273 results about "Signal prediction" patented technology

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention relates to the technical field of modern precision machining and optical scanning, and discloses a motion control method and system of a laser galvanometer. The method comprises the steps that a real-time position signal is collected and preprocessed, and a filtered position signal is obtained; the deviation between the trajectory model and a preset trajectory model is calculated, when the deviation exceeds a threshold value, the trajectory model is identified as a complex trajectory segment, offset vector data is generated, and an offset compensation demand signal is generated accordingly; driving an instruction device in combination with historical data to obtain compensation parameters; when the deviation is lower than an updating threshold value, updating the trajectory model; a stable control signal is generated based on the optimization model and filtering signal prediction, and the environmental interference is calibrated and the signal smoothness is enhanced through the stable control signal; and finally, comparing the enhanced signal with the optimization model, and repeating the compensation process when the deviation exceeds the standard to obtain a final stable control signal. According to the method, through multi-level deviation processing and iterative optimization, the control precision and the anti-interference performance of the laser galvanometer in complex track movement are improved.

The invention relates to the technical field of physiological signal prediction, in particular to a preoperative risk assessment method for the department of cardiology, which comprises the following steps: sliding window segmentation time sequence data to calculate a baseline offset, dynamic time warping alignment parameter fluctuation rate to generate an abnormal mark, and standard deviation comparison amplitude threshold triggering risk signals. K-means clustering multi-dimensional data mapping risk levels, isolated forest detection abnormal fluctuation and electrocardiogram and myocardial zymogram cross validation are combined, and a preoperative risk assessment conclusion is output. According to the method, individual differences and interferences are eliminated through a sliding window algorithm, a time difference problem is solved by aligning a multi-parameter fluctuation rate through dynamic time warping, a quantitative evaluation standard is established by comparing a standard deviation with an amplitude threshold value to avoid limitation of a single threshold value, and objective grading is realized by combining K-means clustering with an Euclidean distance. The isolated forest algorithm, the electrocardiogram ST segment and myocardial zymogram cross validation form a multi-modal evaluation system, and the risk evaluation sensitivity and specificity are improved in a complete closed-loop mode.

The invention relates to the technical field of mechanical arm control, and discloses a bionic self-adaptive variable-stiffness mechanical arm joint control method. The method comprises the steps that multi-source biological signals such as electromyographic signals, tendon tension signals and joint angle signals during joint operation are collected; constructing a stiffness feature vector based on the myoelectricity and tendon tension signals, and generating a motion trail sequence according to the joint angle signals; extracting a corresponding stiffness feature vector for each track point, and calculating stiffness sensitivity; constructing a bionic stiffness reference model containing a mapping relation between the stiffness feature vector and a joint angle signal, and dynamically weighting and adjusting the mapping relation by using stiffness sensitivity; predicting a joint stiffness change sequence based on the adjusted model and the real-time signal, and fusing the sequence with the current joint angle signal to generate a dynamic stiffness parameter; and calculating a driving torque correction amount according to the dynamic stiffness parameter and a preset safety threshold range, iteratively updating the model weighting parameter until the correction amount meets a convergence condition, and outputting a driving torque instruction to an execution mechanism.

The invention relates to the technical field of spraying control, in particular to an anti-corrosion steel pipe inner spraying track self-adaptive control system based on multi-sensor fusion. The feed-forward prediction module generates a feed-forward correction signal through a model compensator and a self-adaptive compensator and predicts and compensates system errors, the feedback control module comprises a track generator and a disturbance observer, a feedback control instruction is generated by comparing a correction track with an original track, an execution mechanism is dynamically adjusted, and the system error is predicted and compensated. The signal driving module adopts a frequency domain decoupling fusion technology, combines a feedforward correction signal and a feedback control instruction, generates a composite control instruction, and ensures the accuracy of trajectory tracking control. According to the invention, through a double-loop control architecture combining feedforward prediction and feedback disturbance suppression, high-precision and strong-robustness self-adaptive control of a spraying track under a complex working condition is realized.

The invention discloses a digital pre-distortion method, system and device and a storage medium, and relates to the technical field of aerospace communication. The method comprises the following steps: acquiring a first input signal and a first actual output signal of the power amplifier, and acquiring a thermal state characteristic; constructing a three-dimensional dynamic baseline; superposing the detection sequence into the first input signal to obtain a second input signal, obtaining a corresponding second actual output signal, and predicting an ideal output signal of the second input signal; extracting a harmonic component generated after the detection sequence passes through a power amplifier, and determining a distortion early warning moment; recording a third input signal after the distortion early warning moment; constructing a virtual test signal according to the third input signal, and predicting to obtain a reference output signal; and comparing the difference between the distortion output signal and the reference output to obtain a distortion feature vector, constructing a dynamic increment compensation function, and correcting the signal processing flow of the power amplifier based on the dynamic increment compensation function. And the accuracy of digital pre-distortion is improved.

The invention discloses a power distribution network fault early warning method and system based on online monitoring, and relates to the field of fault early warning, and the method comprises the steps: continuously collecting real-time electrical signals; signal waveform prediction of the real-time electrical signal is completed according to the real-time signal prediction result, and whether an early fault signal exists in the target power distribution network is judged; if the target power distribution network has the early fault signal, obtaining power distribution network information, environment prediction data and geographic position data; constructing a topological structure of the power distribution network, and extracting early fault information of the target power distribution network; constructing an icing evolution model of the target power distribution network; completing fault risk coupling between the icing evolution model and the early fault information; and carrying out risk accumulation analysis on the topological structure of the power distribution network, and outputting fault early warning information of the target power distribution network according to a risk accumulation analysis result. According to the invention, the power distribution network fault early warning precision and efficiency can be effectively improved.

The invention discloses a hydroelectric generating set vibration signal trend prediction method and system, and the method comprises the steps: S1, collecting vibration signals of key parts in the operation process of a hydroelectric generating set, and generating a vibration time sequence sample with a unified structure; s2, applying an EEMD (ensemble empirical mode decomposition) algorithm to each section of preprocessed vibration time sequence; s3, dividing the constructed data set into a training set and a test set; s4, constructing a time sequence prediction model based on BiLSTM and an attention mechanism; s5, training the model on the training set, using a mean square error as a loss function, and adopting an Adam optimizer to optimize network parameters; calculating a mean value and a standard deviation based on prediction errors of the training set, and setting upper and lower threshold values of early warning judgment; s6, applying the trained model to a test set, predicting an unknown vibration sequence, and if a prediction error exceeds a set threshold range, regarding that the trend is abnormal; according to the method, the problems of insufficient feature extraction capability, poor modeling effect and low prediction precision in the existing hydroelectric generating set vibration signal prediction are solved.

The invention discloses a multi-rotor unmanned aerial vehicle broken propeller fault rapid diagnosis and fault-tolerant stable control system and method, belongs to the technical field of unmanned aerial vehicle flight control, and aims to solve the problems that traditional broken propeller detection is high in cost and unreliable, power compensation robustness is insufficient and return flight logic is lacked. According to the system and the method, on the basis of a high-order nonlinear observation theory, rapid and accurate identification of the abnormal state of the unmanned aerial vehicle power system is realized by constructing a multi-modal signal prediction and residual evaluation system; the method effectively prevents the out-of-control, rolling and even crash of the unmanned aerial vehicle caused by power asymmetry, solves the problem that a traditional fixed control strategy is difficult to adapt to fault working conditions, and adapts to dynamic changes under complex working conditions such as hovering and cruising. Finally, closed-loop control from fault detection to safe return can be realized, the autonomous safe return capability of the unmanned aerial vehicle under the condition of the fault of the propulsion system is ensured, and the method is suitable for unmanned aerial vehicle application scenes with high reliability requirements, such as logistics transportation and inspection monitoring.

The invention discloses a sensor signal prediction method, system and equipment for a humanoid robot. The method comprises the following steps: acquiring a multi-source sensor training sample of the humanoid robot; training the heterogeneous neural network fusion model according to the multi-source sensor training sample through a signal amplitude loss function, a frequency loss function and a transient characteristic loss function to obtain a signal prediction model; obtaining current sensor signals of the humanoid robot, wherein the current sensor signals comprise at least two of a joint angle signal, a motor current signal or an acceleration signal; and inputting the current sensor signal into the signal prediction model, and outputting a predicted sensor signal which is a force sense signal or a torque signal. Through multi-source sensor signal fusion and multi-domain feature learning, accurate prediction of key sensor signals is realized, the number of deployed sensors is reduced, the cost is reduced, the system reliability is improved, and the method can be widely applied to the technical field of robots.

The invention provides a Rowland time delay signal prediction method and system, electronic equipment, a program product and a storage medium. The method comprises the following steps: acquiring an initial Rowland time delay signal; extracting a periodic term of the initial Rowland time delay signal, and inputting the periodic term to a constructed multi-periodic term and trend term model; calculating a residual signal between the observed value of the initial Rowland time delay signal and the output of the multicycle term and trend term model; wavelet decomposition and threshold denoising are carried out on the residual signals; fusing the output of the multi-cycle term and trend term model and the denoised residual signal through an adaptive weight mechanism to obtain a predicted Rowland time delay signal; the deterministic periodic term and the long-term trend drift of the Rowland time delay signal are captured through a multi-periodic term and trend term model, and non-stationary disturbance is processed by using wavelet denoising, so that the Rowland time delay prediction precision is remarkably improved, and the method is particularly suitable for a high-precision timing scene in a complex electromagnetic environment.

The invention discloses a multi-GNSS satellite signal power abnormity real-time monitoring method. The method comprises the following steps: acquiring real-time observation data of an observation station network which is uniformly distributed globally; when the linear relation between the carrier-to-noise ratios of different frequency signals of each satellite in each observation station and the dynamic monitoring threshold value related to the elevation angle of each satellite are determined, one signal is used for predicting another signal by using the linear coefficient, and the deviation between an actual value and a predicted value is calculated; calculating a satellite elevation angle of the real-time observation data, and determining a monitoring threshold value of the current satellite at the current moment according to the satellite elevation angle; and counting an average value of the deviations of all satellites monitored by all observation stations at the current moment, and marking that the power of the current satellite at the current moment is abnormal when the absolute deviation of the current satellite is greater than a monitoring threshold value. According to the invention, on the basis of the carrier-to-noise ratio linear relation of different frequency signals and the dynamic monitoring threshold, real-time monitoring of various power abnormal scenes including natural interference, man-made interference, satellite active power adjustment and the like is realized.

The invention provides an optical fiber transmission receiving signal prediction model training method, a prediction method and equipment, and relates to the technical field of transmission, and the training method comprises the steps: generating a training sample corresponding to a transmitting signal according to the transmitting signal generated by a transmitting end in a continuous spectrum nonlinear frequency division multiplexing system in advance, and training a neural network model based on the training sample to perform mixed domain feature extraction, multi-scale processing and attention feature extraction based on a peak perception attention mechanism on the training sample to obtain received signal prediction result data and optimize the neural network model so as to train the neural network model as an optical fiber transmission received signal prediction model. According to the method and the device, the received signal in the continuous spectrum nonlinear frequency division multiplexing system can be predicted, the accuracy of performing received signal prediction by adopting the trained optical fiber transmission received signal prediction model can be improved, and further the research and development period of the modeling process of the optical fiber communication system and the computing resource consumption of research and development equipment can be reduced.

The invention discloses a VLF propagation signal prediction method based on a three-dimensional finite element and a neural network, and belongs to the technical field of electronic communication. The method comprises the following steps: firstly, constructing a geosphere-ionosphere waveguide model based on a three-dimensional frequency domain finite element method, and solving by using a multi-grid method to improve the operation efficiency; secondly, constructing a VLF signal propagation data set of a plurality of known paths at one time based on a three-dimensional frequency domain finite element method; then, constructing a neural network model reflecting a mapping relation between an ionized layer electron density parameter and a VLF wave characteristic based on the data set; then, obtaining electron density distribution on a plurality of known paths based on neural network inversion; and finally, carrying out inversion by adopting an inverse square weight formula to obtain electron density distribution on the to-be-solved path. According to the method, the VLF signal transmission characteristics on multiple paths can be simulated at a time, the workload of modeling simulation is effectively reduced, and the method is suitable for the fields of inversion of a multi-path electron density parameter model, prediction of very low frequency wave propagation and the like.

The invention belongs to the related technical field of time series prediction, and particularly relates to a multi-scale autoregression time series prediction model construction method based on a Next-Scale prediction normal form, which comprises the following steps: in a training reasoning process, introducing a coarse-to-fine scale-by-scale prediction mechanism, firstly, constructing a token sequence of a preset scale through a residual signal, and then, constructing a multi-scale autoregression time series prediction model; preset-scale residual signal prediction depends on a prefix sequence, the loss of scale-by-scale weighting is calculated based on prediction and a target, advancing is carried out in sequence, the prefix sequence is kept visible when all scales are predicted, and stable global context and consistency constraints are provided for all decoding layers. According to the method, a Next-Scale Prediction normal form is introduced, multi-scale representation based on residual errors is put forward, in training, it is ensured that prediction of all scales is mutually conditional, and global and local prediction performance is improved at the same time through multi-scale loss weighted optimization.

Provided are a reinforcement learning (RL)-based traffic signal control (TSC) method and apparatus, a device, a medium, and a product. The TSC method includes: obtaining traffic state data of a target intersection at a current time point and a road network graph, where the traffic state data includes a quantity of lanes at the target intersection and a traffic flow of each of the lanes; inputting the traffic state data and the road network graph into a preset traffic signal prediction model, and obtaining a target phase action output by the traffic signal prediction model, where the traffic signal prediction model includes a spatiotemporal encoder and a return-based action decoder, and the traffic signal prediction model is obtained through training based on return-based contrastive learning; and controlling, based on the target phase action, a traffic light at the target intersection to execute the target phase action.

A detector system for molecular imaging of a radionuclide comprises a 3D semiconductor detector comprising a plurality of sensor stacks of sensors made of a semiconductor material having an average atomic number Z below 40. A read-out circuitry connected to the pixels is configured to output, for each interaction induced by an incident gamma ray in the detector, a signal representative of a time, a position and an energy of the interaction in the detector. The interactions in the detector belonging to a same event induced by the incident gamma ray are predicted based on the output signals and used to estimate a direction of the incident gamma ray and reconstruct an image based on the estimated directions of incident gamma rays.

Disclosed is a method by which an electronic device predicts the location of a UWB signal. The method of the present disclosure may comprise the steps of: generating an input sequence from input data for a preset number of timesteps; generating an output sequence including prediction data for a next timestep from the input sequence using a trained RNN-based model; and obtaining information about a predicted location of the electronic device at the next timestep on the basis of the prediction data in the output sequence. The input data may include UWB DL-TDoA data.

The invention relates to a hyperspectral signal prediction method based on a hyperspectral remote sensing image and pseudo label guidance, and belongs to the technical field of hyperspectral signal prediction. The method comprises the following steps: collecting remote sensing spectral data and soil spectral data, and carrying out primary preprocessing on the remote sensing spectral data; based on a designed series correction algorithm, correcting the remote sensing spectral data after the primary preprocessing by using the soil spectral data, and performing secondary preprocessing; performing imaging on the remote sensing spectrum data after the secondary preprocessing; constructing a double-branch fusion model comprising a spectrum branch and an image branch; constructing a classification pseudo label, and introducing supervised contrast loss to train the double-branch fusion model; and inputting remote sensing spectral data to be predicted into the trained double-branch fusion model to obtain a hyperspectral signal prediction result so as to realize soil content prediction. The objective of the invention is to solve the technical problems of complex noise, low quality and difficulty in guaranteeing prediction accuracy when remote sensing spectral data is acquired in the prior art.

The invention discloses an online secondary frequency modulation method considering regulation capacity aggregation of a hydrogen fuel cell vehicle. The method comprises the following steps: 1) constructing a power system operation model of the hydrogen fuel cell vehicle HFCV based on a hydrogen fuel cell vehicle cluster; 2) constructing a Minkowski sum-based hydrogen fuel cell vehicle frequency modulation capacity aggregation model; 3) solving the frequency modulation capacity aggregation model of the hydrogen fuel cell vehicle to obtain a frequency modulation capacity feasible region of an HFCV cluster; 4) on the basis of the power system operation model, constructing an online secondary frequency modulation model of the hydrogen fuel cell vehicle; 5) based on the frequency modulation capacity feasible region, the system scheduling mechanism adopts a Lyapunov optimization method to solve the online secondary frequency modulation model of the hydrogen fuel cell vehicle to obtain an AGC instruction signal, and the AGC instruction signal is issued to an aggregator; and 6) the aggregator distributes the AGC instruction to each hydrogen fuel cell vehicle in real time to participate in the secondary frequency modulation of the power system. According to the method, AGC signal prediction information is not needed, and the frequency response and frequency modulation operation cost can be better balanced.

The invention relates to the technical field of satellite navigation and positioning, in particular to a Beidou signal anti-interference method based on space-time state prediction.When the Beidou signal anti-interference method is used, time-space domain parameters, including key information such as directions and phase differences of direct and multi-path signals, of Beidou signals are extracted, and multi-path special suppression measures are combined, so that the anti-interference performance of the Beidou signals is improved; a multi-path signal spectrum peak is subjected to graded attenuation, a positioning result is calibrated in cooperation with a scene correction coefficient, finally, the urban canyon scene positioning precision is achieved, high-precision scene application such as automatic driving and precise surveying and mapping can be stably supported, meanwhile, a prediction model integrating LSTM and Kalman filtering is constructed, a multi-path signal prediction result is output in advance, and the accuracy of urban canyon scene positioning is improved. In cooperation with the rapid matching function of the interference feature library, the method can adapt to the dynamic change of multi-path signals, ensures that interference suppression measures take effect in time, and avoids signal receiving interruption or positioning deviation sudden increase.

The invention discloses a gate valve opening degree control method and device, and relates to the technical field of industrial automation control, and the method comprises the following steps: obtaining valve structure data and actuator types, reading an initial value of a valve position sensor, building a valve position zero point, and loading an initial valve position-flow mapping model; an error is calculated according to the target process variable and the current measured value, and a valve position target value is generated through an outer ring controller; querying the valve position-flow mapping model according to the valve position target value, and performing deviation correction in combination with real-time flow measurement to obtain a corrected valve position target value; the friction state is predicted according to the valve position change rate and the actuator current signal, the friction compensation amount is generated, the corrected valve position target value is corrected, and a final valve position instruction is obtained; and the final valve position instruction is compared with the actual valve position, the driving amount is calculated through an inner ring controller, an actuator is driven to act, and the actual valve position is made to approach the final valve position instruction.

The invention relates to the technical field of communication, in particular to an anti-interference satellite signal capturing antenna suitable for a complex environment. Comprising an environment perception and digital twinning module, a multipath signal prediction and separation module, a synthetic array signal processing module, a beam forming control module, an incremental learning module, a radiation unit module, a temperature compensation module and a multi-system data interface. A direct signal and a characteristic multipath signal can be actively predicted and separated, so that the physical limitation of a traditional antenna is broken through, an anti-interference synthetic wave beam is formed in a polarization domain and a space domain in a combined manner, the wave beam can accurately point to a target satellite, and meanwhile, depth null is generated in a shielding area and an interference source direction; therefore, stable and high-gain acquisition of satellite signals is realized in a complex environment, the antenna system can continuously optimize the model according to real-time signal quality feedback, and the adaptive capability, the anti-interference performance and the long-term reliability of the antenna in a changing scene are improved.

The embodiment of the invention provides an ultrasonic distance measurement chip, an ultrasonic distance measurement device and a self-adaptive threshold generation method. The ultrasonic ranging chip comprises an echo signal processing module, a near-field threshold generator and a near-field decision device, and the echo signal processing module is configured to receive a target echo signal generated by an ultrasonic transducer and generate a near-field echo envelope signal based on the target echo signal; the near-field threshold generator includes a filter configured to obtain a model configuration parameter for the filter, generate an adaptive threshold using the filter based on near-field echo envelope signal prediction, where a gain of the filter is adaptively adjusted based on the model configuration parameter and the near-field echo envelope signal; the near-field decision device is configured to generate first alarm information according to the near-field echo envelope signal and an adaptive threshold. According to the embodiment of the invention, self-adaptive threshold generation during near-field target detection is realized, the false alarm rate and the missed alarm rate of the decision device are reduced, and accurate detection of the near-field target is realized.

The invention provides a medium-voltage grounding fault positioning method and device, a medium and terminal equipment, and relates to the technical field of power grid fault positioning, and the positioning method comprises the steps: obtaining a time sequence traveling wave interference signal based on a historical original voltage signal, and forming a sample data set; based on the sample data set, constructing an interference signal prediction model through an evolutionary algorithm; determining a real-time line mode voltage traveling wave signal, and determining a traveling wave interference prediction signal by using an interference signal prediction model; determining a real-time pure traveling wave signal based on a difference value between the real-time line mode voltage traveling wave signal and the traveling wave interference prediction signal; determining whether a medium-voltage grounding fault occurs or not; and under the condition that the occurrence of the medium-voltage grounding fault is determined, determining the occurrence position of the medium-voltage grounding fault by using the real-time pure traveling wave signal. Through the method provided by the invention, fault traveling waves can be accurately restored, fault detection and positioning are carried out according to the fault traveling waves, the anti-interference capability of medium-voltage grounding fault detection and positioning is improved, and the accuracy of detection and positioning is improved.

The invention discloses a warehouse goods in-out intelligent management system based on Internet of Things equipment, and the system is characterized in that the system comprises an information collection module which is used for carrying out the identity recognition and information collection of to-be-processed goods, and packaging the collected data into a standardized goods information data package; the position distribution module is used for screening storage areas based on the cargo information data packets, calculating storage coordinates and generating an intelligent storage position distribution scheme; the execution planning module is used for performing dynamic path planning according to the storage position distribution scheme and collecting equipment execution state feedback data in real time; the anomaly detection module is used for processing the equipment execution state feedback data, detecting an abnormal state, outputting a monitoring report and generating an early warning signal; the prediction decision-making module is used for predicting equipment maintenance requirements and cargo delivery requirements according to the monitoring report and the early warning signal, and outputting a comprehensive management decision-making scheme; and a feedback optimization module.

The present disclosure is for signaling information related to a beam set for beam management in a wireless communication system. An operation method of a terminal may comprise: determining a first set and a second set of beams for beam management based on artificial intelligence (AI) / machine learning (ML); receiving at least one reference signal corresponding to at least one beam belonging to the second set; on the basis of the at least one reference signal, predicting information related to at least one beam included in the first set; and transmitting the information related to the at least one beam to a base station. The information related to the at least one beam included in the first set may be predicted on the basis of the result of measuring the at least one reference signal corresponding to the at least one beam included in the second set.

The invention relates to the technical field of medical equipment, in particular to an intelligent closed-loop vagus nerve stimulation regulation and control method and system. The method comprises the following specific steps: collecting an electroencephalogram signal and a stimulation signal during real-time stimulation, and obtaining an electroencephalogram signal prediction value of future N steps through an ambulatory electroencephalogram signal prediction model; calculating a depression biomarker estimated value for representing the brain activity state at the next moment according to the electroencephalogram signal predicted values of the next N steps; calculating an optimal stimulation signal at the next time according to a difference value between the depression biomarker target value and the depression biomarker estimated value; and updating the stimulation signal according to the optimal stimulation signal to stimulate the vagus nerve, and updating the stimulation signal. Meanwhile, the invention discloses a system for executing the method, the optimal stimulation parameter is calculated according to the difference between the predicted brain activity state at the next moment and the target state, so that the neural activity is controlled, and the adjusting efficiency and the control precision of the percutaneous vagus nerve stimulation system are improved.

A method for monitoring a prediction error during the inference of an application machine learning model providing predictions based on at least one actual time-series signal from an actual sensor. The method includes: predicting an expected time-series signal from the actual time-series signal; calculating an error based on the expected signal and the actual signal; determining the a stationarity of the error; and determining the an evolution of the stationarity.

The invention relates to a joint angle prediction method and device, wearable lower limb exoskeleton equipment and electronic equipment, and relates to the technical field of exoskeleton equipment control. The method comprises the following steps: acquiring an electromyographic signal when a user walks by wearing the wearable lower limb exoskeleton equipment; classifying the electromyographic signals according to an electromyographic signal classification model, and determining a first electromyographic signal associated with the knee joint and a second electromyographic signal associated with the ankle joint; inputting the first electromyographic signal and the second electromyographic signal into a joint angle prediction model, and determining a joint angle of a knee joint and a joint angle of an ankle joint; wherein the wearable lower limb exoskeleton equipment performs joint control according to the joint angle of the knee joint and the joint angle of the ankle joint. Therefore, the electromyographic signals are disassembled into the signals related to all the lower limb joints, then the joint angles of all the lower limb joints are predicted through the signals related to all the lower limb joints, and therefore the joint angle prediction accuracy is improved.