263 results about "Wavelet thresholding" patented technology

A leakage sound signal denoising method based on a combination of optimized VMD and an improved wavelet threshold, for use in solving the problem in existing noise processing methods of low identification accuracy in processing leakage sound signals of water supply pipe networks. The present invention comprises: acquiring leakage sound signals of a real water supply pipe network, and analyzing noise components and ranges of the leakage sound signals; on the basis of a goshawk optimization algorithm, performing parameter optimization on the number K of decomposition modes and a penalty factor α of VMD to obtain optimal parameters, and using the optimal parameters to construct a variational model; using the variational model to decompose the leakage sound signals to obtain a plurality of intrinsic mode components; using a correlation coefficient method to screen the plurality of intrinsic mode components to obtain high-frequency components and low-frequency components; performing wavelet threshold denoising processing on the high-frequency components to obtain denoised high-frequency components; and reconstructing the low-frequency components and the denoised high-frequency components to obtain denoised leakage sound signals. The beneficial effects are that the signal-to-noise ratio of denoising processing is improved, and the identification accuracy is improved.

The invention relates to the technical field of ecological environment monitoring, and discloses a coastal wetland intelligent monitoring method and system based on artificial intelligence, and the method comprises the steps: collecting unmanned plane data, satellite remote sensing data, Internet of Things sensor data and water quality monitoring buoy data of a coastal wetland; the method comprises the following steps: processing satellite remote sensing data by adopting a wavelet threshold denoising algorithm based on an attention mechanism, calibrating Internet of Things sensor data by adopting an LSTM network, and carrying out data space-time alignment based on a space-time attention fusion model to obtain preprocessed data; inputting the preprocessed data into a Transform-ResNet hybrid model to carry out environmental change evaluation, and outputting an ecological health index; when the predicted ecological health index is lower than a threshold value, a PPO algorithm is adopted to dynamically adjust a monitoring strategy according to the early warning level, and an early warning report is pushed; the whole process is intelligent, manual intervention is greatly reduced, and support is provided for coastal wetland ecological protection.

The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention relates to the technical field of electric power system control, and discloses an electric power system arc harmonic collaborative suppression and intelligent closed-loop control system, which comprises the steps of: acquiring sensor data and uploading the sensor data to an edge computing unit by deploying a sensor; wavelet threshold denoising and electromagnetic interference compensation are adopted; the method comprises the following steps: constructing a coupling model containing arc plasma dynamic resistance, optimizing VMD parameters through PSO, extracting a joint feature vector, filling an arc and harmonic dynamic coupling analysis gap, calculating a total harmonic distortion rate and arc extinction time in combination with a power grid damping ratio, solving an optimal SVG trigger angle, and solving the problem of lack of power grid stability evaluation; correcting the opening time of the circuit breaker, calculating the error of the total harmonic distortion rate and the error of the arc extinction time, and adjusting the SVG trigger angle; calculating the precision of the evaluation model, calculating the full life cycle through the aging coefficient, analyzing and judging whether an early warning signal is generated or not, and triggering equipment replacement early warning based on the generated early warning signal.

The invention discloses a mine water storage layer leakage risk early warning and emergency decision intelligent system, which is characterized in that the system acquires osmotic pressure gradient, microseismic events, tracer migration rate and rock stratum displacement data in real time through distributed sensors, and generates a standardized multi-parameter data set through processing such as wavelet threshold denoising and variation mode decomposition; outputting a leakage probability value P and a potential fracture azimuth angle theta by using a fuzzy neural network model; early warning in three levels according to the P value, wherein Plt is greater than or equal to 0.3; when 0.6, regulating and controlling pore pressure, wherein 0.6 < = Plt; when P is larger than or equal to 0.85, sampling is encrypted, a grouting path is generated, and when P is larger than or equal to 0.85, an optimal evacuation path is calculated; constructing a grouting pressure gradient field according to the theta and the early warning grade, and dynamically matching the ratio of the leaking stoppage material; and online updating of model parameters is realized through closed-loop control. The system realizes multi-physics field coupling monitoring and dynamic adaptive decision making, and improves leakage risk assessment accuracy and emergency response efficiency.

The invention discloses a numerical control machine tool dynamic error cooperative compensation control method adaptive to high-speed machining, and relates to the technical field of numerical control machine tool error control. According to the method, a multi-source dynamic error sensing system comprising a grating displacement sensor, a six-dimensional force sensor and the like is constructed to acquire data; after wavelet threshold denoising and Kalman filtering preprocessing, inputting a three-layer LSTM error coupling prediction model combined with an attention mechanism, embedding a servo motor load characteristic curve in the model, and outputting three types of error compensation amounts; through servo-level compensation and machining-level compensation, the position of a feed shaft, the rotating speed of a main shaft, the cutting feed rate and the behavior of a micro-displacement actuator are corrected, and machining errors caused by deflection and vibration conduction of the main shaft are counteracted. And iteratively updating model parameters by using a gradient descent algorithm. According to the method, through multi-source error synchronous sensing, error coupling modeling and hierarchical cooperative compensation, dynamic error cooperative control more adaptive to a high-speed processing scene is realized, and the method has a wide application value.

The invention discloses a fracturing equipment state monitoring and fault diagnosis system and method, and belongs to the technical field of oil and gas field fracturing equipment intelligence. The invention aims to solve the problems that in the prior art, monitoring depends on a single signal, fault early warning lags behind, and the misjudgment rate is high. The method comprises the following steps: collecting multi-source operation data of the fracturing pump in real time; establishing a theoretical pressure indicator diagram, and comparing the theoretical pressure indicator diagram with an actual indicator diagram generated by real-time data to realize first-stage fault judgment; an improved wavelet threshold noise reduction method is adopted to process the signals, and time domain and frequency domain features are extracted; and inputting the processed feature data into the combined diagnosis model for second-stage fault judgment. The joint diagnosis model combines a principal component analysis (PCA) model used for uncalibrated data anomaly detection and a BP neural network model used for calibrated data fault classification. Through deep fusion of the mechanism model and the data driving model, environmental interference is effectively resisted, and the diagnosis accuracy and the operation and maintenance efficiency are remarkably improved.

The invention discloses a PICC (Peripherally Inserted Central Catheter) tip precise navigation fixing method and system based on electrocardiogram real-time positioning. High-precision positioning and safe fixing of a catheter tip are realized through a multi-modal data fusion and deep learning technology. The method comprises the following steps: collecting intracavity electrocardiosignals (ECG) in real time through a catheter built-in electrode, and obtaining blood vessel wall contact pressure and temperature data in combination with an optical fiber sensor; an improved wavelet threshold algorithm is adopted to dynamically suppress motion artifacts, and P-wave features are enhanced; the preprocessed ECG signals are input into a spatial-temporal feature fused Transformer model, a time attention layer is used for analyzing P-wave time sequence changes, a space attention layer integrates multi-lead space distribution features, and the positioning precision is optimized in combination with preoperative blood vessel anatomical data; the ECG positioning result and the optical fiber sensing data are fused through Kalman filtering, three-dimensional position information of the tip end of the catheter is generated, and real-time navigation is conducted on an augmented reality interface; and when the P wave amplitude reaches a CAJ threshold value, the contact pressure is safe and no abnormal temperature exists, a shape memory alloy (SMA) fixing ring is triggered to contract, and accurate fixing is achieved. The method solves the problems that a traditional method depends on X rays and is poor in anti-interference performance, has the advantages of being free of radiation, high in real-time performance and adaptive to blood vessel deformation, and is suitable for clinical P catheter implantation.

The invention relates to a transformer fault identification method based on voiceprint signals, and belongs to the technical field of cepstrum for extracting parameters in audio decoding or coding. The method comprises the following steps: setting a fault type and establishing a fault identification model for training; arranging an acoustic sensor to collect voiceprint signals of the transformer; utilizing a dream optimization algorithm to optimize the penalty factor and a successive variational mode decomposition method to decompose a plurality of mode components, and dividing the mode components into pure components and noisy components; noise reduction is carried out on the noisy component by adopting a designed threshold function in combination with wavelet threshold noise reduction, and the noisy component and the pure component after noise reduction are input into the recognition model to obtain a probability vector; and finally, fusing into a first fusion probability vector and a second fusion probability vector through fuzzy measurement, and taking the fault type corresponding to the maximum second fusion probability as the fault type of the transformer. The method can accurately capture the mapping relation between the acoustic features and the transformer fault state, and accurately identifies the transformer fault type.

The invention relates to the technical field of intelligent circuit breakers, in particular to a holographic monitoring method based on a circuit breaker intelligent algorithm. The method comprises the steps of collecting operation data of a line where the circuit breaker is located; performing data preprocessing on the operation data; performing data processing on the preprocessed operation data to obtain monitoring parameters; holographic monitoring is carried out on an electrical fault according to the monitoring parameters; wherein when the operation data is preprocessed, a dual-frequency injection method is adopted to realize the separation of resistive residual current, a 2kHz high-frequency test signal is superposed on the basis of a fundamental wave frequency, and the resistive residual current is separated; meanwhile, an intelligent algorithm is adopted to carry out de-noising processing on the signal, that is, a dynamic threshold value is adopted to carry out wavelet de-noising processing, and an interlayer correlation coefficient is fused into wavelet threshold value calculation, so that self-adaptive protection of fault features is realized, and subsequent accurate power grid holographic monitoring is facilitated.

The invention discloses a transformer abnormity identification method based on voiceprint feature analysis, and belongs to the field of power equipment state monitoring and intelligent diagnosis. The method comprises the following steps: firstly, analyzing an iron core acoustic mechanism based on a magnetostrictive effect, and establishing a three-dimensional model through finite element simulation to obtain vibration and sound field characteristics; in a complex substation environment, a hybrid noise reduction method combining density peak clustering and a CEEMDAN-wavelet threshold is provided, and the signal-to-noise ratio is effectively improved. Then extracting Mel-frequency cepstrum coefficients (MFCC) and spectrum features, and performing local linear embedding (LLE) dimension reduction to form a compact feature set; in the recognition stage, a convolutional neural network framework is designed, specifically, a spectrogram and an energy spectrum are modeled through a two-dimensional CNN, an MFCC tensor obtained after dimensionality reduction is modeled through a three-dimensional CNN, and accurate diagnosis of mechanical faults such as core looseness is achieved. The method has the advantages of being non-contact, anti-noise and high in recognition precision, and real-time diagnosis and early warning of mechanical abnormity of the transformer can be achieved under complex working conditions.

The invention discloses a remote monitoring method and system for an explosion-proof distribution box, and the method comprises the steps: synchronously collecting an electrical operation parameter, an explosion-proof structure state parameter and an environment related parameter, carrying out the wavelet threshold noise reduction processing and coupling correction, improving the data precision, encrypting, adding a check code, and transmitting to a remote monitoring center through dual-mode transmission. And the remote monitoring center verifies the data integrity and decrypts the data, carries out graded early warning based on a preset threshold system and a parameter change rate, and sends a corresponding intervention instruction for abnormity of an early warning grade and above. All-dimensional monitoring is achieved, safety and accuracy of data transmission are guaranteed, risks are pre-judged in advance, remote risk control is achieved, the operation safety and operation and maintenance efficiency of the anti-explosion distribution box are improved, and the system is suitable for flammable and explosive dangerous places.

The method comprises the following steps: obtaining a power quality signal containing noise; selecting permutation entropy as an adaptive function of genetic algorithm, calling variational mode decomposition through genetic algorithm, and iteratively optimizing a penalty factor α and a decomposition mode number k of the variational mode decomposition to determine optimal parameters; decomposing signal data into k mode components through the variational mode decomposition, and determining effective mode components and noise mode components through a correlation coefficient; for improved wavelet threshold, a parameter-adjustable threshold function is proposed, and the concept of wavelet energy entropy is introduced into the threshold function; the noise mode components are denoised through the improved wavelet threshold, and the effective mode components and the denoised noise mode components are reconstructed to obtain a denoised power quality disturbance signal. The method can effectively remove noise interference while retaining singular information of mutation points of the collected signal, and provides help for subsequent analysis and treatment of the power quality disturbance signal.

The invention belongs to the technical field of partial discharge detection, particularly relates to a partial discharge signal denoising method based on SVDTQWT, and aims to effectively remove periodic narrow-band interference and white noise in partial discharge signals. Comprising the following steps: performing Fourier transform on a noisy partial discharge signal to obtain a frequency spectrum, determining the number of periodic narrowband interferences through singular value decomposition, constructing a Hankel matrix to eliminate the periodic narrowband interferences, and obtaining a preliminary de-noised signal; and decomposing the preliminarily denoised signal by adopting adjustable quality factor wavelet transform to obtain a plurality of sub-bands. And dividing the plurality of sub-bands into high-frequency sub-bands and low-frequency sub-bands through sample entropy. Wherein the sample entropy indicates measurement of the complexity of the time series. And de-noising the high-frequency sub-band by using a group sparse total variation de-noising algorithm, de-noising the low-frequency sub-band by using an improved wavelet threshold de-noising algorithm, and reconstructing by using adjustable quality factor wavelet transform to obtain a pure partial discharge signal.

The invention discloses a geophysical prospecting signal denoising method based on VMD (variational mode decomposition) combined with an improved wavelet threshold function, and belongs to the technical field of mineral exploration geophysical prospecting signal process.The method includes the steps that firstly, a decomposition mode number K of a signal is determined through VMD in a self-adaptive mode, a plurality of IMFs (intrinsic mode components) are obtained, and then according to the frequency characteristic and noise distribution of each IMF component, a wavelet threshold function is obtained; and carrying out targeted noise suppression by adopting an improved wavelet threshold function containing an adjustment parameter alpha, and finally, carrying out linear superposition reconstruction on all the processed IMF components to obtain a de-noised geophysical prospecting signal. Experimental verification shows that compared with a traditional method, the method has the advantages that the signal-to-noise ratio and the correlation coefficient of the noisy geophysical prospecting signals can be effectively increased, the root-mean-square error can be effectively reduced, the inherent defects of the traditional method are overcome, the geologic features of the geophysical prospecting signals can be effectively reserved, the method is suitable for various mineral exploration scenes, and reliable data support is provided for anomaly recognition in mineral exploration.

The invention discloses a lightweight optical fiber vibration intrusion event identification method and system for perimeter security and protection, and belongs to the technical field of optical fiber sensing technology and mode identification, and the method comprises the steps: collecting an original vibration signal of a perimeter monitoring region through a distributed optical fiber vibration sensing system; performing wavelet threshold de-noising preprocessing on the original vibration signal to obtain a de-noised signal; performing feature extraction on the denoised signal, and constructing a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector by using a linear discriminant analysis method to obtain a low-dimensional classification feature vector; and inputting the low-dimensional classification feature vector into a pre-trained lightweight convolutional neural network model for classification and identification, and outputting a corresponding intrusion event category. According to the method, through cooperation of front-end LDA dimension reduction and a rear-end lightweight network, the model parameter quantity and calculation overhead are greatly reduced while high recognition precision is guaranteed, efficient real-time deployment on edge equipment is achieved, and the method is suitable for intrusion detection in perimeter security and protection.

The invention discloses a high-pressure pipeline leakage detection method and device based on a voiceprint map, and relates to the technical field of pipeline detection. According to the method, sound signals are collected through a distributed microphone array, multi-dimensional features such as wavelet packet frequency band energy and Mel-frequency cepstral coefficients are extracted after variational mode decomposition is combined with wavelet threshold denoising preprocessing, and dimensionality reduction is performed through principal component analysis; the time delay between the sensors is calculated based on a generalized cross-correlation-phase transformation algorithm, a leakage point is positioned through a particle swarm optimization algorithm, the leakage degree can be evaluated by combining a bidirectional long-short-term memory network of an attention mechanism, and the D-S evidence theory is utilized to fuse multi-sensor result early warning. The system realizes high-sensitivity detection and accurate positioning, is high in anti-interference capability, and is suitable for safety monitoring of high-pressure pipelines in the industries of petroleum, natural gas and the like.

The invention discloses a rotor turn-to-turn short circuit diagnosis method based on an exciting current-shaft voltage harmonic nonlinear correlation coefficient. The method comprises the following steps: synchronously collecting parameters, and preprocessing the collected parameters through wavelet threshold denoising and temperature normalization; extracting harmonic analysis according to the shaft voltage harmonic characteristics; identifying magnetic saturation, temperature and load nonlinear factors and calculating corresponding coefficients; performing dynamic correction on the Pearson's correlation coefficient through the nonlinear correction coefficient, and combining with fuzzy logic weighting to obtain fuzzy logic weighting; defining a working condition feature vector, and constructing a working condition adaptive dynamic model comprising a linear regression basic model and an LSTM neural network; and calculating a dynamic threshold value based on the basic threshold value and the nonlinear factor, and judging the turn-to-turn short circuit trend of the rotor according to the comparison of the correlation coefficient and the dynamic threshold value and a prediction result. According to the invention, through combination of the nonlinear correction correlation coefficient and the LSTM neural network, dynamic compensation of nonlinear factors is realized, and the accuracy and reliability of rotor turn-to-turn short circuit fault diagnosis are improved.

The invention relates to the technical field of underground medium density inversion, in particular to an underground medium density inversion method and system based on high-precision microgravity, and the method comprises the steps: obtaining the gravity data of each observation point in an underground medium density inversion ground surface measurement region; calculating a superposition abnormal value of each gravity subsequence of each observation point, extracting a superposition characteristic value of each modal component, obtaining the superposition interference confidence of each gravity subsequence of each observation point, obtaining the superposition influence persistence of each observation point, adjusting the wavelet threshold of the wavelet transform of the gravity data on each observation point, and calculating the superposition abnormal value of each gravity subsequence of each observation point. And correcting the gravity data of each observation point after denoising processing to extract Bouguer gravity anomaly of each observation point, and obtaining an inversion result of the underground medium density through a Parker-Oldenburg density interface inversion algorithm. According to the method, the underground medium density can be effectively and accurately inverted through the high-precision microgravity data.

The invention belongs to the technical field of power transmission line galloping monitoring, and particularly relates to a power transmission line icing galloping on-site monitoring and early warning method based on edge computing, and the method comprises the steps: employing an industrial eMMC storage and "ARM + FPGA" low-power-consumption heterogeneous architecture, filtering strong electromagnetic interference through wavelet threshold denoising, and improving the data quality so as to reduce the equipment misjudgment; an FPGA three-level assembly line framework is used for accelerating calculation-intensive tasks such as FFT spectrum analysis and image segmentation, an ARM is used for being responsible for scheduling and storage management, the computing power bottleneck of low-power-consumption hardware is broken through, a four-dimensional data structure form of multi-modal sensor data is used, a lightweight prediction model subjected to knowledge distillation optimization is adopted for data uploaded by multiple sensors, and a multi-modal multi-sensor multi-dimensional prediction model is used for prediction of the data uploaded by multiple sensors. And the calculation amount is reduced while the icing identification precision is maintained.

The invention discloses a power distribution network harmonic real-time detection method based on artificial intelligence, and particularly relates to the field of harmonic real-time detection. According to the method, intelligent sensing equipment is deployed in a power distribution network, voltage and current signals and temperature and humidity data are collected, and a complete data sequence is constructed through a multi-link redundancy transmission mechanism; carrying out data cleaning and denoising by adopting a 3 sigma criterion and a wavelet threshold method, and carrying out Z-score standardization processing; secondly, an improved CNN-LSTM fusion model is constructed, multi-scale convolution is adopted to capture harmonic characteristics of different frequency bands, key information is enhanced in combination with a double-attention mechanism, and a time sequence dependency relationship is learned through bidirectional LSTM; the model is optimized through a staged training strategy and ensemble learning, and finally integrated output of harmonic existence judgment, frequency identification and content prediction is carried out; and the detection precision and the real-time performance are improved.

The invention relates to a landslide deep deformation monitoring data noise reduction method based on an HBP-VMD combined improvement wavelet threshold. The method comprises the steps of collecting a landslide deep deformation original signal; the sample entropy is used as a fitness function, a badger optimization algorithm is adopted to optimize VMD decomposition parameters, and an optimal combination parameter combination is obtained; substituting the optimal combination parameter into the VMD, and performing VMD decomposition on the original signal to obtain K intrinsic mode components IMF of different frequencies; calculating a variance contribution rate and a correlation coefficient corresponding to each obtained IMF component, and dividing the IMF components into an effective component, a noisy component and a noise component; retaining the obtained effective component, abandoning the noise component, and carrying out noise reduction processing on the noisy component by using an improved wavelet soft threshold; and reconstructing the IMF component after noise reduction and the effective IMF component, and finally realizing signal noise reduction. According to the method, the deformation monitoring signal of the deep part of the landslide can be efficiently stripped from the noisy signal, and the waveform is clearer than that before noise reduction; the SNR of the signal after noise reduction is the highest, the SMES is the lowest, and the excellent noise reduction effect is achieved.

The invention discloses a new energy station cable terminal partial discharge signal identification method and system, and the method comprises the following steps: collecting an original signal, carrying out the preprocessing of the original signal, and carrying out the wavelet threshold noise reduction processing, and obtaining a noise reduction signal; performing sliding slicing processing on the denoised one-dimensional long time sequence signal, and inputting the processed signal into the constructed partial discharge identification model to train the model; and inputting the target signal into the trained partial discharge identification model to obtain a time position and a continuous range of a real partial discharge event in the long time sequence signal, thereby realizing effective identification and separation of the partial discharge signal and corona interference of the cable terminal of the new energy field station. Wavelet noise reduction, a time window and one-dimensional convolutional network depth feature recognition are integrally designed, partial discharge signals can be stably recognized in the strong noise and mixed pulse scene of the new energy station cable terminal, and a reliable data basis is provided for insulation state evaluation and operation and maintenance early warning of the new energy station cable terminal.

The invention discloses a gas pipe network leakage point accurate positioning method based on a multi-source signal attenuation model, and belongs to the technical field of gas pipe network safety monitoring. The method comprises the following steps: arranging monitoring nodes integrating pressure waves, sound waves and an optical fiber vibration module according to a triangular coverage principle, and synchronously acquiring signals through a GPS (Global Positioning System) or the Ethernet; performing targeted denoising on the three types of signals by adopting a wavelet threshold method, a self-adaptive noise cancellation method and an empirical mode decomposition method; constructing a single signal attenuation sub-model associated with the fuel gas viscosity, the density, the pipe diameter and the sound wave dominant frequency, and fusing the single signal attenuation sub-model into a unified attenuation model through information entropy dynamic weight; solving a leakage point coordinate enabling the error between the theoretical value and the measured value to be minimum by utilizing a gradient descent method; through actual coordinate verification, if an error is greater than 5 meters, attenuation coefficient iterative optimization is adjusted. The system adapts to different pressure grades, gas types and complex working conditions, has the advantages of being high in anti-interference capacity and low in operation and maintenance cost, and is suitable for daily operation and maintenance and emergency disposal of the pipe network.

The invention discloses a derrick fatigue damage multi-dimensional diagnosis and early warning method based on a neural network, and belongs to the technical field of safety monitoring. The method specifically comprises the following steps: S1, multi-source data distributed acquisition: acquiring multi-source heterogeneous data according to a set acquisition frequency, and storing and defining a unified data format by adopting a time sequence database; s2, multi-source heterogeneous data hierarchical preprocessing: performing adaptive noise cancellation and wavelet threshold denoising dual-stage processing on dynamic signals, standardizing and unifying dimensions of static data through the Z-score safety monitoring technical field, and removing abnormal data in combination with a 3 sigma safety monitoring technical field criterion and a sliding window; the limitation that only dynamic physical data are focused in the prior art is broken through, dynamic data such as vibration, stress strain and the like, environmental data such as temperature, humidity, wind speed and the like, and historical data such as accumulated use time of the derrick, hoisting load frequency and the like are synchronously collected, and the real fatigue state of the derrick can be comprehensively reflected by combining multi-dimensional data.

The invention discloses a damage identification method and device for an in-service steel wire rope type horizontal lifeline, and belongs to the technical field of high-altitude operation safety facilities. The method comprises the following steps: synchronously acquiring a magnetic flux leakage signal and a surface image of an in-service steel wire rope through magnetic flux leakage detection equipment and a high-definition camera which are carried on a steel wire rope inspection robot; preprocessing the acquired magnetic flux leakage signal, wherein the preprocessing comprises singular value elimination and trend term removal processing; carrying out de-noising processing on the pre-processed signal by adopting an improved wavelet threshold de-noising algorithm fused with a Sigmoid function; extracting a characteristic value for representing the damage of the steel wire rope, and performing normalization processing to form a characteristic vector; and inputting into a BP neural network identification model optimized by a genetic algorithm for identification, and outputting an assessment result of the damage type and positioning of the steel wire rope. The method can realize automatic and quantitative detection and accurate identification of internal and external damages of the steel wire rope.

The invention discloses an urban atmospheric pollution real-time monitoring method and system. The method comprises the following steps: acquiring pollutant concentration, weather and geographic position data through multi-source monitoring equipment; performing data fusion by adopting space-time Kriging interpolation and wavelet threshold denoising to generate a high-quality urban pollution distribution initial field; a machine learning model of an encoder-decoder structure is used for prediction, an encoder is a convolutional long-short term memory network, a decoder is a dynamic graph convolutional network fused with meteorological factors, and a dynamic pollution situation map is generated; polluted area identification is carried out based on a wind field streamline, and accurate tracing is carried out through combination of reverse simulation of a Lagrange particle diffusion model and a control variable method. The whole process optimization of pollution monitoring from data fusion, accurate prediction to quantitative traceability is realized, and the accuracy and timeliness of urban atmospheric pollution supervision are remarkably improved.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

This invention belongs to the field of ship shafting condition monitoring and fault diagnosis technology, specifically relating to a bearing load measurement device and its data acquisition and transmission method. This invention effectively suppresses temperature drift, mechanical vibration, and electromagnetic interference by rationally arranging strain gauges, employing high-precision synchronous sampling, and combined denoising processing, thereby improving the accuracy of signal acquisition. Embedding moment balance and beam strain consistency equations into the machine learning model significantly improves the accuracy of load prediction. High-speed multi-channel synchronous sampling technology ensures data time alignment, and a multi-algorithm denoising scheme combining wavelet thresholding and bandpass filtering effectively filters out mechanical vibration and electromagnetic interference. A multi-mode communication module enables separate channel transmission of real-time load data and raw strain data, balancing the low-latency requirements of online monitoring with the large-scale data archiving requirements of offline analysis. The introduction of edge computing significantly reduces cloud transmission pressure and improves the real-time performance of load measurement.