125 results about "Thresholding algorithm" patented technology

The invention discloses a GIS partial discharge intelligent diagnosis system and method based on one-dimensional ultrahigh frequency signal analysis, and relates to the technical field of power electrical equipment intelligent monitoring, and the system comprises a signal collection and preprocessing module which is used for collecting ultrahigh frequency signals of GIS equipment and obtaining preprocessed signal data through a dynamic threshold algorithm; the discharge initial judgment module is used for performing multi-dimensional sequential judgment to eliminate interference discharge data so as to obtain effective discharge signal data; the feature extraction module is used for performing time domain kurtosis and pulse width analysis, frequency domain energy distribution analysis and time-frequency domain wavelet entropy calculation based on the multi-dimensional features of GIS partial discharge, and generating an optimized feature subset; and the type identification module is used for identifying the partial discharge type by using the integrated learning model to obtain a diagnosis result. According to the invention, the problem of unstable recognition accuracy caused by insufficient signal preprocessing, single feature representation and single classification algorithm in the prior art is solved.

The invention discloses a CPLD-driven IGBT short-circuit current dynamic monitoring method and system, and relates to the technical field of power electronics, and the method comprises the specific steps of S100 real-time data acquisition, S200 dynamic threshold calculation, S300 short-circuit state judgment, S400 rapid protection execution, and S500 fault data recording and uploading. Accurate and prospective judgment of the short circuit state of the IGBT is achieved, two stress parameters including the direct-current bus voltage and the junction temperature serve as independent variables, when the bus voltage rises, the voltage stress borne by the IGBT in the turn-off period is increased, meanwhile, the current peak at the short circuit moment is more remarkable, early warning is conducted in advance by dynamically lowering the threshold value, and therefore the short circuit state of the IGBT is accurately judged. According to the mechanism, the monitoring system does not simply respond to the occurred overcurrent, but can pre-judge the safety boundary of the device under the current working condition, so that intervention is implemented before the current really reaches a dangerous peak value, and a protection blind area under a severe working condition is effectively avoided.

The invention discloses a signal detrending method based on sliding window statistics and dynamic wave crest correction. The method comprises the step of realizing accurate extraction of signal trend and fluctuation characteristics through a double-window dynamic adjustment mechanism of a mean value window and a standard deviation window. A detection threshold value is dynamically adjusted by analyzing the change trend of the historical wave crest amplitude, and the real-time tracking capability of signal fluctuation is ensured; meanwhile, different parameter configurations are preset for different application scenes, the problem of missing detection or misjudgment generated when the amplitude of a fixed threshold algorithm changes suddenly is solved, and the wave crest recognition accuracy is improved. Based on a dynamic threshold algorithm, wave peak start and end points are automatically identified, a linear interpolation technology is adopted to calculate slope of each interval, fitting parameters are dynamically adjusted through residual analysis, and finally low-frequency trend terms and high-frequency characteristic components are effectively separated through difference operation of original signals and fitting signals. The problem of fitting deviation caused by non-uniform sampling can be effectively solved.

The invention provides a supply chain cost anomaly AI positioning system based on multi-mode depth tracing, and relates to the technical field of cost anomaly tracing. According to the supply chain cost anomaly AI positioning system based on multi-modal depth tracing, a three-layer architecture is adopted, a dynamic threshold algorithm and a multi-level association map are combined, real-time detection and root positioning of cost anomaly are achieved, and the three-layer structure is data acquisition, dynamic modeling and visual tracing. The system has the advantages that dynamic adaptability is improved, reasonable fluctuation and abnormal risks are accurately distinguished, the system breaks through the rigid limitation of a traditional static rule, the judgment threshold value is dynamically adjusted by fusing market dynamics (such as bulk commodity prices and policy adjustment) and real-time service data, and a supplier-production-logistics-storage association graph is constructed, so that the risk of abnormal risks is accurately distinguished. And the conduction path of the cost abnormity in each link of the supply chain is visually displayed.

The invention discloses a sparse regularization direction of arrival estimation method based on a risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The method comprises the steps of receiving array signals and establishing an observation model; constructing a sparse representation and over-complete dictionary; establishing and initializing a regularization optimization model; carrying out adaptive weight updating and risk-driven parameter selection; after regularization parameters are determined, a fast iterative shrinkage threshold algorithm FISTA is adopted to carry out optimization solution, and dictionary refinement is carried out on the detected direction after each iteration convergence so as to reduce off-grid errors; and after a small amount of outer layer iteration is repeated, outputting a final DOA estimation result and corresponding power. According to the method, self-adaptive selection of regularization parameters and noise levels can be realized, and the problem of precision degradation under complex conditions of low signal-to-noise ratio, limited snapshot number, signal source correlation, power imbalance and the like is effectively solved without manual parameter adjustment, so that the robustness and practicability of estimation are remarkably improved.

The invention relates to the technical field of community management, in particular to a dynamic monitoring analysis method and system for vehicles in an intelligent community, and the method comprises the steps: collecting multi-source data through a sensor, and generating structured sensing data through a lightweight OCR algorithm; calculating the real-time weight of each sensor by using a self-adaptive dynamic weight algorithm according to the structured sensing data in combination with environmental parameters and equipment states, calculating fusion confidence through a feature confidence fusion formula, rejecting unqualified data, and generating high-precision sensing data; constructing a community digital twinning scene, performing anomaly judgment through a dynamic threshold algorithm, positioning a fault through a three-level mechanism, generating a configuration updating instruction, and generating system state data; and inputting the high-precision sensing data, the system state data and the historical vehicle violation records into the game theory model to calculate the vehicle violation probability, and generating a differentiated management strategy according to the vehicle violation probability. According to the scheme, precise management of community vehicles is realized through multi-source fusion and hierarchical association.

The invention belongs to the technical field of medical health monitoring, and provides a human health prediction method and system based on facial video physiological signal detection, and the method comprises the steps: collecting a facial video stream, and extracting time sequence physiological signals such as heart rate, HRV, respiratory rate and the like through an rPPG algorithm; constructing a graph database individual health portrait in combination with multi-scale time sequence alignment; adopting a dynamic threshold algorithm to detect instantaneous anomaly, and fusing nonlinear dynamics and waveform morphological characteristics to quantify anomaly; constructing a hybrid model, extracting space-time and high-order features, and modeling multi-parameter interaction; a prediction result is dynamically corrected based on a Bayesian algorithm, and health risk layering is realized through clustering; and outputting the visual health report. Through non-contact monitoring, multi-modal fusion and edge-cloud collaborative architecture, the problems that traditional equipment is low in compliance, non-contact technology is insufficient in precision and prediction is shallow are solved, dynamic health prediction and closed-loop management are achieved, and the system is suitable for scenes such as remote monitoring and chronic disease screening.

The invention discloses a continental lake basin grain mud layer sequence image recognition method, device and equipment, a medium and a product, and relates to the technical field of geological exploration. The method comprises the following steps of: after acquiring a smooth surface / slice sample grayscale image of a continental lake basin sedimentary rock core, respectively obtaining a global pixel optimal segmentation threshold value and a local pixel self-adaptive segmentation threshold value of each pixel by applying a maximum between-class variance method and a Sauvola local threshold value algorithm; the two thresholds are fused by taking the local contrast normalized value of each pixel as a weight coefficient to obtain a final segmentation threshold, pixel-by-pixel binarization processing is carried out on the grayscale image to obtain a binarized image, the percentage of the target pixel is counted line by line in the binarized image, and the percentage of the target pixel is calculated. A target pixel percentage trend line is formed according to a statistical result, and finally bright color and dark color texture mud layer segments on the trend line are identified, and quantitative geological parameters of the texture mud layer are obtained according to the identification result, so that the segmentation precision and robustness of the complex texture mud can be remarkably improved.

The invention, which relates to the technical field of industrial equipment state monitoring, discloses a belt conveyor fault detection and early warning system comprising a multi-mode sensing module, an edge calculation module, a feature fusion and analysis module, a risk level determination module, an intelligent early warning module and a visual feedback module. Multi-source data such as images, sound, tension, temperature and vibration in the running process of a conveyor are collected, features are extracted and preprocessed through edge calculation, and a structured feature vector is constructed; through deep fusion and fault identification model analysis, operation state identification and trend score generation, and in combination with a dynamic threshold algorithm, a risk level is determined, the method has a hierarchical response mechanism, supports user feedback driven model online learning and optimization, and improves the accuracy and adaptive ability of fault identification; the system has the advantages of being comprehensive in detection, timely in response and high in intelligent degree, and is suitable for various complex conveying environments.

The invention provides a multispectral visual perception electroslag remelting defect online identification method, which comprises the following steps: constructing a cross-modal perception network through a multispectral camera and an array sensor, and collecting slag bath morphology, multiphase flow parameters and slag system component data; feature fusion is carried out on the multi-source data, a multi-physics field coupling model is established, and a defect inoculation mechanism is analyzed; a hierarchical identification network is constructed based on the fusion features, and defect type determination is realized in combination with a dynamic threshold algorithm; a cross-modal sensing network is constructed to synchronously collect multi-source data, a multi-physics field coupling model is combined to analyze a defect formation mechanism, and a hierarchical identification network is adopted to realize dynamic threshold judgment, so that the technical problem that a traditional method cannot track multiphase flow dynamic and slag system component changes in real time is effectively solved, and the method is suitable for large-scale popularization and application. The method has the capability of capturing multiphase flow dynamic characteristics of the molten pool and real-time evolution of slag system components at the same time, and the identification accuracy of slag inclusion type defects is remarkably improved.

The invention belongs to the technical field of reservoir bank collapse monitoring and early warning, and particularly relates to a reservoir bank collapse real-time early warning system and method based on multi-source data collaboration. The system comprises a distributed optical fiber sensing network, a micro borehole clinometer array and an edge computing terminal, the edge computing terminal comprises a data acquisition module, a data processing module, a model computing module and an early warning module, and the model computing module adopts a dynamic prediction model cooperatively driven by a physical mechanism model and a deep reinforcement learning algorithm. And the pre-warning module analyzes and predicts the data preprocessed by the data acquisition module, and outputs a visual risk cloud picture and a graded pre-warning signal according to a prediction result and a self-adaptive pre-warning threshold algorithm. According to the invention, the strain and temperature information of the bank slope is monitored through the distributed optical fiber network, the change of inclination angles at different depths in the bank slope is monitored through the micro borehole inclinometer array, data fusion and analysis processing are carried out through the edge computing terminal, and finally real-time monitoring and early warning of the bank collapse risk are realized.

The invention relates to a method for monitoring the running state of a lightning arrester for a power grid high-voltage cable terminal tower, which comprises the following steps of: firstly, collecting the running data of the lightning arrester in real time, preprocessing the collected data and carrying out time-space synchronous calibration on the collected data, and then calculating the running state of the lightning arrester based on a cable coupling coefficient and an interphase coupling coefficient; the method comprises the following steps of: firstly, correcting the influence of spatial distribution interference and electromagnetic interference on data according to the pre-processed data, so as to establish a correlation analysis model, then, generating a personalized early warning threshold value by utilizing a dynamic threshold value algorithm based on historical operation data and operation age limit of equipment, and carrying out abnormity judgment on the data subjected to pre-processing and time-space synchronization calibration through the early warning threshold value. And finally, based on a correlation analysis model, carrying out coupling analysis on a lightning arrester leakage current signal and a cable circulation current signal in the extracted data, and outputting and uploading a monitoring report. The method has the advantages of being accurate in early warning and capable of achieving multi-dimensional monitoring.

The invention discloses a thermal infrared imager intelligent temperature measurement platform management method and system. The method comprises the following steps: S1, information acquisition and multi-modal data binding; s2, encoding and encrypting data; s3, wireless transmission and decryption are carried out; s4, the server side performs intelligent processing; s5, result distribution and storage; according to the invention, through deep fusion of multi-modal data binding and environment interference parameters, the motion detection and temperature pre-judgment process can dynamically adapt to interference changes in a complex scene; a multi-modal fusion dynamic threshold algorithm, a space-time attention enhancement model and a layered time sequence temperature prediction model are integrated, the full-dimension requirement from real-time monitoring to long-term trend analysis is covered, and the intelligent level and scene adaptability of the system are remarkably improved; and a whole-process system architecture of sensing, transmission, processing, storage and display is constructed, so that the platform has high efficiency of data processing and convenience in use.

The invention relates to the technical field of new energy automobile thermal management, in particular to a new energy automobile thermal management control method and system, and the method comprises the steps: collecting multi-source data, and generating a structured multi-source data set; according to the structured multi-source data set, a high-precision thermal management parameter is generated in combination with the component temperature deviation, the battery SOH and the driving mode; constructing a double-time-domain LSTM prediction model based on the high-precision thermal management parameters, and obtaining prediction results of short-term motor thermal load and medium-term battery thermal insulation demand; according to a prediction result, a dynamic threshold algorithm is adopted to carry out abnormity judgment on the thermal load data, when abnormity is detected, control parameters are corrected through a PID closed-loop mechanism, an execution instruction is generated, and system optimization data is generated; and calculating personalized control coefficients adapted to different scenes, and generating differential thermal management strategies according to the personalized control coefficients. According to the scheme, the integrated whole vehicle thermal management system is constructed, accurate control over the temperature of the battery, the temperature of the motor and the temperature of the cabin is achieved, and the thermal management efficiency is improved.

The invention discloses a three-dimensional seismic data reconstruction method based on a weighted fast iterative shrinkage threshold algorithm, and the method aims at solving a problem that a conventional three-dimensional seismic data reconstruction method neglects the potential correlation between adjacent slices, and comprises the steps: firstly dividing three-dimensional seismic data into a series of time slices; and carrying out multi-scale multi-direction two-dimensional curvelet transformation to obtain a curvelet domain coefficient, then introducing a fast iterative shrinkage threshold algorithm, and carrying out reconstruction by adopting an index threshold parameter formula. In the process, according to the characteristic that curvelet coefficient support sets of two adjacent time slices have a large intersection, a weighting operator is constructed in a curvelet domain by using a prior support set provided by a previous reconstructed time slice, and then the weighting operator is fused into a fast iterative shrinkage threshold algorithm. According to the method, the signal-to-noise ratio of the reconstructed signal is increased, the calculation speed is increased, the requirement for a computer memory is reduced, weak effective wave signals are protected, and the reflected wave event is more continuous and clearer.

The embodiment of the invention provides a data detection method and a related device, and the method comprises the steps: mapping collected domain name system data to a virtual table, and generating a temporary analysis view; detecting the domain name system data in the temporary analysis view based on a time constraint condition and a node aggregation query rule, and determining abnormal nodes in the domain name system data; and for each piece of domain name system data, determining a data detection result based on the type of the abnormal node and the proportion of each type of abnormal node in the total node. According to the method, the dynamic threshold algorithm based on the proportion is introduced, and the data detection result is determined based on the types of the abnormal nodes and the proportion of each type of abnormal nodes in the total node, so that false alarm and missing alarm caused by abnormity or short-term fluctuation of a single node can be effectively reduced, and the detection accuracy is improved.

The invention provides a central air conditioner energy efficiency intelligent diagnosis method and system, and relates to the technical field of refrigeration equipment control, and the method comprises the steps: collecting monitoring data and equipment information of a central air conditioner water system; performing preprocessing and energy consumption calibration, constructing a data analysis model, analyzing and correcting the monitoring data, and outputting associated monitoring data; based on the host parameters and the energy consumption parameters, a host COP value is calculated by adopting an improved energy efficiency algorithm, a system COP value is calculated by adopting a system comprehensive energy efficiency algorithm, the energy consumption parameters are analyzed through an energy consumption analysis model, and an energy consumption proportion diagram is generated; and performing anomaly recognition on the associated monitoring data, the host COP value, the system COP value and the energy consumption proportion diagram by adopting a dynamic threshold algorithm, generating an alarm event, performing fault positioning on the alarm event in combination with the collaborative fault tree model, outputting a fault analysis result, and performing fault analysis. Therefore, accurate diagnosis of the energy efficiency of the central air conditioner, early warning of abnormity in advance, rapid fault positioning and continuous optimization of the energy efficiency can be realized.

The application discloses a radar high-resolution imaging method based on a fast shrinkage iterative threshold network, which is applied to the field of radar detection and imaging. The low-pass characteristic of a radar antenna pattern makes the deconvolution problem a pathological problem, the frequency band width of which is limited, and direct inverse filtering can cause high-frequency noise amplification and cannot obtain stable inversion results. The application combines the interpretability of an iterative algorithm and the advantages of deep learning, expands a fast iterative shrinkage threshold algorithm into a deep network, and can be used to solve the problems of low azimuth resolution of an existing scanning radar and difficulty in manually selecting parameters, so as to realize super-resolution imaging of the scanning radar. The application has the characteristics that two-dimensional radar data is divided into one-dimensional azimuth vectors, one-dimensional convolution is used to fully learn the characteristics of the azimuth data, the optimal parameters in the fast shrinkage threshold algorithm are obtained, and the azimuth resolution of the radar image is effectively improved.

This invention relates to the field of seismic data processing technology, specifically disclosing a method for removing strong reflection shielding in hidden river channels based on adaptive hybrid L0-L1 norm. The method includes: first, establishing an optimization objective function based on Bayes' theorem using hybrid L0-L1 norm; second, dynamically adjusting the L0-L1 norm weights using an adaptive weight function driven by the seismic signal, combined with reflection coefficient amplitude and residual information, enhancing sparsity constraints in strong reflection zones and reducing constraint strength in weak reflection zones; then, constructing a convex upper bound for the objective function using a minimization framework, and solving it iteratively in stages using an accelerated rapid iterative threshold shrinkage algorithm, while incorporating prior knowledge of seismic wave propagation laws and river channel deposition patterns to ensure the geological rationality of the solution. This invention solves the technical problems of traditional sparse processing methods, such as fixed parameters, lack of geological constraints, and inability to simultaneously address strong reflection suppression and weak signal protection, significantly improving the separation accuracy of strong reflections and the recovery rate of weak signals, effectively overcoming the strong reflection shielding effect.

The application discloses a kind of synthetic aperture assisted multi-channel radar forward-looking learning imaging method, first, the echo data of the region to be imaged is acquired, the data obtained is compressed in distance direction pulse, then the echo data after pulse compression is imaged using the synthetic aperture assisted depth unfolding iterative shrinkage threshold algorithm ISTA network, i.e. in real aperture dimension, the imaging problem is converted into the estimation problem of target scattering coefficient, the target function is solved using the depth unfolding ISTA network, then the azimuth focusing is carried out using back projection BP algorithm in synthetic aperture dimension, then the synthetic aperture dimension result and the ISTA network result are fused to obtain the left-right unambiguous super-resolution imaging result.The method of the application overcomes the problem of left-right ambiguity and low resolution of single-base radar forward-looking imaging, realizes multi-channel radar forward-looking high-resolution imaging, and through depth learning, the parameter setting step is saved, the efficiency is improved, and the problem of signal-to-noise ratio sensitivity is overcome.

The invention provides a transformer fault maintenance virtual training method and system based on multi-modal cooperation. The method comprises the following steps: constructing a multi-modal knowledge graph of transformer fault data; pre-testing the trainee by adopting a preset collaborative training system, determining the initial proficiency of the trainee, and determining an eye movement triggering threshold of the trainee by adopting a preset fault complexity optimization algorithm and a preset eye movement triggering threshold algorithm; according to the eye movement data of the trainee and the eye movement triggering threshold value of the trainee, fault associated data in the multi-mode knowledge graph are called, a preset virtual-real feedback intensity algorithm is adopted, fault interaction data of the trainee and the collaborative training system are obtained, and then a personalized training scheme is generated. According to the invention, trainees are assisted to construct a complete fault maintenance knowledge framework, knowledge fragmentation is avoided, association understanding is enhanced, the problem of missing practical operation is solved, a personalized training scheme is generated, the training pertinence and efficiency are improved, and the skill improvement period is shortened.

The application discloses a kind of multi-source fusion perception systems for exoskeleton robot, belong to robot technical field, system includes:foot bottom pressure acquisition module, is laid in exoskeleton foot component;Inertial measurement module, is laid in exoskeleton limb bar piece;Motor feedback module, is integrated in exoskeleton driving joint;Central processing unit is connected with each module, for receiving and processing multi-source data, method includes: by foot bottom pressure dynamic threshold algorithm real-time determination gait switching point;Fusion inertial measurement data and motor feedback data, carry out limb posture solution and man-machine interaction moment estimation;Based on the redundancy check of multi-source data realizes fault monitoring and safety control.The application solves the problems of high gait recognition delay, poor environmental adaptability and insufficient man-machine interaction transparency in the prior art through deep coupling and synchronous perception of mechanical and kinematic data, and realizes accurate, robust and real-time perception of the wearer's movement intention.

The present application relates to the technical field of electric vehicle reducer fatigue analysis, and particularly relates to a method for preparing a frequency domain of a gear fatigue load spectrum of an electric vehicle reducer based on CCWOA, comprising: S1: constructing a bending stress load spectrum of a reducer gear of an electric vehicle; S2: generating an optimal wavelet base function based on discrete wavelet parameter optimization of an energy leakage criterion; S3: performing threshold optimization based on a CCWOA algorithm to obtain an optimal threshold; the CCWOA algorithm introduces a Logistic-Tent chaotic mapping mechanism and a cosine iteration strategy in the WOA algorithm; S4: performing discrete wavelet transform on the bending stress load spectrum of the reducer gear of the electric vehicle based on the optimal wavelet base function and performing load spectrum frequency domain coding through the optimal threshold; and S5: realizing fatigue analysis of the electric vehicle reducer based on a gear fatigue acceleration load spectrum of the electric vehicle reducer. The present application realizes high-precision compression of the reducer gear load spectrum and equivalent retention of fatigue damage, and provides a reliable scheme for fatigue analysis of the electric vehicle reducer.

The invention discloses a multi-level system hierarchical management and control environment problem investigation and rectification system and method in the technical field of enterprise management. The method comprises the steps of obtaining an initial task allocation parameter set calculated by a superior unit through a task allocation algorithm; the initial task allocation parameter set is issued to a lower-level unit for troubleshooting execution; receiving problems entered by a subordinate unit after checking execution based on the initial task allocation parameter set, and performing severity judgment; on the basis of the severity obtained through judgment, early warning time of auditing of a superior unit is calculated in combination with an early warning time threshold algorithm; and calculating the rectification completion quality index of the subordinate unit based on the early warning time. According to the invention, by integrating the core module, the specialty, the collaboration and the efficiency are balanced, troubleshooting and rectification of hierarchical management and control environment problems of a multi-level system are facilitated, and the management requirements of'controllable whole process and traceable risk 'are met.

The invention discloses a multi-sensor cooperative monitoring system and method for forging and pressing equipment, the system comprises a plurality of sensors, a processor, an alarm module and a control module, the plurality of sensors are distributed at target parts of the forging and pressing equipment, and multi-dimensional operation data are collected in all directions; the processor extracts characteristic parameters by adopting a dynamic threshold algorithm, and accurately finds and locates equipment abnormity reasons through operation data association analysis; the alarm module generates graded alarm signals according to the deviation degree, clearly reflects the abnormal severity, and improves the early warning effectiveness and pertinence. The control module automatically matches a control instruction according to the alarm signal, rapidly adjusts the operation state of the equipment, reduces the risk of manual intervention, and guarantees the production continuity. And by constructing the equipment health state model, the system can also realize preventive maintenance, find potential hazards in advance, prolong the service life of the equipment and reduce the maintenance and operation cost.

This invention provides an adaptive multi-source level wake-up method, system, and medium based on the HiSilicon platform USB. It synchronously captures the first level information, first square wave information, and first voltage information of the USB Hub, and generates a time-aligned trigger signal matrix through delay compensation. Based on the trigger signal matrix, feature signals are extracted, and a wake-up score is generated using a weighted fusion algorithm. An adaptive wake-up threshold is dynamically calculated based on a real-time noise baseline and historical false wake-up records. When only the first slope information exceeds the limit, it is determined to be a Level 1 wake-up, and the wake-up score is increased. When both the first slope information and the first frequency information exceed the limit, it is determined to be a Level 2 wake-up, and a wake-up action is triggered. When the first slope information does not exceed the limit and the wake-up score exceeds the wake-up threshold, it is determined to be a Level 3 wake-up, triggering a wake-up action and initiating wake-up diagnosis. Noise interference is reduced through multi-source signal cross-validation, and a dynamic threshold algorithm is used to adapt to complex industrial environments, improving the success rate and reliability of wake-up.

The invention discloses a method and system for reconstructing and purifying a noise cross-correlation function of seismic background noise, and the method comprises the following steps: collecting an original noise signal, carrying out the non-uniform downsampling of the original noise signal through employing the sparse characteristic of the noise cross-correlation function of the seismic background noise in an effective frequency band, and combining with a compressed sensing theory, and obtaining a non-uniform downsampling signal; obtaining under-sampling data; reconstructing a full-band noise cross-correlation function from the undersampled data with high precision by adopting a fast iterative shrinkage threshold algorithm to obtain a reconstructed noise cross-correlation function; performing frequency-wave number transformation on the reconstructed noise cross-correlation function, and realizing three-component separation by using an FK filtering mask to obtain a separated FK domain effective signal; and carrying out inverse transformation on the separated FK domain effective signal, and finally outputting a purified noise cross-correlation function.

The invention discloses a low-load and anti-fatigue asynchronous brain control interface switching method. The method comprises the following steps: S1, signal acquisition and preprocessing: performing signal acquisition by adopting a 16-bit ADC (Analog to Digital Converter) resolution and 256Hz sampling rate 8-channel dry electrode electroencephalogram cap; high-quality electroencephalogram signals are obtained through the signal collecting and preprocessing module, and then the multi-modal feature extraction module fuses the frequency domain delta beta / alpha energy ratio, the space domain C3-C4 coherence and the nonlinear gamma-band multi-scale entropy to generate robust feature vectors; after the vector is input into an asynchronous detection engine, intention state accurate awakening is achieved based on a dynamic threshold algorithm coupling false triggering history and environmental noise, meanwhile, personalized parameter calibration is completed within 5 minutes through an anti-fatigue initialization process of eye closing resting and multi-mode physiological verification guided by touch, and finally the false triggering rate lt is achieved; and the fatigue degree of the user is reduced by 60% + of the medical-level performance index, so that the use requirements are met.

The application discloses a sparse regularization DOA estimation method based on risk minimization principle, and belongs to the technical field of array signal processing and underwater acoustic signal processing. The application receives array signals and establishes an observation model; constructs sparse representation and an overcomplete dictionary; establishes and initializes a regularization optimization model; adaptively updates weights and selects parameters in a risk-driven manner; after determining the regularization parameter, a fast iterative shrinkage threshold algorithm (FISTA) is used for optimization solving, and after each iteration convergence, dictionary refinement is performed on the detected direction to reduce off-grid errors; after a small amount of outer iteration is repeated, the final DOA estimation result and the corresponding power are output. The application can realize adaptive selection of the regularization parameter and the noise level, effectively overcome the precision degradation problem under complex conditions such as low signal-to-noise ratio, limited snapshot number, signal source correlation and power imbalance without manual parameter adjustment, and thus significantly improve the robustness and practicability of estimation.