626 results about "Wavelet packet decomposition" patented technology

The invention provides a thermal power equipment real-time monitoring method and system based on edge computing, and relates to the technical field of thermal power equipment real-time monitoring, and the method comprises the steps: deploying an edge computing node array to collect multi-source heterogeneous data of thermal power equipment, and carrying out the preprocessing through data screening, multi-scale adaptive filtering and wavelet packet decomposition, a conditional variation auto-encoder is used to extract features, a hierarchical attention mechanism and a deep feature fusion network are combined to generate mixed feature representation, refined distribution estimation and abnormal mode recognition are performed on equipment states, cooperative monitoring modeling is performed based on a multi-scale spatial-temporal feature fusion network and a hierarchical depth deterministic policy gradient network, and a multi-scale spatial-temporal feature fusion network is established. The real-time monitoring accuracy and efficiency of the thermal power equipment can be effectively improved, the equipment failure rate is reduced, and safe and stable operation of a thermal power plant is guaranteed.

The invention relates to a power supply switching control optimization method and system, and the method comprises the steps: constructing a multi-dimensional data fusion monitoring system, integrating a real-time voltage ripple coefficient detection module of a main power supply, a load transient response analysis module and an environment temperature and humidity sensor array, and generating a dynamic health degree index; the method comprises the following steps: establishing a fault prediction model based on transfer learning, pre-training through multi-scene failure data in a historical database, receiving a health degree index in real time, outputting a reliability attenuation curve, and activating a dual verification mechanism when the slope of the attenuation curve exceeds a preset threshold; extracting characteristic energy distribution of a frequency band of 0.1-10 kHz; executing a transient load impact test, and measuring dynamic response parameters of voltage recovery time and overshoot amplitude; and generating a switching confidence score based on cross processing of the dual verification result and the attenuation curve, and triggering a hierarchical switching instruction. The accuracy of the switching time of the main power supply and the standby power supply can be improved.

The invention provides a ground-based radar landslide monitoring and early warning method and system, and relates to the technical field of landslide monitoring, and the method comprises the steps: collecting a monitoring signal, and carrying out the filtering processing; extracting an instantaneous phase and performing wavelet packet decomposition; enhanced features are obtained through morphological filtering; constructing a monitoring feature vector; carrying out nonlinear dimension reduction on the characteristic time sequence matrix; obtaining an evolution trajectory by using a fractional order adaptive neural network; constructing a hierarchical risk reasoning framework; a risk metric value is obtained through multi-source information fusion; and carrying out dynamic evaluation by adopting a fuzzy evolution algorithm and determining an early warning level. The method can improve the accuracy and real-time performance of landslide monitoring and early warning.

The invention relates to the technical field of Internet of Things, in particular to an electric energy meter with a wireless communication module, which comprises a parameter acquisition module, a communication optimization module, an interference analysis module, a fluctuation evaluation response module and a load classification module. According to the invention, a mutual inductor captures a single-phase circuit signal, analog-to-digital conversion is combined to realize parameter quantization, interference is eliminated based on an active power identification algorithm, a structured parameter set is integrated, a wavelet packet decomposition parameter and a quantization threshold are dynamically adjusted according to a load rate, and spread spectrum factor configuration is adopted to improve channel efficiency. The method comprises the following steps: extracting key features by using wavelet packet characteristics; continuously monitoring voltage fluctuation by a sliding window; detecting and identifying an instantaneous interference event by a break variable; inhibiting noise by Kalman filtering and predicting a trend; and the performances of the system in the aspects of acquisition precision, transmission efficiency, interference immunity and detection sensitivity are enhanced.

Disclosed in the present invention is a graph theory-based method for locating an instability fault source in a direct-current microgrid. The method includes: collecting key electrical feature quantities through an instability fault feature collection and extraction link to accurately capture an instability oscillation phenomenon, and utilizing wavelet packet decomposition to construct fault feature vectors; then, according to the structure of the direct-current microgrid, constructing an undirected graph model G=(V, E, X), which lays a foundation for analysis; applying a graph convolutional neural network for deep learning and training to further improve the accuracy and efficiency of diagnosis; deploying the trained model in an actual system to effectively achieve accurate fault diagnosis and location of an instability source. Also provided in the present invention is a device for locating an instability fault source in a direct-current microgrid. The method provided by the present invention can significantly improve a maintenance level of the direct-current distribution network, and greatly improve a detection and adjustment ability of system operation stability through advanced automatic diagnosis technology.

The invention discloses a bearing fault diagnosis method based on fusion of an improved capsule network and zero sample learning, and relates to the technical field of state monitoring and fault diagnosis of electromechanical equipment, and the method comprises the following steps: collecting a multi-mode signal during the operation of a bearing, employing an improved wavelet threshold denoising algorithm for the multi-mode signal to eliminate environmental noise, and then employing a zero sample learning algorithm for the multi-mode signal; according to the method, the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm are adopted to extract the time-frequency domain mixed features as sample data, and the GAN is combined to expand the bearing sample data, so that the data dependence of traditional deep learning is broken through, the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm, and the time-frequency domain mixed features are extracted through the improved wavelet threshold de-noising algorithm and the WPD and VMD fusion decomposition algorithm. Small sample data learning is realized, and by training a pyramid capsule network and optimizing a cross entropy loss function and combining cross-modal joint optimization and a zero sample inference engine, the diagnosis accuracy of known faults is greatly improved, and unknown fault types can be effectively inferred.

The invention relates to the technical field of Internet of Things, and provides a wire and cable online quality detection method and device, electronic equipment and a storage medium. The method comprises the following steps: performing multi-scale wavelet packet decomposition and signal entropy fusion on an original wire and cable signal matrix to obtain a feature tensor, and performing anomaly recognition extraction on the feature tensor through a graph attention network model to obtain a potential defect feature vector; and performing multi-physics coupling simulation inversion on the potential defect feature vector to obtain a defect quantization parameter set, performing dynamic reasoning according to the defect quantization parameter set through a Bayesian network model to obtain an online quality grade decision, and packaging the online quality grade decision according to a block chain intelligent protocol to obtain a quality traceability record. According to the invention, through organic coupling of multiple levels, multiple models and multiple technical means, real-time performance, accuracy, traceability and safety of online quality detection of wires and cables are realized.

The invention relates to the technical field of equipment fault prediction, in particular to a coal mine equipment fault prediction system and method based on big data, and the method comprises the following steps: collecting the thickness of a biological membrane on the surface of equipment, synchronously collecting the vibration acceleration of the equipment and the environmental hydrogen sulfide concentration, and constructing a corrosion multi-mode data set; performing electrochemical noise separation on the corrosion multi-modal data set, and constructing a corrosion activity fingerprint spectrum; decoupling a coupling effect of mechanical vibration and microbial corrosion, and generating a corrosion-fatigue damage correlation map; and constructing a dual-channel life prediction model based on the corrosion-fatigue damage correlation map, and setting an early warning mechanism. Microbial corrosion characteristic current is accurately extracted through wavelet packet decomposition and wavelet entropy analysis, a corrosion-vibration heterogeneous graph is established by adopting a graph attention network, corrosion-induced structural vibration enhancement effect analysis is realized, and compared with a traditional prediction method based on a single signal, the method has the advantages that the method is simple and convenient to operate. And the problems of limited local corrosion influence range, unstable vibration response and the like are effectively solved.

The invention discloses a partial discharge mode recognition method based on a residual neural network and an attention module, and relates to the technical field of partial discharge signal processing and recognition. The method comprises the following steps: acquiring partial discharge signal data to be detected; carrying out multi-scale denoising processing on the partial discharge signal by an improved ensemble empirical mode decomposition adaptive noise algorithm and wavelet packet decomposition combined method so as to remove noise interference and reserve main components of the signal; the time domain, the frequency domain and the phase information of the denoised partial discharge signals are converted into grey-scale maps through a Grubby angle field algorithm, a Grubby angle difference field algorithm and a Markov transfer field algorithm, and the grey-scale maps are fused to generate a feature RGB image. Inputting the RGB image into a residual neural network combined with a convolutional block attention module for feature extraction and classification; and finally, outputting a partial discharge mode identification result for fault detection and early warning of power equipment. According to the method, multi-scale denoising processing and multi-dimensional feature fusion are utilized, and depth feature extraction capabilities of the residual network and the attention module are combined, so that the recognition accuracy and robustness of the partial discharge mode are remarkably improved, and the method can be widely applied to fault diagnosis and early warning of power equipment.

The invention discloses a GIS-based urban underground pipe network monitoring and early warning platform, and relates to the technical field of underground pipe network early warning. A data acquisition module is used for acquiring a pressure fluctuation signal in a pipeline, fluid flow data, a pipe wall vibration spectrum and surrounding soil moisture content change data in real time to form a multi-source time sequence data set; the feature extraction module is used for performing wavelet packet decomposition on the pressure signals, extracting high-frequency-band micro pressure pulsation features, processing vibration data by adopting empirical mode decomposition, and separating a normal operation mode and an abnormal disturbance component of a pipeline, so that the early detection capability of micro leakage is remarkably improved, the false alarm rate is reduced, and meanwhile, high-precision positioning is realized; reliable technical guarantee is provided for safe operation of the underground pipe network, and resource waste and safety accidents caused by tiny leakage are effectively avoided.

The invention belongs to the field of machine tool health state monitoring, and discloses a machine tool feed shaft health monitoring method based on an optimization condition auto-encoder. The sensor collects vibration signals and power signals of the machine tool feed shaft in a healthy state and a partial fault state; preprocessing and denoising the vibration signal and the power signal through wavelet packet decomposition; after denoising, calculating time domain and frequency domain eigenvalues to form a machine tool eigenvector, generating a time-frequency matrix, converting the time-frequency matrix into an RGB image, and constructing a machine tool multi-source data set; a machine tool multi-source data set is divided, a training set, a verification set and a test set are obtained, and the training set only contains health data; the machine tool feature vector and the training set are input into a machine tool feed shaft health state monitoring model based on an optimization condition variational auto-encoder for training, and the trained model is used for machine tool feed shaft health state monitoring. According to the method, the health state of the feed shaft is monitored only by using the health data, so that the influence of imbalance of the fault data and the health data on the data-driven monitoring method is reduced.

The invention relates to the technical field of nondestructive testing, and particularly discloses an automatic detection system and method based on a titanium plate welding part, and the method comprises the steps: obtaining an original physical field signal of a to-be-detected part under the excitation of a single energy field, and extracting a space energy attenuation gradient and time phase lag distribution through wavelet packet decomposition and Hilbert transform; constructing a dynamic propagation model for describing a signal propagation path evolution rule, and performing space-time registration and vector difference calculation with a preset ideal reference model to generate a difference evolution graph; high-dimensional topological feature mapping, density clustering and multi-scale persistence analysis are carried out on the atlas, and a stable abnormal mode caused by defects is identified and confirmed; and backtracking a dynamic evolution path of an abnormal mode, extracting defect core parameters, and completing three-dimensional positioning, type classification and security level evaluation in combination with process information.

The invention relates to the technical field of energy storage batteries, and discloses a yacht energy storage battery internal short circuit detection method, device and equipment and a medium, and the method comprises the steps: sampling the voltage and current of a yacht energy storage battery pack, and obtaining original sampling data; performing parameter identification to obtain RC network dynamic response data; performing sliding window Fourier transform and wavelet packet decomposition to obtain a target feature vector; performing voltage abrupt change feature judgment, current abrupt change feature judgment and impedance spectrum feature judgment based on the target feature vector to obtain a multi-stage fault judgment result; starting the three-ring nested control structure and inputting the three-ring nested control structure into a PWM modulation unit to obtain a control compensation signal; the control compensation signal is input into the current sharing controller, the PWM duty ratio of the parallel branches is adjusted, and a current sharing control signal is obtained, the reliability of fault detection is improved, cooperative control of state protection, power balance and current tracking is achieved, and the current distribution problem of the parallel branches is effectively solved.

The invention discloses a multi-scale feature fusion fault prediction method for a high-speed rail bearing. The method comprises the following steps: acquiring a fault vibration signal of the high-speed rail bearing through a vibration signal comparison technology; noise and interference signals of the high-speed rail bearing fault original vibration signals are removed through an adaptive filter; performing multi-level decomposition on the vibration signal by using wavelet packet decomposition, extracting sub-band signals of different frequency bands, calculating instantaneous frequency and energy distribution of the sub-band signals by using Hilbert-Huang transform, and forming a time-frequency-energy three-dimensional feature map; carrying out weight distribution on each feature dimension in the three-dimensional feature map by adopting a self-adaptive weighting algorithm; constructing a training data set through the time-frequency-energy three-dimensional feature map and the fault type label; and training the long short-term memory network LSTM by using the training data set, constructing a fault prediction model, and performing fault prediction on the high-speed rail bearing. According to the invention, accurate prediction of the high-speed rail bearing fault is realized, and the accuracy of fault detection is improved.

The invention discloses a tunnel convergence deformation monitoring method, and particularly relates to the technical field of tunnel monitoring, through integrating equipment such as a thermal infrared imager, a visible light camera, a distributed optical fiber sensor and a laser radar, a self-adaptive inspection system is constructed, and high-precision appearance and temperature monitoring is realized by adopting an improved ORB algorithm and a temperature field calibration technology. Performing multi-source data fusion through space-time registration and wavelet packet decomposition in combination with optical fiber strain data; and predicting a deformation trend based on an LSTM neural network, and establishing a three-level early warning mechanism. According to the tunnel convergence deformation monitoring method disclosed by the invention, the automation and intelligence level of tunnel structure safety monitoring is remarkably improved, 'perception-analysis-decision 'closed-loop management of the tunnel health state is realized, compared with a traditional method, the efficiency is remarkably improved, meanwhile, the maintenance cost can be greatly reduced, and the tunnel operation safety and economy are remarkably improved.

The invention provides a network information security adaptive threat intelligence analysis and response method and system. The method comprises the following steps: acquiring an encrypted traffic load byte stream, and segmenting the encrypted traffic load byte stream into a time sequence traffic matrix according to a time window; and analyzing the communication metadata, matching the features of the threat intelligence library, and generating a dynamic threat fingerprint based on protocol compliance and behavior abnormality. And performing multi-stage wavelet packet decomposition on the matrix, extracting the energy spectrum density, the information entropy and the time-frequency variable coefficient of the high-frequency component, and performing weighted fusion to generate a frequency-domain composite abnormal index representing heartbeat signal characteristics. And identifying hidden heartbeat periodicity and protocol violation modes through a multi-mode fusion mechanism in combination with the frequency domain index and the threat fingerprint, and outputting a threat score. And if the attack exceeds the threshold value, intercepting the flow, verifying a new attack feature, and then reversely updating the threat intelligence library to form a defense self-evolution closed loop. According to the method, automatic interception and closed-loop updating of the threat intelligence library are realized, and the defense adaptability is remarkably improved.

The invention discloses a bronze ware ornamentation and pattern digital restoration method based on an image enhancement technology, and relates to the technical field of image restoration, and the method comprises the steps: building a space mapping matrix; extracting multi-modal data features by using the surface state of the topological insulator, and registering a joint data volume; forming a super-resolution image through super-resolution reconstruction; obtaining a material degradation coefficient by using a wavelet finite element method and a graph neural network; generating an adversarial network by utilizing physical constraints, and generating an embarrassment repairing result; a material sensing three-dimensional model is constructed by adopting a wavelet packet decomposition and neural radiation field fusion technology, texture mapping is dynamically adjusted based on a graphene Moire effect, and virtual-real fusion is performed through holographic waveguide AR; by combining advanced technologies such as a metamaterial lens, micro-distance laser scanning, a topological insulator film, a graphene heterojunction and a nerve radiation field, high-precision three-dimensional digital restoration and repair of bronze cultural relics are realized, and immersive augmented reality display experience is provided.

The invention provides a multi-sensor dynamic calibration method in rotary geosteering drilling, and relates to the technical field of petroleum engineering and drilling, and the method comprises the following steps: collecting original sensing data in real time through a multi-source sensor array deployed on a rotary geosteering drilling tool, wavelet packet decomposition is combined with an improved sliding window algorithm to carry out online denoising processing on original data, and a three-dimensional feature matrix containing environmental interference factors is established; and based on the three-dimensional feature matrix, constructing a dynamic calibration model based on depth time sequence association, respectively processing sensor body feature flow and environment interference feature flow by using a dual-channel LSTM network, performing dynamic weight distribution and fusion on dual-channel features by means of a gating attention mechanism, and outputting a dynamic deviation compensation coefficient of each sensor. The parameter set is generated and reconstructed through the three-dimensional feature matrix, the two-channel network and cooperative calibration, the drilling efficiency and precision are improved, errors are reduced, and intelligent development is promoted.

The invention belongs to the technical field of semiconductor testing, and discloses a novel automatic detection system and method for the electrostatic protection performance of a power device. A distributed parasitic parameter network model is constructed by acquiring packaging structure parameters of a to-be-tested power device and physical layout data of a test fixture, key parasitic parameter distribution is identified in combination with a time domain reflection measurement technology, a waveform distortion coefficient is calculated, and a spatio-temporal evolution model of electrostatic pulse propagation is constructed. Based on the model, a compensatory predistortion waveform is generated to counteract the parasitic effect of a transmission path, a device port response signal is collected in real time, an energy dissipation characteristic spectrum is extracted through wavelet packet decomposition, and quantitative evaluation of the electrostatic protection performance is achieved. According to the invention, the test precision and repeatability are improved, and the method is suitable for electrostatic protection tests of various novel power devices, especially high-frequency wide-band gap devices.

The invention relates to the technical field of multi-dimensional force sensing measurement, and discloses an anti-interference multi-dimensional force sensing measurement method and system, and the method comprises the steps: optimizing the layout of a heterogeneous sensor array based on a stress field, generating an anti-interference signal through cross-band wavelet packet decomposition and dynamic weight fusion, separating a multi-dimensional force component in combination with a hybrid decoupling algorithm, and carrying out the multi-dimensional force sensing measurement. The weight and the sampling rate of the sensor are dynamically adjusted by adopting fuzzy logic control, and a high-reliability measurement result is finally output through rigid mechanical verification and GAN reconstruction iterative correction; the system comprises a heterogeneous sensor array module, a signal preprocessing module, a dynamic decoupling module, an environment self-adaption module, a redundancy verification module and a data integration module. Through cross-band fusion, a hybrid decoupling algorithm and environment adaptive closed-loop feedback, GAN reconstruction and physical verification are combined, the anti-interference capability and the decoupling precision are improved, and the reliability of abnormal signals and the multi-scene adaptability of the system are ensured.

The invention provides a joint denoising method and system based on adaptive large neighborhood search and modal decomposition, and belongs to the technical field of signal processing and nondestructive detection.The method comprises the steps that an ultrasonic signal and a vibration signal of a detected insulator are synchronously collected and preprocessed; dynamically estimating the noise level based on the preprocessed ultrasonic signal power spectral density, and optimizing decomposition parameters by adopting an adaptive large neighborhood search algorithm; on the basis of the optimized decomposition parameters, wavelet packet decomposition and ensemble empirical mode decomposition are executed in parallel, and effective intrinsic mode function components are screened through cross-correlation verification; extracting the resonance frequency of the preprocessed vibration signal, performing target frequency band weighted enhancement on the low-frequency sub-band, and dynamically adjusting the threshold parameter of the high-frequency sub-band and the low-frequency sub-band according to the resonance frequency; and generating a preliminary de-noised signal from the fused signal, performing affine projection algorithm filtering and multi-modal cross validation, and outputting the verified ultrasonic signal as a final de-noising result.

The invention discloses a transformer bushing high-frequency partial discharge signal on-line monitoring method and system, and relates to the technical field of electrical equipment insulation state monitoring, and the method comprises the steps: synchronously collecting signals based on a high-frequency current sensor and an ultrahigh-frequency sensor; performing mixed noise reduction and feature extraction according to the collected signals; and carrying out discharge type identification on the processed data by constructing a lightweight convolutional neural network, and carrying out early warning and positioning in combination with a dynamic alarm threshold. The method can significantly improve the signal-to-noise ratio and enhance the weak discharge signal detection capability by combining the dual-mode sensor with a hybrid noise reduction strategy of wavelet packet decomposition and adaptive filtering, adopts the lightweight convolutional neural network, supports edge equipment to implement reasoning, realizes intelligent identification of the discharge type, and improves the detection accuracy of the weak discharge signal. The insulation state of the transformer bushing is monitored online in real time, off-line dependence is reduced, the service life of equipment is prolonged, and the intelligent level of operation and maintenance of a power system is improved.

The invention discloses a power grid cross-correlation harmonic suppression method, device and equipment and a storage medium, and the method comprises the steps: carrying out the wavelet packet decomposition of a subnet electric signal, and obtaining the harmonic component of a subnet at each frequency band; calculating the harmonic phase difference between the subnets according to the cross-correlation function between the harmonic components of the subnets and the topological impedance matrix, and calculating the total harmonic distortion rate according to the harmonic components of the subnets in the frequency bands; constructing a harmonic transfer equation according to the harmonic phase difference between the subnets and the equivalent output impedance of the inverter so as to calculate the harmonic responsibility proportion of the subnets; solving the double-layer game model based on the total harmonic distortion rate, the reference value of the total harmonic distortion rate and the harmonic responsibility proportion of the subnet, and obtaining the output of the governance equipment; and inputting the output of the treatment equipment into the fuzzy controller to obtain the impedance adjusting quantity of the subnet, and adjusting the virtual impedance according to the impedance adjusting quantity to update the harmonic backflow path. According to the invention, harmonic suppression among multiple subgrids during high-permeability distributed grid connection is realized, and the operation stability of the power grid is improved.

The invention belongs to the technical field of diving state monitoring, and relates to a diver state monitoring method based on biofeedback, and the method comprises the steps: aligning a sign signal with environment data through a timestamp, extracting motion features through acceleration data, constructing a motion noise model, and removing signal artifacts in real time; in the aspect of EEG signal purification, a db4 wavelet basis is adopted for carrying out wavelet packet decomposition, water pressure related frequency band characteristics are extracted, low-frequency drift is removed, meanwhile, heart rate signals are analyzed through nonlinear dynamics, a Lyapunov index is extracted to evaluate HRV changes, breathing and EEG # imgabs0 # wave coupling are analyzed through wavelet transformation, and the risk evaluation precision is further enhanced; in the aspect of environment compensation, through compensation of a depth and SpO2 relational expression and water temperature on respiratory frequency, in combination with SLTM network extraction time sequence features and a deep causal convolutional layer, risk trend prediction is performed, a diver risk level and a prediction curve are output in real time, a comprehensive risk index is finally calculated, the health state and potential risk of the diver are comprehensively evaluated, and safety is improved.

The invention relates to the technical field of control adjustment, in particular to an intelligent multi-head welding synchronous control system which comprises a data processing module, a modular cooperative control module, a load sensing module, a data transmission module, a self-adaptive pose execution module and a fault-tolerant module. The data processing module extracts three-dimensional geometrical characteristics of a workpiece through laser scanning, PID control parameters are migrated across working conditions in combination with a dynamic time warping algorithm, the inverse kinematics process is optimized, and the self-adaptive pose execution module drives phase synchronization of multiple welding heads based on a global reference trajectory and predicts and compensates thermal deformation errors through a thermal expansion coefficient. And the fault-tolerant module adopts a wavelet packet decomposition identification fault mode, and updates a historical parameter library in combination with an elastic weight algorithm. According to the method, three-dimensional space pose error accumulation caused by mechanical load fluctuation and signal asynchronization is inhibited through cooperation of multiple modules, and the welding process uniformity and the system dynamic stability are improved.

The invention relates to the technical field of magnetic bias monitoring, in particular to a real-time monitoring method and system for direct-current magnetic bias of a transformer. The method comprises the following steps: acquiring a primary side current signal and an iron core vibration signal of the transformer; analyzing the zero-flux closed-loop characteristic of the primary side current signal of the transformer and performing temperature drift elimination on the primary side current signal of the transformer to generate a primary side optimization signal of the transformer; performing magnetostrictive vibration noise separation on the iron core vibration signal to generate an iron core vibration separation signal; dynamically filtering a power frequency fundamental wave of the primary side optimization signal of the transformer to extract a pure direct current component; and performing wavelet packet decomposition on the iron core vibration separation signal, and performing magnetostriction characteristic spectrum extraction on the decomposed iron core vibration separation signal to obtain an iron core magnetostriction characteristic spectrum. According to the invention, through multi-source signal fusion and material characteristic modeling, the accuracy, real-time performance and graded protection response capability of transformer DC magnetic bias monitoring are improved.

The invention provides a magnetostriction-based multi-physical-field defect detection method and system for pressure-bearing equipment, and relates to the technical field of detection, and the method comprises the steps: constructing a reconfigurable magnetic field excitation array and a multi-physical-quantity sensing network through an intelligent excitation and sensing device, and dynamically adjusting the excitation phase to improve the detection precision. According to the layered coupling calculation scheme, a coupling relationship among a magnetic field, a stress field and a temperature field is established, and multi-physical field data calculation is carried out in combination with physical constraint conditions. After multi-level wavelet packet decomposition and noise reduction, the deep neural network is utilized to extract features and generate a defect feature dictionary, so that efficient defect positioning and size evaluation are realized, and the accuracy and reliability of detection are remarkably improved.

The invention discloses a collision identification method and system for a window cleaning robot, and relates to the technical field of robot environment perception, and the method comprises the steps: carrying out the time difference compensation and space synchronization based on a glass material type and collision detection parameters through a distributed perception collaborative analysis method, and generating a three-dimensional space vibration information matrix; enhancing the three-dimensional space vibration information matrix through a dynamic wavelet packet decomposition algorithm and a sliding window analysis method according to the type of a glass material; according to the enhanced three-dimensional space vibration information matrix, collision coordinates are calculated through a time difference positioning algorithm, the characteristic frequency of the vibration waveform is analyzed, and collision characteristic information is obtained; based on the collision feature information, combining safety parameters of glass material types, adopting a hybrid decision algorithm to analyze the collision feature information, and generating and executing a robot obstacle avoidance instruction; according to the method, the high-resolution three-dimensional vibration field is constructed through space-time collaborative modeling, and the collision coordinate calculation precision and the anti-interference capability are improved.

The invention relates to the technical field of crane line fault diagnosis, in particular to a crane line fault diagnosis system and method based on multi-source data fusion, which comprises a data acquisition and processing unit, a mechanical and electrical coupling characteristic unit and a characteristic fusion and fault quantification unit, the three-axis vibration acceleration and the three-phase current waveform of the track are obtained through the data collecting and processing unit, the mechanical and electrical coupling characteristic unit conducts three-dimensional vector synthesis and wavelet packet decomposition on vibration data, and a time-space incidence matrix of harmonic distortion and vibration is constructed. And the feature fusion and fault quantification unit outputs coupling factors by using a bidirectional long-short-term memory network and an attention mechanism, and outputs a fault probability value through a dynamic time warping matching algorithm after time-frequency domain analysis and sample entropy judgment, so that time-space correlation modeling and dynamic fault matching of multi-source data are realized. And the fault positioning precision and the diagnosis accuracy are improved.

The invention relates to an intelligent CNC (computer numerical control) machining method which comprises the following steps: dynamically monitoring a tool-workpiece interaction interface in a CNC machining process through a multi-dimensional torque sensor array to obtain a machining process feature data set, and analyzing a tool motion track in real time to obtain a tool motion state feature quantity; secondly, performing multi-scale analysis by adopting a wavelet packet decomposition technology, and determining processing quality evaluation data; and if the data does not reach the preset standard, performing microstructure reconstruction on the surface of the workpiece based on the data, and generating a workpiece processing state characteristic spectrum. Then, comprehensively analyzing the atlas by utilizing a multi-objective optimization technology to obtain a processing parameter optimization sequence; and finally, implementing closed-loop feedback control on the numerical control system according to the optimized sequence, and generating a real-time processing control instruction. The key problem of how to improve the automation level and the machining precision in the CNC machining process through an intelligent means is solved.