863 results about "Weak signal" patented technology

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 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 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 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 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 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 wide-spectrum interference weak signal enhancement method for high aspect ratio micro-nano structure measurement, and relates to the technical field of semiconductor measurement. The method comprises the following steps: acquiring a wide-spectrum interferogram, and performing two-layer two-dimensional discrete wavelet transform to obtain a first-stage wavelet coefficient group; performing nonlinear enhancement on the wavelet coefficient, and outputting a nonlinear enhanced wide-spectrum interferogram; performing three-layer two-dimensional discrete wavelet transform on the wide-spectrum interferogram after nonlinear enhancement to obtain a second-stage wavelet coefficient group, and calculating a Bayesian threshold value; a neighborhood sliding window function is set, and a Bayesian threshold is combined to carry out local denoising enhancement on the second-stage wavelet coefficient; reconstructing the second-stage wavelet coefficient after local de-noising enhancement to obtain a wide-spectrum interference pattern after local de-noising enhancement; and determining an optical path position by adopting an improved gravity center method according to the wide-spectrum interferogram after local denoising enhancement, and calculating a depth value of the to-be-measured sample. According to the method, the stripe contrast is enhanced, the noise is suppressed, and the depth measurement capability is improved at the same time.

The invention relates to the technical field of encoder communication interface circuits, in particular to a multi-protocol encoder communication interface circuit based on hardware configuration, which comprises a core control and protocol processing module, a hardware configuration and mode recognition module, an RS-485 differential driving and receiving module and an interface protection circuit module. In the invention, the problems that the hardware cost is high, the PCB layout is complicated, the overall size of the system is increased, the types of connectors are diversified and the wiring difficulty is increased due to the fact that an independent physical interface circuit needs to be independently designed for each protocol such as BISS and Tamagawa or a special integrated chip with a solidified factory function is adopted in the existing scheme are thoroughly solved; and meanwhile, the problems that chips need to be replaced or hardware needs to be redesigned and the configuration flexibility is poor in the protocol updating of an existing scheme are solved, the time sequence conflict risk existing in software dynamic switching protocols is avoided, and the defects that a traditional circuit is poor in surge resistance, signal crosstalk is caused and debugging and maintenance are complex are overcome.

The invention relates to the technical field of malicious software detection, in particular to a malicious code detection method based on behavior analysis, which comprises the following steps of: monitoring a system event stream and process resource occupation data, fixed time interval sampling is adopted to capture a network data packet arrival time interval, CPU occupancy rate fluctuation, a target file lock contention state and a mouse cursor pixel position. According to the method, multi-dimensional system event streams and process resource occupation data such as network data packet arrival time intervals, CPU occupancy rate fluctuation, target file lock contention states and mouse cursor pixel positions are monitored, mutual information entropy between different event streams is calculated, and hidden association which cannot be perceived by a traditional detection method can be revealed. The mode is no longer limited to analysis of isolated behaviors of a single process, but captures weak signals generated when malicious software performs communication or data leakage by using a covert channel by quantifying statistical dependency between seemingly unrelated events.

The invention relates to the technical field of vehicle-mounted terminals, and discloses a vehicle-mounted terminal control method and system and a vehicle, and the method comprises the following steps: S1, multi-dimensional environment perception and scene judgment: collecting GPS signal intensity, Beidou signal intensity, mobile network signal intensity, vehicle altitude and environment illumination intensity through a perception module; through Beidou + inertial navigation integrated positioning + null shift calibration, the basement positioning precision is higher, the parking position monitoring and vehicle searching requirements are met, the three-level redundant link (mobile network + LoRa + Bluetooth) and distributed gateway design is adopted, the basement information transmission success rate is higher, emergency information is not interrupted, data security is guaranteed through full-link encryption, and the service life of the system is prolonged. Misjudgment of temporary weak signals on the ground is avoided through multi-dimensional sensing (signal + altitude + illumination + duration), the mode switching accuracy is higher, bandwidth waste is reduced through a priority dynamic adjustment mechanism, the emergency information response speed is increased, and link congestion is avoided through a common information caching strategy.

The invention discloses an echo signal processing method based on a multi-order FRFT domain feature fusion sparse dictionary, and belongs to the technical field of underwater acoustic signal processing and target detection, and the method comprises the steps: screening out a plurality of differential fractional orders through employing the differential features of a target echo and a reverberation signal in different fractional order transform domains; constructing a sparse sub-dictionary based on each differential fractional order, and training a fusion weight of each sub-dictionary through an adaptive gradient optimization strategy; and finally, target signal extraction and strong reverberation suppression are realized through weighted fusion and sparse reconstruction. According to the method, the accuracy and stability of a sparse reconstruction result can be improved, the signal-to-mixing ratio of signals can be effectively improved, target echoes and strong reverberation are further distinguished, good robustness and engineering application value are achieved, and an effective technical means is provided for underwater weak signal detection and strong interference.

The invention relates to the technical field of driving assistance, in particular to an automobile blind area monitoring system and an early warning decision method.The automobile blind area monitoring system comprises an echo feature sensing module, a reflection feature judgment module, an illumination linkage judgment module, a driving behavior recognition module and an early warning sensitivity adjustment module; and comparing the synchronous change trend of the amplitude change and the energy distribution. According to the method, multi-parameter echo signal configuration, reflection intensity gradient evolution, external illumination linkage analysis and driving behavior continuous recognition are adopted, real-state relations between blind area obstacle signals and the environment and between the blind area obstacle signals and the driving state are dynamically established, and structural attributes and environment influence modes of abnormal signals are refined; the method integrates the change characteristics of three risk factors of illumination, signal and driving, improves the recognition capability of weak signals and dynamic obstacles, achieves the linkage adjustment of risk levels, response time periods and blind area event classification, and enhances the perception and response adaptation level of potential risks in a complex traffic environment.

The invention relates to the technical field of wireless communication and signal processing, in particular to a short-wave spectrum efficient sampling method and system based on compressed sensing, and the method comprises the steps: collecting full-band power spectrum density data through a low-speed scanning receiver; determining a strong interference frequency point set; generating a binary null observation sequence; loading the binary null observation sequence to an analog front-end frequency mixing circuit, performing analog domain orthogonal frequency mixing and integral processing on the input radio frequency signal, and outputting a voltage sampling value; resolving to obtain a signal sparse coefficient through a compressed sensing reconstruction algorithm; calculating a reconstruction residual error according to the voltage sampling value and the signal sparse coefficient; in response to the condition that the reconstruction residual error is greater than a preset error threshold, triggering a reacquisition process of the power spectral density data; outputting a signal sparse coefficient in response to the condition that the reconstruction residual error is smaller than or equal to a preset error threshold; according to the invention, the problems of limited dynamic range and weak signal submerging are solved, and the receiving sensitivity of weak signals in a strong interference environment is obviously improved.

The invention relates to the technical field of signal processing, and provides a hidden weak signal feature recognition method and system, and the method comprises the steps: carrying out the time-frequency transformation of a to-be-recognized signal, and generating a time-frequency diagram; inputting the time-frequency graph into the feature recognition model, and extracting multi-scale features from the time-frequency graph through a backbone network; sending a feature map with the highest semantic hierarchy in the multi-scale features into a convolution attention module, and sequentially executing channel attention weighting and space attention weighting in the convolution attention module to obtain an enhanced feature map; fusing the enhanced feature map and other scale features in a feature fusion layer to obtain a fused feature map; and based on the fused feature map, identifying a weak signal through a detection head. Compared with the prior art, the method has the advantage that the recognition accuracy of weak signals in communication signals is greatly improved.

The invention relates to the technical field of microscopic optical imaging, and discloses a semiconductor conductive particle detection task-oriented differential interference optical system, which comprises an objective lens group and a differential interference module, the objective lens group comprises a front group, a middle group and a rear group which are arranged in sequence; the front group is used for ensuring a large aperture angle and less introduction of spherical aberration and chromatic aberration, the middle group is used for compensating various aberrations introduced by the front group, and the rear group is used for correcting field curvature; the differential interference module comprises a Nomarski prism, a polarizer and an analyzer which are arranged in sequence; the Nomarski prism is used for splitting and combining the linearly polarized light beams; the polarization analyzer is used for converting linearly polarized light which passes through the Nomarski prism and is orthogonal in polarization direction into linearly polarized light with the same polarization direction; the system further comprises a beam splitter and a barrel lens group. According to the invention, long working distance and large numerical aperture can be realized in the visible light wave band, the light path design is optimized so as to reduce aberration introduction, and the anti-interference capability to weak signals is enhanced.

The invention discloses a surface diaphragm electric signal acquisition system, which adopts a processor and integrates a metal dry electrode and an acquisition front end to realize high-integration-level non-invasive diaphragm electric signal acquisition. The acquisition front end comprises a signal front end amplification circuit which is constructed by an LM324 chip and an LM358 chip, has the characteristics of high common-mode rejection ratio and low power consumption, and ensures stable amplification and anti-interference capability of weak signals. The lower computer takes STM32F407VET6 as a core, adopts multi-channel ADC sampling, and combines timer control and a DMA transmission mechanism to realize high-real-time and high-stability signal acquisition. The upper computer is developed based on a QT framework and has the functions of real-time display, storage and signal processing. The system has the advantages of high real-time performance, high acquisition precision, low interference and the like, and can provide efficient and reliable technical support for respiration synchronous analysis and clinical medical diagnosis.

The invention discloses a shielding effectiveness monitoring method and system. The method comprises the following steps: modeling a space electromagnetic environment based on multi-sensor fusion and ray tracing; performing joint analysis on the mixed signals acquired by the sensor network in real time, and extracting fingerprint features representing different signal sources; dynamically defining an effective action boundary of the current shielding field based on the extracted fingerprint features and the type identification result, and distinguishing effective signals and boundary-crossing interference in the shielding area; meanwhile, a deep residual network is adopted to identify weak signals under strong interference, and power compensation is estimated; and finally, outputting a multi-dimensional evaluation result of shielding effectiveness monitoring. According to the invention, by using signal fingerprint analysis and intelligent separation, the anti-confusion and anti-interference capabilities are enhanced, and the misjudgment rate is reduced. And under strong background interference, weak shielding test signals can still be effectively identified, the root cause of a monitoring failure phenomenon can be accurately diagnosed, and the accuracy of a shielding effectiveness monitoring result is improved.

The invention provides an AI-driven multi-wireless-signal candid photographing detection device which comprises a mounting shell, a hardware acquisition layer, an edge computing layer, a cloud federation layer and a man-machine interaction layer, and all the layers achieve signal interaction through PCIe4.0 and SPI hardware interface communication protocols. The broadband patch antenna, the ceramic antenna and the helical antenna are adopted to cover a full target frequency band, the millimeter wave radar chip is matched, the 16 * 16 metamaterial RIS panel is additionally configured, and the weak signal receiving power is improved by synchronously controlling a reflection phase focusing signal, so that the problem of missing detection is solved; the multi-signal tensor decomposition unit and the CNN-LSTM signal classification unit construct frequency, intensity, time and phase four-order tensors, and overlapped signals are separated through Khatri-Rao space-time coding; a three-layer CNN and a two-layer LSTM are combined, precise classification of candid photographing signals is realized, the misjudgment rate is reduced, millimeter wave assisted precise distance measurement is combined with millimeter wave flight time data and an RSSI attenuation model, a distance measurement error is controlled through a dynamic weighting algorithm, and the problem of positioning misalignment is solved.

The invention provides a taxi-taking interaction method based on equipment cooperation and related products, and the specific implementation mode of the method comprises the steps: collecting taxi-taking request voice data of a user through an earphone, forwarding the taxi-taking request voice data to a cloud server through a watch, and carrying out the multi-round interaction refinement, so as to confirm the getting-on and getting-off position information; and the cloud server determines a positioning result based on the real-time positioning data of the earphone cabin, generates confirmation interface data based on the getting-on and getting-off position information and the positioning result and sends the confirmation interface data to the watch for display, and a user carries out taxi taking order confirmation operation on the watch through a confirmation interface, so that after the watch detects the confirmation operation, the taxi taking order is obtained. And the confirmation instruction is sent to the cloud server, so that the cloud server submits the taxi-taking order data to the taxi-taking platform, the voice interaction efficiency is improved, the scene adaptability is enhanced, the equipment resources are fully utilized, and the positioning accuracy in the weak signal scene of the mobile phone is remarkably improved.

The invention relates to the technical field of temperature early warning, and discloses a JP cabinet bus connection point temperature early warning method and device, and the method comprises the steps: collecting the voltage drop time sequence data of a JP cabinet bus connection point, and carrying out the wavelet packet decomposition of the voltage drop time sequence data, and obtaining a conduction characteristic coefficient; performing contact resistance prediction based on the conduction characteristic coefficient to obtain a contact resistance value, and calculating a transient temperature rise characteristic value based on the contact resistance value; a temperature early warning threshold value is calculated based on the transient temperature rise characteristic value and the current change rate, and a temperature early warning signal is generated based on the temperature early warning threshold value, the change characteristics of the contact interface conduction state on different time scales can be effectively captured, and compared with a simple time domain analysis method, the time domain analysis method has the advantage that the accuracy is high. The identification precision and the anti-interference capability of the weak signal change are improved, the problem of insufficient adaptability of a fixed threshold value method under a dynamic load condition is effectively solved, and the accuracy of temperature early warning is further improved.

The invention belongs to the technical field of GIS fault detection, and discloses a GIS latent insulation fault diagnosis method and system under strong noise, and the method achieves the omnibearing signal capture through synchronously obtaining optical, mechanical, electrical and acoustic signals, and solves a problem of weak signal leak detection. For strong noise interference, noise and fault characteristic frequency bands are effectively separated by using multi-modal signal difference and constructing a time domain and wavelet domain cascaded double-layer dictionary. A discriminative sparse model is combined with a K-SVD algorithm to carry out end-to-end iterative optimization, sparse features with higher discriminative force are automatically mined, and the problem of deep feature mining is solved. For the problem of weak generalization ability of small samples, a classifier error term is introduced into an objective function, a joint optimization objective function fusing reconstruction and classification errors is constructed, dictionary learning and classifier training are combined into one, the generalization ability of the model is significantly enhanced, and the method is suitable for large-scale popularization and application. Therefore, timely and accurate identification of the latent insulation fault under the conditions of strong noise and small samples is realized.

The invention relates to the technical field of discharge monitoring, and provides a high-precision monitoring method and system for partial discharge current pulse signals. The method comprises the following steps: activating distributed broadband monitoring equipment to continuously monitor a transformer to obtain distributed monitoring information; extracting first monitoring information, and performing feature collection on the first pulse signal to obtain a pulse feature set; establishing a multi-conductor transmission line model based on the multi-dimensional characteristic parameters of the transformer, and performing simulation to obtain simulation information; and calling a partial discharge positioning strategy to analyze the pulse feature set and the simulation information, and determining the partial discharge source of the transformer. According to the invention, the technical problems that the partial discharge pulse signal in the transformer cannot be accurately extracted and analyzed and the accurate positioning of the partial discharge source is influenced because the detection blind area is easily influenced by electromagnetic interference and the weak signal identification is insufficient in the existing partial discharge monitoring method are solved; the technical effect of high-precision partial discharge source positioning is achieved.

The invention discloses a carrier ring auxiliary code delay phase-locked loop tracking method, system and device and a medium, and belongs to the technical field of signal tracking, and the method comprises the steps: obtaining a digital intermediate frequency signal, and constructing a code loop model; executing code tracking through the code loop model to obtain a code phase error measurement value, and determining a relationship between a carrier-to-noise ratio and a noise covariance; a measurement noise covariance matrix model is obtained through combination of a code phase error measurement value and a carrier-to-noise ratio dynamic state and is used for first adjustment of a filter. According to the invention, a measurement noise covariance matrix is dynamically adjusted based on real-time carrier-to-noise ratio estimation, so that the filter can accurately adapt to signal quality change, fully trust an accurate measurement value when a signal is strong, greatly reduce tracking jitter, achieve higher pseudo-range measurement precision, and automatically reduce dependence on the measurement value under noise interference when a signal is weak. In turn, the method depends on smooth prior model prediction.

The invention relates to the technical field of nondestructive testing of constructional engineering, and discloses a laser measuring instrument for nondestructive testing of concrete cavities, which comprises a laser interference measuring assembly, a paraxial sound wave excitation assembly, coaxially arranged anti-interference equipment and a cooperative control processing system. The sound wave excitation assembly performs wide-area excitation on the concrete surface, and the laser interference measurement assembly receives returned light through a clean light path constructed by the anti-interference equipment. The cooperative control processing system establishes acousto-optic time-frequency association by using an adaptive control module, generates a phase compensation angle according to ranging data to eliminate phase lock loss caused by sound wave propagation delay, and monitors a spectral gradient to automatically trigger fixed-frequency locking to capture resonance response. And the signal analysis module uses the phase compensation correction signal, and outputs cavity features through dual data cleaning and thermodynamic diagram analysis. According to the invention, the problems of time-space synchronization and weak signal extraction of long-distance non-contact detection in a complex environment are effectively solved.

The invention discloses an early cancer screening model based on a cloud architecture and a construction method, and particularly relates to the technical field of medical artificial intelligence. The method comprises the following steps: firstly, collecting and normalizing heterogeneous medical image and biomarker data of a plurality of geographic areas, and constructing a unified data set; then extracting image features and marker features by adopting multi-task learning, fusing equipment and region attributes, establishing a joint representation vector, and training a preliminary screening model; after the model is deployed to a cloud end, target area data is adapted through online transfer learning, and an area model is generated; further extracting an activation heat map, identifying a weak signal high response area in combination with sensitivity analysis, comparing with manual annotation, constructing a local weight adjustment strategy based on errors, optimizing the model, and outputting a final model; the method improves the recognition capability of early lesions under the condition of low signal-to-noise ratio, is suitable for remote and basic screening scenes, and has a high practical value.

The invention belongs to the technical field of remote sensing image processing and deep learning, and particularly relates to a remote sensing erosion gully segmentation method combining multi-scale features and an attention mechanism. Comprising the following steps of 1, data preparation and data preprocessing; 2, constructing and enhancing a data set; step 3, model construction and strategy training; and 4, performing comparison experiment and result evaluation. According to the method, the segmentation precision of the extremely long and thin erosion gully target can be remarkably improved; the topological connectivity and the morphological fidelity of the linear landform are enhanced; pixel-level accurate space positioning and noise suppression are realized; the method has extremely high slant feature capture and multi-scale adaptability; and the stability and weak signal perceptibility of the training process are improved.