1105 results about "Weak signal" patented technology

The invention discloses a turbofan engine operation monitoring method and system based on digital twinning, belongs to the technical field of turbofan engine monitoring, and aims to solve the problems that weak fault signals such as early cracks and abrasion are difficult to extract and the prediction precision of a multi-source fault propagation path is low under a strong noise background. An original operation signal is collected through a sensing array, and is processed by an adaptive resonance demodulation chain to generate a demodulation signal. The method comprises the following steps: carrying out time-frequency transformation on a demodulation signal, constructing an initial candidate feature set by combining feature frequency prior matching actual measurement and theoretical feature frequency, and generating an independent feature set by fusing multi-scale decoupling network separation features of digital twin constraints; for independent features, effective causal pairs are screened by adopting a physical coupling relationship combining Granger causal analysis and digital twinborn simulation, a dynamic Bayesian network is constructed to simulate fault propagation, a posterior probability is calculated through digital twinborn verification and Monte Carlo simulation, early warning is triggered, and a maintenance decision is generated. And weak signal extraction and accurate fault prediction under strong noise are realized.

The invention relates to the technical field of battery detection, and particularly provides a battery fault early warning and diagnosis method, which comprises the following steps: acquiring operation parameters of a battery pack in real time, processing the operation parameters based on a preset judgment condition, generating a corresponding preprocessing signal according to a noise environment state, and extracting spatial-temporal characteristics to perform weak signal enhancement processing, so as to obtain a battery fault early warning and diagnosis result. Generating an enhanced feature set; based on the enhanced feature set, performing space-time fusion calculation according to a preset weight relation to obtain a fault energy accumulation value; dynamically correcting a fault judgment threshold according to the health state of the battery and the real-time environment parameters; and when the fault energy accumulation value exceeds the fault judgment threshold after dynamic correction, outputting a graded early warning signal. By capturing weak fault features of the battery pack and combining spatial domain feature extraction of temperature difference and voltage distribution and a signal enhancement technology, the detection sensitivity of early hidden faults is improved, and in a lithium battery safety early warning scene, the fault detection time is shortened, the false alarm rate is reduced and the like.

The invention discloses an unmanned aerial vehicle cluster communication system based on weak signal connection. According to the invention, through the intelligent cooperation mechanism of the dynamic relay path optimization module, the communication reliability in a complex environment is significantly improved. The system analyzes cluster network topology and node states in real time, combines multi-dimensional parameters such as signal stability, energy consumption efficiency and task load, dynamically screens an optimal relay node and generates an efficient transmission path. For example, in a weak signal area, the system can automatically start an adjacent high-stability node as a relay, the problem of rigidity of a traditional fixed relay strategy is avoided, meanwhile, the signal attenuation risk is avoided in advance through predictive switching, and continuous return of key data is ensured. The capability of actively adapting to network fluctuation effectively reduces the probability of communication interruption, and can ensure the non-inductive connection of task instructions and key information especially in scenes with strict requirements on real-time performance, such as search and rescue, exploration and the like.

The invention belongs to the technical field of bearing fault diagnosis, and particularly relates to a transmission casing typical small bearing fault weak vibration signal extraction method, which comprises the following steps: reconstructing a bearing vibration signal to obtain a modulation impact signal, and calculating a weighted kurtosis value based on the modulation impact signal to improve a Protugram algorithm; noise interference is added to a bearing fault signal, and extraction of a small bearing fault weak impact signal is realized by optimizing a filtering frequency band based on a weighted kurtosis value improved Protugram algorithm; bearing impact characteristics can be enhanced based on a simulation sensor resonance enhancement algorithm (SPM); the impact quantification of the bearing can eliminate the influence of different rotating speeds and load working conditions, and realizes the quantitative diagnosis of bearing faults. And finally, the fault data of the rolling body, the inner ring and the outer ring of the flight attachment case small bearing part test bed are verified, and effective extraction and enhancement of bearing fault weak signals can be realized.

The invention provides an unmanned aerial vehicle high-precision autonomous navigation method and system based on GNSS and IMU deep fusion, and aims to solve the problems of insufficient navigation precision and poor robustness in a complex electromagnetic environment. Through a tight coupling architecture, GNSS original observed quantity and IMU pre-integration results are jointly modeled in an observation layer, and multi-source constraints are introduced in combination with factor graph optimization, so that the positioning precision and consistency in weak signal and shielding scenes are remarkably improved. For abnormal observation, a robust kernel function is adopted to dynamically adjust the weight, and the influence of electromagnetic interference and a multipath effect is effectively inhibited. Meanwhile, navigation calculation and model prediction control MPC are combined, and sub-meter hovering and high-precision trajectory tracking are achieved. According to the method, in high-voltage transmission line inspection, dependence on a high-cost sensor is reduced, the engineering application value is high, the method can be widely applied to the fields of electric power inspection, disaster emergency, infrastructure monitoring and the like, and reliable technical support is provided for high-precision autonomous navigation of the unmanned aerial vehicle in a complex environment.

The invention discloses a chemical process fault diagnosis method and system, and relates to the technical field of chemical process fault diagnos.The scheme aims at the diagnosis bottleneck of catalyst inactivation type progressive faults, fine parameter offset is captured in real time through a dynamic reference model, weak signals are accumulated and amplified in combination with an attenuation weighting mechanism, and the fault diagnosis accuracy is improved. The problem that a traditional method is not sensitive to slow drifting, and consequently report omission is caused is solved, and a fault recognition window is remarkably advanced. The dynamic threshold value is updated in real time based on mobile statistics, and raw material fluctuation and sensor noise can be self-adapted; during working condition switching, the model is automatically reset and the threshold value is relaxed, so that false alarm triggered by parameter mutation is avoided, and the stability of production scheduling is guaranteed; the design that fault half-life period weight and moving window length are associated with an inactivation period is introduced, so that the model autonomously adapts to different catalyst characteristics.

The invention discloses intelligent part damage identification and quantitative analysis based on multi-modal fusion, and particularly relates to the technical field of intelligent part damage identification. According to the method, synchronous or asynchronous real-time data acquisition is carried out on a target part, multi-modal features are extracted in combination with a heterogeneous feature extraction network, confidence scores of all modals are calculated, mutual information between the modals is fused, and an attention weighted fusion process is guided; when it is detected that the modality is abnormally suppressed, feature enhancement and dynamic weight adjustment are implemented, key weak signals are prevented from being ignored, fusion features are input into a damage identification model, and a damage identification result, modal weight visualization and early damage risk scoring are output; the technology effectively improves the recognition capability of the model for early and hidden damage, is especially suitable for sensitive capture and fusion judgment of weak modal signals in high-safety scenes such as wind power and aviation, and significantly enhances the early warning accuracy and maintenance foresight of the system.

The invention provides a lithium battery internal short circuit fault early warning and positioning method and system based on deep learning. The early warning and positioning method comprises the following steps: step S10, multi-dimensional data semantic acquisition and working condition self-adaptive preprocessing; step S20, collaborative extraction and alignment of cross-scale spatio-temporal features; step S30, performing dynamic threshold adaptive internal short circuit early warning and feedback optimization; s40, fault accurate positioning and verification of topology perception are carried out; and step S50, performing embedded collaborative optimization and self-diagnosis deployment. According to the invention, early weak signals of an internal short circuit fault can be found in time, and the efficiency and timeliness of battery safety monitoring are improved; the sensitivity of early warning is improved; the robustness in high-temperature, low-temperature and rapid charging and discharging environments is enhanced; and the positions of the single battery and the electrode with the fault can be accurately identified.

The invention discloses a millimeter wave radar meteorological target detection method based on deep learning, and relates to the technical field of meteorological radar processing. The method comprises the following steps: acquiring a millimeter wave radar original signal by using a transmission control protocol for preprocessing; the distance and the angle between an object and an antenna are measured through a millimeter wave radar, so that three-dimensional space coordinates of the measured object are obtained; converting the three-dimensional space coordinates through time alignment and a space coordinate system, and projecting the three-dimensional space coordinates into a visual coordinate system; pre-training a feature extractor, analyzing a data label by using ECMWF, and then performing end-to-end fine tuning; and automatically optimizing the detection threshold according to the signal-to-noise ratio, and outputting target meteorological classification, meteorological intensity and meteorological motion prediction. According to the method, the detection threshold is automatically optimized according to the signal-to-noise ratio, target classification, intensity estimation and motion vector prediction are output, weather weak signal leak detection is avoided, and the target tracking capability and the extreme weather generalization capability are improved.

The invention discloses a full waveform decomposition method based on a multi-branch convolutional neural network, which belongs to the technical field of airborne depth sounding laser radars, is used for airborne depth sounding laser radar echo signal decomposition, and comprises the following steps: obtaining and preprocessing an original echo sequence, constructing a multi-branch one-dimensional convolutional neural network model, and performing neural network training; and based on the trained multi-branch one-dimensional convolutional neural network, outputting a three-channel probability heat map, performing multi-peak sub-pixel decoding on the three-channel probability heat map, and outputting a peak accurate position, a peak accurate position normalized value and a peak confidence after de-weighting. According to the method, the multi-branch one-dimensional convolutional neural network model is constructed and multi-peak sub-pixel decoding is carried out, so that stable decomposition of full-waveform echoes and high-precision positioning of multi-echo peak values are realized under the conditions of multi-peak superposition, high noise and weak signals, and false detection and false peaks caused by noise are remarkably reduced.

The invention discloses a wide-frequency-domain weak signal data acquisition method, system, equipment and medium, and relates to the technical field of power system monitoring and fault diagnosis, and the method comprises the steps: collecting an input signal, analyzing the signal characteristics in real time, obtaining the frequency composition and amplitude change information of the signal, and dynamically adjusting the sampling rate according to the signal characteristics. Performing noise reduction processing on the collected signals, eliminating noise interference and retaining effective signal components, performing time alignment processing on the data to form a data set with a unified time reference, extracting multi-category features based on the data set, performing fusion judgment, identifying whether the system is in a fault state, and when it is judged that a fault occurs, judging whether the system is in a fault state or not; if yes, fault analysis and positioning are executed, and an analysis result is generated and output. According to the method, fast Fourier transform analysis is carried out on the signals, the dominant frequency and harmonic components can be accurately recognized, the energy ratio can be calculated, and a reliable data basis is provided for signal feature extraction and fault diagnosis.

The invention relates to the technical field of information retrieval, in particular to a vertical website deep crawling method based on intention recognition, which comprises the following steps: crawling data from a plurality of heterogeneous information sources, and constructing a dynamic knowledge graph; identifying a weak signal association path formed by connecting a plurality of relation edges in the dynamic knowledge graph, and converting the weak signal association path into a to-be-verified risk hypothesis; generating a data acquisition intention, and based on the to-be-verified risk hypothesis, generating a rejection query for searching reverse evidence as a first data acquisition intention; receiving an input query instruction, and performing semantic analysis on the query instruction to generate a second data acquisition intention; a multi-source crawling strategy is generated based on the type of the data collection intention, and the multi-source crawling strategy comprises a plurality of vertical website sources and crawling priorities of the vertical website sources; executing a deep crawling task according to the multi-source crawling strategy to obtain target data; and processing the target data, verifying the to-be-verified risk hypothesis, and judging whether the reverse evidence exists or not and judging the intensity of the reverse evidence.

The embodiment of the invention provides a multi-frequency-band signal processing method for a high-performance 5G router, and the method comprises the steps: constructing a dynamic radio frequency front-end gain regulation and control model based on signal intensity monitoring results of different frequency bands; acquiring real-time multi-band signal intensity data, and inputting the real-time multi-band signal intensity data into the dynamic regulation and control model; generating a corresponding gain adjustment parameter instruction according to a result output by the model; and sending the gain adjustment parameter instruction to a radio frequency front end to realize precise gain regulation and control so as to improve the communication quality in a weak signal environment. According to the scheme provided by the embodiment of the invention, the radio frequency front-end gain can be accurately regulated and controlled according to the signal intensity change of different frequency bands, so that the problem that the communication quality is reduced in a weak signal environment is solved.

The invention discloses a meeting window call system based on a voice sensor, and relates to the technical field of communication. Comprising a full-space voice acquisition module, a dynamic confidence evaluation module, a phase coupling enhancement module, a multi-dimensional speech mode recognition module, a dynamic gain adjustment and tone quality enhancement module and a full-link voice integrity verification module, an array type voice sensing unit is deployed based on audio reflection characteristics, a multi-band full-space acquisition matrix is constructed, voice signals of two parties meeting are acquired, and a basic audio data stream with a high signal-to-noise ratio is output. According to the invention, through array sensing acquisition, dynamic confidence evaluation, phase coupling and harmonic enhancement, multi-dimensional speech recognition and dynamic gain adjustment, accurate recognition and fidelity enhancement of low-volume speech are realized, and through combination of full-link integrity verification and mistaken killing backtracking, weak signals are ensured not to be missed and distorted, so that the accuracy of speech recognition is improved. And the privacy, the security and the information integrity of the meeting call are improved.

The invention relates to an abnormal battery positioning method and system, equipment, a medium and a product. The abnormal battery positioning method comprises the steps of determining operation data of a battery pack; performing abnormal value detection and normalization processing on the operation data to obtain target operation data; performing time sequence analysis on the target operation data, determining time characteristics, and obtaining spatial characteristics according to the position information of each battery and the target operation data; inputting the spatial-temporal characteristics into a thermal imaging model to generate a thermal image of the battery pack according to the spatial-temporal characteristics; and determining that the battery corresponding to the target area is abnormal in response to the fact that the target characteristic value of the target area in the thermal image exceeds a preset threshold value. By adopting the method, the problems that the traditional infrared thermal imaging technology is greatly influenced by the environment, the installation space is limited, the cost is high and the weak signal space-time resolution capability is insufficient can be solved, the thermal state of the battery pack can be accurately reflected, and the abnormal positioning and accurate early warning of the thermal runaway early stage can be realized.

The invention belongs to the technical field of flow field optical measurement, and particularly relates to a high-enthalpy plasma gas temperature measurement method, system and device based on an absorption spectrum. Generating a composite modulation signal through a high-frequency sine wave and a low-frequency sawtooth wave, and enabling incident light to pass through a flow field measurement area in parallel through laser modulation and beam shaping; the detector receives the transmission light and converts the transmission light into an electric signal containing harmonic information, the signal acquisition device is used for performing signal acquisition, the phase-locked demodulation algorithm is used for extracting second harmonic, and the temperature is inversely deduced by combining the double-spectral-line temperature measurement method and the Fourier coefficient amplitude ratio of the second harmonic with the HITRAN database. The system comprises a signal driving module, a laser emission module, a detection acquisition module and a temperature measurement module. The device is composed of a signal generator, an in-phase adder and the like, and a laser is matched with a driving power supply to guarantee stable work. According to the scheme, aiming at the problems of weak signal absorption and insufficient signal-to-noise ratio under complex working conditions, the measurement accuracy and reliability are improved by composite modulation, harmonic extraction and a bispectral line algorithm, and an effective means is provided for research on thermal characteristics of the high-enthalpy plasma.

The invention is applicable to the technical field of geophysical detection, and provides an underwater target detection method and device based on acoustoelectric joint and width learning, and the method comprises the steps: carrying out the signal collection through an acoustoelectric joint detection network which is disposed in an underwater monitoring region in advance, the method comprises the following steps: acquiring an acoustic signal and an electromagnetic signal synchronously acquired by each detection node in an acoustic-electric joint detection network, performing feature extraction on the acoustic signal and the electromagnetic signal based on a data sliding window mechanism to obtain an acoustic-electric fusion feature matrix, and calculating the acoustic-electric fusion feature matrix according to the acoustic-electric fusion feature matrix. Whether a target object capable of autonomously radiating an acoustic signal and an electromagnetic signal underwater exists in an underwater monitoring area or not is determined through a pre-trained width learning network, so that false alarm interference caused by background noise in an underwater environment is effectively reduced, and the underwater target recognition capability under a weak signal condition is remarkably improved; and the accuracy of underwater target detection is improved.

The invention discloses an underground structure leakage intelligent detection system based on multi-modal image fusion and deep learning recognition. The system comprises an image acquisition and preprocessing module, a significance guide image fusion module, a bimodal target detection module and a feature fusion and output module. Compared with the prior art, the method has the following advantages: saliency guidance, channel attention and multi-modal joint training are combined, an information closed loop is constructed by a triple mechanism, and the image fusion quality is improved; bimodal parallel recognition and ANN fusion judgment are adopted to adapt to the image degradation condition in a complex environment, and the recognition accuracy of a weak signal area is improved; the system can be deployed in various underground structure scenes such as subways, tunnels, underground garages and pipe galleries, and is compatible with various hardware terminals; a lightweight feature extraction and rapid fusion module is provided, and the real-time processing requirement of edge computing nodes is met; a closed-loop detection system of image acquisition, fusion enhancement, depth identification and intelligent output is formed, the overall efficiency is high, and the false detection rate is low.

The invention belongs to the technical field of wireless communication, and particularly relates to an OFDM (Orthogonal Frequency Division Multiplexing) semantic signal detection device and method integrating deep learning and energy detection. The method comprises the following steps: firstly, acquiring I / Q sampling data of an OFDM signal to be detected, wherein a data subcarrier of the I / Q sampling data bears a continuous value complex symbol generated by a semantic encoder; then parallel features are extracted and fused; a signal energy value of I / Q sampling data is calculated to obtain energy features; inputting the I / Q data into a one-dimensional convolutional neural network, and extracting deep semantic features; and performing normalization processing on the energy features, splicing the energy features with the preliminary detection probability output by the deep semantic features and the deep learning branches to form a fusion feature vector, inputting the fusion feature vector into a fusion network, and outputting a detection result that the signal exists or does not exist. According to the invention, through a parallel feature extraction-fusion decision-making architecture, in combination with the sensitivity of a traditional energy detection method to strong signals and the extraction capability of deep learning to weak signals and complex features, the method is specially aimed at the detection requirements of noise-like OFDM semantic signals.

The invention provides a polarization modulation coupling time control phase locking high-temperature noise suppression in-situ Raman method and device, and relates to the technical field of spectrum detection. The method comprises the following steps: exciting pulse linear polarization laser to irradiate a sample to obtain Raman scattering light; a parallel vibration component (P) and a vertical vibration component (S) are obtained, and original spectral data containing polarization information and residual high-temperature noise are collected by adopting an ISCCD detector synchronously triggered by gating. Background noise is removed and a parallel vibration component (P) is extracted through polarization differential processing in combination with adaptive filtering (dynamically adjusting parameters according to signal distribution) and principal component analysis (PCA); and finally, performing signal-to-noise ratio optimization on the pure component by applying a phase locking algorithm to finally obtain a Raman spectrum result with a high signal-to-noise ratio. According to the invention, the in-situ Raman spectrum of the material within the temperature range of room temperature to 3000 DEG C can be measured, and the detection of weak signals is more sensitive.

The invention discloses an LIBS rare earth multi-element quantitative detection method based on a deep learning model, and the method comprises the steps: constructing a multi-scale feature fusion deep learning architecture MSFF-Net, carrying out the weighted fusion of local features extracted by a 1D-CNN and long-range dependence features of BiLSTM modeling through a channel attention mechanism, and enhancing weak signal features in combination with a first-order derivative spectrum. Meanwhile, a synthetic data enhancement strategy based on a physical model is adopted to simulate a matrix effect and a spectral line overlapping scene, and spectral feature migration under different matrixes is achieved in cooperation with a matrix self-adaptive migration learning strategy. And after data acquisition and preprocessing, a trace element concentration prediction value is synchronously output through a multi-task learning framework. According to the method, the detection precision and the anti-interference capability of the LIBS technology on trace elements are effectively improved, and the method has remarkable application value in the industrial fields of mineral resource exploration, strategic metal recovery and the like.

The invention discloses a semi-aviation transient electromagnetic signal adaptive gain method and system, and relates to the technical field of geophysical exploration, and the method comprises the steps: obtaining a differential signal of a receiving coil, and carrying out the chopping self-stabilization zero amplification processing of the differential signal; the method comprises the following steps: dividing a total time window of a differential signal into a plurality of time channels under a set time reference, adjusting the length of each time channel according to a signal attenuation characteristic, calculating the time length and data point number of each time channel according to a sampling rate and the total time window, and calculating the time length and data point number of each time channel; the data points are distributed to an early time channel, a middle time channel and a late time channel, so that different gain multiples are switched, and adaptive gain amplification is carried out; and the differential signal subjected to adaptive gain amplification is transmitted to a receiver after being collected by an ADC (Analog to Digital Converter). By dynamically adjusting the gain coefficient in different time channels, the technical problems of large dynamic range of transient electromagnetic signals and low signal-to-noise ratio of weak signals are solved, and high-fidelity acquisition of full-time-domain signals is realized.

The invention provides a water body motion state recognition method and device based on spectral moment characteristics and a readable storage medium thereof, and belongs to the technical field of hydrographic survey and intelligent analysis. According to the method, an acoustic Doppler flow meter is used for collecting a flow velocity sequence, the flow velocity sequence is converted into frequency domain power spectral density through short-time Fourier transform, multi-dimensional features including zero-order to fourth-order spectral moments, frequency band energy, statistics and spectrum features are extracted, a feature time heat map is constructed and input into a multi-scale residual convolutional neural network, and the multi-scale residual convolutional neural network is obtained. And identification of laminar flow, turbulent flow and backflow is realized. The method accurately depicts essential differences of three types of flow states through frequency domain features, retains weak signals in combination with multi-scale convolution kernel and residual connection, solves the problems that a traditional method is poor in interpretability and insensitive in distinguishing, and has the advantages of being high in recognition accuracy, high in robustness and suitable for multiple scenes such as rivers and dams.

The invention discloses a radar target detection method and system based on deep learning and a storage medium, and relates to the technical field of target detection, and the method comprises the steps: carrying out the standardization processing of a millimeter wave radar, obtaining the standardized data, inputting the standardized data into an LSTM-KF model, and carrying out the filtering, combining data obtained by filtering with echo data to realize target detection; in order to solve the problems that a small target echo signal is weak, is susceptible to clutter interference, is complex in motion mode and the like, a weak signal change mode and a nonlinear motion characteristic of continuous multi-frame echoes are captured through LSTM, and dynamic trajectory constraint of KF is combined, so that time sequence enhancement and false alarm suppression of the weak signal are realized; the problems that traditional linear filtering is high in small target omission ratio, false alarm is difficult to control, nonlinear motion tracking lags and the like are effectively solved.

The invention discloses a system and a method for automatically measuring pipe network parameters based on three-dimensional scanning of a wellhead of an inspection well, and belongs to the technical field of municipal engineering measurement. The system comprises a wellhead fixing assembly, a three-dimensional laser scanning assembly, a posture sensing assembly, a distance measuring assembly, a coordinate input assembly, a control processing assembly and a telescopic rod assembly. According to the method, through system deployment leveling, coordinate reference establishment, working condition judgment, point cloud acquisition, data processing and result output, automatic measurement of inspection well depth, pipe network pipe diameter, pipeline trend and pipe orifice center coordinates is realized. The system is fixed at a wellhead, does not need to descend, and adapts to scenes such as shallow wells / deep wells and good / weak signal areas; based on a high-density point cloud and an automatic algorithm, the measurement precision is high, the efficiency is improved, and output data can be directly used for pipe network modeling. The method solves the problems of low efficiency, strong manual dependence and poor safety of traditional measurement, and is suitable for parameter measurement of various inspection wells and pipe networks.

The invention discloses an AC arc fault weak signal detection method, device and equipment and a medium, and the method comprises the steps: obtaining a bus current signal of a low-voltage power distribution system, carrying out the amplitude standardization preprocessing of the bus current signal, and generating a normalized signal; performing time-frequency domain feature fusion extraction on the normalized signal to generate a comprehensive feature vector; performing fault diagnosis on the comprehensive feature vector by adopting a nested sliding window mechanism to generate a fault diagnosis mark; and based on the comprehensive feature vector and the fault diagnosis mark, utilizing a pre-trained classification model to generate an arc fault classification result. According to the method, through the synergistic effect of signal noise reduction, feature fusion, real-time diagnosis and intelligent classification, the detection sensitivity is improved to reliable identification in a low signal-to-noise ratio-10dB scene, the real-time performance meets the UL1699 standard requirement, and the core problem that weak alternating current arc fault detection sensitivity and real-time performance are insufficient is solved.

The invention discloses a hyperspectral remote sensing image weak end member unmixing method suitable for methane detection and quantification, and particularly relates to the field of computer vision technologies. Acquiring a hyperspectral remote sensing image, and determining a background end member matrix, a gas end member matrix and an abundance matrix of each pixel of the hyperspectral remote sensing image; reconstructing the hyperspectral remote sensing image; determining a reconstruction error of the hyperspectral remote sensing image and the reconstructed image; determining the degradation amount of the priori knowledge according to the priori knowledge of the gas and the gas matrix; performing sparsification on the abundance matrix to obtain a sparse abundance matrix; according to the reconstruction error, the degradation amount of the priori knowledge and the sparse abundance matrix, an objective function is established, and constraint conditions of a gas matrix, a background end member matrix and the abundance matrix are established; and obtaining an optimal gas end member matrix and an optimal abundance matrix according to the objective function. Based on the method, the detection precision of weak signals can be improved.

The invention particularly relates to a method for quickly capturing high-dynamic and low-carrier-to-noise-ratio signals, which comprises the following steps of: preprocessing input digital intermediate-frequency signals, performing correlation operation on stored local pseudo code data and baseband data after down-conversion, and accumulating correlation results of which the length is a set value; if the local code phase is aligned with the pseudo code phase modulated by the baseband data, a peak value is obtained through FFT, the index of the peak value indicates the Doppler frequency, and the number of FFT calculation times indicates the code phase; calculating a module value based on a real part and an imaginary part output by the FFT; judging whether capturing succeeds or not based on the maximum module value, and obtaining the Doppler frequency of rough capturing if capturing succeeds; and refining the roughly captured Doppler frequency to obtain a refined Doppler frequency. According to the method, the Doppler frequency and the code phase are accurately searched in a short time, and the method is suitable for capturing high-dynamic weak signals.

The invention belongs to the technical field of laser spot positioning, and discloses a method and a device for extracting a spot centroid of a four-quadrant detector. A first linearly polarized light beam and a second linearly polarized light beam which are consistent in wavelength and orthogonal in polarization are generated through an emission optical system, the two linearly polarized light beams are modulated, different modulation frequencies are given to the two linearly polarized light beams, and the two linearly polarized light beams are combined into one beam of composite light to be emitted; receiving the composite light by using a receiving optical system; separating the composite light into first linearly polarized light and second linearly polarized light by using a polarization bifocus prism, and enabling the two beams of light to respectively form focuses which are staggered front and back; a four-quadrant detector is used for receiving a pre-focus light spot and a post-focus light spot, and the four-quadrant detector is arranged at the middle position of the two focuses; a signal processing system is used for processing signals output by the four-quadrant detector, and light spot centroid information is obtained through calculation. According to the invention, the weak signal detection efficiency of the four-quadrant detector is improved, and the precision of light spot position calculation is improved.

The invention relates to the technical field of sensing, and discloses a capacitive touch screen input event classification and processing system which comprises an input capture module, an event classification module, an intention prediction module, a dynamic response module, an energy efficiency optimization module and a multi-channel cooperation module. When an input event of the capacitive touch screen is identified, a dynamic noise suppression model is established, and adaptive filtering strategies are configured for different use scenes, so that the reliability of signal analysis in various electromagnetic interference environments is ensured, and meanwhile, the spatial and temporal distribution characteristics of capacitance variation are analyzed in real time; real touch operation and environmental electromagnetic noise can be effectively distinguished, the identification fidelity of basic operations such as clicking and sliding is ensured, the false triggering rate is reduced, the operation accuracy in a weak signal scene is improved, and when multi-finger operation is detected, the consistency of contact coordinates in a curved surface state and a plane state is maintained, so that the accuracy of the touch operation is improved. And the track continuity of writing and drawing type fine operation is ensured.