627 results about "Adaptive filter" patented technology

The invention provides an automatic early warning method for sudden weather in a target area, which belongs to the technical field of weather early warning, and comprises the following steps of: establishing a primary dense matrix by adopting adaptive filtering processing and a frequency domain signal separation technology, and generating a secondary dense matrix by applying a marine meteorological recognition model of a spiral progressive network structure; a dynamic statistical equation is used to calculate the physical coupling relationship of each parameter to establish a multi-scale weather process balance matrix, a maximum flow and minimum cut algorithm is used to optimize a weather system coupling relationship network to calculate a coupling degree matrix, and a dynamic threshold adjustment mechanism is established according to coupling strength parameters to adjust the early warning detection frequency. And based on a comparison result of the coupling degree moment order maximum characteristic value and a preset risk threshold value, establishing a grading early warning system and outputting a corresponding early warning signal to control an offshore oil and gas platform emergency response system. The technical problem that a multi-time scale weather process coupling relationship cannot be effectively processed is solved.

The invention relates to the technical field of ground resistance detection, in particular to a single-core cable ground resistance detection method capable of resisting strong power frequency interference. High-frequency signals are injected in a non-contact mode through a coupling type capacitor clamp, background voltage and current signals are collected firstly, and injection frequency far away from power frequency and harmonic waves is selected through Fourier analysis; cancelling background noise by adopting a normalized least mean square adaptive filter to generate an enhanced signal; the signal is down-converted to a base band through digital quadrature demodulation, a Kalman filter estimates the amplitude and the phase in real time, and the state and noise parameters are periodically calibrated through a compressed sensing algorithm; and finally, loop impedance is calculated according to the voltage and current amplitudes and the phase difference to obtain grounding resistance. The method has the advantages of being non-contact, high in safety, strong in anti-interference capability, high in measurement precision and the like, and is suitable for monitoring the grounding state of the cable in a complex electromagnetic environment.

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

The invention relates to the technical field of underwater detection, and discloses an underwater comprehensive detection system. The system collects multi-dimensional acoustic data including sound wave reflection intensity, propagation time delay and frequency dispersion characteristics of a target water area through an underwater sonar array; dividing the detection area into a plurality of layered units according to a water attenuation coefficient and a substrate reflection characteristic of the acoustic data, and extracting an environment intrinsic parameter of each layered unit; constructing an acoustic propagation path model based on the environmental intrinsic parameters, and generating adaptive filtering parameters in combination with a preset noise suppression strategy; calling sonar array hardware configuration data, matching filtering parameters with an array beam forming mode, and calculating a detection sensitivity threshold value of each layered unit; and integrating the sensitivity threshold and the acoustic data, generating a three-dimensional acoustic characteristic spectrum of the target water area, and marking the space coordinates and confidence level of the abnormal area. The system can improve the comprehensiveness and accuracy of underwater detection, and meets the requirement of high-precision detection.

The invention relates to the technical field of power systems, in particular to a UPQC harmonic compensation method and system based on repetitive control, and the method comprises the steps: obtaining a load current signal and a voltage signal of a power distribution network, and extracting a harmonic instruction current based on the load current signal; and inputting the harmonic instruction current into a feed-forward channel constructed based on an adaptive filter, and generating a feed-forward control quantity. The phase and frequency of the voltage signal of the power distribution network are tracked in real time through a digital phase-locked loop, the fundamental frequency of the power grid is accurately solved, and the power distribution network can be accurately controlled. The internal model period and the phase lead compensation amount of the frequency adaptive repetitive controller are dynamically calculated, and the internal model structure of the repetitive controller is always matched with the current power grid frequency by updating the two core parameters in real time, so that the compensation precision decline caused by frequency deviation is avoided; and high-precision suppression of the periodic harmonic waves can be realized in a normal fluctuation range of the fundamental wave frequency of the power grid.

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a rigid-elastic coupling-oriented active and passive integrated control method for a hypersonic flight vehicle. The invention aims to realize stable tracking control of the elastic hypersonic flight vehicle. The method comprises the following steps: constructing a longitudinal dynamic model of the elastic hypersonic aircraft; self-adaptive identification of the elastic vibration frequency is realized through a cascaded self-adaptive filter; an elastic modal filtering estimation method is designed, and high-precision and low-cost elastic modal state quantity is provided for subsequent active feedback controller design; and then rigid-elastic coupling model decomposition is carried out, the control performance is ensured by using active disturbance rejection passive control for a rigid body subsystem, an RBF neural network is introduced for an elastic subsystem, an elastic mode is actively inhibited by using sliding mode control, and stable tracking of a reference instruction is realized. The method is an active and passive integrated control method for the hypersonic flight vehicle oriented to rigid-elastic coupling, and the application prospect is wide.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal, and belongs to the field of gas detection. The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal. The infrared CO2 high-precision detection system comprises a sensing module, a signal conditioning and collecting module and a self-adaptive filtering processing module. According to the method, the problems of contradiction between response speed and stability, discrete signal tracking lag and sensitivity to environmental interference in the prior art are solved, the weight of a filtering channel is adaptively and dynamically adjusted, and the advantages of a fast filtering channel and a slow filtering channel are combined, so that balance between dynamic response and steady-state precision is realized; the problem of tracking lag of discrete signals is solved by analyzing the change rate of the signals in real time and dynamically adjusting a filtering strategy; and through a hybrid filter and an environment compensation algorithm, the influence of temperature and humidity changes on a detection result is reduced, so that the long-term stability of the sensor is improved.

The present invention relates to the technical field of signal processing and fault diagnosis. The method of the present invention comprises: collecting a flame chemiluminescence signal, and performing adaptive filtering noise reduction processing; using an improved proper orthogonal decomposition method and a high-order mode to obtain comprehensive combustion feature information; developing a real-time synchrosqueezing transform algorithm to perform time-frequency analysis on data, and updating energy distribution in real time; taking into account sensor data to perform multi-parameter joint analysis to obtain a combustion state evaluation, using a plurality of high-speed cameras to acquire flame images from different angles, reconstructing three-dimensional flame morphology by means of a computer vision technology, and providing fault detection information; and constructing a fault prediction model to give an early warning of a combustion fault. The present method implements nondestructive and rapid detection of oscillating combustion faults, keeps rich information of flame images, reflects overall flame pulsation characteristics, increases the speed and accuracy of detection, and compensates for the defect of low frequency resolution of conventional time-frequency analysis methods.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention discloses a sudden death risk real-time assessment system and method based on multi-modal physiological signal fusion, relates to the field of human physiological state monitoring and early warning, and solves the problems of low accuracy and poor real-time performance of sudden death risk assessment by single-modal signals. The system comprises a signal acquisition module, a preprocessing module, a high-dimensional feature extraction module, a multi-modal feature fusion module, a sudden death risk quantification module, a model updating engine module and an early warning feedback module, and each module integrates a self-adaptive filtering unit, a time sequence convolutional network unit, an improved multi-head self-attention mechanism unit and the like. According to the scheme, multi-mode signals such as electrocardio are synchronously collected through multiple channels, a dynamic risk index is calculated through self-adaptive noise reduction, parallel extraction of time-frequency domain nonlinear features and mutual information weighted fusion in combination with kernel density estimation, and online incremental updating and multi-stage early warning of a model are achieved; the sudden death risk can be accurately evaluated in real time, the anomaly detection sensitivity and the early warning timeliness are improved, and the method is suitable for daily health monitoring and high-risk group risk management and control.

The invention relates to the technical field of adaptive filtering, discloses a variable-step robust adaptive filter and a filter network, and aims to solve the technical problem that a traditional adaptive filter cannot effectively consider convergence speed, steady-state precision and impulse noise interference resistance. A non-linear error factor is introduced, large errors caused by impulse noise are effectively suppressed through a generalized function form of the non-linear error factor, and the robustness of the filtering process is remarkably improved; meanwhile, a variable step length mechanism is provided, the theoretically optimal candidate step length is calculated on line by minimizing the mean square deviation of the next moment, and a self-adaptive target variable step length is generated through truncation and time smoothing processing, so that dynamic balance is realized between rapid convergence and low-steady-state maladjustment. According to the invention, the filter is expanded to the distributed network, each node follows a diffusion strategy of first updating and then combining, and global information sharing and performance collaborative optimization are realized by using a combined coefficient.

The invention relates to the technical field of large-scale structure measurement, and discloses a robot laser vision three-dimensional scanning measurement system for a large-scale structural member. The system comprises a robot motion platform, a laser vision sensing module, an ambient light sensing module, a point cloud processing module and a motion control module, wherein the laser vision sensing module is arranged at the tail end of the robot motion platform. The ambient light sensing module collects ambient light intensity distribution data in real time, and the motion control module combines the data with three-dimensional model data of the large-scale structural member to be measured to generate a multi-view dynamic scanning path instruction. The robot motion platform executes an instruction to drive the laser vision sensing module to move, the module projects laser stripes to the surface of a to-be-measured structural member and synchronously collects an image, the point cloud processing module performs adaptive filtering processing on the image and then generates three-dimensional point cloud data, and the requirement for efficient and accurate measurement of a large structural member in a complex industrial environment can be met.

The invention relates to the technical field of motor control and sensors, and discloses a displacement error dynamic compensation method, device and equipment and a medium, and the method comprises the steps: obtaining the temperature data and original displacement data of a plurality of temperature measurement points in a moving part and an environment, calling a pre-built nonlinear mapping relation between a temperature gradient and a displacement deviation, and calculating the displacement error of the moving part; based on the nonlinear mapping relation, adaptive filtering is carried out on the temperature data and the original displacement data, a trend component representing temperature drift is separated out, displacement data without the trend component is obtained, and a dynamic compensation instruction is generated based on the trend component and the nonlinear mapping relation; and adjusting the displacement data of which the trend component is removed and outputting compensation displacement data. According to the method, the nonlinear mapping model of the temperature gradient and the displacement deviation is constructed, the drift trend is extracted in combination with multi-point temperature sensing and self-adaptive filtering, the compensation instruction is dynamically generated, accurate real-time correction of the displacement error is achieved, and the responsiveness and accuracy of compensation are improved.

The invention discloses a self-adaptive rotating baseline direction finding method, device and equipment, a medium and a terminal. Signals are continuously collected through the base line rotatable antenna array; rapidly identifying and outputting environment parameters by using the trained environment evaluation model, and evaluating the environment grade; constructing a dynamic direction-finding angle model by combining the attitude parameters and the motion parameters of the mobile platform, the control parameters of the antenna array and the environmental parameters; according to the environment parameters and the environment grades, a dynamic parameter optimization rule is formulated, and control parameters, direction-finding resolving parameters and signal processing algorithm parameters of the antenna array are adjusted; and based on the environmental parameters, the adjusted control parameters of the antenna array, the adjusted direction-finding resolving parameters and the adjusted adaptive filter parameters, calculating an incoming wave angle by using a dynamic direction-finding angle model. According to the method, high-precision incoming wave signal direction finding is realized in a dynamic environment, and the method is suitable for mobile scenes such as unmanned aerial vehicles and vehicle-mounted communication.

The invention relates to the field of mine risk prediction, in particular to a mine dynamic risk prediction method and system based on multi-modal parameter fusion analysis. The method comprises the following steps: obtaining a multi-modal mine omnibearing data stream based on a multi-modal sensor cluster, carrying out adaptive filtering noise reduction and time-space synchronization space mapping, and constructing a cross-modal mine data stream model; performing mine geological micro-fluctuation behavior deep analysis according to the cross-modal mine data flow model, performing dynamic geological risk situation evolution, and constructing a multi-modal geological risk situation map; and obtaining a mine historical risk event log, performing risk event extraction, performing adaptive risk chain deep learning on the multi-modal geological risk situation map, and constructing a mine risk state dynamic evolution model. According to the invention, intelligent prediction and dynamic early warning of mine risks are realized, and the mine intrinsic safety level and the emergency response efficiency are improved.

The invention relates to the technical field of biological process intelligent control, and discloses a pomegranate leaf fermentation process optimization and intelligent control system based on digital twinning. According to the system, the Internet of Things and a visual sensor are integrated through a multi-source data sensing module, so that synchronous acquisition and fusion of temperature, pH, dissolved oxygen, flora density and image data are realized; the intelligent data preprocessing module extracts high-quality features by adopting a self-adaptive filtering technology; the digital twin simulation engine module drives the virtual model to dynamically update based on real-time data; the deep prediction module accurately predicts the fermentation trend through a deep learning algorithm; the intelligent diagnosis module realizes abnormal root cause analysis by using a knowledge graph; the reinforcement learning optimization module autonomously generates an optimal control strategy based on the diagnosis result; and the self-adaptive execution module accurately regulates and controls fermentation parameters. According to the system, multi-dimensional perception, high-precision prediction and adaptive optimization control of the fermentation process are realized, and the accuracy and stability of process control are effectively improved.

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention belongs to the technical field of intelligent wireless sensing and radar signal processing, and particularly relates to a millimeter wave radar gesture recognition feature processing method, which is particularly suitable for scenes with environment interference (such as walking of others and static clutter), and specifically comprises the following steps: S1, preliminary filtering by a self-adaptive filter; s2, carrying out improved I CEEMDAN decomposition; s3, I MF component classification and processing; s4, signal reconstruction; the experimental result shows that the method provides a robust and efficient solution for gesture recognition, and can be widely applied to the fields of man-machine interaction, virtual reality, intelligent equipment control and the like.

The invention provides a three-dimensional positioning method and system for a satellite-borne multi-base radar system based on weighted linear iteration, and the method comprises the steps: obtaining radar echo data of a plurality of receivers, carrying out the effective detection of a target, and carrying out the searching of a pitch angle and a space taper angle of a target signal through a two-dimensional adaptive filtering technology, obtaining two-dimensional angle estimation information of the target at different bases and different visual angles; constructing a nonlinear equation set according to two-dimensional angle information and slope distance information of the target under different visual angles, and performing first-order Taylor expansion on the nonlinear equation set to obtain a corresponding linear overdetermined equation set; and forming a precision matrix according to the known measurement precision of the radar system, and carrying out normalization operation and iterative solution on the linear overdetermined equation to obtain the three-dimensional coordinate of the target in the earth-centered earth-fixed coordinate system, thereby obtaining the latitude, longitude and height information of the target, and realizing the high-precision three-dimensional positioning of the multi-static radar system.

The invention discloses a distribution network power line anti-interference communication method based on adaptive filtering, and belongs to the technical field of distribution network power line communication. According to the method, a communication signal adopting a frequency shift keying modulation mode is acquired from a power line coupler of a distribution network through a signal acquisition module, the communication signal is preprocessed by using a digital signal processing unit, spectral analysis is performed on the preprocessed signal, and characteristic parameters of interference noise are extracted; a coefficient vector of a finite impulse response adaptive filter is initialized based on the characteristic parameters, the order of the filter is in direct proportion to the reciprocal of the dominant interference frequency, fine adjustment is carried out based on noise energy distribution through a table look-up method or a linear mapping relation, and the weight coefficient of the filter is adjusted in real time in the communication process. The adjustment step length is dynamically adjusted according to the real-time estimated signal-to-noise ratio, and finally, the processed signal is output through the adaptive filter and data demodulation is carried out. The method is mainly used for improving the anti-interference capability and the communication reliability of the power private line communication of the distribution network.

The invention discloses an acousto-optic combined detection and fusion positioning method and device for underground gas leakage, and belongs to the technical field of industrial safety monitoring. In order to solve the problems of high false alarm rate, insufficient positioning precision, poor dynamic environment adaptability and the like of a single sensor in the prior art, the invention provides a multi-modal signal space-time collaborative fusion technical scheme which specifically comprises an acousto-optic heterogeneous sensor, an integrated MEMS ultrasonic array and a tunable laser spectrum module. Microsecond-level synchronous acquisition of acousto-optic signals is realized through a GPS taming clock, and a cross-correlation time delay compensation model is designed to eliminate space-time mismatch errors. And designing a three-stage dynamic fusion framework: improving LMS adaptive filtering to suppress industrial noise, establishing a light intensity attenuation model in combination with dust environment concentration, carrying out multi-modal feature extraction of acousto-optic signals, carrying out signal preliminary fusion, and judging a gas leakage condition. And constructing an acousto-optic Bayesian joint probability model, and calculating the leakage posterior probability. And optimizing positioning parameters based on a double-Kalman filter group, predicting sound wave time delay by forward filtering, and correcting a light diffusion coefficient by backward filtering to realize three-dimensional coordinate solution. Temperature, humidity and dust concentration parameters are fused in real time, a sound velocity calculation model and a light intensity attenuation factor are corrected, and the robustness of complex working conditions is improved. According to the invention, the advantages of acousto-optic signals are combined, errors of various complex environmental factors are further fused and compensated, the overall error is greatly reduced, and the method has the characteristic of dynamic adaptation, and is very suitable for gas leakage detection and positioning in complex environments such as underground environments and the like.

The invention relates to the technical field of anti-dry-burning liquid level monitoring, in particular to an anti-dry-burning liquid level monitoring control system based on a PCB capacitance sensing circuit, in the anti-dry-burning liquid level monitoring control system based on the PCB capacitance sensing circuit, a signal preprocessing module adopts self-adaptive filtering to dynamically suppress noise interference in an original capacitance signal, and a reliable preprocessing signal is generated; the boiling state recognition module decomposes the signal into a steady state component and a transient state component, judges the boiling state according to the matching degree of the transient state component and a boiling feature library, and outputs a freezing instruction to maintain operation when the matching degree is high, so that mistaken shutdown is avoided; meanwhile, in combination with the judgment that the steady-state component is continuously lower than the warning line, a trigger instruction is output when the matching degree is low, and the real dry burning risk is accurately captured; the intelligent execution module responds instantly according to the instruction, guarantees continuous operation of equipment during freezing, executes power cut-off and sound-light alarm during triggering, and eliminates the potential safety hazard of dry burning; according to the process, accurate distinguishing between boiling interference and real dry burning is achieved, and equipment safety and operation continuity are synchronously optimized.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention relates to the technical field of man-machine collaboration and information processing, discloses an AI processing-based motion capture recognition pre-judgment system, and aims to solve the problems of difficulty in physiological signal monitoring and performance drifting of a myoelectricity intention recognition model in a high-noise environment, and the AI processing-based motion capture recognition pre-judgment system comprises a processing module, a visual capture module, a myoelectricity acquisition module, a bioacoustics acquisition module and an information prompting module, according to the method, myoelectricity and visual information decoding operation intentions are fused, a self-adaptive filter is guided through the intentions, accurate noise suppression is carried out on biological acoustic signals of a target object, and therefore the physiological state risk of the target object is accurately evaluated, meanwhile, an online self-adaptive calibration mechanism is introduced, the actual action of visual recognition serves as a supervision true value, and the accuracy of the physiological state risk of the target object is improved. And the myoelectricity model is continuously calibrated, so that the accuracy of long-term identification is ensured. According to the method, the decoding intention and the physiological risk level are integrated, the composite cooperation instruction is intelligently generated and fed back to the operator, the situation awareness ability of the operator is enhanced, and the safety and efficiency of man-machine cooperation are improved.

The invention relates to the technical field of power electronic control and new energy power generation, and discloses an energy storage type photovoltaic off-grid inverter control method and system. The method comprises the following steps: acquiring an analog voltage signal and an analog current signal, and inputting the analog voltage signal and the analog current signal into a preset adaptive filter to obtain a disturbance component sequence; calculating a disturbance current sudden change degree, and if the disturbance current sudden change degree exceeds a preset sudden change threshold, constructing a to-be-counteracted current vector; determining a target phase lead angle according to the to-be-counteracted current vector and a preset transformer phase lag model, and generating a correction time domain compensation signal; generating a comprehensive modulation signal and an inverter switching instruction sequence to drive an inverter; updating a filter coefficient based on the distortion rate and determining a compensation voltage gain; and performing closed-loop correction on the sampling trigger time sequence of the analog current signal according to the system loop transmission delay to obtain a calibration current sequence. The method can effectively overcome the phase lag of the transformer and the transmission delay of hardware, and improve the electric energy quality under the nonlinear load.

The invention discloses an adaptive filtering Beidou navigation and inertial navigation positioning and attitude determination method and device, and the method comprises the steps: constructing a short-baseline carrier phase and pseudo-range double-difference observation equation through BDS satellite second pulse signals received by a first BDS receiver and a second BDS receiver, and solving the short-baseline carrier phase and pseudo-range double-difference observation equation to obtain the positioning information of a carrier where a second antenna is located; assigning the positioning information of the carrier to the inertial navigation system as an initial positioning value, carrying out positioning and attitude determination on the carrier through the inertial navigation system based on the initial positioning value, and outputting the latest positioning and attitude determination information of the carrier; carrying out Kalman filtering processing on the positioning information of the carrier and the latest positioning and attitude determination information of the carrier, and outputting the optimal attitude and positioning information of the carrier; the Kalman filtering is used for adaptively adjusting noise by using a carrier state vector through innovation analysis; the state vector is constructed by using the positioning information of the carrier and the latest positioning and attitude determination information of the carrier. The problem of error accumulation in the navigation process can be effectively solved, and the positioning precision is improved.

The invention relates to the technical field of telephone communication, and discloses a robust recursive least square adaptive filter based on inverse QR decomposition and a storage medium, and the method comprises the steps: calculating an inner product of an adaptive weight vector at a moment and an input signal vector, and obtaining an output signal at the moment of the adaptive filter; calculating a difference value between the expected signal and the output signal at the moment, obtaining an estimation error signal, and constructing a nonlinear weighting function; recursively updating and acquiring an inverse correlation matrix of the moment input signal vector based on the moment input signal vector and transpose thereof, a nonlinear weighting function, the inverse correlation matrix of the input signal vector and the covariance of the estimated error signal, and constructing a block matrix containing the inverse correlation matrix of the moment input signal vector; inverse QR matrix decomposition and unitary rotation operation are sequentially carried out on the block matrix, an array before inverse QR decomposition and an array after inverse QR decomposition at the moment are obtained, a gain vector at the moment is constructed, and a self-adaptive weight vector at the moment is updated and obtained.

The invention discloses an adaptive control method and system for paying-off tension of a ground wire, particularly relates to the technical field of automatic control, and is used for solving the problem of control misalignment caused by incapability of distinguishing and stripping multi-span coupling vibration interference in an existing tension control method. Firstly, high-frequency fluctuation components are identified by monitoring frequency band energy distribution of tension signals, then state space trajectory similarity and causal network topology of multi-point vibration signals are cooperatively analyzed, and local or global coupling of vibration sources is accurately judged; then, extracting a dominant vibration frequency and a phase difference, and separating a vibration interference component from the original tension signal through a phase compensation adaptive filter according to the dominant vibration frequency and the phase difference; and finally, subtracting the interference component from the original tension to obtain a real tension value for closed-loop adjustment of the output of the tension equipment. Accurate identification and dynamic compensation of vibration interference are achieved, and the control precision of the pay-off tension and the system stability are remarkably improved.