238 results about "Adaptive filtering algorithm" patented technology

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention discloses a combined navigation method and system of an unmanned surface vehicle. The method comprises the following steps: forming a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) integrated navigation system by using a GNSS (Global Navigation Satellite System) and a strapdown inertial navigation system (INS), and establishing a mathematical model of a GNSS / INS integrated navigation system filter; the mathematical model comprises a state equation and a measurement equation; establishing a mathematical model of the influence of the GNSS auxiliary information on the GNSS positioning precision so as to preliminarily adjust the measurement noise in the measurement equation; through an improved Sage-Husa adaptive filtering algorithm, adaptive estimation is carried out on the preliminarily adjusted measurement noise, and a measurement noise estimation final value is obtained; and according to the motion characteristics of the unmanned surface vehicle, adding motion constraint conditions to the measurement equation. The GNSS auxiliary information mathematical model based on multivariate function fitting is established, the influence of the external environment on the positioning precision is objectively reflected, the measurement noise in the filter is dynamically adjusted, and the positioning precision of the integrated navigation system under the interference condition is remarkably improved.

The invention relates to the technical field of adaptive filters, and discloses a wide linear adaptive filter, which comprises the following steps of: performing tensor integral solution on a system weight vector at the moment of the wide linear adaptive filter to obtain two adaptive weight vectors; constructing an input matrix based on the sampling value of the input signal, and calculating an estimation signal of the moment based on the product of the input matrix of the moment and the two adaptive weight vectors; and calculating the difference value between the expected signal and the estimation signal at the moment, obtaining an estimation error signal, and updating the gain vector at the obtaining moment and the estimation error signal by using a minimum error mode criterion, updating the two adaptive weight vectors at the obtaining moment, and reconstructing the system weight vector at the moment of the wide linear adaptive filter. According to the invention, the recursive adaptive filtering algorithm resistant to impulse noise interference is realized with low calculation complexity by using weight decomposition and selection of a robust cost function.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality are improved.

The invention discloses a BIM (Building Information Modeling)-based large-span post-tensioned bonded prestressed beam construction method. The BIM-based large-span post-tensioned bonded prestressed beam construction method comprises the following steps: collecting a vibration signal of a working rib and a matt rib reference signal of a matt rib; inputting the dummy rib reference signal into an adaptive filtering algorithm to which phase preserving constraint is applied, and performing noise cancellation processing on the working rib vibration signal to generate a purified pressure wave signal; performing multi-scale decomposition on the purified signal, extracting propagation time delay of the direct wave based on coherence analysis among components, and generating a phase-time delay characteristic matrix; and based on pre-configured geometric parameters of the BIM three-dimensional model, a real-time stress distribution field is calculated through inversion according to the phase-time delay characteristic matrix, and the real-time stress distribution field is synchronously updated to the BIM three-dimensional model. According to the method, the phase distortion problem under noise interference is solved through a physical reference channel and phase preserving filtering, the accuracy of time delay extraction is improved through a multi-path separation technology, and high-precision and high-robustness real-time monitoring of the prestress is achieved.

The invention provides a power factor correction method and system for AC-DC conversion of a server power supply, and relates to the technical field of server power supplies, and the method comprises the steps: collecting an input AC signal and a DC bus output signal, calculating a real-time power factor value, constructing a compensation function based on the power factor value, and calculating a deviation value; and generating a compensation parameter matrix through recursive calculation, processing a direct current output signal by adopting an adaptive filtering algorithm, fusing the direct current output signal with the compensation parameter matrix, calculating to obtain a current adjusting signal and a duty ratio adjusting signal, and accurately adjusting a power factor.

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 a detection-interference integrated unmanned aerial vehicle electromagnetic interference device, which belongs to the technical field of electronic countermeasure, and comprises an integrated design of an antenna module, a radio frequency front end module, a digital signal processing module and a main control module, the radio frequency front-end module comprises a detection receiving channel and an interference transmitting channel to process signal conversion, the digital signal processing module executes signal detection and analysis, intelligent interference decision and anti-self-interference signal processing based on a self-adaptive filtering algorithm, and the main control module is responsible for overall control and power supply. According to the invention, the technical problems of large response delay, complex system cooperation and incapability of continuous reconnaissance in the interference process of traditional anti-unmanned aerial vehicle equipment are solved. Real-time signal cancellation is achieved through the normalized least mean square algorithm, the reconnaissance capability is kept while interference is applied, the method has the advantages of being rapid in response, accurate in interference, low in power consumption and the like, and the countering efficiency is remarkably improved.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

The invention relates to the technical field of sensing data processing, and discloses a sensing data chip method based on multi-source information fusion and dynamic parameter adjustment. The method comprises the following steps: collecting original data streams of a plurality of heterogeneous sensors, and generating synchronized sensing data through timestamp alignment and format standardization processing; extracting an environment noise component, and dynamically suppressing noise by using an adaptive filtering algorithm to obtain de-noised sensing data; performing multi-dimensional feature decomposition on the de-noised data, calculating time domain, frequency domain and space domain feature vectors, and generating a multi-dimensional feature set; dynamically adjusting a feature weight distribution strategy in combination with a current system load state and a multi-dimensional feature set historical distribution rule, and generating an optimized fusion weight matrix; and carrying out weighted aggregation on the multi-dimensional feature set and the optimized fusion weight matrix, and outputting a multi-source fusion feature vector. According to the method, the problems of data synchronization, noise suppression and feature fusion suitability in multi-source heterogeneous sensing data processing can be solved, and the processing requirements of different scenes are met.

The invention discloses an unmanned aerial vehicle airborne geophysical prospecting method and system based on big data, and the method comprises the steps: obtaining the positioning data of a global navigation satellite system and the measurement data of an inertial measurement unit, carrying out the correction and compensation through the combination of a differential positioning technology and a terrain height model, and obtaining an accurate three-dimensional coordinate data set; acquiring a magnetic field signal and a spectral signal according to the three-dimensional coordinate data set, and performing de-noising processing on the magnetic field signal and the spectral signal by adopting an adaptive filtering algorithm to obtain preprocessed signal data; judging whether the signal data volume of the preprocessed signal data exceeds a preset threshold value or not, if the signal data volume exceeds the preset threshold value, performing fragmentation processing on the preprocessed signal data through a distributed computing framework, extracting feature vectors in each piece of fragmented data by adopting a convolutional neural network, and generating a feature vector set; according to the invention, full-link optimization from data acquisition to ore body prediction is realized, and the precision and efficiency of geological exploration are improved.

The invention provides a method for monitoring cracks in a high-pressure vibration state of a wind tunnel in real time, and belongs to the technical field of wind tunnels. A piezoelectric ceramic sensor array is arranged on the wall surface of the wind tunnel, a vibration fatigue matrix and a structure resonance matrix are established, and a self-adaptive filtering algorithm is adopted to preprocess sensor signals; establishing a vibration coupling equation set to extract crack characteristic parameters, constructing a dynamic threshold discrimination matrix and a risk assessment matrix, utilizing a multi-sensor data fusion algorithm to accurately position crack positions and sizes, calculating risk levels according to a crack risk assessment index equation, and performing three-level safety discrimination. A complete crack real-time monitoring and risk assessment system in the high-pressure vibration environment is formed, and the technical problems that in the prior art, crack monitoring precision is low in the high-pressure vibration environment, and real-time risk assessment cannot be achieved are solved.

The invention relates to the technical field of intelligent power regulation, discloses a charger control method and system based on intelligent power regulation, and dynamically adjusts a power distribution strategy by monitoring equipment power consumption and a battery state in real time. The method comprises the following steps: firstly, acquiring an equipment power consumption curve and a battery health index, judging the priority of a power consumption demand and a charging demand according to a preset threshold value, determining a preliminary power distribution scheme, optimizing power distribution by adopting a pulse width modulation technology and a digital signal processor according to the actual conditions of equipment and a battery, and generating a frequency-coordinated power control signal; and finally, calculating progressive power transfer parameters among multiple energy paths through a proportional-integral-differential control algorithm to realize smooth power transfer, and processing feedback data by using an adaptive filtering algorithm to ensure stable power output. Intelligent power balance between equipment operation and battery charging can be achieved, the energy utilization efficiency is effectively improved, the service life of the battery is prolonged, and stable operation of equipment is guaranteed.

The invention provides a voice dialogue interaction method and system for industrial equipment, and relates to the technical field of man-machine interaction, and the method comprises the following steps: obtaining a voice signal of a user, and carrying out the voice enhancement processing of the voice signal of the user through an incremental adaptive filtering algorithm, and obtaining an enhanced voice; according to the enhanced voice, using an information gain transfer learning method to identify an interaction intention of the user, and generating a to-be-interacted voice based on the interaction intention of the user; based on the enhanced voice, utilizing a time delay estimation method to identify an interaction position of the user, and performing position prediction on the position of the user; according to the position prediction result, an industrial equipment horn output strategy is constructed; and outputting the to-be-interacted voice based on the industrial equipment loudspeaker output strategy so as to realize voice dialogue interaction between the user and the industrial equipment. According to the invention, convenience, high efficiency and accuracy of interaction between the user and the equipment in an industrial scene are greatly improved, and intelligent development of industrial production is facilitated.

The invention relates to the technical field of laser radar detection, in particular to a frequency-modulated continuous wave laser radar high-precision distance measurement method, which comprises the following steps of: 1, firstly, performing noise suppression by using an improved FTRLS (Fast Transverse Recursive Least Squares) adaptive filtering algorithm aiming at a low signal-to-noise ratio signal; 2, adjusting the added limitation and filter parameters, iterating the optimal parameters, and applying the optimal effect to subsequent spectrum correction; 3, windowing the denoised signal, and carrying out FFT (Fast Fourier Transform) to obtain a frequency spectrum of the signal; and 4, smoothing the signal by using a Butterworth filter, suppressing a side lobe of a frequency spectrum due to the characteristics of the filter, and highlighting the energy of a main lobe of the signal and the position of a spectrum peak. According to the method, a redundancy feedback-dynamic restart mechanism is applied to an adaptive filtering algorithm RLS, the FTRLS algorithm which is added with a transverse filter and leads to reduction of denoising precision is greatly improved, numerical divergence of the FTRLS algorithm is inhibited by using a regularization factor, and the signal-to-noise ratio is improved.

The invention discloses a radio frequency chip phase deviation calibration method based on multichannel sampling data analysis, and relates to the technical field of radio frequency signal processing and multichannel data synchronous calibration. Processing the sampling sequence of each channel by adopting a phase estimation algorithm to calculate a phase value to obtain initial phase distribution; according to the initial phase distribution, if the number of detection channels is increased or decreased, the reference phase is recalculated by fusing the time sequence relation adjustment information to obtain an updated reference phase; acquiring an updated reference phase, and extracting a reference signal from the residual channels and compensating a missing part through an adaptive filtering algorithm to obtain compensated phase distribution if a failure channel signal is lost according to partial channel failure judgment; the radio frequency chip phase deviation calibration method based on multichannel sampling data analysis has long-term self-calibration and self-adaption capabilities, and the robustness, reliability and signal processing precision of a system are remarkably improved.

The invention discloses a hearing aid health analysis method based on a physiological signal monitoring function. The method comprises the following steps: collecting physiological signal data of a user in real time through a multi-mode biosensor array integrated on a hearing aid body; the hearing aid body comprises a digital signal processor and a wireless communication module; the physiological signal data at least comprises a brain wave signal, a skin electric response signal and a temporal artery pulse signal; performing motion artifact elimination processing on the physiological signal data through an adaptive filtering algorithm; performing feature extraction on the preprocessed physiological signal by using an embedded health analysis engine to generate a composite health feature vector containing a sympathetic nerve activity index, cognitive load measurement and a cardiovascular function index; and dynamically generating a health risk assessment report based on an association relationship between the composite health feature vector and a preset hearing scene, the bone conduction vibration unit and the audio synthesis module of the hearing aid body output graded health alarm signals.

The invention provides a low-altitude logistics aircraft take-off and landing point and vehicle dynamic connection positioning calibration method, and aims to solve the problems of insufficient positioning precision, poor collaboration and single path optimization in dynamic connection. The method comprises the following steps: firstly, constructing a multi-source fusion positioning module, fusing multi-sensor data through hardware terminal integration and data synchronization and an adaptive filtering algorithm, and realizing high-precision positioning by combining environment adaptive signal switching; secondly, a vehicle-aircraft cooperative calibration link is established, and real-time cooperation is guaranteed through low-delay data transmission, dynamic deviation prediction calibration and communication exception handling; and finally, establishing a multi-agent collaborative path optimization model, modeling based on multi-target parameters, modifying a path in real time by an improved algorithm, and improving a connection effect. The method is suitable for high shielding scenes such as an operation area, centimeter-level positioning calibration can be realized, and the accuracy and collaboration of dynamic connection are enhanced.

The invention belongs to the technical field of biological signal processing and monitoring, and particularly relates to a multi-mode-based non-contact heart rate intelligent monitoring method which comprises the steps that a ballistocardiogram signal of a user is collected through a piezoelectric film sensor, meanwhile, a photoelectric volume pulse wave signal of the user is collected through a photoelectric sensor, and a multi-mode physiological signal is formed; performing space-time alignment processing on the multi-modal physiological signal, eliminating low-frequency breathing interference and motion noise by adopting an adaptive filtering algorithm, and outputting a pre-processed signal; inputting the preprocessed signal into a space-time coupling feature analysis engine, extracting time sequence features through a bidirectional long-short-term memory network, and extracting spatial features through a full convolutional network; adopting a multi-head self-attention mechanism to carry out dynamic weight fusion on the time sequence features and the spatial features to obtain fusion features; and calculating a real-time heart rate value based on the fused features, and judging a heart rate abnormal state. The problem that the heart rate monitoring difficulty is large is well solved.

The invention discloses an anti-interference method suitable for a satellite navigation signal, and belongs to the field of satellite navigation signal anti-interference, and the method comprises the steps: collecting multi-channel satellite navigation signal data through a receiver array, processing an initial signal through a self-adaptive filtering algorithm to preliminarily reduce noise interference, and obtaining a preliminarily purified signal; extracting a spatial correlation matrix according to the preliminary purification signal, decomposing the matrix by adopting a principal component analysis algorithm to identify dominant spatial features, and determining an interference direction vector; obtaining the spectrum distribution of the direction suppression signal, further separating the spectrum overlapping part by adopting a principal component analysis algorithm, and judging the residual interference residue; through fusion of the direction suppression signal and the residual interference residue, an adaptive filtering algorithm is adopted to iteratively optimize the weight so as to enhance the navigation signal strength, and an optimized enhanced signal is obtained; and calculating a positioning parameter according to the optimized enhanced signal, judging whether the parameter stability meets a preset threshold value, and obtaining a final stable signal.

The invention discloses a sea target detection method, device and equipment and a storage medium, and the method comprises the steps: carrying out the coarse filtering of an initial coordinate point set through employing a self-adaptive filtering algorithm, obtaining a first screening coordinate point set, and dynamically obtaining a wake flow region according to the first screening coordinate point set; performing fine filtering on the first screening coordinate point set based on the wake flow region to obtain a second screening coordinate point set; and clustering the second screening coordinate point set by adopting an adaptive clustering algorithm based on physical parameters of the laser radar to obtain a detection target. The comprehensiveness of laser radar detection information is improved through fusion of point cloud data, coarse filtering and fine filtering are performed on an initial coordinate point set obtained after fusion, and filtered results are aggregated based on physical parameters of the laser radar to complete detection of each target obstacle. Therefore, in a complex sea surface environment, the laser radar can be used to carry out efficient and real-time detection on the water surface target obstacle.

The invention discloses a ship waste heat utilization system and an energy recovery method, and the method comprises the steps: monitoring and collecting waste heat parameters in real time through a multi-sensor array, processing data fluctuation through a self-adaptive filtering algorithm, and carrying out the quality evaluation in combination with an effective energy model; according to the real-time effective energy value, the fluctuation index and the host load, the waste heat resource allocation priority is dynamically adjusted, and a multi-mode waste heat resource allocation strategy is synchronously formulated based on the output predicted by the extended Kalman filtering algorithm; a full-state prediction model based on an ORC system is constructed, and a prediction-optimization-compensation adaptive control framework is established; a heat charging and discharging sequence is optimized based on model prediction control, and energy loss is minimized; according to the method, performance indexes of the whole waste heat utilization system are monitored, control parameters are optimized by using a reinforcement learning algorithm based on real-time data, the optimized parameters are applied to the system, and closed-loop control is performed. The problem of insufficient waste heat utilization is solved, energy loss is remarkably reduced, and the waste heat utilization efficiency of the system is improved.

The invention relates to the technical field of automobile chassis dynamics control, and discloses a hydrogen energy heavy truck omnidirectional degree-of-freedom optimization method, which comprises the following steps: acquiring multi-source sensor data and hydrogen fuel cell system state data of a hydrogen energy heavy truck, and executing time synchronization and noise preprocessing; carrying out real-time identification on the key state parameters of the vehicle by utilizing a nineteen-degree-of-freedom vehicle dynamics model and a self-adaptive filtering algorithm; a layered omnidirectional coordination control architecture is constructed and comprises a main control layer, a scheduling layer and an execution layer, and the main control layer generates a global motion control target; carrying out multi-objective optimization solution on the scheduling layer, and calculating to obtain an expected acting force distribution instruction; and the execution layer converts the instruction into a bottom layer execution control signal and drives the subsystems to cooperatively act so as to realize omni-directional degree-of-freedom optimization control. According to the invention, the limitations of poor coordination and difficulty in multi-target optimization caused by independent control of subsystems of a traditional chassis can be broken through, and accurate control of the hydrogen energy heavy truck under complex working conditions is realized.

The invention discloses an intelligent shock absorption and noise reduction control method for rail transit, and belongs to the technical field of rail transit noise and vibration control. The method aims at solving the problems that traditional passive control measures are poor in adaptive capacity and limited in frequency band, and effective prospective offset is difficult to achieve due to system delay and inaccurate reference signals in existing active control. The method comprises the following steps: synchronously acquiring vibration and noise signals through a sensor array; calculating the contribution weight of vibration to noise by using a cross-domain modal correlation module based on an attention mechanism, and fusing the features; predicting an evolution sequence of vibration and noise in a specific time period in the future by adopting a long-short-term memory network model; synthesizing a target reference signal according to the prediction sequence, and generating an anti-phase control signal through an adaptive filtering algorithm; finally, it is ensured that the total delay of the system is smaller than the feedforward advance time, and an actuator is driven to achieve accurate foresight counteracting. The method is mainly used for improving the self-adaptive capacity and the control effect of the rail transit damping and noise reduction system.

The invention provides a human body posture recognition and rehabilitation training evaluation method and system based on deep learning, and relates to the technical field of human body posture recognition. According to the method, multiple human body key points are extracted from a pre-acquired user image by using a deep learning attitude estimation model, then measurement data of human body key angles are acquired based on the human body key points, and then dynamic smoothing processing is performed on the human body key angle data based on an adaptive filtering algorithm introduced with a multi-level smoothing factor switching mechanism. And finally, outputting the smoothed key angle data of the human body in real time, and performing human body posture recognition and rehabilitation training evaluation according to the smoothed key angle data of the human body. Compared with the prior art, different dimensions such as real-time performance, measurement precision, equipment portability, functional integrity and user experience are remarkably improved.

The invention relates to the technical field of image processing, in particular to a brake disc safety performance detection method and system. When the safety performance of the brake disc is detected, appearance detection, size detection and material performance detection are carried out on the brake disc, and detection is comprehensive; meanwhile, an improved adaptive filtering algorithm is adopted to carry out de-noising processing on the to-be-detected brake disc image; performing multi-scale decomposition on the to-be-detected brake disc image, and decomposing the to-be-detected brake disc image into sub-images of multiple scales; performing local feature extraction on each sub-image, and dynamically allocating weights according to the local features; performing adaptive filtering on the sub-image of each scale according to the weight so as to improve the filtering effect; the filtering intensity is dynamically adjusted according to the local variance, the gradient intensity and the texture information, so that the edges and details of the image can be better reserved, and excessive smoothness is avoided; in combination with global and local information, a self-adaptive weight is distributed for each pixel, the filtering effect is optimized, and the adaptability and robustness of the algorithm are improved.

The invention discloses a belt-disengaging prevention combined control method and device for an electric crawler and a medium, and relates to the technical field of crawler control. The method comprises the following steps: S1, acquiring multi-source sensing data of the electric crawler, wherein the multi-source sensing data at least comprises vehicle attitude data, crawler tension data, crawler sag data, driving wheel speed data and environment sensing data which take a quantum inertial sensor as a reference; and S2, carrying out space-time alignment and consistency correction on the multi-source sensing data, and fusing the corrected data by adopting an adaptive filtering algorithm to obtain a real-time fusion state quantity of the vehicle. And S3, on the basis of the real-time fusion state quantity, a comprehensive risk index is calculated through multi-dimensional feature coupling analysis, and a current off-band risk level is determined. And S4, a preset hierarchical control strategy is matched according to the determined belt disengaging risk level, a combined control instruction is generated and sent to an execution mechanism of the vehicle, and the execution mechanism comprises a motor driving system and a mechanical tensioning system.

The invention relates to a running speed detection method and device based on radar echo analysis and a running machine. The method comprises the steps that original echo signals are collected, fixed frequency interference is suppressed through time-frequency transformation processing and an adaptive filtering algorithm, and denoised time-frequency domain signals are obtained; performing short-time Fourier transform processing on the denoised time-frequency domain signal, and extracting a motion signal spectrum sequence; based on the motion signal spectrum sequence, a Kalman filtering algorithm is adopted to estimate the motion state at the current moment, and a motion state estimation vector containing position, speed and acceleration components is obtained; and generating real-time speed data based on the vector, wherein the real-time speed data is used for representing the movement speed of the runner. According to the method, real-time detection of the non-contact running speed is realized through a radar signal processing and state estimation technology.

The invention discloses a drill string state real-time monitoring and analyzing method under limit drilling parameters, which comprises the following steps of: acquiring multi-source sensor data of a drill string in real time, and transmitting the acquired original multi-source sensor data to a data processing center in real time; de-noising and drift correction are carried out on the multi-source sensor data by adopting a self-adaptive filtering algorithm; the preprocessed multi-source sensor data are input into the multi-parameter fusion analysis model subjected to data equalization processing optimization, and the current drill string state is recognized; outputting a drill string state identifier by using a multi-layer neural network structure and a nonlinear activation function, and transmitting the drill string state identifier to a monitoring terminal on a drilling site in real time; and the data processing center classifies and stores the information in a distributed database. According to the invention, the working state information of the drill string under the complex working condition can be comprehensively and timely obtained, and the state of the drill string can be accurately identified. Major accidents are effectively avoided, and the personal safety of drilling operators and the safety of drilling equipment are guaranteed.