1463 results about "Time frequency domain" patented technology

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses a lithium ion battery internal short circuit fault detection method based on time-frequency fusion, and the method comprises the steps: firstly constructing a fractional order equivalent circuit model fused with an electrochemical aging mechanism, and simulating the paths of conductivity reduction, active material loss and lithium inventory reduction in combination with an aging empirical formula; joint modeling of the aging process and the random internal short circuit is achieved, and multi-cycle voltage and current and electrochemical impedance spectroscopy data are obtained; secondly, extracting time domain features by using a time domain attention enhanced long-short-term memory network, and analyzing frequency domain information by using a multi-scale frequency sensing convolutional neural network; and then weighting and screening two types of modal features through a dynamic gating fusion module, introducing a cross attention mechanism to establish dependency mapping between time-frequency domain features, and finally outputting an internal short circuit fault detection result. The method can solve the problems that in the lithium ion battery aging process, due to lithium dendrite growth, the internal short circuit early fault is high in concealment, and single time-frequency characteristics are difficult to detect, and early high-precision recognition of the internal short circuit fault can be achieved.

The invention belongs to the field of power system engineering, and discloses a power distribution automation terminal diagnosis method and system based on multi-source wave recording feature fusion, and the method comprises the steps: obtaining the electric quantity data and equipment operation state data collected by a power distribution automation terminal; performing adaptive decomposition on the electrical quantity data by using a variational mode decomposition algorithm to obtain an intrinsic mode function; constructing a deep residual network model, carrying out fusion analysis on the time-frequency domain features of the intrinsic mode function, and generating a fault feature vector; establishing a multi-dimensional evaluation matrix based on the fault feature vectors, and integrating a plurality of indexes to output fault types and credibility scores; according to the fault type and the credibility score, generating a fault isolation strategy based on a Petri network model; and executing a dynamically adjusted self-adaptive self-healing control algorithm. According to the method, complex and changeable fault modes can be effectively identified, a complete collaborative verification mechanism is formed, seamless connection from fault diagnosis to self-healing control is realized, and the operation reliability of the power distribution network is remarkably improved.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The invention relates to a signal detection and accurate positioning method for a lift-off platform unmanned aerial vehicle, and the method comprises the steps: deploying an unmanned aerial vehicle-mounted platform, capturing a global electromagnetic signal in real time, outputting multichannel original signal data, carrying out the time-frequency domain joint analysis, extracting a frequency hopping period, a modulation mode and pseudo-random code features, and recognizing a target unmanned aerial vehicle signal. A control instruction link signal and an image transmission link signal are separated; the arrival time difference and the arrival phase difference of the target unmanned aerial vehicle signal are converted into a dynamic space coordinate system, a three-dimensional positioning equation set is constructed, and real-time space position coordinates of the target unmanned aerial vehicle are obtained through calculation; and extracting transmitting end characteristic parameters of control instruction link signals, constructing a control signal propagation path model in combination with the real-time position of the target unmanned aerial vehicle, and positioning geographic coordinates of an unmanned aerial vehicle control source by reversely tracking a signal transmitting direction and an attenuation law. The terrain shielding influence is effectively avoided, the target identification accuracy and positioning precision are improved, and the unmanned aerial vehicle control source traceability is realized.

The invention discloses a bearing degradation trend prediction method and system based on multi-domain feature dynamic fusion and dimensionality reduction, and the method comprises the steps: collecting full-life vibration signals of a bearing, synchronously marking three stages of health, degradation and fault, constructing multi-dimensional features such as a time domain, a multi-scale frequency domain, a time-frequency domain, and the like; evaluating the cross-stage difference of the features by using double criteria of mahalanobis distance and information entropy, and adaptively adjusting the weight to complete optimization; threshold cutting, linear proportion, Softmax or hierarchical weighting strategy empowerment are automatically selected according to data distribution, and energy is reserved through PCA for dimension reduction. And a TCN-GRU deep network model is constructed. Real-time data are input into the model to predict the degradation state, if errors exceed the limit, feature reconstruction and model retraining are triggered, and full-life-cycle high-precision high-robustness multi-stage continuous online monitoring is achieved. The method aims at solving the problems that the diagnosis precision is limited and the working condition adaptability is insufficient due to the fact that single time domain or frequency domain features are excessively depended and the features of each stage of fault evolution are difficult to comprehensively characterize.

The invention discloses a resting electroencephalogram quality evaluation method and system based on double-branch comparative learning, and the method comprises the steps: collecting an original EEG signal X, carrying out the data preprocessing and data enhancement, generating two different enhanced views, transmitting the two different enhanced views to a double-branch encoder in parallel, respectively extracting a time domain waveform and a time-frequency domain rhythm feature, and carrying out the deep fusion, the fused features X1 and X2 are sent to a projection head, and through a self-supervised contrast learning mechanism, the network weight is optimized by using contrast loss; a small amount of labeled fine-tuning data sets including X and corresponding labels y are adopted, after flowing through a pre-trained double-branch encoder, the fine-tuning data sets are directly sent to a classification head connected with the back of the double-branch encoder so as to output a prediction result of an input data segment, and a mixed loss function including classification loss and comparison loss is adopted for training in the optimization process. According to the invention, a complete online real-time quality control system is established, and end-to-end real-time closed loop from data acquisition to quality evaluation is realized.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention relates to the technical field of geotechnical mechanics and engineering, and particularly discloses a soft soil foundation settlement monitoring method and system based on multi-field multi-source information, and the method comprises the steps: collecting multi-source data of a target soft soil area; performing constitutive parameter inversion, data standardization and discrete Fourier transform frequency domain conversion on the data to obtain a standardized frequency domain multi-field multi-source data set; based on the data set, a soft soil constitutive parameter library and a soil mechanics physical equation, constructing an FD-PINN frequency domain physical information neural network, embedding the physical equation as a prior constraint, and training the model by adopting an alternating optimization strategy; inputting the real-time frequency domain data flow into the model, and outputting the current settlement amount, the settlement rate and the multi-physical field frequency domain distribution; and in combination with a pre-established large scale model test result, through IDFT inverse transformation, a time-space domain settlement field is reconstructed, multi-stage early warning is triggered, a targeted reinforcement scheme is recommended, the soft soil foundation settlement monitoring precision and the engineering practicability are remarkably improved, and the method is suitable for construction, operation and maintenance of infrastructures such as high-speed rails and highways.

The invention belongs to the technical field of power system load prediction, and relates to a long-range multivariable load prediction method and system based on time-frequency domain collaboration, and the system carries out the normalization and stabilization of a multivariate load time sequence through a data preprocessing module; the feature embedding module performs linear embedding on the block sequence to construct high-dimensional feature representation; the state space coding module extracts long-range dependency features and generates depth time sequence representation; the decoding prediction module maps the coding features into a preliminary prediction sequence; the time sequence alignment module identifies a leading-lagging relation among multiple variables and aligns a time sequence; the frequency domain optimization module realizes frequency domain component fusion based on adaptive filtering; and the model training optimization module is used for performing training and optimization through a signal attenuation loss function. The method can effectively improve the precision and robustness of long-range multivariable load prediction, and especially has obvious advantages in the aspects of processing complex dependency relationships and dynamic time delay.

The invention discloses a high-precision pressure sensing and self-calibration system, and relates to the technical field of pressure sensing, the system comprises a pressure sensing module, an environment monitoring module, a data processing module, a digital twinning module and a self-calibration module, the pressure sensing module collects and converts pressure signals through a high-precision sensor, and the environment monitoring module obtains environment parameters in real time; the data processing module carries out amplification, filtering, analog-to-digital conversion and timely frequency domain analysis on the initial electric signal, and outputs preprocessed data in combination with environmental parameter dynamic compensation. The digital twin module constructs a sensor virtual model, and simulates and outputs an ideal pressure predicted value; and the self-calibration module compares the predicted value with the preprocessed data, generates a final pressure value through deviation calculation, iterative learning and data fusion, and can also calculate the health index of the sensor and perform early warning maintenance. The system realizes high-precision pressure induction and real-time self-calibration, improves the measurement accuracy and prolongs the service life of the sensor.

The invention provides a power load prediction method and device, and belongs to the technical field of power load prediction.The method comprises the steps that current waveform data are obtained, and fundamental wave and harmonic components in the current waveform data are extracted; carrying out waveform spatial form geometric analysis to obtain a real-time load characteristic sequence, and then carrying out segmentation processing; the current effective value sequence of each time window is converted into a time-frequency domain energy distribution vector, and then a three-level feature library is constructed; constructing a three-dimensional tensor model through equipment start-stop event identification, inputting the three-dimensional tensor model into a multi-target optimizer to evolve feature weights, and filtering abnormal samples to obtain a feature cluster; performing random masking processing on the time sequence data of the feature cluster to generate a mask sequence, inputting the mask sequence into an encoder to reconstruct masking data, comparing, learning and judging abnormal output correction data, and inputting the corrected data into a prediction network to generate a feedback signal flow; and analyzing the feedback signal flow to update the prediction network weight. Based on the method, the invention also provides power load prediction equipment. According to the invention, the precision of power load prediction is obviously improved.

The invention relates to the technical field of machine learning, and provides a building equipment abnormity identification method and system based on machine learning, which are used for realizing accurate detection and accurate early warning of building equipment abnormity. The method comprises the following steps: acquiring a continuous operation data set of target building equipment, wherein the continuous operation data set comprises multiple segments of equipment state recording units with timestamp marks; performing time-frequency domain feature extraction processing on the continuous operation data set to obtain a time-frequency domain feature set of the equipment state recording unit; calling a pre-constructed hybrid machine learning model to perform anomaly detection processing on the time-frequency domain feature set, and generating an anomaly recognition result of the equipment state recording unit; determining the anomaly type of the target building equipment and the distribution feature information of the anomaly type in the time dimension according to the anomaly recognition result; and generating a target early warning instruction containing a time positioning identifier based on the exception type and the time distribution feature information, and sending the equipment early warning instruction to a target equipment management terminal.

The invention relates to the technical field of fault diagnosis, in particular to a pole-mounted circuit breaker fault diagnosis method based on multi-information fusion, and the method comprises the steps: firstly obtaining electric quantity modal data such as three-phase current and coil current and mechanical quantity modal data such as mechanism vibration and voiceprint, and constructing a time-frequency domain alignment feature tensor; then, through a graph space-time attention fusion model, deeply mining physical structure association and time sequence evolution laws among heterogeneous data, and generating a dynamic space-time feature matrix; then, respectively constructing a mechanical evidence body for representing the state of the transmission chain of the operating mechanism and an electrical evidence body for representing the working condition of the vacuum arc-extinguishing chamber by adopting a feature level-decision level mixed framework; an improved D-S evidence theory is applied for fusion, when high-conflict evidences are detected, a self-adaptive arbitration mechanism is automatically triggered, conflict weights are dynamically attenuated or distributed to uncertain items, misjudgment is effectively avoided, and high-robustness collaborative diagnosis of the electromechanical state is achieved.

The invention relates to the technical field of lithium ion battery testing, and discloses a lithium ion battery life loss evaluation method and device, a medium and equipment, and the method comprises the following steps: S1, collecting multi-modal dynamic data of a lithium ion battery in a charge-discharge cycle process; s2, performing time-frequency domain conjoint analysis on the multi-modal dynamic data, and extracting a battery aging sensitive feature set; s3, constructing a multi-scale coupling model of battery life loss; by constructing a multi-modal data fusion mechanism and a dynamic feature extraction system, during lithium ion battery life loss evaluation, change trends of electrochemical impedance spectroscopy and heat distribution key parameters are captured in real time based on time-frequency domain conjoint analysis, sensor signal distortion and drift problems can be identified, the extraction precision of aging sensitive features is improved, and the accuracy of lithium ion battery life loss evaluation is improved. The problem of characteristic errors caused by signal interference in traditional evaluation is solved, and the accuracy and reliability of life loss evaluation are ensured.

The invention relates to the technical field of electrical safety early warning, and discloses an electrical fire early warning method and system. The method comprises the following steps: acquiring a multi-dimensional dynamic parameter set of an electrical circuit in real time, wherein the multi-dimensional dynamic parameter set comprises a current fluctuation sequence, temperature gradient distribution data and an insulating medium loss value; performing time-frequency domain joint decomposition processing on the dynamic parameter set, and extracting current harmonic distortion characteristics, a temperature field spatial evolution mode and a dielectric loss accumulation rate; generating a current anomaly index according to a harmonic distortion characteristic and a preset threshold deviation degree, calculating a thermal runaway risk level in combination with a temperature field spatial evolution mode, and determining an insulation degradation coefficient based on a dielectric loss accumulation rate; fusing the three to construct a comprehensive fire risk index; and when the index exceeds the dynamic warning threshold value, a grading early warning signal is triggered and a hidden danger positioning map is generated. According to the method, the electrical fire early warning effect is optimized through multi-dimensional parameter acquisition and fusion analysis.

The invention discloses a nondestructive testing method for a welding seam of a steel structure. The method comprises the steps that firstly, morphology data of the surface of a building steel structure are obtained through a high-precision laser scanner and an electromagnetic induction technology; coating thickness distribution and weld surface roughness are analyzed based on the morphology data, and a surface information matrix is constructed; then automatically adjusting ultrasonic wave beam parameters of the ultrasonic phased array by using the adaptive parameter regulation and control model and executing detection to obtain a preliminary ultrasonic echo signal; the time-frequency domain difference between the preliminary echo and the reference signal is calculated through an echo signal compensation model, and secondary compensation is completed to obtain an optimized ultrasonic echo signal; fusing the optimized echo signal, the morphology data and the surface information matrix to construct a detection data set, and adopting a pre-trained steel structure detection model to realize defect detection; and finally, evaluating the confidence of a detection result by adopting a three-dimensional confidence evaluation model, performing grading, and performing local re-detection and parameter adjustment on a low-confidence region to form closed-loop optimization. According to the method, the surface state of the steel structure can be adaptively matched, the accuracy of echo signals and the defect detection precision are improved, and the detection reliability is guaranteed through three-dimensional confidence evaluation.

The invention belongs to the technical field of drainage pipeline detection, and particularly relates to a voiceprint recognition detection method for internal defects of a drainage pipeline, and the method comprises the following steps: S1, collecting sound signals in the pipeline through an acoustic sensor array; s2, preprocessing the collected sound signals, wherein the preprocessing comprises noise filtering, signal enhancement and segmentation processing; s3, extracting time domain, frequency domain and time-frequency domain features of the sound signals, and constructing voiceprint fingerprints; s4, establishing a defect classification model based on the deep neural network, and identifying different types of pipeline defects; s5, carrying out confidence evaluation on the detection result, and determining the position of the defect in the pipeline; s6, outputting a detection result and carrying out graded alarm; according to the method, various defect forms of cracks, blockage, damage and the like of different degrees of the drainage pipeline can be recognized, the recognition accuracy of the internal defects of the drainage pipeline can reach 95% or above through multi-dimensional voiceprint feature extraction and a deep learning algorithm, and continuous monitoring and real-time alarming can be achieved.

The invention discloses an aero-engine residual life prediction method based on multi-modal deep learning, and relates to the field of aero-engine prediction and health management. The method comprises the following steps: acquiring and preprocessing multi-sensor time sequence data; constructing a degradation sensitive feature set through multi-scale analysis of a time domain, a frequency domain and a time-frequency domain; constructing a multi-modal deep learning model comprising an original data processing module and a multi-scale feature processing module, and introducing an attention mechanism and an uncertainty quantization module into the model; the model is subjected to lightweight processing to support embedded deployment. According to the method, multi-scale features and multi-modal deep learning are fused, the uncertainty quantification capability is achieved, high-precision and interpretable residual life prediction with uncertainty quantification is achieved, and reliable support is provided for engine maintenance decision making.

The invention belongs to the technical field of battery management, and discloses a lithium ion battery state-of-charge estimation method for heterogeneous sensor data missing. The space-time mask auto-encoder comprises a parallel multi-branch channel encoder, a feature level mask module and a space-time collaborative attention decoder; the parallel multi-branch channel encoder performs time-frequency domain feature decoupling on signal sequences from the plurality of heterogeneous sensors, and extracts and fuses transient response of voltage and periodic load characteristics of current; learning and accurately reconstructing missing sensor channel information in a high-dimensional feature space through a feature level mask module; and cascading the estimated value output by the space-time mask auto-encoder with the extended Kalman filtering unit to obtain a final result. According to the method provided by the invention, the robustness, the accuracy and the generalization capability of SOC estimation in a sensor channel part missing scene are remarkably improved, and a key technical support is provided for realizing accurate state monitoring and intelligent safety management of the lithium ion battery under a complex working condition.

The invention relates to the technical field of mine fans, in particular to a mine fan fault recognition and detection system and method, and the method comprises the steps: collecting mine fan data, and obtaining time-frequency domain features and working condition features; a CNN-based fault diagnosis model is constructed and trained, and fault identification is carried out; meanwhile, building a health index of the mining fan, evaluating a health state, building a health index of the mining fan, building a mining fan residual life prediction model based on a long short-term neural network LSTM model, and predicting the residual life of the mining fan; in order to adapt to a new fault type, an incremental learning method is adopted to dynamically update a diagnosis model, and a knowledge distillation technology is utilized to learn a new fault. According to the method, multi-source heterogeneous data are comprehensively utilized, the model can be dynamically updated, the evaluation health state of the mining fan is quantified, and a comprehensive solution is provided for reliable operation of the mining fan.

The invention discloses a lower limb muscle fatigue assessment method and system based on multi-modal physiological signals, and relates to the technical field of biomedical engineering, target muscles are selected, sEMG signals, AUS signals and respiratory flux VE data are synchronously collected, and RPE is recorded; preprocessing various signals to remove interference and abnormal points; extracting sEMG time-frequency domain features and AUS muscle thickness features; fusing features and constructing vectors in a standardized manner; dividing five fatigue grades; and a data set is divided according to stratified sampling, a model is optimized through five-fold cross validation, and real-time features are input to output a fatigue level. Multi-modal physiological signals are fused, fatigue evaluation accuracy is improved, different fatigue stages are finely adapted, muscle load and energy consumption are reduced through personalized adaption and a dynamic adjustment strategy, rehabilitation training safety and comfort are improved, and efficient recovery of lower limb movement functions is assisted.

The invention discloses an intelligent temperature control mattress system based on multi-mode sensing and adaptive learning, and the system comprises a data collection module which is used for collecting a mattress surface vibration signal, a mattress surface temperature and a mattress surrounding environment parameter; the data analysis module is used for performing deep analysis on the time-frequency domain features according to a biological recognition model to obtain human body or non-human body classification, performing self-learning to obtain body movement changes, accurately recognizing various typical sleeping postures and filtering out error data; and the model prediction module is used for monitoring the physiological parameters of the user according to the spectral analysis, inputting the physiological parameters, the collected temperature and the parameters of the surrounding environment of the mattress into a temperature control model composed of three neural networks of LSTM, GNN and Transform for comprehensive analysis and decision making, and outputting an optimal temperature control strategy. According to heart rate changes or body heat fluctuations, the sleep stage and the sleep and waking time are intelligently recognized, and therefore sleep environment adjustment better conforming to the human body rhythm is achieved.

The invention discloses an insulation state fusion detection method, system, medium and equipment under transient impact in a cable operation process, and the method comprises the steps: synchronously collecting a transient impact signal and a partial discharge signal of a cable under the transient impact effect; performing time-frequency domain separation on the acquired transient impact signal and the partial discharge signal to obtain a transient impact component and a partial discharge component after separation; when it is detected that the amplitude of the transient impact signal exceeds a preset threshold value, a partial discharge detection window is started, and whether a partial discharge signal exists or not is detected in the window period; if a partial discharge signal is detected, multi-dimensional characteristic parameters of the partial discharge signal are extracted, a state vector is constructed in combination with environmental parameters, the state vector is input into a pre-trained machine learning model, and a cable insulation state grade is output; if the partial discharge signal is not detected, performing inversion calculation on the transient impact signal based on a transmission line model, and reconstructing an overvoltage waveform; and extracting propagation characteristic parameters according to the reconstructed overvoltage waveform, and judging the insulation defect type according to the propagation characteristic parameters.

The invention provides an urban solid waste incineration NOx emission prediction method based on MSGSE-NSCF, and the method comprises the steps: obtaining a key variable and nonlinear dependence of NOx emission, and constructing a non-stationary cross converter; constructing a non-stationary gated spectrum enhancement sub-module and a gated convolution attention fusion sub-module, extracting space-time enhancement representation, and realizing time-frequency domain feature fusion modeling; performing hyper-parameter verification to obtain reasonable parameters of the model; according to the NOx emission prediction method based on the non-stationary cross converter and the multi-scale time-frequency feature enhancement, the non-linear dependence, the original structure and the dynamic change of data can be reserved, the non-stationary time sequence is modeled, the output scale is kept, and the NOx emission concentration is obtained. In addition, time domain-frequency domain and global-local cooperative enhancement can be achieved, selective modeling of periodicity and key frequency components is enhanced, and an accurate NOx emission prediction result is obtained.

The invention relates to the technical field of artificial intelligence, in particular to an electric energy quality disturbance denoising method based on artificial intelligence. The invention discloses a power quality disturbance denoising method based on artificial intelligence. The method comprises the following specific steps: S1, collecting and marking signal data; s2, time-frequency domain weighted fusion preprocessing including time domain normalization processing, frequency domain wavelet transform, adaptive weight calculation and time-frequency fusion representation is carried out; comprising a dual-path feature encoder module, a self-adaptive feature fusion module, a gating jump connection decoder module, a denoising signal purification module, calculation of purification constraint terms, calculation of a total loss function and model iterative training and parameter updating, and S4, power quality disturbance denoising. The problems that in the prior art, information loss is likely to happen, feature extraction is insufficient, the denoising effect is unstable, and the precision of electric energy quality analysis is limited are solved.

The invention relates to the technical field of connector detection, and discloses a reliability detection method based on a connector. The method comprises the following steps: acquiring real-time monitoring data of a multi-channel electrical sensor of a connector in a power-on state, wherein the real-time monitoring data covers a contact impedance fluctuation sequence, temperature rise gradient distribution and an electromagnetic leakage intensity spectrum; performing time-frequency domain fusion processing on the monitoring data, and extracting dynamic characteristic parameter sets such as impedance attenuation rate, heat conduction lag coefficient and field intensity distortion factor of each contact point; then, according to the parameter space distribution difference, a three-dimensional degradation model comprising a material performance attenuation layer, a contact surface deformation topology layer and an insulating medium loss layer is constructed; coupling mechanical stress and electric heating field intensity distribution based on the model, and generating life prediction indexes of each region; and finally, completing reliability grading according to the indexes, and outputting a detection report with key failure position marks. According to the method, comprehensive and accurate evaluation of the reliability of the connector is realized, and an effective technical path is provided for quality control of the connector.

The invention relates to the technical field of chronic kidney disease screening, and discloses an intelligent chronic kidney disease early screening instrument which comprises a physiological parameter detection module configured to synchronously collect multi-dimensional bio-electricity signals, optical characteristic parameters and body fluid biochemical parameters of a subject and output original signals; the multi-modal data processing module is in communication connection with the physiological parameter detection module and is configured to receive original signals, perform time domain and frequency domain conjoint analysis and construct a parameter incidence matrix; the wireless communication module is configured to realize encrypted interaction between the screening data and the cloud server and block chain evidence storage; and the man-machine interaction module is configured to display the risk level map in real time. According to the method, holographic dynamic correlation modeling of kidney function related micro-electrophysiological fluctuation and optical metabolism characteristics is realized, and a specific cross-modal pathological correlation mode of early renal injury can be effectively identified based on a time-frequency domain combined characteristic extraction and dynamic coupling algorithm.

The invention relates to the field of lithium battery health state prediction, in particular to a lithium battery health state prediction method based on TKAN and PxLSTM. According to the scheme, the method comprises the steps of collecting multi-dimensional time sequence data, including voltage, current and temperature, of a lithium battery, forming an input sequence by the multi-dimensional time sequence data, and performing denoising and standardization processing on the data; hilbert-Huang transform is applied to the input sequence, the input sequence is decomposed into a plurality of intrinsic mode functions and residual errors, the time-frequency domain joint information entropy of each intrinsic mode function is calculated, the first k intrinsic mode functions and corresponding entropy values are selected according to entropy value sorting, and a compact feature vector is constructed in combination with the residual errors; the coded compact feature vectors are input into a TKAN module and a PxLSTM module at the same time; and performing weighted combination on the first hidden state sequence and the second hidden state sequence through a fusion weight to obtain a fusion feature, and outputting a lithium battery health state prediction result according to the fusion feature. The method is suitable for battery health state prediction.

The invention relates to an intelligent control method and system for extrusion forming of an aluminum profile, and the method comprises the following steps: carrying out acoustic emission sensor array measurement and preprocessing on a mold inlet region of the aluminum profile in an extrusion process, and obtaining a preprocessed acoustic emission signal sequence; performing time-frequency domain energy distribution analysis on the acoustic emission spectrum to generate an acoustic emission energy spectrogram; carrying out material flow stability analysis based on the spectrogram to obtain a flow analysis result; if the flow instability degree exceeds the target stability range, multi-constraint optimization calculation is conducted on the main cylinder pressure and the extrusion speed based on the analysis result, and optimization control parameters are obtained; and finally, closed-loop feedback control is performed on the extruder through a distributed PID controller, so that the material flow stability is kept in a target range, intelligent high-precision forming control is realized, and the problem that in the traditional extrusion process, the material flow stability is often influenced by factors such as the extrusion speed, the main cylinder pressure and the mold design is solved. And a flow instability phenomenon is easy to occur.