8532 results about "Frequency spectrum" patented technology

The invention discloses a closed-loop fault diagnosis method based on the combination of an AI intelligent agent and power equipment simulation, which is applied to the field of power system fault diagnosis, and comprises the following steps: on the basis of a preset knowledge graph basic data set and power system multi-source data, performing data cleaning, feature extraction and knowledge integration; constructing a unified power equipment fault diagnosis knowledge graph, and performing reasoning on the knowledge graph and time sequence characteristics in combination with large model fine tuning or a mixed reasoning engine of a graph neural network to generate M candidate fault hypotheses; calculating the similarity between simulation and actual measurement waveforms through a DTW algorithm and a frequency spectrum comparison method, and screening high-consistency hypotheses; based on the logic verification rule base, performing causal graph reasoning, constraint checking and anti-factual thinking on the high-consistency hypotheses, and eliminating non-logic hypotheses; feeding back the abnormality found in the verification link to the AI agent, dynamically adjusting the reasoning strategy through reinforcement learning, and generating a convergent diagnosis result; and outputting a target diagnosis conclusion based on the converged diagnosis result.

The invention relates to a wireless communication test method and system based on a portable frequency spectrograph and a medium, and belongs to the technical field of radio frequency spectrum analysis, and the wireless communication test method comprises the steps: collecting electromagnetic signal original data of a target area; constructing a dynamic electromagnetic environment baseline model; performing filtering and background subtraction processing on the original data of the electromagnetic signal; carrying out modulation mode identification, protocol analysis and terminal identification extraction on the preprocessed signal matrix, generating a multi-dimensional feature vector, carrying out convolution similarity matching on the multi-dimensional feature vector and a legal signal feature template, and distributing a threat level label; dynamically reconstructing scanning parameters of the portable spectrum analyzer according to the threat level label, generating an equipment parameter adjustment record and updating the electromagnetic fingerprint database; and generating a spectrum optimization control instruction based on the threat level label and the equipment parameter adjustment record, and issuing the spectrum optimization control instruction to the target base station or the terminal equipment. According to the application, the identification precision of the threat signal and the control closed-loop linkage capability in a complex electromagnetic environment are improved.

The invention relates to the technical field of line arc abnormity monitoring, in particular to a data center operation and maintenance service environment monitoring system which comprises a multi-mode sensing unit, a dynamic baseline modeling unit, a transition state abnormity extraction unit and a grading early warning unit. The dynamic baseline modeling unit builds a three-layer safety baseline model, the base layer builds harmonic references in a segmented mode according to the load rate, the environment layer generates voiceprint feature template libraries of different temperature and humidity intervals, the time layer fits a 24-hour change trend envelope line, and the transition state anomaly extraction unit separates and decouples an arc voiceprint feature frequency band through a blind source and calculates the energy ratio. The harmonic abrupt change phase deviation amplitude is analyzed in combination with window sliding correlation, the abnormal evolution index is generated through fusion, the graded early warning unit triggers response according to the index, transition state abnormity is accurately recognized, the perspectiveness and reliability of operation and maintenance early warning are improved, and the method is suitable for safe operation and maintenance of data center equipment.

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention relates to the technical field of equipment testing, and discloses a motor operation state vibration noise combined diagnosis generation method, which comprises the following steps: when a motor operates, performing bimodal data extraction on operation state data to obtain a vibration signal and a noise signal; performing order tracking analysis on the vibration signal to obtain an electric vibration characteristic order component; calculating a time domain energy envelope line, and performing synchronous time domain segmentation on the noise signal to obtain noise signal slices; filtering non-related components of the vibration characteristic order component in the noise signal slice, and extracting a vibration synchronous noise component of the vibration characteristic order component synchronous with the current rotation period of the motor according to a filtered result; constructing a sound-vibration fusion spectrum; analyzing the distribution rule of the sound and vibration intensity in the sound and vibration fusion spectrum, identifying a correlation mutation point, and diagnosing a specific operation state fault based on the distribution mode of the correlation mutation point; according to the invention, the efficiency of vibration noise combined diagnosis generation of the motor operation state can be improved.

The invention relates to the technical field of audio and video control, and discloses an audio and video player control method based on a voice instruction. The method comprises the steps that an original voice instruction stream of a user is collected, the instruction stream comprises a time domain audio signal sequence, an environment noise spectrum and user pronunciation characteristic parameters, and voice information can be comprehensively captured; multi-modal instruction analysis processing is carried out on the original voice instruction stream, a structured control instruction set containing acoustic control intention identification, semantic operation object description and context correlation parameters is generated, and the analysis precision is improved; then executing player state adaptation based on the set, generating a dynamic control response sequence containing an equipment state adjustment command, a media content positioning parameter and an interface interaction logic identifier, driving a player to execute a multi-dimensional control operation and generating real-time play control effect feedback data; and finally, multi-modal analysis parameters are optimized according to feedback data, a self-adaptive instruction analysis strategy is generated, and the control experience of a user on the audio and video player is optimized.

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 the technical field of wireless communication, in particular to a wireless communication network system and method for intelligently fusing a multi-mode heterogeneous network. Comprising a multi-mode access unit; a protocol compatibility and conversion unit; the heterogeneous network intelligent fusion unit completes cross-network dynamic flow collaboration and resource optimization decision through cross-layer feature association mining, service-network two-dimensional weight calibration and a double-closed-loop feedback optimization mechanism, generates a resource scheduling instruction and transmits the resource scheduling instruction to the intelligent scheduling and resource control unit; an intelligent scheduling and resource control unit; and a system control and cooperation unit. According to the invention, the service-network two-dimensional dynamic fusion weight is constructed through the intelligent fusion unit of the heterogeneous network, and bandwidth, frequency spectrum and power resources are allocated according to the priority and margin balance principle in combination with the intelligent scheduling unit, so that the problem of insufficient two-dimensional coordination of resource scheduling is solved, the resource scheduling is matched with the service demand and the network load, and the resource scheduling efficiency is improved. And resource waste or incapability of meeting business requirements is avoided.

The invention relates to the technical field of industrial control, in particular to an intelligent centerless grinding machine cooling system and method for difficult-to-machine materials, and the system comprises a multi-source sensing unit, an edge calculation module, a decision center module, a precise execution module and a digital immune memory bank; compared with the defects that in the prior art, monitoring depends on a single temperature sensor, thermal field sensing is incomplete, thermal damage early warning lags behind and the like, according to the scheme, grinding power frequency spectrum, acoustic emission signals, triaxial vibration and coolant mass spectrum data are collected in real time through a multi-source sensing unit, and a micron-sized temperature field is reconstructed in combination with an edge end space-time diagram convolutional network; full-dimensional monitoring of the grinding thermal-mechanical coupling effect is achieved, and thermal anomaly recognition sensitivity and early warning timeliness are remarkably improved.

The invention provides a wind turbine generator voiceprint fault recognition method, and relates to the technical field of wind turbine generator state monitoring and fault diagnosis, and the method comprises the steps: carrying out the noise reduction of an original audio signal through variational mode decomposition, screening a target mode of which the frequency, energy and kurtosis accord with features, and reconstructing the signal; extracting a Mel frequency cepstrum coefficient and a sensing noise robust coefficient, and generating multi-dimensional voiceprint data in combination with statistical characteristics such as a frequency spectrum gravity center, a spectrum entropy, energy, kurtosis and a zero-crossing rate; constructing a support set based on the prototype network, realizing small sample fault classification by calculating the Euclidean distance between the feature vector and the prototype vector, and outputting a preliminary result; judging whether the voiceprint is abnormal according to a preset threshold value, if so, storing the voiceprint into a dynamic abnormal voiceprint knowledge base; frequently occurring abnormal samples are manually labeled and added into a support set, the prototype network is retrained to update the model, and continuous optimization of the fault recognition capability is achieved.

The invention relates to a bridge structure vibration monitoring and analysis method based on artificial intelligence, and the method specifically comprises the following steps: setting an acceleration sensor network at a key part of a bridge, collecting and marking a vibration signal sample, and forming a data set; a high-resolution time-frequency matrix of sample adaptive optimal kernel time-frequency distribution is calculated, a damage sensitive resonance frequency band is positioned according to spectrum kurtosis, and the time-frequency matrix is enhanced by the resonance frequency band subjected to adaptive gain enhancement; dividing a matrix frequency axis multi-scale binary tree, calculating and normalizing sub-band average energy, quantifying energy distribution uniformity through information entropy, and splicing multi-scale entropy values into feature vectors; then constructing and training a bridge detection data model; and finally, preprocessing new data, inputting the preprocessed new data into the trained model, and automatically analyzing to obtain a bridge health monitoring result. Through time-frequency optimization, multi-scale entropy and deep learning technologies, the problems of large noise, difficult feature extraction and low automation and accuracy of traditional monitoring can be solved.

The invention provides a cement equipment maintenance decision-making method and device based on a knowledge graph and large model reasoning, relates to the field of cement industry intelligent operation and maintenance, and solves the technical problem of decision-making response delay caused by knowledge fragmentation. The method comprises the following steps: extracting real-time characteristics from vibration spectrum signals, temperature curves and torque waveform data collected by an edge gateway, and extracting a work order entity triple from a natural language work order text of an EAM system; based on an equipment BOM list, a historical maintenance record and an FMEA analysis table, physical assembly constraint conditions are defined through ontology modeling to generate a cement equipment topological relation and a fault rule chain, and a knowledge graph is created to output a fault rule base with confidence coefficient weights. And inputting the real-time feature vector and the work order entity triple into a multi-modal collaborative inference engine, triggering a matched fault rule chain by combining real-time features and semantic features, outputting a fault root cause and an associated maintenance strategy ID, and labeling a logic chain. And activating the associated maintenance strategy ID and obtaining the real-time characteristic deviation degree of the maintenance strategy ID, quantifying the decision credibility through a tracing rule matching path, obtaining an executable maintenance instruction packet with a logic chain, executing the maintenance instruction packet and dynamically updating the knowledge graph based on a maintenance result. The method is used in the maintenance decision-making process of the cement equipment.

The invention discloses a charging method and system based on battery dynamic health state evaluation and learning prediction, belongs to the technical field of electric vehicle charging, and aims to solve the technical problem of how to break through a traditional BMS information barrier, realize deep perception and accurate prediction of a microcosmic dynamic health state in a battery, and improve the charging efficiency. According to the technical scheme, when the electric vehicle is connected to a charging pile, the charging pile establishes a communication link with a BMS (battery management system) of the vehicle through a standard protocol; a current or voltage disturbance signal with a specific frequency spectrum is injected into the battery, a response signal of the battery is measured synchronously, and dynamic health state parameters of the battery of the electric vehicle are collected in real time; and analyzing the dynamic health state parameters of the battery of the electric vehicle by using a pre-trained deep learning model, predicting the health state evolution trend of the battery, and generating a multi-target charging parameter optimization scheme.

The invention discloses a concrete internal defect quantitative detection method and system based on reflected waves, and belongs to the technical field of nondestructive testing. A reflected wave data matrix is obtained through multi-angle excitation and synchronous receiving; calculating energy characteristics of each channel, and constructing an energy response residual field; extracting waveform offset, spectrum jitter and phase change caused by defects by adopting a disturbance comparison algorithm to form a disturbance feature vector set; a defect-response function curved surface is further constructed, a defect topological structure is inversed based on gradient and curvature analysis, and defect geometric parameters are output; and finally, inputting the multi-moment defect parameters into the recurrent neural network, and predicting a defect evolution path and a failure risk. The method has high resolution and trend prediction capability, and is suitable for detection and early warning of concrete structures in bridges, tunnels and nuclear power projects.

The invention relates to a microphone array sound source positioning method and system based on cross-correlation-beam forming closed-loop optimization, and belongs to the technical field of sound source positioning. The method comprises the following steps: collecting multichannel sound signals through a microphone array and preprocessing the multichannel sound signals to extract time-frequency features and suppress noise interference; time delay information among the microphones is estimated by adopting a generalized cross-correlation phase transformation algorithm, and an optimization strategy is introduced to improve estimation stability and anti-interference performance; enhancing the target sound source signal in combination with a minimum variance undistorted response beam forming algorithm and an adaptive Kalman filtering mechanism; constructing a closed-loop feedback optimization mechanism based on the beam output signal to realize feedback adjustment; and adopting a hybrid network architecture, taking the beam output signal amplitude spectrum as input, and outputting the frequency spectrum or mask of the obtained target sound source signal. The method has the advantages of high calculation efficiency, high positioning precision and strong anti-interference capability, and is suitable for real-time acoustic signal processing in a complex environment.

The invention relates to a dynamic precision compensation control method based on an intelligent PCB drilling machine, and the method comprises the steps: collecting a dynamic parameter set in real time through a multi-source sensor group disposed on a drilling main shaft, the dynamic parameter set comprises displacement deviation caused by axial deformation of a main shaft, thermal deformation analyzed based on interlayer temperature gradient and frequency spectrum characteristics reflecting tool-material coupling vibration, and according to the multi-dimensional compensation vector and current drilling process parameters, a space trajectory correction instruction and a motion parameter optimization instruction are dynamically synthesized, so that the dynamic parameter set is obtained. Wherein the trajectory correction instruction reconstructs a real-time interpolation path through a B-spline curve, the motion controller receives a space trajectory correction instruction and a motion parameter optimization instruction, at least three times of compensation iteration are completed in a single drilling period, a linear motor and a spindle motor are driven to execute online precision compensation, and the execution delay of the compensation instruction is smaller than 2 ms.

The present invention relates to the technical field of communication anti-interference, and relates to an interference confrontation method, system, device and medium for a wireless communication network, the method comprising: obtaining spectrum data, the spectrum data comprising spectrum data deployed and collected by a wireless network according to an initial deployment scheme; performing feature extraction and fusion processing on the spectrum data to generate a spectrum situation map; performing reinforcement learning decision processing according to the frequency spectrum situation map to generate a dynamic anti-interference strategy; according to transmitting node combination parameters in the dynamic anti-interference strategy, a collaborative bait node group is obtained through distributed consensus algorithm processing; sending a virtual signal to an interference source according to the dynamic anti-interference strategy and the collaborative bait node group; and calculating the strategy effect of the virtual signal by using a reinforcement learning reward function to obtain an optimized anti-interference strategy, thereby breaking through the limitation of insufficient static coping and cooperation of a traditional anti-interference method, and realizing communication reliability and anti-interference adaptability in a complex dynamic interference environment.

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 combustion monitoring, and discloses a combustion instability boundary prediction system based on complex environment condition analysis, and the system comprises a multi-source sensing module, a data fusion module, an anti-interference processing module, an instability feature library, an intelligent decision engine and a digital twinborn body. A dynamic environment interference compensation mechanism is constructed, spectrum correction parameters are configured according to different environment working conditions during real-time prediction of a combustion instability boundary, the problem of spectrum distortion caused by environment disturbance on combustion dynamic parameters is eliminated in real time, spectrum interference deviation of a combustion measurement position can be automatically sensed and corrected, and real-time prediction of the combustion instability boundary is realized. The accuracy of combustion instability sensitive feature extraction is guaranteed, the instability early warning error is reduced, a flame pose real-time tracking system is deployed, fuel control parameter self-adaptive adjustment is triggered immediately when it is detected that the pose is abnormal, autonomous correction of the combustion instability feature recognition position is achieved, and it is guaranteed that the feature capture space is accurately positioned.

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 relates to the technical field of motor detection, and discloses a motor instantaneous torque measurement method and system based on a high-bandwidth sensor, and the method comprises the steps: synchronously collecting a torque signal and a multi-source interference variable through the high-bandwidth sensor, and forming an original torque sequence after time synchronization superposition; extracting disturbance frequency characteristics through multi-scale wavelet transform and spectral analysis; a pre-trained adaptive filter model is utilized to generate a filter coefficient matched with the working condition, and primary filtering is completed; residual fluctuation is suppressed in combination with Kalman state estimation, and an optimized torque estimation value is obtained; parameter self-adaptive compensation is realized through online calibration weight updating, and a stable torque measurement sequence is output; load abrupt change pre-judgment and threshold value dynamic updating are achieved based on time-frequency correlation analysis, and finally rapid measurement convergence after working condition abrupt change is ensured through a closed-loop reset mechanism. According to the method, the problem of low instantaneous torque measurement precision in the prior art can be solved.

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 belongs to the technical field of power supply management, and particularly relates to a high-precision DCDC power supply voltage stabilization calibration system and method, which comprises a DCDC power conversion module, a traditional feedback control module, a high-frequency ripple sampling and digitization module, a dynamic load prediction module, a feedforward compensation generation module and a calibration management module, through high-frequency ripple sampling and digital output of ripple signals, real-time spectrum analysis is carried out to predict load transient changes, prospective compensation signals are generated, and traditional feedback control is combined to finely adjust PWM signals of the DCDC module. According to the scheme, feedback lag is overcome, transient overshoot / undershoot of voltage is remarkably restrained, the dynamic voltage stabilization error is controlled within + / -0.3%, and high-precision voltage stabilization under both static and dynamic working conditions is achieved.

The invention relates to the technical field of power distribution station inspection, and discloses a power distribution station automatic inspection method based on image recognition. According to the method, multi-spectral image data of multiple areas in a power distribution station are collected in real time, and a multi-channel feature tensor of an equipment state is generated through a feature extraction network; performing feature fusion of space and frequency spectrum dimensions on the multichannel feature tensor by using a multi-scale convolutional attention network, and outputting an enhanced device feature map; inputting the data into a cascade anomaly detection module, positioning an equipment surface defect region by adopting a region segmentation algorithm, and analyzing and generating a defect evolution trend vector in combination with time sequence characteristics; on the basis of the vector, probability distribution of equipment fault risks is predicted through a space-time propagation model, and a dynamic risk field is generated; and finally, constructing a self-adaptive early warning decision tree for the dynamic risk field, and generating an inspection maintenance instruction according to risk probability threshold grading. According to the method, automation and intelligentization of power distribution station inspection are realized, and support is provided for efficient maintenance of the power distribution station.

The invention discloses a fault diagnosis method and device, computer equipment and a computer readable storage medium, relates to the technical field of power systems and automation thereof, and solves the problem of fault misjudgment caused by important fault characterization of a method which is easy to lose when single modal information is used for fault diagnosis at present. The method comprises the following steps: converting a bus voltage signal into a two-dimensional time-frequency diagram based on a continuous wavelet transform model, and converting the bus voltage signal into a one-dimensional frequency spectrum sequence based on a fast Fourier transform model; performing feature extraction on the two-dimensional time-frequency graph based on a time-frequency image feature extraction model to obtain a first feature vector, and performing feature extraction on the one-dimensional frequency spectrum sequence based on a frequency spectrum sequence feature extraction model to obtain a second feature vector; performing feature fusion on the first feature vector and the second feature vector based on a feature fusion model, and obtaining a category probability vector by using a fault diagnosis model based on the fused feature vector; and determining the highest probability value in the category probability vector, and taking the corresponding fault type as a target fault type.

The invention relates to the technical field of distributed optical fiber acoustic sensors, in particular to a distributed optical fiber sensor vibration identification method and system, and the method comprises the steps: obtaining original data, and carrying out the preprocessing; inputting the preprocessed vibration time sequence into a Mamba model, and extracting a time domain feature vector reflecting a time evolution rule; after obtaining a time-frequency spectrogram of the preprocessed vibration time sequence, extracting to obtain a frequency domain feature vector representing different frequency components and a global association relationship thereof; inputting the time domain and frequency domain feature vectors into a time-frequency feature fusion model, and outputting a vibration event recognition result of the distributed optical fiber acoustic sensor; training the time-frequency feature fusion model, and storing the optimal model weight for prediction; and performing prediction by using the optimal model, storing a prediction result, performing verification, calculating an evaluation index, and finally performing result comparison. The invention aims to solve the problems of insufficient performance and the like of a distributed optical fiber acoustic sensing technology in the prior art.

The present application discloses a method for calculating the risk of failure of a component on the basis of vibration load simulation, comprising: inputting a determined PSD spectrum load into predetermined target software, so as to convert the PSD spectrum load into initial time domain information; executing a signal repetition operation, obtaining target time domain information, and calculating fatigue damage information; converting the information into target frequency domain data, and generating compressed spectrum data; and inputting the data into a vibration simulation model, obtaining a model output result, and determining a risk of failure evaluation result of a target component.

The invention relates to the technical field of intelligent regulation and control of fusion data, in particular to a rolled finished product-based size precision intelligent regulation and control system and method, which comprises a data acquisition unit, a data fusion unit, a deep learning prediction unit and a compensation execution unit, multi-source data such as temperature gradient, roll gap fluctuation and tension spectrum are collected in real time through a sensor array, thermal expansion compensation correction is carried out through a data fusion unit based on a rolling elasticoplastic deformation equation, an anti-interference coupling feature vector is generated by using adaptive Kalman filtering, and a deep learning prediction unit is used for predicting the rolling elasticoplastic deformation through a dual-channel space-time convolutional network. The thickness error and the width error are predicted by combining the constraint of a rolling force balance equation, the compensation execution unit dynamically generates the roll gap adjusting amount and the rolling force compensation amount according to a three-level response mechanism, phase lag compensation current is injected to suppress resonance, the high-end steel yield and the rolling precision are improved, and the rolling quality is improved. And an intelligent closed-loop control scheme is provided for high-precision rolling under complex working conditions.

The invention provides an underwater acoustic target recognition method based on autonomous task perception, and belongs to the field of underwater acoustic signal processing and artificial intelligence. S3, performing feature extraction on the time-frequency spectrogram by the task type extraction network to obtain a task embedding vector, calculating a similarity score, when the maximum similarity score is greater than a set threshold value, outputting a task representation vector, and entering S3, otherwise, inputting features output by the last Transform layer into a classifier; s3, selecting a router according to the task representation vector, selecting a trained expert network by the router, calculating a door control weight, processing universal acoustic features in parallel by the expert network, fusing output of the expert network, fusing deep features and fused features to obtain enhanced features, and enabling the enhanced features output by the last Transform layer to enter a classifier; the invention further provides a system. The problems that the task category autonomous recognition capability is insufficient and the correlation between tasks is ignored are solved.

The invention discloses a tunnel monitoring method and system based on a vibrating wire sensor, and relates to the technical field of tunnel engineering construction safety monitoring, and the method comprises the steps: collecting the vibration frequency data and spatial position coordinates of the vibrating wire sensor in a monitoring region; the method comprises the following steps: calculating a time difference and an energy attenuation ratio between adjacent sensors by extracting spectrum energy density distribution, and constructing a stress wave propagation field; dividing grids in the propagation field to calculate node stress values, generating a stress field distribution cloud picture and extracting a stress concentration area; calculating a gravity center position coordinate change rate and a stress time sequence curve of the stress concentration area, and determining fracture extension parameters; and calculating a surrounding rock bearing capacity attenuation curve based on the parameters, and outputting an early warning signal when the slope exceeds a threshold value. According to the invention, real-time monitoring and early warning of the tunnel surrounding rock instability risk are realized.