network and reinforcement learning, the present invention can not only classify the IoV data in combination with the actual situation, but also better allocate broadband resources and ensure that emergency data is preferentially uploaded to the cloud.

The application provides an insulator contamination grade evaluation method based on an infrared image, and comprises the following steps: S1, acquiring an infrared image of a sample insulator by using an infrared imaging device, wherein the image is acquired under different temperatures, different humidities and different contamination grades; S2, preprocessing the infrared image; S3, extracting a temperature characteristic parameter from the preprocessed image; S4, constructing a probabilistic neural network, inputting the temperature characteristic parameter, the environmental temperature and the environmental humidity into the probabilistic neural network as input features, and training the probabilistic neural network; S4, acquiring an infrared image of a to-be-tested insulator in real time, inputting the temperature characteristic parameter of the to-be-tested insulator and the environmental temperature and the environmental humidity of the to-be-tested insulator into the trained probabilistic neural network after processing through steps S2 and S3, and outputting a contamination grade of the to-be-tested insulator.

The invention discloses an electric energy quality disturbance identification method based on WEE and LVQ-PNN, and belongs to the field of electric energy quality signal analysis, and the method comprises the steps: obtaining an electric energy quality disturbance signal, decomposing the electric energy quality disturbance signal, and extracting the wavelet energy entropy of the decomposed signal; according to the wavelet energy entropy, a probabilistic neural network is trained and optimized through a learning vectorization method, an optimized network is obtained, the input of the probabilistic neural network is the wavelet energy entropy, and the output of the probabilistic neural network is an electric energy quality disturbance classification recognition result; acquiring actually measured data, and identifying the actually measured data through the optimized network to obtain a power quality disturbance classification identification result; according to the technical scheme, the disturbance types of the electric energy quality signals can be effectively classified.

The invention is suitable for the technical field of decayed tooth recognition, and particularly relates to a decayed tooth recognition method based on computer vision, and the method comprises the steps: obtaining original decayed tooth image data, and carrying out the marking and preprocessing of the original decayed tooth image data; constructing a probabilistic neural network model, and training the probabilistic neural network model based on the preprocessed decayed tooth image data; and after the training is completed, processing the new image data by using the trained probabilistic neural network model, and identifying decayed teeth contained in the new image data. Through physical prior embedding and weight matrix optimization strategies, the decayed tooth classification recognition capability of the model is improved, through a weight coupling synchronization mechanism and a self-adaptive noise injection strategy, the stability of the model in different image quality and noise environments is improved, the classification result is more reliable, and the classification accuracy is improved. Physical priori knowledge enables the model to maintain high classification performance under the condition of small samples, and dependence on large-scale annotation data is reduced.

One method includes training, using data collected from sensors, a probabilistic neural network (NN) model comprising a set of model weights. The probabilistic NN model is trained to filter out data samples that induce a threshold of model uncertainty. The method includes training, at each cycle of training the probabilistic NN model and based on the set of model weights, a joint estimator to generate gradient updates to the set of model weights that are used to predict whether model updates from the sensors are anomalous. The method includes assigning, to each sensor, a confidence coefficient value that estimates a degree of confidence of the model updates.The method includes transferring the set of model weights to a subset of the sensors for which the confidence coefficient value meets a threshold.

The present invention relates to a method, device, electronic device and storage medium for classifying spam text messages. The method for classifying spam text messages includes the following steps: S1, pre-processing text messages, downloading a text message data set from a communication network as a basic data set for generating word vectors, and using a vector space model to form a word-text matrix from the word set; S2, generating text message feature vectors including a topic feature vector and a context feature vector, the topic feature vector is obtained by performing a singular value decomposition of the word-text matrix, and the context feature vector is used to predict the probability of the context based on the central word; S3, neural network fusion and training, a training task for a classifier for processing text messages, the classifier is used to distinguish whether the text message is a spam text message; S4, text message classification and discrimination. According to the spam text message classification method of the present invention, the topic feature and the context feature can make up for each other's shortcomings, realize multi-feature fusion, and thus realize accurate classification of spam text messages.

The present invention discloses a method and device for matching well-seismic data, which includes: obtaining a reservoir parameter time-shift curve of a target well target layer; determining a porosity time-shift curve of the target well target layer using a trained probabilistic neural network model based on the reservoir parameter time-shift curve of the target well target layer; determining a well velocity time-shift curve of the target well target layer using a constructed rock physics model based on the predicted porosity time-shift curve of the target well target layer; and correcting an original velocity curve of the target well target layer using the well velocity time-shift curve of the target well target layer to obtain a corrected well velocity curve of the target well target layer. The present invention predicts the porosity time-shift curve using a probabilistic neural network, determines the well velocity time-shift curve using a rock physics model, and eliminates the time variation of the original velocity data through the well velocity time-shift curve correction, thereby improving the matching accuracy of well-seismic data in the time dimension.

The application discloses a kind of monitoring method and equipment for reducing the pressure of high-pressure pipeline of drilling and production ship, and relates to high-pressure pipeline technical field, the method comprises: obtaining the real-time performance data of high-pressure pipeline of drilling and production ship, the real-time performance data includes high-pressure pipeline pressure data, motor rotating speed data of high-pressure mud pump, drill pipe motion state data and top drive motion state data;The real-time performance data is preprocessed;The high-pressure pipeline pressure data after preprocessing is input into the high-pressure mud pipeline probabilistic neural network agent model trained in advance to predict the safety condition level;According to the safety condition level predicted, control signal is generated to control the high-pressure pipeline of drilling and production ship.The application can realize closed-loop control of control process, with high accuracy and strong pertinence.

The present invention discloses an ECG identity recognition method based on local segment sparse representation, which includes collecting an electrocardiogram signal, using wavelet transform based on a weight threshold to perform signal denoising to obtain the denoised ECG signal, implementing signal division on the denoised ECG signal by using a sliding window method, extracting local segments, obtaining local features of each signal by using principal component analysis to achieve data dimensionality reduction, using the orthogonal matching pursuit algorithm to find the optimal matching atoms during the sparse representation process for the dimensionality-reduced data, using the K-singular value decomposition algorithm to construct a dictionary for sparse representation to obtain a processed sparse coefficient matrix, and performing probability neural network recognition on the final sparse coefficient matrix to obtain the recognition accuracy of the ECG signal. The present invention can well capture global and local information, and sparse representation and dictionary construction can improve the reliability of identity recognition based on electrocardiogram signals.

The invention discloses a rotating machine fault feature optimization extraction method based on variational mode extraction and comprehensive detection indexes, and the method comprises the following steps: S1, decomposing an input vibration signal through employing a variational mode extraction method, and calculating a corresponding sample entropy value; s2, constructing a fitness function of a particle swarm optimization algorithm by using the comprehensive detection index; s3, parameters of the variational modal extraction method are optimized in combination with the comprehensive detection index and a particle swarm optimization algorithm; s4, using the variational mode under the optimal parameter to extract and decompose the input vibration signal; s5, calculating the sample entropy of each modal component to obtain a fault feature set; and S6, sending the fault feature set into a probabilistic neural network for fault classification. According to the method, a solution is provided for parameter selection of variational mode extraction, so that the optimal fault feature of the rotating machine is obtained, the signal decomposition efficiency and the fault classification precision are improved, and intelligentization of fault diagnosis of the rotating machine is promoted.

The invention discloses a data driving-based heavy truck electric drive bridge fault diagnosis method, which comprises the following steps of: firstly, acquiring operation state data of a heavy truck electric drive bridge in various use scenes, marking fault categories, and constructing an original data set; carrying out sample generation by adopting a generative adversarial network based on weight noise to realize data expansion, and merging the expanded data with the original data to form a training data set; selecting a self-encoder based on boundary smoothing as a feature dimension reduction model, and training the feature dimension reduction model by using the training data set; adopting a probabilistic neural network based on quantum probability guidance as a classifier model, and training the classifier model by using features output by the feature dimension reduction model; and finally, collecting original data of a to-be-evaluated heavy truck electric drive bridge, and inputting the original data into the trained feature dimension reduction model for feature processing. And inputting the processed features into the trained classifier model for classification to obtain a classification result. According to the invention, high precision and high robustness of electric drive bridge fault diagnosis can be realized.

A flexible power distribution network voltage drop identification method based on a reinforcement learning theory comprises the following steps: carrying out dimension reduction processing on original data through principal component analysis, and inputting an optimized probabilistic neural network-learning vector quantization model to carry out voltage drop disturbance source classification; and voltage sag identification caused by three common single disturbance sources and two composite disturbance sources can be realized. The invention provides a principal component analysis-probabilistic neural network-learning vector quantization voltage sag source classification identification model based on learning algorithm optimization, which is based on analog voltage sag data, performs dimension reduction on the data by using principal component analysis, extracts data features based on a deep learning method probabilistic neural network, and performs classification identification on the voltage sag source based on the deep learning method probabilistic neural network. And classifying voltage drop types by utilizing learning vector quantization. The model can effectively identify the voltage drop type. Compared with other models which only use the probabilistic neural network, the principal component analysis-probabilistic neural network and the probabilistic neural network-learning vector quantization, the method has the best recognition precision on the voltage drop source, and has very good performance on small and medium-sized sample data sets.

The application discloses a fuel cell multi-dimensional state prediction method based on a multi-task probability network and dynamic gate pruning, belongs to the technical field of fuel cell health management, and specifically relates to the following: obtaining fuel cell state data, including multi-cell voltage, partitioned cell current density and multi-dimensional operating condition parameters, performing double-target collaborative feature screening on the multi-dimensional operating condition parameters to obtain an optimal collaborative feature subset; constructing a parallel multi-task probability neural network architecture containing multiple base models, and independently training each base model based on a training set; and utilizing an online rolling monitoring and dynamic gate collaborative pruning integrated mechanism to output final multi-cell voltage global probability prediction curves and cell current density global probability prediction curves. The application can improve the accuracy, robustness and reliability of fuel cell multi-dimensional state prediction.

The invention discloses a single-foot jumping robot motion control method based on coupling flow model driving, and the method comprises the steps: outputting a decision action through an intelligent agent strategy model, and carrying out the online interaction with a single-foot jumping robot, and collecting a real environment sample; an environment dynamic model is constructed through a probabilistic neural network, and the model is trained in a supervised learning mode to simulate the motion state of the robot; building a coupling flow model by utilizing a multi-layer coupling structure, converting the output distribution difference into a reward signal, and realizing dynamic optimization of an environment model by reconstructing a Markov decision process and combining with a reinforcement learning algorithm; and finally, interacting the intelligent agent strategy with the calibrated environment model to generate a high-precision simulation sample, and combining with a real sample to complete strategy iteration updating. According to the method, accumulated rewards equivalent to model-free reinforcement learning can be achieved only through a small amount of environment interaction, and the learning efficiency and the model generalization ability of a single-foot jumping robot strategy are remarkably improved.