253 results about "Dominant frequency" patented technology

A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.

The invention discloses a multivariable fusion power load prediction method and system, and relates to the technical field of load prediction, and the method comprises the following steps: obtaining first data, and synchronizing the first data into second data based on a physical constraint interpolation method; shielding the harmonic dominant frequency band based on the second data, and de-noising the load waveform by combining the real fluctuation of the filtering separation load; a load prediction model is constructed based on the denoised load waveform in combination with a harmonic distortion rate weighted double-flow network, prediction model parameters are corrected in real time, and a load prediction value is output; the real-time correction is a dynamic correction strategy based on wavelet transform. Through a dynamic selection interpolation method, the load data, the meteorological data and the new energy output data can be synchronized on a unified time scale, the synchronism and precision of different data sources can be ensured, high-quality input data is provided for a prediction model, and the reliability of a prediction result is improved.

The invention relates to the technical field of nondestructive testing, in particular to a leak detection and partial discharge digital imaging detection method and system based on acousto-optic fusion, and the method comprises the following steps: based on channel microphone array sound wave data in a partial discharge signal suspicious region, extracting a sound wave abnormal section, positioning a sound source, matching image edge features, and synchronously marking; and analyzing the phase change of the multi-frequency signal to judge a sound source concentration area, tracking the moving trend of a disturbance point, and outputting an acousto-optic fusion positioning trend track. According to the method, the partial discharge feature recognition sensitivity is improved through high-frequency peak paragraph screening and dominant frequency recognition, the abnormal region positioning precision is enhanced in combination with image edge extraction and sound source space matching, and acousto-optic synchronous positioning and trend trajectory display are achieved through multi-frequency signal phase analysis and image frame disturbance tracking. Through fusion of frequency domain feature extraction, image recognition, dynamic comparison and other actions, the spatial precision of abnormal source recognition and the multi-source fusion analysis efficiency are improved, and the partial discharge traceability and dynamic monitoring capability are enhanced.

The invention provides a joint denoising method and system based on multi-source noise suppression and sparse representation, and belongs to the technical field of noise suppression in ultrasonic detection of power equipment, and the method comprises the steps: synchronously collecting an ultrasonic echo signal, a vibration signal and an electromagnetic signal of a composite insulator; decomposing the ultrasonic echo signal into a plurality of modal components and undecomposed signal margins; extracting a dominant frequency component in the vibration signal, and identifying a resonance noise feature overlapped with the frequency band of the ultrasonic signal; detecting a pulse interference event in the electromagnetic signal, marking occurrence time and a frequency range, and generating a pulse event feature; performing frequency band suppression and time domain threshold processing on the modal component according to the resonance noise characteristics and the pulse event characteristics; performing sparse representation coding on the processed modal components, executing multiple rounds of iterative screening and orthogonalization processing, and screening an atom set matched with defect echoes; and reconstructing the de-noised ultrasonic signal by using the screened atom set, and combining the de-noised ultrasonic signal with the undecomposed signal margin to output a final result.

The invention discloses a steam turbine regulating valve state evaluation method, and belongs to the technical field of thermal power plant steam turbine valve state monitoring, and the method comprises the steps: collecting the video stream data and operation data of a to-be-evaluated steam turbine regulating valve rod, recognizing the contour of the valve rod based on a YOLOv5 model according to the video stream data, and outputting the feature points of the valve rod; tracking the two-dimensional coordinates of the feature points of the valve rod to calculate the vibration parameters of the valve rod; the method comprises the following steps: calculating a Pearson correlation coefficient of operation data, screening vibration parameters related to a jam fault to generate a time domain displacement sequence, performing time alignment with the operation data, performing fast Fourier transform to obtain a frequency spectrum, and identifying a dominant frequency component, a frequency multiplication component and a sideband frequency; calculating a frequency deviation and an energy distribution difference based on a preset reference spectrum library; and judging the jam grade according to the frequency deviation and the energy distribution difference. The technical problem that the jamming fault level cannot be accurately quantified by an existing turbine regulating valve state monitoring method is solved.

The invention discloses a multivariate monitoring data fusion method and system for a slope tunnel model test, particularly relates to the technical field of geotechnical engineering physical model test, and aims to solve the problem that dynamic stress waves in an existing model test integrating static and dynamic measurement cause static monitoring signal distortion and further influence the model state evaluation accuracy. Coupling interference in static signals is identified by synchronously collecting dynamic excitation and static response signals; obtaining signal samples before and after a model state sudden change event to perform frequency domain comparative analysis, identifying characteristic frequency components related to sudden change, and evaluating a coupling interference dominant frequency band; separating an effective static response component from the original static signal based on the frequency band; and finally, fusing the effective component with a soil dynamic parameter derived from dynamic excitation to generate comprehensive stability evaluation data. Dynamic interference is effectively suppressed, the authenticity of static signals is improved, and the reliability of model stability state evaluation is improved.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention relates to a cable joint resonance real-time diagnosis method and device and a medium, and belongs to the technical field of power equipment state monitoring, and the method comprises the following steps: obtaining a high-frequency current signal collected by a high-frequency current sensor and an ultrasonic vibration signal collected by an ultrasonic sensor array; performing signal synchronization on the high-frequency current signal and the ultrasonic vibration signal, and extracting a high-frequency current feature and an ultrasonic array feature; calculating a dominant frequency synchronization index, a normalized phase stability index and a nonlinear coupling index based on the high-frequency current characteristics and the ultrasonic array characteristics, and performing weighted summation to determine a fusion confidence coefficient function; and judging whether resonance exists or not based on the fusion confidence coefficient function, and if so, carrying out resonance source positioning. Compared with the prior art, the method has the advantages of high precision, low false alarm and high adaptability, and is suitable for key power system scenes such as new energy grid connection, urban smart power grids and wind power.

The invention relates to a switch cabinet operation reliability evaluation method, system and device and a medium, and belongs to the technical field of power distribution equipment monitoring, and the evaluation method comprises the steps: receiving real-time monitoring data of a to-be-detected switch cabinet; performing fast Fourier transform on the current waveform data, and calculating to obtain a harmonic distortion rate sequence; performing feature extraction on the vibration spectrum data to obtain a vibration dominant frequency amplitude sequence; correlation analysis is carried out on the harmonic distortion rate sequence and the vibration dominant frequency amplitude sequence, a sliding covariance matrix is constructed, and a dynamic coupling coefficient is calculated; generating a three-dimensional feature space distribution diagram according to the harmonic distortion rate sequence, the vibration dominant frequency amplitude sequence and the dynamic coupling coefficient sequence; and dividing the real-time monitoring data into a plurality of feature regions based on density clustering, determining a current fault state corresponding to the to-be-detected switch cabinet according to the feature regions mapped by the real-time monitoring data, determining a corresponding maintenance strategy, generating a reliability evaluation report, and sending the reliability evaluation report to the management terminal. The operation stability of the switch cabinet equipment can be improved.

The invention discloses a control method and device of a wind driven generator, a terminal and a storage medium, particularly relates to the technical field of operation control of the wind driven generator, and is used for solving the problem that a supporting structure bears violent asymmetric loads due to dynamic response differences of wind and wave load directions in a marine environment in an existing control method. Wind direction and wave direction data are synchronously obtained in real time, the wind direction change rate is calculated, supporting structure vibration signals are analyzed to recognize wave excitation dominant frequency components, and when it is detected that wave excitation energy suddenly changes and the wind direction change rate exceeds the limit, the wind wave direction difference is analyzed; meanwhile, a wave spectrum energy concentration frequency band is determined by analyzing the tower top movement track, the load asymmetry risk level is evaluated by combining the direction difference and the wave energy concentration frequency band, finally, the yaw speed and the variable pitch speed are adjusted in a self-adaptive mode according to the risk level, and accurate recognition and early warning of the wind wave load direction mismatching working condition are achieved. And the operation reliability of the wind driven generator in extreme weather is improved.

The invention relates to the technical field of real-time monitoring and early warning, in particular to a real-time monitoring and early warning method and system for house safety, and the method comprises the following steps: obtaining a dominant frequency amplitude and an inclination change rate, screening abnormal nodes through combining a distance, correlating temperature to eliminate thermal interference, analyzing a phase and energy to recognize an abnormal region, and evaluating aging influence through combining harmonic waves and stress. And analyzing the conduction relationship to generate early warning information. According to the method, the abnormal recognition accuracy is improved through linkage judgment of the vibration dominant frequency and the inclination change rate, space aggregation screening is achieved in combination with the node spacing, thermal disturbance misjudgment is effectively eliminated through dominant frequency offset and temperature gradient joint analysis, and the regional continuous abnormal recognition capacity is enhanced through vibration phase and energy difference coupling analysis; harmonic response is compared with material aging data to establish conduction analysis of a degradation determination path, a phase difference, inclination and temperature change to strengthen structure chain type risk identification, and risk identification precision and early warning effectiveness are integrally improved.

The invention discloses an acousto-optic radar wind measurement data correction system based on adaptive noise suppression, and relates to the technical field of data correction. Environmental parameters and acousto-optic radar system parameters are combined through a dynamic adjustment module to dynamically adjust a signal-to-noise ratio threshold; the echo signal quality analysis module inputs the frequency spectrum characteristic data and the dynamically adjusted signal-to-noise ratio threshold value into an echo quality model and judges whether the current echo signal is valid or not, if yes, interpolation is carried out by adopting a spatial smooth interpolation method, if yes, the dominant frequency of the echo signal is extracted, Doppler frequency shift is calculated, and according to the detection angle and the laying direction, the Doppler frequency shift is calculated; and calculating a radial wind speed, inverting a three-dimensional wind speed vector and carrying out physical consistency correction. According to the correction system, the dynamic adjustment module is introduced, real-time environment parameters and system operation parameters are combined, and a signal-to-noise ratio threshold value required by judgment is dynamically adjusted, so that a judgment basis can be intelligently self-adapted along with a measurement environment, and the accuracy and robustness of echo signal quality judgment are remarkably improved.

The invention discloses a new energy missing data completion method and system based on multi-model fusion. The method comprises the following steps: performing linear and nonlinear correlation two-stage feature screening on a collected new energy data set; normalizing the data set, constructing a missing data mask according to a missing proportion parameter, and dividing the data set; the designed AFMFormer model is constructed and trained; a self-adaptive frequency domain feature extraction module constructed in the model utilizes a data-driven dominant frequency extraction and frequency spectrum noise suppression mechanism to enhance the modeling capability of the model for a long-sequence dominant mode structure; the multi-granularity time sequence coding module realizes semantic consistent feature fusion through short-term features and long-term trends of a multi-scale modeling time sequence and through a learnable weight and an adaptive alignment mechanism, and generalization ability training of the model under a complex time sequence structure is further improved. Compared with a traditional method, the method shows higher precision, robustness and generalization ability in a high-fluctuation and high-missing-rate data scene.

The invention discloses a contact service life prediction method based on big data, and relates to the technical field of contact service life prediction.The contact service life prediction method comprises the steps that operation data of a contact is collected, multi-modal feature fusion is conducted on the operation data, an arc energy dominant frequency, an impact damage amount, a temperature rise rate and an environment corrosion index are extracted, and an N * 8-dimensional feature matrix is constructed; on the basis of the final predicted life data calculated in the step S2, vibration correction factors and resistance change rates are obtained in real time, the final predicted life data are verified in combination with a digital twin platform, and model parameters are optimized. And the contact stability of the contact is combined, so that the precision of predicting the service life of the contact is remarkably improved. And meanwhile, real-time prediction and early warning are realized, the adaptability is enhanced, the equipment failure rate and the maintenance cost are reduced, and a powerful guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of electrical sensor measurement, and provides a fault test method, system and equipment suitable for a fan converter, and the method comprises the steps: collecting current data in an IGBT module of the fan converter, and taking the current data as an original current signal; screening a plurality of dominant frequencies of the original current signal; decomposing the original current signal to obtain a plurality of component signals, and corresponding to each dominant frequency to obtain a plurality of dominant frequency signals; screening to obtain a reference signal; according to the variation trend difference between each component signal and the reference signal, the variation abnormity of each component signal is obtained; analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix to obtain the energy difference index of each component signal; obtaining an anomaly index of each component signal; and performing fault detection on the fan converter based on the anomaly index of each component signal. The invention aims to solve the problem that high-frequency electromagnetic noise affects signal performance of a current sampling loop and covers real fault features.

The invention discloses a sound source localization method based on multi-frequency separation and Newton optimization deconvolution, relates to the technical field of array acoustic signal processing, and is used for solving the problem that weak sound sources and multiple sound sources are difficult to identify. According to the method, dominant frequency is extracted through multichannel frequency domain analysis, a cross-spectrum matrix is constructed in combination with a near-field propagation model and a guide vector, delay summation beam forming is executed to obtain sound source preliminary distribution, then the distribution is regarded as a convolution result, a maximum likelihood model is introduced, and a two-stage deconvolution strategy of coarse estimation and Newton method fine optimization is adopted to obtain a high-resolution sound source. According to the multi-sound-source positioning method, subgrid-level analysis of sound source positions and amplitudes is achieved, finally, all frequency results are fused, continuous sound source images are smoothly output through a two-dimensional Gaussian kernel, the resolution and real-time performance of multi-sound-source positioning are remarkably improved, and the multi-sound-source positioning method is suitable for high-precision acoustic imaging in a complex sound field.

The invention relates to the technical field of digital signal processing, and particularly discloses a mixed precision low-resource digital control oscillator implementation method and system based on an FPGA (Field Programmable Gate Array), and the method comprises the steps: dividing a 32-bit phase accumulator into a high-bit 24-bit dominant frequency control section and a low-bit 8-bit dynamic compensation section, and carrying out the phase accumulation in parallel through double carry chains, the high-order section receives high 24 bits of a frequency tuning word FTW to control a dominant frequency, and the low-order section receives low 8 bits of the FTW and overlaps an overflow remainder to dynamically compensate for a phase error. According to the implementation method of the mixed precision low-resource digital control oscillator based on the FPGA disclosed by the embodiment of the invention, through collaborative innovation of mixed precision phase accumulation, hierarchical mixed storage, high-order interpolation reconstruction and dynamic phase compensation, the core problems of a traditional NCO in resource occupation, phase continuity, waveform precision and time sequence performance are systematically solved.

The invention relates to the technical field of sleep quality evaluation, in particular to a sleep monitoring optimization system based on artificial intelligence. The system comprises the steps of collecting sleep monitoring data of a user at different collection moments, extracting dominant frequency components in snore audio data through spectrum envelope analysis, extracting audio primitives based on the dominant frequency components, inputting the audio primitives into a snore classification model, outputting a snore category corresponding to the snore audio data of the user, and outputting the snore category corresponding to the snore audio data of the user. Carrying out Bayesian inversion analysis on the brain wave data to obtain a posterior sample of random field model parameters, executing reliability analysis based on the posterior sample to calculate a posterior failure probability, obtaining a first sleep quality index of the user based on the posterior failure probability, obtaining a second sleep quality index of the user based on vital sign data analysis, and sending the second sleep quality index to the user; and obtaining a user sleep quality evaluation result based on the snore type, the user first sleep quality index and the user second sleep quality index. The accuracy and efficiency of user sleep quality evaluation can be improved.

The invention provides a sleep breathing condition detection method and system and electronic equipment, and relates to the technical field of human body sign monitoring, and the method comprises the following steps: obtaining a first breathing signal sampled by an optical fiber micro-vibration sensor; performing low-pass filtering processing on the first respiratory signal to obtain a second respiratory signal; envelope analysis and frequency analysis are carried out on the second respiration signal, and a target envelope width sequence and a target respiration dominant frequency sequence are determined; and according to the target envelope width sequence, the target respiration dominant frequency sequence, the reference envelope width baseline and the reference respiration frequency baseline, determining the sleep respiration condition of the user. According to the sleep breathing condition detection method, a large number of electrodes and wires do not need to be pasted on the human body, the use convenience is high, and the sleep comfort of the user is improved. In addition, the envelope width is combined with time-frequency analysis, the accuracy is improved through a multi-dimensional feature cross validation mode, and the accuracy of abnormal breathing event detection is high.

The invention discloses a transformer substation AC power supply residual current detection method and device, and the method comprises the steps: carrying out the vector synthesis of three-phase current data and zero line current data, forming a current difference sequence, exporting a residual current reference curve, and carrying out the fluctuation analysis of the residual current reference curve, and judging the leakage characteristics. After effective leakage features are formed through harmonic elimination, dominant frequency components are locked through frequency spectrum decomposition; calibrating a leakage type identifier according to the dominant frequency component and forming segmented leakage distribution; a deviation degree is formed by means of segmented leakage distribution and residual current reference curve deviation analysis, a correction deviation value is formed through load fluctuation compensation, and a standard exceeding coefficient is formed according to threshold value comparison to detect abnormal nodes to form hidden danger data; establishing a judgment rule based on the hidden danger data and the leakage type identifier to form a detection evaluation index; and executing path tracking on the detection evaluation indexes to form leakage position information, forming a disposal priority through intensity analysis to generate a residual current detection report, and realizing leakage current classification identification and self-adaptive evaluation.

The invention relates to the technical field of industrial automation, and discloses an energy consumption optimization control method and system for an automatic industrial production line. The method comprises the steps of collecting an equipment energy consumption gradient sequence through a fixed period and extracting a load demand, and identifying a high-load section exceeding a threshold value; performing frequency domain transformation on the segment to obtain a standard feature vector, extracting a dominant frequency, and mapping the dominant frequency into a discrete control instruction; if the concurrent change of multiple devices is detected, starting a distributed protocol to collect node feedback, constructing a power equation set to solve active, reactive and phase angle corrections, and generating a power supply adjustment parameter set; screening the subset corresponding to the high load to update the power distribution strategy, outputting an optimization control signal, and performing closed-loop iteration with an efficiency threshold, and finally forming a cooperative adjustment instruction sequence, thereby improving the power supply stability and energy-saving robustness of the industrial production line in a load sudden change scene.

The invention relates to a method for obtaining credible blasting vibration dominant frequency, which solves the problem of dominant frequency prediction under complex geology through EMD-HHT multi-scale decomposition and statistical confidence interval analysis and is difficult to adapt to dynamically changing construction conditions. EMD decomposition adaptively eliminates low-frequency noise caused by nonlinear response of the rock mass, and the signal-to-noise ratio is increased; a main frequency determination strategy is automatically switched through confidence interval width (RW) to adapt to complex geological conditions; 95% confidence coefficient ensures that a dominant frequency predicted value accords with actual energy distribution; compared with a traditional Samsu formula, the main frequency prediction error is reduced by more than or equal to 40%.

The invention discloses a battery mechanical damage monitoring method and system in the transportation process of an electric vehicle based on acoustic emission spectrum analysis, and the method comprises the steps: carrying out the multi-scale decomposition of an original signal sequence through wavelet transform, separating out a high-frequency transient component and low-frequency background noise, and obtaining a denoised elastic wave signal; calculating time domain features including peak amplitude and duration according to the denoised elastic wave signal, and combining frequency domain features such as a main frequency component to obtain a comprehensive feature vector; if the peak amplitude of the comprehensive feature vector exceeds a preset threshold value, judging that the event is a potential damage event, and extracting a damage related subset from the feature vector to obtain a damage candidate feature; training the damage candidate features through a support vector machine classifier to obtain damage type labels; and aiming at the damage type label fusion transportation environment data, a sliding window is adopted to analyze and track the signal change trend, and the damage evolution degree is determined. According to the invention, accurate identification, classification and dynamic monitoring of transportation damage of the battery pack are realized.

The invention discloses a partial discharge signal feature extraction method and system, and the method comprises the following steps: collecting a current signal on a grounding loop cable of detected electrical equipment, and carrying out the preprocessing of the current signal; converting the preprocessed signal into a two-dimensional matrix, and performing singular value decomposition on the two-dimensional matrix to obtain a principal component; matrix reconstruction is carried out based on the principal component to generate a de-noising matrix, and the de-noising matrix is restored to a time domain signal sequence; performing discrete wavelet transform on the time domain signal sequence to obtain a multi-level approximation coefficient and a detail coefficient; the discrete wavelet transform selects a wavelet basis function from the candidate wavelet basis set based on a preset adaptive wavelet basis selection mechanism, and adaptively determines the number of wavelet decomposition layers based on the signal dominant frequency characteristics; and inputting the approximation coefficient and the detail coefficient into an inverse wavelet transform module to obtain a final partial discharge signal. According to the method, efficient, accurate and universal partial discharge signal feature extraction can be realized.

The invention provides a power distribution network line fault monitoring and early warning method, which comprises the following steps of: acquiring line vibration data through a sensor array, analyzing vibration frequency distribution by utilizing fast Fourier transform, extracting dominant frequency, comparing the dominant frequency with an inherent frequency database, judging a resonance risk, and further calculating an amplitude change trend by adopting a sliding window algorithm. And evaluating the fatigue accumulation degree by combining a material fatigue model. A Bayesian probability model is innovatively introduced, the influence of current load and wind action is fused, the resonance risk posterior probability is calculated, a support vector machine algorithm is adopted to classify and evaluate the risk, accurate early warning is achieved, finally, an early warning signal is transmitted through wireless communication, a vibration suppression control signal is generated to adjust a damping device, and the vibration suppression effect is achieved. And stable line operation is realized. According to the invention, accurate identification, real-time early warning and active suppression of the resonance risk of the power transmission line are realized, and the operation safety and reliability of a power grid are improved.

The invention belongs to the field of seismic data processing, relates to a stable deconvolution method based on mixed norm constraint and frequency band optimization, and aims to solve the problems that the prior art is easily interfered by noise and is insufficient in data resolution and dynamic optimization in a low signal-to-noise ratio and complex geological environment. The method comprises the following steps: obtaining a linearization frequency division operator according to collected seismic data; based on a linearized frequency division operator, establishing a stable deconvolution objective function of sparsity constraint and mixed norm constraint; decomposing the target function into a plurality of sub-problems based on an ADMM algorithm, alternately optimizing and updating each variable, and solving an approximate optimal solution; and stopping iteration until a termination criterion is met, taking the variable obtained at the last time as an approximate optimal solution, and outputting a stable deconvolution result. According to the method, the mixed norm sparsity constraint and the linearization frequency division operator are introduced, so that the sparsity of data can be effectively kept, meanwhile, the dominant frequency band range is accurately controlled, and the authenticity and precision of a deconvolution result are ensured.

The invention belongs to the technical field of intelligent oil field and oil and gas yield prediction, and particularly discloses an oil well yield prediction method and system based on a frequency domain enhanced BiLSTM-Transformer hybrid model, and the method comprises the steps: collecting oil well historical production time sequence data, and carrying out the abnormal value elimination and normalization preprocessing; constructing a multi-scale feature system, and integrating a time domain lag term, a rolling statistic, and frequency domain dominant frequency and amplitude features extracted through Fourier transform; introducing an oil well embedding vector to explicitly compensate the inter-well static difference; the features are input into a BiLSTM-Transform hybrid model, a local dynamic state is captured by the BiLSTM, and a global trend is modeled by the Transform; an oil well level adaptive optimization strategy is adopted, sample imbalance is relieved through sample dynamic weighting and adaptive learning rate scheduling, and the generalization ability is improved in combination with gradient cutting and an early stop mechanism; and finally outputting an oil well yield prediction value. According to the method, the problem of prediction deviation caused by yield sequence non-stationarity, inter-well difference and sample imbalance is effectively solved, and the prediction precision is remarkably improved.

The invention relates to the technical field of power system state monitoring and fault diagnosis, and discloses a fault detection method in the operation state of power distribution equipment. Arranging current acquisition channels at a bus and a branch end, and acquiring a current sequence at a sampling frequency in a time period; a multi-scale primary function family based on sine and exponential attenuation envelope is constructed, attenuation parameters and center frequency are set according to scales and frequency bands and normalized, sliding window segmentation is combined, window subsequences are projected to a multi-scale primary function set, and an energy spectrum is calculated; in a stable stage, averaging energy spectrums to construct an energy reference, calculating an energy offset rate for a subsequent window and extracting a maximum offset index, forming a robust fault threshold by using a median and an absolute deviation thereof, positioning a fault time period and a dominant frequency band of an abnormal window, and outputting a fault channel and characteristics; therefore, misjudgment of sampling and fixed threshold values on transient feature recognition is reduced, and the fault detection accuracy is improved.

The invention discloses a settlement degree measuring method and system for bridge design, and belongs to the field of bridge engineering. Geological profile data in a bridge site range is collected, a settlement influence function is constructed based on geological parameters, and a bridge pier load matrix is extracted in combination with bridge design load working conditions; multiplying the settlement influence function by the load matrix to obtain a design settlement initial value of each pier; further calculating a settlement difference value between adjacent piers, constructing a bridge floor elevation deviation function, performing discrete Fourier transform on the bridge floor elevation deviation function, and extracting a dominant frequency characteristic value to judge whether a high-frequency settlement fluctuation behavior exists or not; if the dominant frequency exceeds a preset threshold value, marking the corresponding pier as a settlement sensitive area, and outputting a load adjustment parameter and a foundation elevation correction suggestion; the method can realize prospective identification and parameterized regulation and control of the non-uniform settlement trend in a bridge design stage, has the advantages of high structural adaptability, high prediction precision and the like, and is suitable for bridge foundation optimization design in a complex geological environment.

The invention provides a non-stationary signal fault diagnosis method and a storage medium, and the method comprises the steps: carrying out the discretization of a low-pass fractional order filter, and obtaining a digital filter; optimizing the parameters of the digital filter according to the objective function by using a QPSO algorithm; filtering the non-stationary signal by using the optimized digital filter; extracting characteristic parameters from the filtered non-stationary signals; and performing fault type identification by using a fault diagnosis model based on the characteristic parameters. According to the method, discretization processing is carried out on the low-pass fractional order filter, and parameters of the low-pass fractional order filter are optimized, so that the filtering effect on non-stationary signals is remarkably improved. By means of the optimized digital filter, fault features, such as amplitude entropy, fractional order spectrum kurtosis and dominant frequency components, in non-stationary signals can be extracted more accurately. The support vector machine is adopted as a fault diagnosis model, and the radial basis function is selected as a kernel function, so that the accuracy and robustness of fault recognition are further improved.