187 results about "Wavelet denoising" patented technology

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention provides a gait analysis method and an early warning system based on multi-source data fusion, three types of original signals are collected through a wearable inertial sensor, a plantar pressure insole and an edge calculation camera, and multi-dimensional motion characteristic parameters are fused through wavelet denoising, low-pass filtering, interpolation alignment and data consistency correction, so that the gait analysis method based on multi-source data fusion is realized. And constructing a user individualized gait feature prototype library. The method further adopts a dynamic causal graph network to model a motion relation between joints, determines an abnormal coupling mode based on causal weight and historical reference, and combines activity context and Bayesian rules to generate a dynamic threshold value to realize risk grading early warning, so that heterogeneous data collaborative analysis precision and abnormal gait detection sensitivity are improved, and the method is suitable for large-scale popularization and application. And support is provided for gait health monitoring and personalized risk management and control under multiple scenes.

The invention provides a lithium ion battery safety valve opening and failure early warning method based on expansive force, and belongs to the technical field of lithium ion batteries. Battery expansive force and cycle data under different pre-tightening force conditions are collected, statistical features are extracted to construct a state feature set, health state groups are divided by adopting a fuzzy clustering algorithm, and the early warning result is obtained. Establishing a segmented nonlinear mapping model of the expansive force and the internal pressure, performing wavelet denoising and robust differential calculation on expansive force signals, and optimizing an initial expansive force derivative threshold value by analyzing time dispersion at different heating rates; a multi-scale feature fusion algorithm based on hierarchical attention aggregation is utilized to construct a state self-adaptive early warning model to correct a threshold value, and a four-stage early warning mechanism is set to monitor the opening and failure states of the safety valve. The technical problem that the opening time of the safety valve cannot be accurately predicted and self-adaptive early warning cannot be realized under different battery health states and pretightening force working conditions is solved.

The invention provides a multi-modal information acquisition and roof global attitude monitoring method and system, and relates to the technical field of mining safety monitoring and roadway surrounding rock control. The method comprises the following steps of: acquiring acoustic, optical, electrical and mine pressure multi-source data by arranging a fan-shaped drill hole, an annular electrode array and various mine pressure monitoring devices; a wave velocity profile, a fracture parameter set and a resistivity equivalent cloud picture are generated through wavelet noise reduction, fracture recognition and resistivity inversion, space-time registration with a mine pressure data sequence is carried out, and roof partition probability distribution is output through a space-time feature pyramid network. Furthermore, improved XGBoost is adopted for joint inversion of the thickness of the loose circle, the Poisson's ratio and the elastic modulus, probability distribution serves as a space weight, the separation amount and anchoring stress data are fused, and a roof displacement field, a curvature field and a rotation field are reconstructed. And finally, generating an early warning based on a preset safety threshold value, outputting a posture partition map and a monitoring report, and realizing top plate posture transparent deduction and support coordinated regulation and control.

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 relates to a power grid equipment dispatching control method and device based on artificial intelligence, equipment and a medium, and the method comprises the steps: collecting equipment operation data through a multi-mode sensing network, and generating a noise reduction data set through wavelet noise reduction processing; fusing electrical, thermodynamic and mechanical characteristics to calculate a real-time aging factor; utilizing a matrix transformation function to dynamically correct equipment factory parameters to generate current operation parameters; an optimal power distribution path is constructed through graph neural network topology analysis, and a control instruction set is generated; and updating the mapping function by combining the historical aging factor sequence based on the deviation quantized value of the execution feedback result and the theoretical parameter. According to the method, the problems of parameter drift and scheduling model mismatch caused by equipment aging are solved, real-time sensing of the aging state, dynamic parameter correction and closed-loop optimization are achieved, and the accuracy and safety of power grid scheduling are remarkably improved.

The invention discloses a bridge disease detection method based on a diffusion model and a bitter fish optimization algorithm, and relates to the technical field of bridge detection. The method comprises the following steps: fixing a visual angle and a distance at an easy-to-peel or crack position of a bridge, and collecting and aligning visible light and near-infrared images; carrying out multi-scale downsampling on the image, and carrying out wavelet denoising, brightness correction and texture smoothing; inputting the preprocessing result into an improved diffusion model, weighting the edge during forward diffusion, and reversely generating and applying texture and contour smoothing; the multi-source feature channel and the reconstructed image are combined and input into a deep segmentation network, shadow and stain are eliminated by using a local difference function, and global search is performed on a segmentation threshold, a noise coefficient and the like based on a disease detection rate, a false detection rate and the like by using a bitter fish algorithm; training and correcting the high-noise area again according to the optimal parameters; and uniformly marking disease areas. According to the method, the recognition recall rate of tiny spalling and irregular cracks in an extreme environment can be greatly improved, and the intelligent level and the practical effect of bridge disease detection are improved.

The invention provides an ultrasonic detection method for internal defects of an insulating part of a power system based on a piezoelectric ultrasonic transducer, and belongs to the technical field of insulating part detection. After wavelet denoising and deep attenuation compensation are carried out on echo signals, sound path distance time delay and sound pressure amplitude parameters are extracted, defect identification and position estimation are carried out by utilizing a layered variational auto-encoder, a high-frequency transducer is switched to aiming at tiny defects, a harmonic component is extracted by adopting a nonlinear ultrasonic method, and a characteristic size is calculated; a defect distribution space feature matrix is generated based on a synthetic aperture focusing technology, a unified or personalized processing strategy is selected according to an acoustic characteristic homogenization degree evaluation value, a complete defect detection result is output, and the technical problem that deep weak defects in the electric insulation part are difficult to accurately detect and recognize is solved.

The invention discloses a side slope displacement monitoring method and device based on quantum single photons and a medium, and belongs to the technical field of displacement monitoring. Rectangular light pulses are emitted periodically, echo detection is completed by using a single photon detector, a photon counting time sequence is constructed, wavelet denoising and reconstruction processing is performed on the photon counting time sequence, main peak position offset, energy change and form distortion are extracted to match multiple types of feature vectors, and feature fusion and resolving are performed through a multi-source feature fusion BP neural network. And finally outputting a high-precision slope displacement result. According to the method, the quantum single-photon detection technology is combined with the wavelet denoising and neural network fusion algorithm, noise interference is effectively suppressed, the displacement feature extraction precision is improved, and the method is suitable for high-precision and long-distance slope micro-deformation real-time monitoring.

The invention discloses a cable partial discharge signal noise separation mode identification method and system, and particularly relates to the technical field of cable partial discharge detection, in a multi-cable stacking environment, partial discharge signals are synchronously collected through multiple types of sensors such as a high-frequency current sensor, an ultrasonic sensor and an electromagnetic antenna; performing band-pass filtering, wavelet denoising and multi-channel synchronous alignment on the acquired signals to suppress background noise and retain key features of partial discharge pulses; separating the mixed signals by adopting independent component analysis; calculating a decision coefficient based on an inter-channel correlation coefficient, a signal-noise power ratio and an amplitude dynamic range, and judging whether to introduce a denoising method based on deep learning; for the waveform after noise separation, executing an amplitude re-calibration process so as to correct amplitude scaling uncertainty, and extracting multi-dimensional time domain, frequency domain and phase features; and a support vector machine and other machine learning algorithms are combined to realize automatic identification of partial discharge types.

The invention belongs to the technical field of intelligent control of industrial heating equipment, and particularly relates to a big data self-learning evolution method for an intelligent burning furnace model of a hot blast stove. According to the method, multi-source data such as fuel components and temperature fields are collected in real time, and a standardized input data set is constructed after wavelet noise reduction and principal component analysis processing. The model can dynamically optimize the air-fuel ratio, predict the hot air temperature and correct the equipment parameter deviation, and stable combustion is achieved. When effective samples are accumulated to a threshold value, the system automatically triggers parameter iteration, key parameters are reserved and secondary parameters are updated by utilizing transfer learning, encryption gradient aggregation and knowledge sharing among multiple furnaces are realized by virtue of federated learning, and distribution and deployment are performed after a global optimization model is formed. And finally, according to the model output, a closed-loop regulation and control fuel valve, a fan and other execution mechanisms are realized, the temperature and energy consumption are monitored in real time to evaluate the evolution effect, and a continuously optimized intelligent control cycle is formed. According to the method, the control precision and the combustion efficiency are remarkably improved, and energy conservation and consumption reduction are effectively achieved.

The invention discloses a smart agricultural crop growth analysis system based on big data. Initial crop data is acquired through a sensor array; performing noise reduction processing on the initial crop data by using a wavelet denoising-Kalman filtering coupling algorithm, performing feature extraction on the image data based on an improved MobileNetV3 network, and outputting a crop morphological feature vector; constructing a sequential network through LSTM to perform correlation analysis on the environment and physiology, learning a dynamic relationship between parameters and photosynthetic efficiency, and establishing a crop state prediction model; inputting the noise-reduced crop data into a prediction model for prediction, and outputting a growth health score; performing crop health grade judgment according to the growth health score, and generating a crop growth intervention strategy according to a judgment result. The growth health score of the crop can be predicted more accurately, and the resource utilization efficiency is improved.

The invention discloses a substation danger area judgment method and system based on electric field gradient perception, and relates to the field of safety monitoring, and the method comprises the steps: arranging an electric field sensor array in a key equipment area of a substation; performing noise reduction processing on the data of each sensor node to obtain an electric field vector set; obtaining the electric field intensity change rate of each monitoring point through a space vector differential algorithm, and generating an electric field gradient tensor; dividing the electric field gradient value, and constructing a dynamic danger level mapping rule; performing spatial interpolation through a Delaunay triangulation algorithm to generate an electric field gradient equipotential surface; constructing an electric field gradient space distribution model, and generating a dynamic danger boundary envelope body in real time; and correcting the boundary of the dangerous area through the pre-trained gradient compensation coefficient matrix and triggering a graded early warning signal. The method has the advantages that the electric field gradient change of the transformer substation is monitored in real time through the electric field sensor array and the wavelet denoising technology, the dangerous area is dynamically evaluated, and the accuracy and real-time performance of dangerous area judgment are effectively improved.

The invention discloses a method and a system for acquiring the output power of a turbine, relates to the technical field of turbine monitoring, and provides a five-stage closed loop aiming at a ship high-temperature wet vibration environment, and millisecond-stage alignment torque, rotating speed, fuel oil, exhaust and vibration signals are synchronously acquired in a multi-source manner; secondly, a credibility matrix is constructed through null drift detection, wavelet denoising and consistency verification, and initial power is obtained through Kalman fusion; performing real-time comparison by using a digital twinborn model, and adaptively adjusting the weight of the sensor to output a correction power curve; then decomposing the virtual-real residual into slow drift, transient and harmonic factors, and dynamically modifying the sensitivity and the sampling rate to realize source end self-compensation; and finally, performing windowed compression, partitioning and salting hash on the correction curve, quickly confirming the right of the three-node lightweight block chain, and writing the fingerprint back to a twin model to realize minute-level tamper-proof traceability. The method has the advantages of high-resolution measurement, rapid deviation correction and real-time credible evidence storage.

The invention discloses a partial discharge signal noise suppression method, system and device and a medium, and the method comprises the following steps: receiving an original partial discharge signal, calculating the kurtosis value of the original partial discharge signal, and constructing a kurtosis criterion; constructing a variational mode decomposition model, and optimizing parameters of the variational mode decomposition model by using a northern eagle algorithm; variational mode decomposition is performed on the original partial discharge signal based on the optimized parameters, the kurtosis value of each mode component is calculated, and an effective mode component and a noise mode component are divided according to the kurtosis criterion; reconstructing the effective modal component to obtain an intermediate signal; and performing signal enhancement on the intermediate signal by using a wavelet denoising method based on an improved threshold to obtain a denoised partial discharge signal. According to the method, the noise suppression precision, the algorithm stability, the operation efficiency and the signal integrity of partial discharge signal processing can be effectively improved.

The invention discloses a power equipment partial discharge signal classification method and system based on multi-feature extraction, relates to the technical field of power equipment state monitoring and fault diagnosis, and is suitable for online monitoring of high-voltage equipment such as transformers, reactors, power cables and gas insulated switchgear. According to the method, through multiple feature extraction technologies such as wavelet denoising, variational mode decomposition, multi-scale dispersion entropy, mutual information and correlation entropy, and in combination with principal component analysis dimension reduction and support vector machine, random forest and K-nearest neighbor multi-model fusion classification, high-precision classification of partial discharge signals in a complex electromagnetic interference environment is realized. According to the method, the partial discharge type can be quickly identified and classified, and the accuracy and robustness of signal classification are improved. Compared with a traditional classification method, the method can effectively improve the recognition accuracy, improves the online monitoring precision of the partial discharge signal, and provides a scientific basis for state evaluation and intelligent maintenance of power equipment.

The invention relates to the technical field of insulator fault early warning, in particular to an insulator mechanical pulse electrification early warning system. The system comprises a pulse signal acquisition module which is used for setting sampling points at equal intervals at each height of an insulator and acquiring vibration pulse signals of the sampling points; the vibration abnormity identification module is used for determining the transverse vibration abnormity degree and the longitudinal vibration abnormity degree of the vibration pulse signal of each sampling point; the pulse signal denoising early warning module is used for acquiring a standard comprehensive abnormal index of the vibration pulse signal of each sampling point; identifying the non-low-frequency energy intensity of the vibration pulse signal of each sampling point, and adjusting a fixed threshold value in a wavelet denoising algorithm in combination with the standard comprehensive abnormal index so as to perform defect early warning on the insulator by using the denoised vibration pulse signal; according to the invention, the accuracy of defect early warning of the insulator is improved.

The invention discloses a partial discharge signal wavelet denoising method based on all-parameter space traversal optimization. The method comprises the following steps: S1, initializing parameters; s2, a noisy signal is read; s3, constructing a wavelet basis traversal cycle; s4, constructing a decomposition layer number traversal cycle; s5, denoising and index calculation are executed; s6, comparing and updating a global optimal solution; and S7, carrying out loop iteration until all preset wavelet base order and decomposition layer combinations are traversed. According to the method, the parameter search space containing various wavelet bases and different decomposition layer numbers is constructed, so that automation and optimal matching of denoising parameters are realized; compared with a traditional method of selecting wavelet parameters depending on artificial experience, the method has the advantages that the influence of subjective factors on the denoising effect is effectively avoided, and the adaptability and robustness of the algorithm to different field environments and different types of partial discharge signals are remarkably improved.

The invention discloses a mutual inductor operation error on-line monitoring system, and belongs to the technical field of detection and analysis. The method is used for solving the technical problems of secondary load and error characteristics of the mutual inductor in the existing scheme. Electrical characteristics and environmental parameters are fused through fuzzy neural reasoning, so that the identification error of error characteristic parameters can be effectively reduced; wavelet denoising and momentum item gradient descent are combined, so that the parameter updating stability and the system robustness can be effectively improved under sensor noise and temperature drift; by correcting a secondary load coupling dynamic model in real time, the goodness of fit of secondary load-error nonlinear mapping can be effectively improved; through real-time compensation of the magnetic control reactance, dynamic error suppression is realized, and ratio error drift and angular error drift caused by secondary load change can be effectively reduced; and the reactance-error mapping relation is corrected by using the dynamically updated output error characteristic parameters, so that the overall error suppression capability of modeling, identifying and compensating a closed loop can be effectively improved.

The invention discloses a centrifuge full life cycle intelligent analysis and operation and maintenance method. The method comprises the following steps: constructing a health scoring model which is based on an SAE-SOM neural network and comprises wavelet denoising and future parameter prediction functions; constructing an equipment life prediction model; a data set obtained from detection equipment is subjected to problem sample screening and normalization operation, and then a feedforward neural network is trained. And the hyper-parameter of the feedforward neural network is determined through a genetic algorithm. And a fault tracing model is constructed. The system is composed of a feature extraction network, a middle layer, a relation measurement network and a fault classification network. And establishing an equipment operation environment cross validation module. According to the invention, through mutual cooperation of the equipment health assessment module, the whole machine life estimation module, the fault alarm module, the early warning and traceability module, the operation environment cross validation module and the warning module, fusion analysis is carried out on key operation parameters of the centrifugal machine and external environment data; the method can be widely applied to water plants and other complex industrial scenes with high requirements for the operation reliability of key equipment.

The invention relates to the technical field of signal processing, in particular to a partial discharge signal sharpening method based on VMD decomposition-wavelet denoising-depth refinement, which provides a cascade processing framework of VMD decomposition-wavelet adaptive threshold-depth residual refinement and combines a parameter adaptive selection and uncertainty evaluation mechanism, so that the partial discharge signal sharpening method based on VMD decomposition-wavelet denoising-depth refinement is realized. And the robustness and auditing performance of the method under different sensing channels and variable working conditions are improved, so that a reliable signal basis is provided for diagnosis of the insulation state of the power equipment.

The invention discloses a heat supply pipe network leakage diagnosis method based on multi-modal data fusion and dynamic feature modeling, and aims to solve the problems of low diagnosis accuracy, poor real-time performance and weak adaptability of a traditional method in a complex pipe network. The method comprises the following steps: acquiring multi-modal data such as pressure, flow and the like, and carrying out wavelet denoising and Transform fusion preprocessing on the multi-modal data; secondly, local multi-scale features are extracted by adopting a double-branch CNN, and global hydraulic association is modeled in combination with an adaptive Transform; then, lightweight deployment and continuous optimization of the model are realized through dynamic pruning and incremental learning; and finally, constructing a full-process closed-loop optimization mechanism, dynamically adjusting parameters, and fusing operation and maintenance positive marks. Experiments show that the diagnosis accuracy of the method reaches 90.30%, the reasoning time is 35 ms, the accuracy of 95% or above is still kept under 30% noise interference, and the reliability and efficiency of heat supply network leakage diagnosis are remarkably improved.

The invention provides a few-lead physiological signal emotion recognition method based on prototype feature learning, which comprises the following key steps: carrying out band-pass filtering and wavelet denoising processing on an original EEG signal and an original ECG signal, extracting a differential entropy DE feature from the EEG signal, and extracting a heart rate variability HRV feature from the ECG signal; based on training set sample feature data, performing time window alignment and splicing processing on the DE features of the extracted EEG signals and the HRV features of the extracted ECG signals, and performing feature fusion by using a multi-head self-attention mechanism; learning prototype features of each emotion category; training set sample feature data is used as input of paired learning, and the similarity between samples is learned by defining a loss function; paired learning is carried out on test set sample feature data, the similarity between sample features is calculated, and sentiment classification is achieved. According to the method, high accuracy is achieved on a few-lead physiological signal emotion recognition task, and the method is remarkably superior to a traditional machine learning method and a deep learning method.

The invention provides a backpack multi-sensor data fusion-based motion attitude anomaly real-time detection method, which comprises the following steps of: acquiring multi-channel original data through a backpack-type integrated triaxial accelerometer, a gyroscope, a magnetometer and a barometer, implementing synchronous sampling and time-space alignment, and constructing a standardized time sequence data stream through wavelet denoising and multi-dimensional normalization preprocessing; high-dimensional features representing motion differences are extracted, nonlinear dimensionality reduction is achieved through principal component and Laplacian feature mapping, and a motion mode prototype library is generated; according to the method, a new motion situation can be continuously and adaptively learned, and the motion recognition accuracy, the system robustness and the attitude anomaly detection capability in a complex scene are effectively improved.

The invention discloses a DSP (Digital Signal Processor) online self-adaptive wavelet denoising and concentration inversion system and method for TDLAS (Tunable Diode Laser Absorption Spectroscopy) measurement, and relates to the technical field of gas spectrum detection. Comprises: a laser module for emitting semiconductor laser light; the absorption cell module is used for absorbing specific components of the laser through a gas absorption cell formed by the to-be-detected gas; the detection and amplification module is used for converting the transmission light signal into an electric signal and amplifying the electric signal through a photoelectric amplifier; the phase-locked amplification module is used for filtering and extracting noise; the digital signal processing module is used for performing digital sampling, performing wavelet denoising and signal reconstruction, performing wavelet inverse transformation reconstruction on a denoised waveform, and extracting a second harmonic characteristic parameter; and the upper computer module is used for obtaining the concentration of the gas to be detected based on a pre-established gas concentration inversion model according to the second harmonic characteristic parameters. The method is suitable for high-precision detection of trace gas, and has the obvious advantages of strong real-time performance, high precision, good system stability, good portability and the like.

The present application belongs to the technical field of engineering structure health monitoring, and discloses a photovoltaic flexible support cable force non-contact monitoring method and system based on video vibration. First, a cable video sequence is acquired, and a dynamic sensitive area is adaptively divided along the cable image. The time sequence average value of the pixel intensity in each area is calculated, the two-dimensional image information is converted into a one-dimensional vibration time history signal, and the light drift and image noise are suppressed through difference and wavelet denoising. After filtering the signal of each dynamic sensitive area, the initial frequency set is formed by preliminarily extracting the multi-order dominant frequency estimate value. The frequency observations from all areas are fused and analyzed, and the real frequency cluster is identified. The weighted centroid frequency of the cluster is used as the multi-order vibration frequency. Finally, the multi-order frequency is substituted into the model, and the real-time cable force value is output. The whole process of the method does not need to contact the measured cable, and provides an innovative and reliable solution for long-term online safety monitoring of the flexible support cable force in complex environments.

This invention discloses a preprocessing method for highly polluted pediatric EEG data, belonging to the field of EEG signal processing technology. The invention proposes a robust motion artifact detection method based on a multi-channel voting mechanism. This method overcomes the sensitivity of single-channel detection to transient noise through multi-channel joint decision-making, automatically identifying time periods requiring restoration. A comprehensive preprocessing workflow integrating PCHIP interpolation restoration, adaptive Bayesian wavelet denoising, bandpass filtering, and independent component analysis is designed. This workflow is validated using real pediatric continuous task EEG data. This invention demonstrates excellent performance in preserving frequency band information and improving the weighted signal-to-noise ratio, achieving an average accuracy of 96.4% (205 test cases) and a maximum of 100% based on permutation entropy feature classification. This invention effectively solves the preprocessing challenge of pediatric EEG data in high-noise environments, significantly improving the accuracy and reliability of subsequent classification tasks.

The present application relates to the technical field of transformer fault diagnosis, and in particular to a transformer body vibration signal analysis and fault diagnosis method, device and medium. The present application combines the kurtosis characteristics of the vibration signal, adopts a semi-soft threshold function wavelet denoising method based on the threshold selection method of the 3sigm rule, and achieves better denoising effect. Through the fault diagnosis model of the organic fusion of the two improved HHT transforms-mobilenetV2 model, combined with different feature extraction methods, it is more conducive to retaining the effective features of the vibration signal; the improved mobilenetV2 model designs a multi-scale deep convolution model, introduces a channel attention mechanism before the channel-by-channel convolution, and after multi-scale deep convolution feature extraction, a multi-source data attention mechanism is introduced; without affecting the safe and reliable operation of the transformer, the intelligent diagnosis of the vibration state fault of the transformer is realized.

The invention relates to the technical field of mobile outdoor self-adaptive energy storage regulation and control, in particular to an outdoor mobile robot self-adaptive charging method based on environmental perception, which comprises the following steps of: periodically acquiring environmental data and processing the acquired data by using a wavelet denoising and dynamic time warping algorithm; dynamic generation of the outdoor mobile robot charging method is realized through an adaptive charging model after data fusion, meanwhile, the quality of a charging strategy is judged and evaluated by constructing a charging power-time curve and calculating a fluctuation power proportion after derivation, and an abnormal reason is judged and parameters are corrected when the charging strategy is unqualified, so that the charging efficiency is improved. Therefore, dynamic optimization and self-adaptive adjustment of the charging strategy are realized. According to the self-adaptive charging method for the outdoor mobile robot, the problem that an existing self-adaptive charging method for the outdoor robot is weak in resistance to irresistible factors is effectively solved, and powerful technical support is provided for wide application of the outdoor mobile robot.

The application provides a charging pile state prediction method, a computer readable medium and equipment, the method is a health index and double attention optimization GCN charging pile state prediction method, including load data acquisition, dynamic wavelet denoising, health index construction, health state prediction. The principle of the application is: firstly, the charging pile load data is collected and processed, and the server platform data and the voltage, current and power data in the charging station are integrated and processed; secondly, the original data is denoised by using dynamic threshold wavelet transform to obtain a charging pile state data matrix; thirdly, based on the voltage, current and power matrix of the charging pile load data, the health factors of the single state indicators of the charging pile are constructed by using the distance sparsity algorithm, and the health factors are fused into a health index according to the improved objective weighting method; finally, the health state of the charging pile is predicted by using a double attention optimization graph convolutional neural network.