2857 results about "Mechanical vibration" patented technology

The invention provides a pressure sensor data precision compensation method and system for industrial control, and the method comprises the steps: firstly obtaining an original pressure signal sequence outputted by a target pressure sensor in a preset collection period, the original pressure signal sequence containing a plurality of pressure signals arranged according to a timestamp; meanwhile, a real-time environment parameter set, covering temperature, humidity and mechanical vibration parameters in an acquisition period are detected, then the real-time environment parameter set is input into a pre-trained compensation parameter generation model, a corresponding dynamic compensation parameter set is generated, then each signal of an original pressure signal sequence is adjusted step by step according to the dynamic compensation parameter set, and a dynamic compensation parameter set is generated; and finally, the compensated pressure signal sequence is output to a signal processing module of an industrial control system to drive an execution mechanism, so that the data precision of the pressure sensor can be dynamically and accurately compensated by integrating multiple environmental factors, and the accuracy and stability of industrial control are improved.

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention provides a filling control method and system based on online parameter identification. According to the method, the synchronous error data of each servo shaft in the multi-shaft servo system is acquired, the error time sequence matrix is constructed by the synchronous error data according to the time sequence, and the displacement feedback data of each servo shaft in the multi-shaft servo system is acquired in real time through the high-precision rotary encoder; a dynamic compensation signal is generated according to the deviation between the displacement feedback data and a preset track, online estimation is carried out on mechanical vibration disturbance of the multi-axis servo system based on the error time sequence matrix, a disturbance parameter estimation value is obtained, the dynamic compensation signal and the disturbance parameter estimation value are fused, and a servo synchronous compensation amount is generated; according to the technical scheme, through fusion of real-time parameter identification and dynamic compensation, the filling precision and efficiency are remarkably improved, meanwhile, the manual debugging complexity is reduced, and a reliable solution is provided for high-stability production under multiple working conditions.

The invention relates to the technical field of intelligent power grids, in particular to an intelligent power distribution room sensing system and method based on data fusion. The multi-source data acquisition module is used for synchronously acquiring electrical parameters, mechanical vibration signals, temperature distribution data and environment monitoring data of power equipment in a power distribution room; the edge computing gateway is connected to the multi-source data acquisition module and is configured to perform time alignment, abnormal value elimination and feature extraction on the original sensing data; the data fusion analysis server is connected to the edge computing gateway through a network and comprises a space-time alignment unit used for unifying monitoring data of different sampling frequencies to the same time reference; the self-adaptive weight fusion unit is used for dynamically adjusting fusion weight according to the reliability of the data of each sensor; the state evaluation unit is used for generating an equipment health degree score and a fault early warning signal based on a fusion result; according to the scheme, monitoring blind areas and misjudgment risks caused by data islands can be fundamentally solved.

The invention relates to a GIS fault prediction method and system based on multi-modal data fusion. The method comprises the following steps: acquiring temperature infrared imaging data, a partial discharge signal, a mechanical vibration signal and gas component data of GIS equipment, and preprocessing the temperature infrared imaging data, the partial discharge signal, the mechanical vibration signal and the gas component data; acquiring a low-level feature and a high-level semantic feature of each mode in the preprocessed data, extracting a spatial feature and a time sequence feature in the low-level feature and the high-level semantic feature, acquiring an association relationship between the spatial feature and the time sequence feature according to a self-attention mechanism, and fusing the spatial feature and the time sequence feature; and predicting the fault state of the GIS according to the obtained fusion features through a full-connection network to obtain a prediction result including fault possibility, fault position and fault type. According to the method, low-level features and high-level semantic features of each mode are extracted through a layered architecture, and local spatial features and global time sequence features are captured to ensure that the model can simultaneously keep local details and global association, so that the fault prediction accuracy for the GIS can be improved.

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 relates to the technical field of mine mining, and discloses an underground multi-parameter environment monitoring method, which comprises the following steps of: acquiring equipment pose, vibration spectrum and environment parameters in real time through a multi-source sensor, performing space-time alignment, filtering noise reduction and feature extraction on original data, outputting a standardized state vector, and performing data processing on the standardized state vector; fusing the pose data, the vibration signals and the environmental parameters under a unified space-time reference to construct a multi-dimensional feature matrix; and carrying out dimension reduction and redundant information elimination by adopting principal component analysis, and outputting a fused equipment state vector. According to the method, through real-time fusion of the equipment pose and the airflow dynamic state, the ventilation equipment is cooperatively regulated and controlled, the interference of mechanical vibration on the gas monitoring precision is eliminated and the reliability of gas concentration detection is guaranteed based on coupling modeling of vibration spectrum and pose drift and intelligent compensation of sensor reading errors, and through space correlation analysis and dynamic air volume optimization, the gas concentration detection accuracy is improved. Wind speed sudden drop caused by tramcar passing is actively eliminated, and roadway global continuous monitoring is achieved.

The invention discloses an operation and maintenance debugging monitoring system suitable for an electric field, and relates to the technical field of operation and maintenance debugging monitoring, and the system comprises a collection unit which collects the operation data of an electric field device at a multi-dimensional monitoring point, the operation data comprises an electrical parameter, a mechanical vibration parameter, an environment parameter and chemical gas component data, and a multi-modal monitoring data set is constructed; the feature analysis module is used for carrying out spatial-temporal feature extraction on the multi-modal monitoring data set, generating a multi-dimensional feature tensor containing a time domain feature, a frequency domain feature and a spatial distribution feature, obtaining a spatial-temporal coupling relationship among the features through a tensor decomposition technology, and sending the spatial-temporal coupling relationship to the multi-modal monitoring data set; according to the method, a whole-process intelligent solution from monitoring, early warning to decision making is provided for electric field operation and maintenance, the equipment fault risk is greatly reduced, the power failure time is shortened, and the safety and economical efficiency of power grid operation are improved.

The invention relates to the technical field of fault diagnosis, in particular to an oil monitoring fault diagnosis method and system based on the Internet of Things, and the method comprises the following steps: collecting a mechanical vibration signal through oil monitoring, setting a time window, executing short-time Fourier transform, converting a time domain signal into a frequency domain signal, and obtaining frequency spectrum recognition data. According to the method, batch data can be processed in real time through the Internet of Things technology, a classifier is trained through a machine learning method, the fault type recognition precision is further improved, the operation state and efficiency of the machine are allowed to be evaluated through introduction of entropy calculation, and through comparison with a set fault level standard, the fault type recognition accuracy is improved. The fault risk of the machine is found and sorted in time, and the combination of real-time monitoring and automatic identification greatly improves the capability of prevention and maintenance and the operation efficiency of the equipment, reduces the risk of accidental shutdown, and brings remarkable benefits in the aspects of cost control and productivity improvement.

The invention relates to the technical field of power equipment state monitoring, in particular to a power equipment operation state evaluation and prediction method based on big data, which comprises a multi-source data acquisition module, a feature engineering processing module, an intelligent evaluation and prediction module and a decision support output module. By setting a multi-source sensing end, when the operation state of the power equipment is evaluated, the definiteness of state evaluation of different types of equipment is ensured by formulating multi-modal data fusion standard parameters and setting different state sensing weights for different types of equipment; and meanwhile, real-time state sensing is performed by fusing electrical parameters, mechanical vibration, thermal distribution and environmental stress data, so that whether a sensing blind area problem caused by a single data dimension occurs in an equipment evaluation process or not can be detected in real time, the comprehensiveness and accuracy of equipment operation state evaluation are ensured, and state sensing fragmentation errors are further reduced.

The invention belongs to the field of machining, and relates to a machining process quality online monitoring and feedback adjusting system and method. The system comprises a multi-modal data acquisition module used for acquiring a mechanical vibration signal and a high-frequency micro-damage signal; the feature preprocessing and cross-modal fusion module is used for carrying out time domain, frequency domain and time-frequency domain feature extraction on the collected mechanical vibration signals and high-frequency micro-damage signals, carrying out edge, texture and shape feature extraction on workpiece machining surface image signals, and introducing a cross-modal attention mechanism to achieve weighted fusion of heterogeneous signals. And finally generating a fusion feature vector with high discrimination. The machine tool / cutter state and workpiece quality intelligent analysis module is used for constructing an intelligent analysis model for the equipment operation state and the workpiece machining quality; inputting the fusion feature vector into an intelligent analysis model to obtain a quality evaluation result; and the quality judgment and feedback adjustment module is used for adopting a dynamic threshold adjustment mechanism to realize adaptive comparison with a process standard according to a quality evaluation result.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a motor control method and device based on an encoder, a medium and a program product, and relates to the field of motor control, and the method comprises the steps: receiving a position signal and a speed signal outputted by the encoder, and carrying out the filtering processing to obtain an operation state parameter; collecting mechanical vibration and electromagnetic noise generated in the operation process of the motor to obtain acoustic parameters of the motor, and extracting frequency characteristic parameters representing the rotating speed and motion characteristics of the motor; performing data fusion on the operation state parameters and the frequency characteristic parameters according to a preset weight to generate an operation state model; the operation state model is compared with a standard motion trail, and the position deviation value and the speed deviation value of the actual motion trail of the motor are calculated; and calculating a compensation control quantity according to the position deviation value and the speed deviation value, converting the compensation control quantity into a driving signal and outputting the driving signal to a motor driver so as to drive a motor to perform operation adjustment. According to the invention, the control precision of the motor in actual operation can be improved.

The invention discloses a hard and brittle material ultra-precision turning damage monitoring method based on multi-sensor fusion, and relates to the technical field of precision / ultra-precision machining state monitoring, a multi-sensor signal acquisition device is built on an ultra-precision turning machine tool to capture different types of sensor signals generated in the turning process in real time; performing signal preprocessing operation of minimum entropy deconvolution filtering on the acquired sensor signal; feature extraction is carried out on the signals subjected to minimum entropy deconvolution filtering, and 95 time domain and frequency domain statistical features reflecting turning damage are extracted; by integrating data from different sensors, the precision and reliability of monitoring are remarkably improved, more comprehensive and more accurate information can be obtained by capturing and fusing acoustic characteristics, cutting force and mechanical vibration information in the machining process, so that the abrasion degree of a tool and the damage condition of a workpiece are judged more accurately, and the machining accuracy is improved. And false alarm and missing alarm can be reduced, and the monitoring reliability can be improved.

The invention relates to the technical field of mechanical vibration testing, in particular to a method for testing dynamic rigidity and damping characteristics of an engine support, which comprises the following steps of: 1, simulating a boundary; step 2, double-source excitation loading is carried out; step 3, dynamic response acquisition: arranging vibration measurement points in the main shaft direction of the rigidity of the support to acquire acceleration signals in three directions, synchronously acquiring excitation force signals, and recording all the signals at a set sampling rate after anti-aliasing filtering; 4, constructing a frequency response matrix: performing time-frequency transformation on the exciting force signal and the acceleration signal, and calculating a cross-point frequency response function matrix; 5, parameter decoupling calculation is carried out, wherein parameter decoupling is achieved through cross iterative optimization; and 6, outputting parameters. Through the decoupling calculation of the low frequency band and the high frequency band, the cross iteration optimization method can effectively reduce the calculation error, improves the parameter decoupling precision, and guarantees the reliability of the test result under different frequency bands.

The invention relates to the technical field of flow measurement and control, and particularly discloses a cheese filling flow real-time calibration control method and system.The cheese filling flow real-time calibration control method comprises the following steps that working condition information of cheese in a filling pipeline is collected in real time, and the working condition information comprises the temperature, the pressure, an original flow signal, the pump rotating speed and the proportional valve opening degree; dynamically selecting a filtering order according to the signal-to-noise ratio of the original flow signal, and outputting the filtered flow; and based on the acquired working condition information, predicting the dynamic viscosity of the cheese in real time through a neural network, fitting a flow-working condition relation function and calculating a target flow value. The signal-to-noise ratio of an original flow signal is dynamically adapted to optimize filtering processing, signal distortion caused by interference such as temperature drift and mechanical vibration is effectively eliminated, and the flow detection precision is improved; by means of the neural network, the dynamic viscosity of cheese is predicted in real time, the model is dynamically adjusted, the problems that traditional viscosity estimation lags behind, and the batch adaptability is poor are solved, and an accurate basis is provided for flow target value calculation.

The invention discloses a switch loop data extraction method, device and equipment based on phase locking, and the method comprises the steps: carrying out the interval change analysis of operation data in a ring main unit, and recognizing a communication abnormal node through a phase locking technology; associating a mechanical vibration source based on the communication abnormal nodes, detecting a vibration conduction path, extracting current fluctuation characteristics, and determining position deviation; the operation current intensity is adjusted in combination with fluctuation characteristic parameters, and accurate off-position control is achieved through reverse braking force; constructing a dynamic threshold reference based on frequency domain analysis, and generating a complete operation record according to the dynamic threshold reference and the accurate parking data; performing typed grouping and transmission optimization on the data, and controlling data switching by adopting a gradually migrated beat sequence; and finally, through signal reconstruction and data fusion technologies, a switch loop data set with continuous time sequence and complete space is generated. The method can effectively cope with communication interference, mechanical vibration and other complex environmental factors, and improves the integrity and accuracy of data acquisition.

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

The invention relates to a multi-parameter fusion motor performance evaluation method, equipment and a medium. The method comprises the following steps: firstly, acquiring electrical signals, mechanical vibration signals and thermal temperature data in operation of a motor, and performing preprocessing and standardization to form a first data set; secondly, dimension reduction is carried out on multi-dimensional parameters of the first data set, and core parameter combinations are extracted to generate a second feature data set; then, classifying and marking the second feature data set by utilizing a classification algorithm, and determining a key performance index distribution interval to obtain a third classification result set; and finally, constructing a time sequence anomaly detection model based on the third classification result set, analyzing an index change trend, identifying an abnormal point deviating from a preset threshold value, and generating a fourth anomaly detection result. According to the method, the data quality and consistency can be improved, the abnormal state in motor operation can be effectively identified, data support is provided for motor performance evaluation and fault early warning, and the method is suitable for real-time monitoring and operation optimization of a motor system.

The invention belongs to the technical field of parallel robot controllers, and discloses a high-performance parallel robot controller based on an arm + fpga architecture, through deep heterogeneous fusion of an ARM and an FPGA, the control period is shortened to be within 10 microseconds, the trajectory tracking error is controlled to be 0.1 mm or below, and the performance bottleneck of a traditional architecture in a high-speed scene is solved; the multi-core ARM undertakes complex tasks such as global trajectory planning and dynamics solution, and realizes parallel processing by means of an NEON instruction set; the FPGA fully releases the hardware parallel characteristic of the FPGA, real-time tasks such as multi-axis motion control and sensor data fusion are synchronously completed through a distributed logic unit, and a complex decision-real-time execution assembly line cooperation mode is formed. Inertial parameters and load changes of the mechanical arm are estimated in real time through an LSTM neural network, and feedforward compensation is carried out on interference such as mechanical vibration and load abrupt change in combination with an extended state observer achieved through FPGA hardware; the innovatively designed double closed-loop control architecture supports seamless switching between a force control mode and a position control mode.

The invention discloses a power distribution equipment on-line monitoring system based on multi-modal data fusion, and relates to the field of power distribution equipment management, and the system comprises a sensing module which is used for collecting an electrical signal, a thermal infrared signal, a mechanical vibration signal and an acoustic signal generated in the operation process of power distribution equipment through a multi-type sensing channel, converting the collected various signals into processable equipment state original data to form an equipment operation state original data set; according to the invention, through accurate acquisition of multi-dimensional signals, combination of space mapping and time delay compensation, data quality is optimized, the reliability of original information is ensured, key features are extracted according to working conditions, subtle state changes are captured by means of temperature field analysis and a variable-resolution spectrum technology, the comprehensiveness, accuracy and response timeliness of equipment operation monitoring are effectively improved, and the real-time performance of equipment operation monitoring is improved. Misjudgment and missed judgment are reduced, and fault risks are avoided in advance.

The invention relates to the technical field of urban public safety, in particular to an underground gas pipe network safety pre-warning system, which comprises an acquisition module, a monitoring module, a monitoring module and a control module, and is characterized in that the acquisition module is used for arranging sensing nodes at key positions of a gas pipeline body and a surrounding environment to form a distributed monitoring network; the method comprises the following steps: synchronously acquiring mechanical deformation, methane concentration and mechanical vibration data of a pipeline through a distributed monitoring network to obtain a multi-modal sensing data set; the fusion module is used for performing humidity and temperature interference correction on the methane concentration data in the multi-modal sensing data set to obtain corrected methane concentration data; and performing fusion processing on the mechanical deformation data, the mechanical vibration data and the corrected methane concentration data to obtain an initial pipeline safety state evaluation index. Through multi-source data fusion, environmental interference correction and spatial domain calibration, transmission and dynamic analysis are combined, and precise early warning and efficient linkage disposal of the safety state of the gas pipe network are achieved.

The invention relates to the technical field of automatic detection data processing, in particular to a cooling fan automatic detection cleaning system which comprises an electromagnetic compatibility strengthening sensing module, a multi-physical field feature decoupling module, a layered fault diagnosis module, a dynamic cleaning strategy generation module, an anti-corona cleaning execution module and a closed loop feedback optimization module. Temperature field distribution, mechanical vibration signals and partial discharge characteristics are collected in real time through a distributed sensing network and are subjected to tensor fusion with SVG operation parameters to generate a multi-physical field joint characteristic space, and a closed-loop feedback module associates cleaning efficiency data with fault characteristics, reversely corrects model parameters and optimizes a cleaning strategy. According to the invention, traditional threshold limitation is broken through, and closed-loop control of electromagnetic interference suppression, unknown fault dynamic diagnosis and cleaning accurate response is realized.

The invention discloses an intelligent safety monitoring method and system for coal mine excavation, and belongs to the technical field of coal mine safety, and the method specifically comprises the steps: deploying a plurality of adaptive sensing nodes in an excavation area, each node comprising an environment state collection unit and an equipment vibration collection unit, and collecting environment data and mechanical vibration waveforms respectively; then, according to the position of the mining machine and the tunnel structure change, node physical connection is adjusted to form a dynamic topology network, and when mechanical movement exceeds a threshold value, adjacent nodes are subjected to high-density monitoring; then, synchronously generating a coupling data stream according to the collected data timestamps, inputting the coupling data stream into a pre-constructed model, and predicting the overlap ratio of mechanical vibration abnormity and a target gas accumulation area space; when the overlap ratio exceeds a critical value, an emergency response is triggered, the speed is reduced firstly, inert gas is released, and power is cut off if set duration is continued; and finally, reversely correcting surrounding rock stress parameters in the model according to emergency data. According to the invention, the accuracy and response timeliness of early warning of coal mine disasters are improved.

The invention relates to the technical field of power equipment state monitoring, and particularly discloses a power cable electrical performance detection method and system, and the method comprises the steps: synchronously collecting a broadband electromagnetic signal, a mechanical vibration signal and a temperature signal at a cable monitoring point; calculating a wavelet coherence coefficient between the signals through continuous wavelet transform, and constructing a multi-modal coupling tensor fusing amplitude and cross-modal time-frequency correlation characteristics; performing time slicing and high-order singular value decomposition on the tensor to obtain a time-varying core tensor sequence, mapping the time-varying core tensor sequence into a high-dimensional manifold curve, and generating a system state fingerprint by calculating local curvature distribution and topology invariants of the curve; inputting the fingerprints into a pre-trained defect prediction model, and directly outputting defect inoculation probability and evolution stage judgment; according to the method, the limitation that early weak defect detection is not sensitive in a traditional method is broken through, and early warning and accurate diagnosis of cable insulation latent defects are achieved.

The invention relates to a numerical control lathe turning process energy consumption prediction system, which belongs to the technical field of data processing and prediction, and comprises a multi-source data acquisition unit, a steady-state power prediction unit, a numerical control lathe turning process energy consumption prediction unit and a numerical control lathe turning process energy consumption prediction unit, according to the invention, the prediction precision under an abnormal working condition is improved, the limitation of an energy consumption gradual change hypothesis is broken through by fusing transient vibration information of a physical world, and the prediction precision is improved. A new abrupt change sensing type energy consumption prediction normal form is created; the conversion from passive response to active predictive maintenance is realized, and the risks of workpiece scrapping and accidental shutdown are greatly reduced.

The invention relates to the technical field of metal smelting, and provides an electrolytic refining device which comprises an anode assembly, a cathode assembly and an electrolytic bath, the cathode assembly comprises a cathode structure, the cathode structure comprises an ultrasonic transducer and a cathode head, the cathode head is connected with the ultrasonic transducer, and the ultrasonic transducer converts ultrasonic electric signals into mechanical vibration; the electrolytic bath is used for containing electrolyte, and at least part of the anode assembly and at least part of the cathode head are arranged in the electrolytic bath. The ultrasonic transducer drives the cathode head to vibrate, so that dendritic crystals gathered on the surface of the cathode head fall off, dendritic crystal accumulation on the surface of the cathode head is reduced, the surface of the cathode head keeps active, the service life of the cathode structure is prolonged, the electrolytic refining reaction rate and stability are improved, the electrolytic refining reaction can be continuously and uniformly carried out, and energy consumption in the electrolytic process is reduced. The maintenance cost and the production downtime are reduced, and the current efficiency and the collection rate of target metal are improved.

The invention discloses an intelligent image classification method based on big data, and relates to the technical field of image classification, and the method comprises the steps: image feature extraction: employing a deep learning algorithm to integrate and classify the extracted features, the method has the advantages that information of two different physical quantities can be integrated into a visual representation by constructing a vibration and temperature data fusion time-frequency diagram, and vibration data generally reflect dynamic characteristics of a mechanical system, such as running balance of a bearing, collision or friction between parts and the like; temperature data is related to energy loss, heat conduction, potential fault heating and the like, associated information of mechanical vibration and heat change in time and frequency can be captured at the same time through fusion of a time-frequency diagram, a more comprehensive view angle is provided for equipment state monitoring and fault diagnosis, and when a fault occurs, the fault diagnosis accuracy is improved. Vibration and temperature signals often change at the same time, and when a bearing has a local abrasion fault, the vibration amplitude may be increased.

The invention relates to the technical field of track foreign matter real-time detection, and particularly discloses a track foreign matter real-time detection method based on YOLOv5 improvement. Comprising the steps of video vibration synchronous acquisition, space offset compensation parameter generation, dynamic feature enhancement processing, feature weight thermodynamic diagram generation, hierarchical perception feature aggregation, cross-scale target verification, foreign matter positioning instruction packaging and multistage early warning execution control. Image distortion caused by mechanical vibration and illumination fluctuation is effectively overcome by synchronously acquiring track video and vibration time sequence signals, constructing a spatial offset compensation parameter set and combining with a dynamic feature enhancement template to correct video frames, a feature weight thermodynamic diagram is generated through visual saliency detection, features are aggregated through a layered perception model, and the visual saliency detection accuracy is improved. Early warning levels are dynamically matched according to the sizes and the positions of the foreign matters, and a sound-light alarm and braking system is linked; according to the invention, the reliability of data transmission is ensured, the operation and maintenance cost is greatly reduced, and the safety requirement of millisecond response of modern rail transit is met.

The invention relates to the technical field of motor state monitoring and intelligent fault diagnosis, in particular to a motor test and diagnosis system and method based on multi-data acquisition, and the system comprises a multi-mode sensing array module, a heterogeneous data fusion module, a coupling feature mining module and a migration diagnosis decision module. Wherein the multi-mode sensing array module is used for collecting mechanical vibration time domain signals, temperature field space distribution data and near-field electromagnetic radiation spectrums of a motor; the heterogeneous data fusion module is used for generating a time-frequency matrix, a thermal field characteristic matrix and fundamental frequency harmonic energy distribution characteristics; the coupling feature mining module is used for generating a comprehensive diagnosis factor; and the migration diagnosis decision module is used for constructing a feature mapping network based on meta-learning and outputting a fault type and a confidence score. According to the invention, through multi-modal feature fusion and a metalearning-based migration diagnosis mechanism, high-precision identification and cross-model adaptive diagnosis of multi-source fault information under a complex working condition of the motor are realized.