873 results about "Wavelet decomposition" patented technology

The invention provides a real-time face blind restoration method and device based on identity constraint and frequency domain enhancement, and relates to the technical field of image processing, and the method comprises the steps: S1, generating an identity code through the fusion of CLIP and ArcFace, extracting spatial structure features in combination with ControlNet, and obtaining a high-frequency component through the decomposition of learnable wavelets; s2, an input image is mapped to a potential space, and identity, space and high-frequency features are fused to construct a condition vector; s3, performing iterative denoising based on the consistency model, and superposing lightweight wavelet residual correction to enhance high-frequency details; s4, according to the high-frequency energy dynamic distribution weight, reconstructing a multi-scale fusion image through inverse wavelet transform; according to the method, the problems of identity distortion, detail loss and low calculation efficiency of a low-quality face image in restoration are solved, and semantic consistency, detail fidelity and real-time performance of a restoration result are improved through identity-space-frequency domain ternary constraint, lightweight residual correction and degradation perception enhancement.

The invention relates to the technical field of brain-computer interfaces, in particular to an electroencephalogram signal amplification method, device and equipment based on topological gradient field coding. The method comprises the following steps: acquiring a first data set corresponding to a scalp electroencephalogram of a testee through electroencephalogram acquisition equipment, and performing Hilbert transform to extract phase information so as to calculate a topological gradient field; key phase features meeting the energy concentration ratio feature and the duration feature are extracted from the topological gradient field, so that the key phase features with the energy concentration ratio threshold value larger than a preset value and the duration exceeding a time threshold value are screened out; inputting the key phase features to a pre-constructed generative adversarial network, and generating an enhanced phase field by a generator of the generative adversarial network through adversarial training; and combining the enhanced phase field with the low-frequency amplitude component, subjected to wavelet decomposition, of the first data set to construct a complex field, simulating spatio-temporal evolution of the complex field based on a nonlinear dynamic model, and generating a finally enhanced and amplified electroencephalogram signal. And efficient characterization of electroencephalogram phase information is realized.

The invention discloses a mechanical arm welding detection intelligent diagnosis method and system based on laser ultrasonic guided waves. According to the system, a high-precision multi-degree-of-freedom steering engine, an infrared distance measurement array, a temperature sensor, a laser vibration exciter and a laser vibration meter are integrated at the tail end of a mechanical arm, synchronous operation of welding and online detection is achieved by presetting a welding track, and the system collects an excitation signal, a reflection ultrasonic signal and temperature data through a high-speed data collection card; and the signal is preprocessed through a temperature matching model, phase compensation and baseline correction. Then, wavelet decomposition is adopted to extract time-frequency features, intelligent diagnosis is achieved through a pre-trained one-dimensional convolutional neural network, a dynamic threshold value is set based on the 3 sigma criterion, and recheck feedback is automatically triggered when detection data exceed the limit, the welding detection efficiency and precision are remarkably improved through the scheme, data closed-loop management and intelligent quality tracing are achieved, and the reliability of welding detection is improved. And the automatic production requirement of modern steel structure manufacturing is met.

The invention belongs to the technical field of computer data processing, and provides a biodiversity inversion method based on multi-source remote sensing image fusion. Comprising the steps of remote sensing image data acquisition, image preprocessing, image fusion processing, multispectral resolution image data generation, spectral feature extraction, final frequency feature extraction, feature integration and target ecological variable prediction. According to the invention, through wave basis adaptive selection and multi-scale wavelet decomposition, spectrum fidelity and space structure maintenance are considered, differential fusion of high and low frequency components under different scales is realized, and multi-source data complementarity and fusion image quality are improved; through spectral resolution refinement processing and multi-bandwidth scale simulation, the limitation of single resolution is broken through, and the capability of capturing complex spectral features of vegetation is enhanced; the spatial correlation is enhanced through spatial neighborhood feature fusion; and through an ecological variable inversion estimation model, multi-index synchronous prediction is realized, and the universality of the model is improved.

The invention relates to the technical field of crop growth monitoring, in particular to a crop growth monitoring method based on unmanned aerial vehicle remote sensing, which comprises the following steps of: acquiring a time sequence remote sensing image through an unmanned aerial vehicle and performing time phase processing to solve the problem of data inconsistency of a traditional method; a crop segmentation network based on wavelet transformation and edge guidance is constructed, high-frequency details and low-frequency semantic features are captured through wavelet decomposition, multi-scale dynamic interaction is achieved through cross-resolution feature fusion, and the problems of high-frequency detail loss and fuzzy segmentation are solved; based on a twin network, extracting dual-temporal global semantic features, and combining a difference compensation module to enhance the significance of the growth change, suppress noise interference and improve the weak change detection capability; and finally, fusing the segmentation mask and the difference characteristics through a multi-task framework, synchronously generating a pixel-level spatial distribution diagram and a time sequence thermodynamic diagram, realizing spatio-temporal conjoint analysis of the crop growth state, solving the problem of spatio-temporal information segmentation in a traditional method, and providing high-robustness monitoring decision support for precision agriculture.

The invention discloses a bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention provides a hydro meteorology early warning method for an offshore oil and gas platform, and belongs to the technical field of offshore hydro meteorology. Extreme weather events are identified by adopting minimum probability abnormal event identification vectors to match abnormal characteristic parameters, and abnormal signal characteristic parameters are input into an ocean dynamics prediction model to calculate real-time sea condition parameters; calling a multi-temporal-spatial-scale early warning fusion matrix to combine with a wavelet decomposition technology and a recurrent neural network to realize multi-scale information integration, analyzing an environmental parameter change trend through a sea condition jump identification model and triggering an emergency response, dynamically adjusting system parameters according to a stability evaluation index vector, and optimizing prediction precision by adopting an early warning residual value compensation matrix. And finally, multi-level early warning information is generated and a real-time early warning notification is sent to an operator, so that the technical problem of insufficient early warning precision of an offshore oil and gas platform hydro meteorology early warning system in multi-spatio-temporal scale data fusion processing is solved.

The invention provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention discloses an aircraft control surface oscillation fault monitoring method based on multi-scale wavelet transformation, and the method comprises the steps: carrying out the calculation through a PID (Proportion Integration Differentiation) control law through an autopilot according to an input flight path command, and obtaining a control surface control instruction deltadepth; a control surface deflection prediction value delta est is generated based on the nonlinear model and the control surface control instruction, the control surface deflection prediction value and an actual deflection value delta mea collected by the sensor module are subtracted to generate a residual signal Rn, and the residual signal is decomposed into a plurality of sub-band signals through wavelet decomposition; wavelet energy calculation is carried out on the sub-bands possibly containing the control surface oscillation fault signals, whether a fault occurs or not is judged according to the magnitude of an energy comparison threshold value, and the judgment threshold value is determined through Monte Carlo simulation. According to the method, instruction signals are separated by generating residual errors, wavelet transform processing is carried out on residual error signals, interference of noise and irrelevant frequency signals on monitoring performance is reduced, meanwhile, small-amplitude oscillation signals can be effectively monitored according to sub-band wavelet energy serving as a fault criterion, and the monitoring coverage rate of oscillation faults is remarkably increased.

The invention discloses a high-resolution hyperspectral image reconstruction method based on wavelet domain feature fusion and contrast learning, and belongs to the technical field of image fusion and super-resolution reconstruction. The method comprises the following steps: constructing a fusion network model comprising a wavelet transformation module, a cross-modal feature fusion module, a high-frequency contrast learning module and an image reconstruction module; performing end-to-end supervised training by using a training data set containing the low-resolution hyperspectral image and the high-resolution multispectral image; and after training is completed, inputting a test image pair to realize image reconstruction. According to the method, the detail retention capability is improved by combining wavelet decomposition and a directional fusion mechanism, the cross-modal high-frequency feature alignment capability is enhanced through comparative learning, a fusion image with high spatial resolution and high spectral consistency is finally generated, and the method is suitable for multi-modal image reconstruction tasks such as remote sensing, medical and natural images.

The invention relates to a multi-branch power distribution network double-end traveling wave fault positioning method and system, and the method comprises the steps: deploying a plurality of measurement points based on the head and tail ends of a radial power distribution network and branch terminals, collecting topological information, and calculating the linear mode wave velocity; using the three-phase voltage data after the fault to extract traveling wave line mode and zero mode components through modulus decoupling; a line mode component with the strongest global mutation is selected as a reference, and a wave head time sequence table is generated through wavelet decomposition and mutation detection; and correcting the zero-mode wave velocity by combining the topology, the time sequence table and the line-mode wave velocity, and determining a fault interval and outputting a fault position after compensation and correction by using the line-mode and zero-mode wave head time difference. According to the invention, through a wave head time sequence analysis and time difference compensation mechanism based on cooperation of multiple measuring points, positioning errors caused by clock asynchronization and parameter disturbance are effectively overcome, accurate distance measurement which is not influenced by clock errors is realized, and the distance measurement precision is significantly improved; and meanwhile, the point distribution requirement is simplified, accurate judgment and positioning of a fault interval can be realized only by a branch single measuring point, and high economy and deployment flexibility are both achieved.

The invention relates to the technical field of nondestructive testing, and discloses a crack detection device and method for precise automobile parts. The method comprises the following steps: carrying out ultrasonic multi-dimensional pre-scanning and acoustic impedance abnormal region identification on a to-be-detected automobile part, carrying out influence range calculation and sensitive threshold classification based on abnormal region distribution data, and obtaining stress-acoustic response time sequence data through partition grabbing force distribution and stress loading; three-dimensional stress field reconstruction and stress concentration area identification are realized; performing dual-frequency stress modulation excitation and multi-physics field synchronous detection on the stress concentration area to obtain a multi-physics field response signal, performing multi-parameter fusion calculation and multi-scale wavelet decomposition of acoustic components on the multi-physics field response signal, and performing feature dimension splicing to obtain crack feature classification data; and calculating and generating a crack detection result based on feature similarity matching and extension prediction. The precision and reliability of crack detection of the automobile parts are remarkably improved, and the damage risk in the detection process is avoided.

The invention discloses a lightweight super-resolution system and method of an adaptive wavelet attention network, and belongs to the technical field of image processing. The system is composed of a multistage wavelet attention module, a dynamic convolution kernel generation unit, a convolutional neural network and an output unit. The method comprises the following steps of: extracting features of a low-resolution image and performing Haar wavelet decomposition; calculating a cross-scale attention weight on each high-frequency sub-band and carrying out weighted fusion to highlight details; adaptively generating a dynamic convolution kernel of a Haar wavelet basis kernel weighted combination based on the fused features, and performing directional convolution enhancement on the features; high-resolution image reconstruction is realized through a lightweight residual network and pixel rearrangement; and during training, pixel domain mean square error and wavelet coefficient compensation loss joint optimization is adopted. The parameter quantity of the system model is smaller than 450KB, a 1080p video super-resolution task can be processed on mobile equipment in real time, and the texture recovery performance is improved by about 1.2 dB compared with that of an existing lightweight model.

The invention relates to the technical field of power system communication, and discloses a fault diagnosis method and system for a substation communication network, and the method comprises the steps: obtaining multi-band signal data, and carrying out the fast Fourier transform to obtain spectrum distribution data; according to the spectrum distribution data, performing frequency band division to obtain an initial power ratio; performing adaptive filtering according to the initial power ratio and the multi-band signal data to obtain a fundamental wave amplitude fluctuation characteristic; performing wavelet decomposition according to the fundamental wave amplitude fluctuation characteristics to obtain abnormal signal time-frequency energy distribution; performing K-means clustering division according to the abnormal signal time-frequency energy distribution, and matching a harmonic fault database to obtain a fault type identifier; and according to the fault type identifier and the abnormal signal time-frequency energy distribution, ring network topology propagation time delay analysis is carried out, a fault position coordinate is positioned, and a fault diagnosis report is generated. According to the method, the network fault can be accurately diagnosed for multi-band complex signal interference.

The invention provides a pavement defect analysis and detection method and system, and relates to the technical field of defect identification. Through multi-source data fusion processing, a dynamic anchor frame adjustment mechanism, an improved feature extraction network and a multi-stage wavelet decomposition technology, the problem of missing detection of a traditional detection method under shadow interference is effectively overcome, the defect recognition precision in a complex scene is remarkably improved, refined quantitative evaluation of the pavement condition is achieved, and the method is suitable for large-scale popularization and application. The method has the technical advantages that the small-scale defect recognition precision in a complex scene is improved, the shadow interference resistance is enhanced, and multi-dimensional quantitative evaluation is realized.

The invention discloses a panchromatic sharpening method and system based on cross-resolution adversarial learning and a Mama network. The method comprises the following steps: acquiring a low-resolution multispectral image LRMS and a high-spatial-resolution panchromatic image PAN; extracting features of the low-resolution multispectral image LRMS to obtain a first feature map; extracting features of the high-spatial-resolution panchromatic image PAN to obtain a second feature map; performing wavelet decomposition and weighted fusion on the first feature map and the second feature map to obtain a multi-channel feature map, and performing dynamic fusion on the multi-channel feature map through a cross attention gating mechanism to obtain a fused feature map; and carrying out residual connection on the fused feature map and the low-resolution multispectral image, and then carrying out feature reconstruction to obtain a high-resolution multispectral remote sensing image. According to the method, the panchromatic sharpening performance of the remote sensing image can be improved, and the high-quality and high-resolution multispectral remote sensing image is obtained.

The invention relates to the technical field of data processing, and discloses a secondary equipment hidden danger mining method and system based on a wave recording file and monitoring data. The method comprises the steps of calculating a conditional probability and intervention probability difference identification causal relationship by analyzing a recording file in combination with equipment topology and protection logic to construct a correlation graph, and performing Fourier transform and wavelet decomposition on a sampling sequence to extract multi-scale features; small signal test excitation is injected, a system identification estimation transfer function is fused with monitoring data to form a health feature vector to judge a health state, and a correlation map and the health state are input into a map neural network to calculate weighted attention coefficients and aggregate neighbor information to predict a fault propagation path; and establishing a degradation model, correcting the failure rate, predicting the residual life and generating a graded early warning report. According to the method and the device, the transformation from passive post analysis to active predictive maintenance is realized, and the timeliness, accuracy and systematicness of hidden danger identification of the secondary equipment are improved.

The invention relates to the technical field of electric digital data processing, in particular to a data processing method for quality monitoring of a power distribution equipment production line, which comprises the following steps of: calculating a time decay dynamic fluctuation index based on acquired continuous power distribution equipment sensor data flow, and recording data points and timestamps of which variable quantities exceed a threshold value; and obtaining a time decay sampling point sequence. According to the dynamic sensing and time decay modeling mechanism of the continuous power distribution equipment sensor data flow, the time decay dynamic fluctuation index is constructed to capture numerical value abrupt change in a short period, key change points where quality abnormity possibly exists can be positioned in time, timestamps can be recorded, and the response sensitivity to data behaviors before and after event triggering is improved. Dense original data segments are actively intercepted after anomaly recognition, multi-stage discrete wavelet decomposition of time domain signals is carried out, smooth trend and multi-scale mutation features are extracted in a refined mode, and the depicting capacity of key process detail changes of the circuit breaker is enhanced.

The invention discloses a distributed ground well data processing method, and relates to the technical field of data processing, and the method comprises the steps: collecting the vibration data of an air conditioning unit of an airport distributed ground well through an acceleration sensor; de-noising processing is carried out on the collected vibration data of the air conditioning unit by combining a time segmentation clustering algorithm on the basis of the wavelet decomposition layer number dynamically adjusted based on the signal-to-noise ratio and a threshold value strategy; performing segmented feature coding on the vibration data of the air conditioning unit; the deep neural network model based on feedback rewards carries out training optimization through a feedback reward mechanism, and the performance of the model in a complex fault diagnosis task is improved; and performing fault diagnosis classification on the vibration data of the air conditioning unit by using the trained deep neural network model based on the feedback rewards. According to the method, a signal-to-noise ratio driven wavelet decomposition layer number and threshold value control mechanism is adopted, the decomposition depth and the threshold value function are dynamically adjusted according to the signal local standard deviation, and the capability of retaining fault features under different working conditions is improved.

The invention discloses an ancient textile image restoration system based on artificial intelligence. The system comprises a multi-source image acquisition module, a damaged area detection module, a pattern generation module, a color restoration module, a texture synthesis module and a multi-scale fusion module. The system introduces a wavelet guidance-frequency domain attention mechanism and a rotation invariant Haar wavelet basis function to realize accurate identification and classification of a damaged area; a saliency-guided wavelet decomposition control and self-adaptive threshold denoising method is combined, so that the perception capability of slant textures and edge details is improved; the texture synthesis module constructs a hierarchical modeling strategy fusing Gram style loss, Wasserstein style loss and a total variation regular term, and realizes generation of high-quality textures with unified styles and smooth edges; the system can be widely applied to cultural relic digital repair and display scenes.

The invention discloses a single image super-resolution reconstruction method and system based on wavelet transform and cross-domain feature fusion. According to the method, firstly, a low-resolution RGB image is mapped to a high-dimensional feature space through a shallow feature extraction module; performing up-sampling and discrete wavelet decomposition on the features by using a wavelet feature mixing module to obtain multi-band features; low-frequency and high-frequency depth features are respectively extracted through a double-branch structure, cross-domain fusion is realized by means of a deformable cross attention mechanism, and the feature expression ability is enhanced in combination with residual connection; and finally, reconstructing a high-resolution image through convolution, up-sampling and regularization processing. In the training process, a pixel-level loss function is adopted to optimize network parameters, the multi-frequency-domain feature sensitivity is effectively improved, texture and structure information is balanced, the image contrast, definition and structural integrity are improved, and high-quality real-time super-resolution reconstruction can be achieved.

The invention relates to the technical field of photovoltaic energy storage systems, and discloses a power regulation method, device and equipment for a photovoltaic energy storage system and a storage medium, and the method comprises the steps: carrying out the real-time collection and wavelet decomposition of voltage and current parameters of a photovoltaic array, and obtaining power fluctuation components and power trigger signals of different frequencies; executing power vector analysis to obtain an oscillation mode characteristic parameter set and an oscillation suppression strategy; constructing a photovoltaic side feedforward trigger channel and an energy storage side feedback trigger channel, and obtaining a coordination trigger instruction of the photovoltaic side and the energy storage side; and performing multi-objective optimization decomposition and slope limitation processing on the coordination triggering instruction to obtain a voltage reference value of the photovoltaic MPPT controller and a current reference value of the battery charging and discharging controller, thereby realizing accurate identification and prediction of different frequency power fluctuation components, realizing collaborative cooperation of photovoltaic side feed-forward and energy storage side feedback, and improving the reliability of the system. And the power oscillation risk caused by control mode switching is avoided.

The invention provides a power supply optimization model and method for a converter station. The method comprises the steps that firstly, a converter station energy consumption characteristic analysis and electric quantity prediction model is constructed, and the converter station energy consumption characteristic analysis and electric quantity prediction model comprises user converter station energy consumption characteristic analysis based on wavelet transformation and an optimization fast density peak value clustering algorithm and a converter station electric quantity prediction model based on an XGBoost algorithm; then, constructing a wind power and photovoltaic novel power supply output prediction model, and adopting a wavelet decomposition-bidirectional long-short term memory network based on an Attention mechanism as a prediction model; and finally, in combination with the electric quantity prediction model and the novel power supply output prediction model, constructing a power supply combination optimization design model for the converter station. Through the model, the power optimization capability and the use efficiency of the converter station can be improved, electric energy resources are managed and reasonably utilized, and the economical efficiency and the safety of a power system are improved. The power supply optimization model and method for the converter station are innovative and have practical value.

The invention discloses an assembly type building node stress monitoring and design feedback system based on BIM, and relates to the technical field of building engineering structure monitoring, a building information modeling model is constructed, component geometric information, a connection mode and a load path associated with nodes are extracted, and a node mechanical attribute initial parameter set is formed; in combination with node real-time stress monitoring data, Fourier transform and wavelet decomposition are carried out, and frequency domain characteristic parameters are extracted; constructing a finite element correction model, and simulating a stress response path under a multi-load combination; the predicted stress peak value is compared with the actually measured stress peak value, structural abnormal nodes are identified, parameter optimization is executed according to node construction information, the component size, the steel bar anchoring length or the concrete grade are automatically adjusted, an optimized parameter set is generated and written back into a building information modeling model, and closed-loop correction is formed; continuous monitoring, abnormity diagnosis and intelligent optimization of the node stress state can be achieved, and the safety and the intelligent level of assembly type building structure design are improved.

The invention relates to the technical field of asthenopia monitoring and health monitoring, and discloses a remote control tower air traffic controller asthenopia monitoring method. The method comprises the following steps: synchronously collecting eye movement, electrocardio, workload and environment multi-modal data, generating a labeling label in combination with a visual function test, and constructing a high-dimensional feature data set; key features are extracted through principal component analysis dimensionality reduction, and real-time prediction of the asthenopia state is achieved through LightGBM model training; a sliding median filtering and fixation event merging strategy is adopted for the eye movement data, and baseline drift is removed and HRV features are extracted from the electrocardio data through Bior4.4 wavelet decomposition; generating an asthenopia label based on the weighted fusion visual function index; optimizing model hyper-parameters by using TPE; in real-time monitoring, the system outputs a trigger alarm according to the model. According to the invention, non-intrusive and high-precision asthenopia monitoring is realized, the system effectively adapts to the complex environment of a remote control tower, and the health management efficiency of controllers and the aviation operation safety are remarkably improved.

The invention relates to the technical field of building electrical, and discloses a building electrical safety intelligent evaluation method and system based on Internet of Things dynamic monitoring, and the method comprises the steps: collecting multi-dimensional data in real time through an Internet of Things sensor network deployed in a building electrical system; carrying out wavelet decomposition on the collected multi-dimensional data, extracting time domain and frequency domain features, carrying out feature fusion and noise removal on multi-scale wavelet coefficients by using a convolutional neural network, and obtaining a denoised multi-dimensional feature vector; inputting the multi-dimensional feature vector into a dynamic evaluation model, and outputting a risk level, fault probability prediction and a key risk factor through the dynamic evaluation model; according to an output result of the dynamic evaluation model, performing graded early warning on the building electrical system; according to the invention, dynamic evaluation and early warning are carried out on the safety state of the building electrical system, evaluation is comprehensive and accurate, and the efficiency and level of building electrical safety management are improved.

The invention discloses an unmanned aerial vehicle power distribution network equipment identification method based on mutual neighbor density peak value clustering, and relates to the technical field of power distribution network equipment inspection, and the method comprises the steps: obtaining an original RGB image of a power distribution line; outputting the contrast characteristic coefficient of each channel; constructing a saturation retention item; calculating a contrast feature retention item, constructing a total energy function, solving an optimal channel weight by adopting a discrete search strategy, and outputting an initial grayscale image; sequentially carrying out weighted guide filtering, morphological reconstruction and super-pixel segmentation operation; performing two-dimensional wavelet decomposition on the super-pixel segmented image, and extracting a feature vector; and calculating the local density and the relative distance, selecting a clustering center, and completing sample distribution based on the shared mutual neighbor similarity to realize a power distribution network equipment identification effect. According to the method, the problems of detail loss, noise interference, edge breakage, disordered classification of multi-scale equipment and the like under complex illumination are effectively solved, and the identification precision and efficiency of unmanned aerial vehicle inspection are effectively improved.

The invention discloses a gas pipeline operation risk dynamic evaluation system based on big data analysis, and relates to the technical field of gas pipeline safety monitoring, and the system comprises a multi-source data processing module, a feature extraction module, a local risk modeling module, a risk evolution prediction module and a dynamic early warning module. The multi-source data processing module is used for acquiring original multi-source time sequence data from a pressure sensor, a flow sensor, a temperature sensor, a geological monitoring device and a third-party construction disturbance data interface of a gas pipeline network, and performing time alignment, abnormity elimination, normalization and characteristic standardization processing on the data; a uniform input feature vector is formed; and the feature extraction module is used for decomposing the normalized time series data into a low-frequency trend component and a high-frequency residual signal by using discrete wavelet decomposition. The gas pipeline risk assessment system solves the problems of low gas pipeline risk assessment precision, insufficient risk propagation modeling and lack of dynamic early warning and emergency strategies in the prior art.

The invention relates to the technical field of sealing performance testing, in particular to a breather valve performance online testing method for oil and gas storage and transportation, which comprises the following steps: acquiring a vibration signal of a breather valve body, a multi-band acoustic signal of a valve port and a total pressure signal in a storage tank; performing multi-scale complex wavelet decomposition, and constructing a time frequency-energy correlation feature tensor; the total pressure signal and the time frequency-energy correlation characteristic tensor serve as a combined observation value and are input into a preset continuous Gaussian mixture hidden Markov model containing four hidden states of sealing, transient micro-leakage, continuous leakage and full-amount opening, and the posterior probability is calculated; and calculating to obtain a real-time leakage rate. According to the method, the opening pressure can be accurately determined, the real-time leakage rate can be quantitatively calculated after the leakage state is recognized, comprehensive and accurate quantitative online evaluation of core performance parameters of the breather valve is achieved, and the multi-source information fusion degree and the anti-interference capacity are improved.