1260 results about "Vibration response" patented technology

The invention relates to an electric power material intelligent detection method based on multi-modal data fusion, and aims to improve the accuracy and automation level of material state recognition. According to the method, in the electric power material operation or circulation process, multi-modal data such as images, infrared thermal imaging, radio frequency identification, vibration response and environmental parameters are acquired through a unified time index, and a structured time sequence data set is constructed. And after normalization and exception elimination processing, multi-dimensional feature vectors including structural strength, temperature distribution, label continuity and dynamic stability are extracted, and weighted statistics and correlation calculation are executed to generate a comprehensive state index. And further through comparison with a historical reference, identifying an abnormal state according to a deviation threshold value, outputting corresponding labels and feature information, and obtaining material state evaluation and disposal suggestions based on rule reasoning. According to the method, accurate monitoring and abnormal early warning of the electric power materials under the driving of the multi-source data are realized, and the method has good practicability and expansibility.

The invention relates to the technical field of discharge detection, in particular to a high-precision detection method and device for partial discharge of totally-enclosed GIS equipment. The method comprises the following steps: carrying out metal shell three-dimensional structure modeling on the GIS equipment, and carrying out electromagnetic shielding evaluation so as to obtain a multi-point electromagnetic shielding evaluation value; acquiring an original discharge signal of each sampling point based on the electromagnetic wave sensor, and performing interference suppression processing and back propagation attenuation compensation according to a multi-point electromagnetic shielding evaluation value, so as to obtain an attenuation compensation discharge signal waveform; micro-vibration response parameters are collected based on an MEMS micro-vibration sensor, actual influence waveform point detection marking and physical vibration interference filtering are carried out on attenuation compensation discharge signal waveforms, and therefore pure optimized discharge signal waveforms are obtained. According to the invention, through efficient and accurate partial discharge detection, real-time early warning of the state of the GIS equipment is realized, and the stability and safety of the equipment are improved.

The invention relates to the technical field of geological landslide monitoring, in particular to a tower footing geological landslide monitoring and early warning system and method. The method comprises the following steps: arranging an inclination angle sensor, a Beidou-GNSS module, a soil moisture content probe and a micro-seismic accelerometer on a tower footing and the periphery of the tower footing, and collecting the inclination amount, three-dimensional displacement, soil moisture content and vibration response information of the tower footing to form a monitoring original data set; timestamp unification, WGS-84 projection coordinate system conversion and multi-frequency vibration elimination are carried out on the monitored original data set, and an air-ground integrated displacement profile is constructed; the whole process realizes high integration and automation in the aspects of data acquisition, processing, fusion and transmission, so that the timeliness, accuracy and data integrity of landslide risk monitoring are ensured, and scientific decision support is provided for disaster prevention and reduction.

The invention relates to the technical field of aircraft structure detection, and discloses an aircraft structure crack intelligent identification method based on deep learning. The method comprises the following steps: acquiring original vibration response signals and electromagnetic field distribution data on the surface and inside of an aircraft structure in parallel through a multi-source sensor network; synchronously processing the data by using a multi-scale convolutional neural network, and extracting time-frequency domain abnormal fluctuation features and space magnetic field distortion features; constructing a cross-modal correlation model, analyzing a topological dependency relationship of the two types of features through a graph attention mechanism, and generating a fused damage sensitive feature vector; inputting the vector into a pre-trained deep belief network to obtain a probability distribution mapping relation for different crack types; and according to the mapping relation, carrying out adaptive weighted fusion on original multi-sensor data, inhibiting environmental noise and structural background interference, and separating and reconstructing an accurate three-dimensional morphology map of the target crack. According to the method, multi-source data information can be effectively fused to improve the accuracy of aircraft structure crack identification.

The invention discloses a large-span bridge vibration response prediction method based on data driving, and relates to the technical field of bridge vibration response prediction, and the method comprises the following steps: arranging a plurality of sensors at a plurality of key structure parts of a bridge, collecting vibration response data of the bridge during operation in real time based on an initial sampling frequency set based on the inherent frequency of the bridge structure; performing frequency domain analysis on the collected vibration response data, extracting a main excitation frequency component under the current working condition, and constructing a current excitation frequency spectrum; the method comprises the following steps: calling structural modal parameters obtained in a design stage, a static load test or a modal test of a bridge, extracting the natural vibration frequency of each order of structure, and constructing a bridge inherent frequency feature library; according to the method, the resonance state is dynamically identified through frequency domain analysis and the fuzzy risk index, the sampling frequency is intelligently adjusted, high-frequency response accurate capture and resource efficiency optimization are realized, and the sensitivity, the real-time performance and the safety of a bridge health monitoring system are remarkably improved.

The invention discloses a pre-twisted damper fatigue life prediction method for an extra-high voltage ground wire, and particularly relates to the technical field of power transmission line vibration prevention. Collecting multi-source vibration and environment data, and constructing a vibration characteristic time sequence; amplitude, frequency and strain energy are extracted based on the multi-frequency vibration response, and a multi-frequency collaborative vibration model is established; obtaining a damper structure and material parameters, establishing a finite element model, coupling the finite element model with a vibration model, and simulating local stress response; a Rainflow counting method and a Miner damage criterion are adopted to construct a fatigue damage factor distribution matrix; predicting the residual fatigue life of the damper based on the damage evolution trend, and evaluating the replacement opportunity; according to the method, accurate fatigue life prediction and optimal replacement strategy recommendation of the pre-twisted damper under complex working conditions can be realized, and the method has high precision, high adaptability and engineering practicability.

The invention relates to the technical field of intelligent transportation control, in particular to a tomato transportation speed self-adaptive adjustment method based on path road condition feedback, which comprises the following steps: acquiring road images, vibration waveforms and altitude data through a multi-source sensing unit, and constructing real-time road condition information; identifying a driving mode and extracting a corresponding bumping parameter; detecting the maturity grade of the tomato by combining multispectrum and thermal imaging, and querying a maturity-compressive strength corresponding table to calculate a cargo damage threshold value; constructing a dynamic mapping model under multiple working conditions, predicting vibration response and converting the vibration response into equivalent pressure; comparing the equivalent pressure with a damage threshold value to obtain a safety margin, constructing a speed adjustment decision tree and generating a maximum allowable speed value of each road section; and dynamically generating a segmented variable-speed control instruction based on the speed decision matrix, and controlling a throttle valve and a braking system to cooperatively change speed. The method has the advantages of accurate working condition identification, dynamic fruit adaptation, closed-loop speed regulation and control and the like, and is suitable for fine speed control of a high-sensitivity fruit and vegetable transportation scene.

The invention discloses a disk array fault early warning method and related equipment, and relates to the technical field of computer storage, and the method comprises the steps: responding to a preset period triggering condition, and applying a preset mechanical excitation signal to a target disk array; collecting a vibration response frequency spectrum and an acoustic response waveform of the target disk array under the action of the mechanical excitation signal; performing fusion analysis on the vibration response frequency spectrum and the acoustic response waveform through a preset feature fusion model to obtain a fault probability prediction vector; and determining a real-time fault early warning instruction for the target disk array according to the fault probability prediction vector and a preset health reference vector. Through periodic mechanical excitation and multi-mode signal fusion analysis, high-precision, non-intrusive and real-time fault early warning of the disk array can be realized, the reliability of the disk array is improved, and the operation and maintenance cost is reduced.

The invention relates to an existing building glass curtain wall operation and maintenance method and system based on a digital base. The method comprises the following steps that curtain wall vibration response data, visible light image data and three-dimensional point cloud data are obtained; based on the curtain wall vibration response data, comparing the current modal parameter with the historical modal parameter, and determining the dynamic characteristics of the curtain wall panel; identifying the curtain wall apparent damage type and the corresponding damage degree based on the visible light image data; based on the three-dimensional point cloud data, a curtain wall live-action three-dimensional point cloud model is constructed and mapped into a numerical model, a local stiffness reduction coefficient is calculated based on curtain wall panel dynamic characteristics, curtain wall apparent damage types and corresponding damage degrees, and a local stiffness matrix of a corresponding unit or node in the numerical model is corrected; and performing finite element analysis on the corrected numerical model to realize performance evaluation and deduction of the glass curtain wall. Compared with the prior art, the method has the advantages that the performance change of the glass curtain wall can be accurately and dynamically sensed and predicted, and the like.

The invention relates to a vehicle vibration response prediction method based on multi-modal feature depth fusion, which belongs to the field of computer vision and signal processing, and is characterized by comprising the following steps: constructing a vehicle vibration excitation and response triple data set based on multi-modal data; constructing a vehicle vibration response prediction model based on multi-modal feature deep fusion; training, verifying and testing the vehicle vibration response prediction model; and predicting a vehicle vibration response signal. According to the method, the problems that the traditional road excitation measurement precision is relatively poor and the calculation complexity and the structure fineness are difficult to balance by vibration signal prediction are solved, and the image feature extraction advantage of an EfficientNet framework and the time sequence feature extraction advantage of a Transformer framework are applied to the regression prediction of the vehicle vibration response signal; multi-modal features are fused through a bidirectional cross attention mechanism obeying Dirichlet distribution, and high prediction accuracy is obtained. And a method with relatively high prediction accuracy is provided for the field of vehicle vibration response signal prediction.

The invention discloses a large-model-based steel bar engineering quality detection method, system and equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring vibration response data and three-dimensional point cloud data of a to-be-detected reinforcement project; inputting the vibration response data into a pre-trained vibration characteristic analysis model to obtain a stress distribution map and a stress transmission path, and performing segmentation processing on the three-dimensional point cloud data along the stress transmission direction to obtain segmented point cloud data; based on the segmented point cloud data, extracting morphological characteristics of each section of reinforcing steel bar in the to-be-detected reinforcing steel bar project, and establishing a stress-geometry coupled digital twinborn model according to the stress distribution map and the morphological characteristics; performing dynamic mapping on the digital twinborn model and a pre-calibrated BIM design model, and determining steel bar deformation parameters of the stress abnormal region; and based on the steel bar deformation parameters, generating a quality detection report of the to-be-detected steel bar project. By implementing the technical scheme provided by the invention, the accuracy of reinforcing steel bar engineering quality detection can be improved.

The invention relates to the technical field of fan defect detection, in particular to a fan blade inspection method based on an unmanned aerial vehicle, which comprises the following steps: acquiring static basic data of a fan; constructing a blade reference model based on the static basic data of the fan, and collecting dynamic vibration response data according to a preset short-time dynamic vibration excitation rule; identifying a blade high-risk area according to the dynamic vibration response data, and generating a layered surrounding route in combination with a blade reference model; the unmanned aerial vehicle is controlled to collect blade multi-angle images according to the layered surrounding route, the blade multi-angle images are processed to recognize surface defects, and visual confidence is generated; and mapping the dynamic vibration response data to a blade reference model by using a space-time alignment model, extracting power spectral density characteristics of corresponding positions, calculating a damage index by fusing visual confidence, and outputting an inspection report. The objective of the invention is to solve the technical problem that blade internal damage is difficult to identify in a static inspection mode.

The invention relates to the technical field of engineering mechanical structure health monitoring, and discloses a carry-scraper structural member crack real-time detection system and method based on vibration characteristics, and the carry-scraper structural member crack real-time detection system based on vibration characteristics comprises a sensing acquisition module which is used for acquiring structural member vibration response and obtaining an original signal; the signal processing module is used for carrying out wavelet denoising on the original signal and extracting effective vibration data; the feature extraction and dynamic calibration module is used for extracting multi-domain features and performing dynamic calibration in combination with working conditions; the crack recognition and intelligent diagnosis module is used for inputting an intelligent model to recognize a crack state; and the crack positioning and alarm feedback module is used for positioning cracks and giving an alarm in combination with the diagnosis result. According to the invention, the multi-modal sensor array is constructed, piezoelectric and MEMS sensors are cooperatively arranged, a magnetic snap-in type installation structure is combined, broadband response signals are stably collected under different working conditions, and then front-end signal optimization is completed in cooperation with a multi-scale wavelet denoising and standardization mechanism.

The invention discloses a loss tuning method of a power transformer, which is applied to a transformer body sleeved with a winding and comprises the following steps: applying scanning current excitation containing fundamental waves and harmonic waves to the winding, synchronously acquiring a body vibration signal and converting the body vibration signal into a frequency spectrum; extracting a formant from the frequency spectrum, matching the formant with a theoretical electromagnetic force wave and a structure inherent frequency library, and identifying a coupling formant to be optimized; aiming at each formant, installing a vibration exciter in a corresponding area, sending out an anti-phase periodic pulse force, and dynamically and finely adjusting a pulse force parameter by monitoring a vibration response in real time and taking equivalent mechanical impedance minimization as a target; when the optimal damping state is achieved, the vibration exciter output rod is locked, and static pre-tightening force is formed; and after all formants are adjusted and optimized in sequence and the prestress is locked, final pressing and fixing of the transformer body are completed in the state that the pretightening force is kept. According to the invention, the dynamic loss source of the individual transformer can be actively inhibited and cured before assembly and curing, the operation loss and noise are effectively reduced, and the structural stability is improved.

The invention provides a vibration signal space-time reconstruction method based on a multi-modal condition diffusion model, and relates to the technical field of vibration signal reconstruction, and the method comprises the steps: firstly collecting structure vibration response through multiple sensors, constructing a multi-dimensional vibration signal matrix, and automatically recognizing a space continuous missing region and a time random missing region; performing coarse reconstruction on the missing region by adopting self-adaptive multi-scale interpolation so as to recover the basic trend and frequency band characteristics of the signal; a pseudo-missing mask is further applied to complete data, a training sample is constructed through a self-supervision strategy, and the model is guided to learn spatio-temporal correlation features and missing modes; in a training stage, a diffusion model is used as a generation framework, Gaussian noise disturbance is applied to a missing region, four types of condition embedding of time, space, trend and frequency domain are introduced in a denoising inversion process, signal periodicity, multi-sensor space coupling, low-frequency change and a physical frequency spectrum structure are respectively described, and the noise is reduced; and high-fidelity signal reconstruction under multi-modal information joint constraint is realized.

The invention discloses a subway depot upper cover building vibration response prediction method based on deep learning, and the method comprises the steps: constructing a feature library containing vibration signals and working condition data, generating enhanced data through a mechanical model, and fusing the enhanced data into a data set; a mixed deep learning model embedded with physical prior is constructed, and training and dual-objective parameter optimization are carried out; a prediction result is output after working conditions of real-time data are recognized through the lightweight model; parameters are finely adjusted through regular incremental learning, and transfer learning adaptation is carried out when working conditions suddenly change; verifying precision and rationality, and adjusting the weight of a regular term or suggesting to add a sensor; according to the method, measured data sparseness is made up by enhancing data fusion; the double-branch architecture overcomes the deep nonlinear mapping problem, and physical constraints are prevented from violating physical rules; the incremental learning reduces the cost, and the transfer learning solves the time-varying vibration capture problem; precision is improved through closed-loop verification, accurate real-time prediction of vibration response is achieved, and safety and comfort of a building are guaranteed.

The invention relates to a bridge non-stationary wind speed intelligent prediction and abnormal vibration early warning method and system, and a storage medium, and the method comprises the steps: constructing a first training set based on historical wind speed-vibration response data with a timestamp; and meanwhile, constructing a second training set based on the historical wind speed feature vector marked with the abnormal state. And respectively training the CNN-LSTM network and the KNN classification model by using the first training set and the second training set. And inputting real-time wind speed data into the CNN-LSTM network to obtain a non-stationary wind speed prediction feature vector in a future time period. And the prediction feature vector is imported into a t-SNE-KNN dimension reduction classification algorithm, unstable abnormal wind and normal wind are distinguished, and an early warning boundary and safe and dangerous areas are established. Compared with the prior art, the method has the advantages that an accurate intelligent prediction and abnormal vibration early warning decision method can be constructed for extreme wind events such as downburst, the problems in the prior art are solved, and reliable guarantee is provided for safe operation of a large-span bridge under the extreme wind events.

The invention discloses an intelligent quality inspection system for medicinal empty capsules, particularly relates to the field of intelligent detection, and comprises a multi-dimensional optical characteristic acquisition module, a dynamic thermodynamic response analysis module, a near infrared spectrum component inversion module, a multi-source data fusion decision module and a self-adaptive feedback optimization module. Omnibearing nondestructive detection of the capsule is realized through non-contact multispectral imaging and vibration response analysis, traditional destructive sampling loss is thoroughly avoided, and the multisource data fusion module integrates optical texture features, mechanical frequency domain energy density and near infrared spectrum inversion values based on the D-S evidence theory, so that the detection accuracy is improved. And the self-adaptive optimization module dynamically adjusts an optical wavelength weight, a mechanical sweep frequency range and a spectrum modeling parameter by applying reinforcement learning, is self-adaptive to fluctuation of different batches of gelatin materials and environmental interference, remarkably reduces a misjudgment rate and ensures continuous and stable operation of a production line.

The invention discloses an optical cable fitting fatigue damage detection method and system, and relates to the technical field of power transmission line state monitoring, and the method comprises the following steps: S1, constructing a wind field and structure coupling observation baseline, obtaining a full-time-domain vibration response signal of an optical cable fitting in a non-uniform wind field environment, generating a phase consistency distribution map, and obtaining a phase consistency distribution map; establishing a corresponding relation between the multi-path reflection source group and the time correlation sequence as a traceable reference for phase analysis; and S2, based on the phase consistency distribution map, performing causal beam demixing processing, performing arrival time difference densification calculation and curvature spectrum separation analysis on each sound wave propagation path, and extracting crack propagation pointing data. According to the method, through wind field-structure coupling observation, path unmixing, phase regression and error checking, multi-path propagation recognition and correction are achieved, crack propagation topology is reconstructed, polarization rotation and a phase suppression mechanism are combined, and direction recognition stability and detection adaptivity are improved.

The invention discloses a submarine pipeline scour-vibration response analysis method based on a cross-scale coupling framework. The method comprises the following steps: establishing a multi-physics field coupling model according to geographical and structural information of a pipeline layout area and initializing the multi-physics field coupling model; marine environment data are obtained through real-time monitoring, the model is imported for scouring dynamic deduction, and a scouring boundary data set is generated. And reconstructing a multi-physics field solving environment, and forming a real-time physics solving environment package. After the model is updated, pipeline vibration response is analyzed, and a vibration data set is obtained. And performing fatigue damage analysis, deducing a flow guide field disturbance coefficient, updating the model again, performing next time step coupling calculation, and finally obtaining a full-period coupling analysis data set. And evaluating the pipeline operation safety situation based on the data set, and generating and pushing a safety report. Various physical field effects on the submarine pipeline in the marine environment are comprehensively considered, the safety of the submarine pipeline is evaluated more comprehensively, and cross-scale coupling analysis between the scouring effect and the vibration effect is achieved.

The invention discloses an MEMS and MHD combined gyroscope low-high frequency composite calibration method, which comprises the following steps: synchronously acquiring vibration response data of a combined gyroscope sensor, separating low-high frequency signals, calculating low-frequency static error parameters and high-frequency dynamic error parameters, constructing a static orthogonal matrix based on a mounting angle error, and calibrating the low-high frequency composite of the MEMS and MHD combined gyroscope. The method comprises the following steps: generating a normalized error transfer coefficient by utilizing a high-frequency dynamic error parameter cross response ratio and a rejection ratio, generating a dynamic error matrix according to the normalized error transfer coefficient, establishing an expression containing error output of a sensor by utilizing the dynamic error matrix, and inversing the dynamic error matrix to obtain a dynamic error transfer matrix, and the static orthogonal matrix and the dynamic error transfer matrix are combined to construct a composite error compensation model, error parameters after dynamic compensation are evaluated, a residual feedback signal is generated in combination with an error evaluation result, calibration error parameters are iteratively corrected until convergence, and the calibration parameters are output.

The invention discloses a planetary gear system digital twin model updating method based on deep reinforcement learning. The planetary gear system digital twin model updating method comprises the steps of S1, simplifying a planetary gear system; s2, describing the kinetic model by using a lumped parameter method, and constructing a planetary gear train oscillatory differential equation set; s3, a frequency response function family is calculated through finite element analysis, and a planetary gearbox rigid-flexible coupling model, namely a digital twinborn body, is constructed; s4, vibration response of the planetary gearbox shell under the stable rotating speed is obtained through a vibration signal collecting device; s5, defining an action space and a state space, and establishing a reinforcement learning environment; s6, solving the oscillatory differential equation set to obtain a simulation response, and constructing a reward function according to the simulation response; step S7, setting neural network parameters; step S8, optimizing by using a deep reinforcement learning algorithm; and step S9, loop iteration is carried out, the optimal physical parameters of inversion are output, and updating of the digital twinborn body is realized.

Provided is a method for predicting vibration response and stiffness degradation of a helical gear. The method includes: establishing a lumped parameter dynamic model of a gear system according to a meshing condition of a pair of gears, considering that the gear system is a multi-degree-of-freedom system under the action of a deterministic force and a random force, establishing a digital twin model of the system at multiple time scales of characteristic time and running time, calculating a translation-vibration coupling control equation, establishing a grey box model by combining unscented Kalman filter with machine learning, performing combined state parameter estimation upon collected data to construct a state prediction framework, and predicting stiffness degradation at a running time scale. Response of a nonlinear multi-degree-of-freedom system can be predicted, and the residual stiffness of the gear is predicted through the collected data.

The invention relates to the technical field of intelligent detection, and discloses a building quality evaluation method and system based on concrete nondestructive testing and a storage medium. The method comprises the steps that a piezoelectric ceramic sensor array is arranged to collect micro-vibration response signals, and an original vibration data set is obtained; extracting an energy distribution coefficient of each frequency band by using a wavelet packet decomposition algorithm, and constructing a damage feature vector matrix; establishing a physical constraint neural network model, and outputting a damage variable time sequence; fusing the damage variable with ultrasonic and rebound data, and calculating comprehensive strength and damage degree indexes; and calculating the remaining service life by using a time sequence prediction algorithm, and generating an evaluation report. According to the method, the technical problem that the existing concrete nondestructive testing technology cannot realize microstructure damage evolution dynamic monitoring and residual life prediction is solved, and the accuracy of building quality evaluation and the scientificity of predictive maintenance decision are improved.

The invention provides a marine engine turbocharger fault prediction method based on digital twinning. The marine engine turbocharger fault prediction method comprises the steps that operation parameters and vibration signals of a marine turbocharger are acquired; performing signal processing on the vibration signal; establishing a three-dimensional solid model of the supercharger; establishing a fluid excitation reconstruction model and a vibration response model according to the three-dimensional entity model; performing all-condition fault simulation based on the vibration response model to obtain fault data; constructing a full-condition fault data set and training a fault prediction model, and screening sensitive vibration characteristic parameter categories based on various vibration characteristic parameters; according to a data transmission relationship among the fluid excitation reconstruction model, the vibration response model and the fault prediction model, constructing a corresponding data twinborn model; and inputting the operation parameters and the sensitive characteristic parameters corresponding to the real-time vibration signals into a digital twinborn model, and obtaining an output fault type and a fault degree. According to the method, the fault type and degree of the supercharger are accurately predicted, and the reliability and safety of equipment are improved.

The invention relates to a cable bridge fault diagnosis method and related equipment based on a sensor network, and the method comprises the following steps: carrying out the vibration monitoring of a cable bridge, and obtaining dynamic response data; obtaining an abnormal vibration time period sequence through time window segmentation and abnormal identification; tracking a vibration propagation path, and positioning a fault source area; a bridge weak link distribution map is generated by combining structural stress concentration degree analysis; and performing fault grade judgment according to the map, and outputting a maintenance decision suggestion, thereby solving the technical problems that most schemes still stay in a static parameter acquisition stage, an effective analysis mechanism for dynamic vibration response is lacked, and the occurrence position and evolution trend of the abnormal behavior of the bridge are difficult to accurately recognize.

The invention provides a tunnel backfill compactness estimation method and device, a terminal and a medium. The method comprises the steps that vibration response information and real-time settlement information are obtained; performing feature extraction on the vibration response information to obtain response frequency spectrum information; inputting the response frequency spectrum information and the real-time settlement information into various machine learning models to obtain various backfill compaction degree estimation data; and performing fusion processing on the multiple backfill compactness estimation data through a Bayesian triangular cap fusion method to obtain fusion compactness estimation data. According to the method, the Bayesian triangular cap fusion method is utilized to integrate the backfill compactness estimation data based on the machine learning algorithm, the final fusion compactness estimation data is generated, and the prediction precision of the tunnel backfill compactness is improved. The efficiency of tunnel backfill compactness detection is improved, the detection period of the tunnel backfill compactness is shortened, and continuous automatic monitoring of a large-range tunnel backfill layer is achieved.

The invention discloses an aviation thin-wall composite component robot edge milling process parameter optimization method, which comprises the following steps: constructing a milling force prediction model, and predicting a milling force based on machining equipment parameters and machining process parameters; a robot edge milling machining system is constructed, the predicted milling force serves as input, the robot tail end cutter vibration response and the workpiece vibration response are obtained, and the surface roughness of the workpiece is detected; based on different machining equipment parameters and / or machining process parameters, a milling force-workpiece surface roughness database is obtained; optimizing by taking the minimum milling force and the optimal milling quality as targets, screening a milling force-workpiece surface roughness database, and outputting an optimal combination; the edge milling machining quality of the aviation thin-wall composite component robot is remarkably improved, and the problems of large milling vibration and poor machining quality caused by the fact that technological parameters of the aviation large weak-rigidity component robot are difficult to regulate and control are solved.

The invention discloses an axle coupling system analysis method based on a physical information neural network, relates to the technical field of vehicle-axle coupling systems, and aims to solve the problem that the calculation precision of the dynamics problem of the vehicle-axle coupling system is not high. The axle coupling system analysis method based on the physical information neural network comprises the following steps: establishing a vehicle model and a bridge model, and establishing an axle coupling system according to the vehicle model and the bridge model; taking the motion equation of the axle coupling system as a constraint condition, taking integration of an initial condition and a boundary condition of the axle coupling system as a loss function, and constructing a physical information neural network; selecting a plurality of random points in the axle coupling system to carry out iterative calculation on the physical information neural network to obtain a trained neural network; and calculating the motion equation of the axle coupling system through the trained neural network to obtain the vibration response of the axle coupling system.

The invention provides an iron tower voiceprint intelligent detection method and system, and belongs to the voiceprint detection technology. The method comprises the following steps: firstly, outputting a sweep frequency signal in a specific frequency range, collecting iron tower vibration response data, extracting candidate frequency through spectral analysis, and dynamically adjusting excitation frequency by using a gradient descent algorithm; hardware filtering is carried out on collected sound signals, frequency band signals related to excitation frequency are reserved, and space beam forming is carried out through a microphone array to enhance iron tower voiceprint signals. Separating iron tower vibration components from the mixed signals by utilizing independent component analysis, performing time-frequency analysis, extracting an energy ratio of a specified frequency band by adopting wavelet packet transformation, and performing deep learning processing in combination with a one-dimensional convolutional neural network to generate an energy ratio and zero-crossing rate feature vector; and finally, inputting the feature vectors into a support vector data description model, setting an initial threshold value and dynamically adjusting the initial threshold value, thereby realizing graded judgment of bolt looseness and improving detection efficiency and stability.