567 results about "Detection threshold" patented technology

The invention discloses a display panel defect detection method and system. The method comprises the following steps: acquiring original image data of a display panel; preprocessing the original image data to generate standardized image data; extracting multi-dimensional defect features based on the standardized image data; inputting the multi-dimensional defect features into a pre-trained defect classification model, and outputting defect types and confidence coefficients; generating a defect distribution map according to the defect type and the confidence coefficient, and adjusting a detection threshold value based on the defect distribution map; and optimizing the defect classification model based on the adjusted detection threshold, and generating a dynamic quality control strategy. The method has the following advantages and effects: the limitation of a single data source is broken through through multispectral fusion and multidimensional features, and surface and internal defects are covered; through dynamic threshold and model optimization, the method adapts to the environmental change of a production line, and guarantees the long-term detection precision.

The invention discloses an Internet of Things intelligent gas meter leakage detection and early warning system and method, and relates to the technical field of gas leakage detection, and the method comprises the following steps: S1, collecting data through multiple sensors; s2, identifying an equipment operation state based on the data set and outputting a state confidence coefficient; s3, a stable monitoring window period is judged, a corresponding strategy is selected to compensate pressure data, and reliability is marked; s4, dynamically generating a detection threshold in combination with the historical mode, the real-time parameters and the data reliability; s5, dynamically adjusting a risk assessment weight according to the confidence coefficient and the reliability, calculating a risk score and determining an early warning level; s6, safety operation is executed according to grades; and S7, updating the model by using process data to realize self-optimization. According to the invention, by deploying a multi-sensor array and adopting a multi-modal signal fusion algorithm, the system can accurately identify the running state of the gas appliance, provides reliable preposition information for subsequent analysis, and overcomes the defect that data of a traditional single sensor is easily interfered.

The invention relates to the technical field of wind power generation. The impeller imbalance detection method based on multi-source data fusion comprises the steps that operation state data of a draught fan unit and environment data of corresponding time are obtained, and a multi-source data set is obtained; preprocessing the multi-source data set to obtain a preprocessed data set; feature parameters in the preprocessed data set are extracted, data fusion is carried out, and a multi-source feature fusion vector is constructed; according to the multi-source feature fusion vector, constructing a time sequence prediction model based on a gating circulation unit, and predicting a theoretical vibration baseline value under the current working condition in real time; and calculating a deviation degree between an actual vibration value and a predicted baseline through residual analysis, dynamically adjusting a detection threshold, and triggering imbalance early warning when the deviation degree exceeds the threshold. The problems that the accuracy of a detection result of a traditional impeller imbalance detection method is affected by various factors, misjudgment and missed judgment are prone to occurring, and the requirement of a modern wind power generation system for high-precision fault detection cannot be met are solved.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention relates to an electric power internet of things operation and maintenance management method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining sensor data, carrying out the cleaning and time alignment of the sensor data, and obtaining standard operation data; based on a historical data window corresponding to the standard operation data, calculating a mean value and a standard deviation of the historical data, and further generating a dynamic detection threshold value; comparing the standard operation data of the current time node with a dynamic detection threshold value, and screening out potential abnormal data; calculating an abnormal trend factor corresponding to the potential abnormal data, and determining an abnormal development trend of the potential abnormal data according to the abnormal trend factor; and determining a corresponding causal influence weight based on a preset causal relationship network, and calculating a fault score according to the causal influence weight and the abnormal trend factor, so as to generate an early warning report according to the fault score. The method has the effect of improving the management efficiency of the power equipment.

The invention relates to a data access prediction scheduling method and system based on a dynamic threshold value, and belongs to the technical field of data processing. The method comprises the following steps of: dynamically adjusting an abnormal detection threshold in an access behavior by utilizing reinforcement learning and an error feedback mechanism, realizing preposition identification of potential high-frequency access data by combining abnormal point classification processing and access trend prediction, and generating a scheduling request to drive cache preheating or interface priority scheduling of the data. According to the method, access behavior modeling, anomaly recognition, trend prediction and strategy feedback mechanisms are combined, interface access optimization requirements in a mass data storage system are met, and the key problems that access behaviors are unpredictable, scheduling response lags and the like are solved.

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 target object targeting agent monitoring method and device based on AI visual identification, belongs to the technical field of termite monitoring, and aims to solve the problems of high manual dependence degree, high misjudgment rate, low data transmission efficiency and the like in traditional target object monitoring. A slow-release targeting agent and a solar hybrid power supply system are arranged in the device, and low-power-consumption endurance is supported. According to the core scheme, vibration and infrared sensor signals are fused, and image acquisition is triggered through feature analysis; recognizing a target object through background difference, a multi-scale image and feature matching, and dynamically optimizing a detection threshold value; after video acquisition, activity intensity is calculated in combination with target tracking and gridding analysis, and transmission is optimized based on intensity and quota; light supplement is automatically adjusted according to ambient light, and image quality is ensured through pixel compensation and brightness adjustment. According to the scheme, a closed-loop system with multi-mode accurate triggering and intelligent identification is constructed, and the intelligence and efficiency of termite monitoring are improved.

The invention discloses a data processing method and device, equipment and a medium. The method comprises the steps that a detection threshold value is determined based on a first access traffic log; determining an access behavior for the service domain name based on the number of domain name requests determined by the access traffic indicated by the second access traffic log and a detection threshold; when the access behavior aiming at the service domain name belongs to the abnormal access behavior, taking a behavior determination time point corresponding to the abnormal access behavior as a critical time point, and taking a third access traffic log obtained based on the critical time point as an abnormal access traffic log; when a normal traffic feature determined based on the first access traffic log is obtained, determining an abnormal traffic feature of the service domain name based on the abnormal access traffic log, and performing feature comparison on the abnormal traffic feature and the normal traffic feature to obtain an attack feature of the service domain name; a protection policy for the service domain name is determined based on the attack feature. According to the invention, the security and stability of the business server can be maintained.

The invention relates to the technical field of ecological system service function evaluation. The invention relates to a frequency offset correction algorithm in HPLC (High Performance Liquid Chromatography) + HRF (High Radio Frequency) dual-mode communication. The method comprises the following steps: S1, collecting phase sequence data from an HPLC communication link, collecting frequency offset estimation data and corresponding confidence data from an HRF communication link, and adaptively adjusting a preset detection threshold value of the phase sequence data according to the confidence data; s2, performing pulse abnormal sample point detection and elimination on the phase sequence data according to the adjusted preset detection threshold value; according to the method, the detection threshold is dynamically adjusted through the confidence data, so that the anomaly detection process can be matched with the current interference environment in real time, the integrity of the phase sequence data is maintained, and the problem of insufficient adaptability of a fixed detection threshold in an electromagnetic interference dynamic change scene is solved; and a more reliable phase data basis is provided for subsequent interpolation compensation and frequency offset estimation, so that the frequency offset correction precision of the dual-mode communication system is improved.

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 network information operation and maintenance system based on AI multi-modality, and belongs to the technical field of network information operation and maintenance. Heterogeneous operation and maintenance data such as network flow, equipment state, audio alarm and thermal imaging are acquired through an acquisition unit, and various features are extracted in parallel by using a multi-modality feature extraction module to form a unified multi-dimensional feature vector; a hierarchical anomaly detection architecture is established, lightweight and deep anomaly detection models are deployed on an edge side and a cloud end respectively, different modal features are intelligently fused through an attention mechanism by adopting a multi-modal feature fusion model, and a detection threshold is optimized in real time according to a network state by using an adaptive threshold dynamic adjustment Bayesian algorithm. And a multi-modal fusion decision engine is constructed to perform weighted fusion on the anomaly detection result and dynamically adjust the modal weight, so that the technical problem of insufficient accuracy of multi-modal operation and maintenance data fusion processing is solved.

The invention discloses a signal acquisition and processing method and system based on a multifunctional radar, and relates to the technical field of signal acquisition and processing, and the method comprises the steps: employing a quantum genetic algorithm to optimize radar transmission waveform parameters, including center frequency, bandwidth and frequency modulation slope, and generating a nonlinear frequency modulation waveform through FPGA hardware; a non-uniform sparse array is adopted to receive a target echo signal, time domain random interval sampling and frequency domain pseudo-random frequency point selection are synchronously implemented, and time-space-frequency three-dimensional compressed sensing observation data are formed; performing trilinear tensor joint sparse reconstruction on time-space-frequency three-dimensional compressed sensing observation data, decomposing a polarization scattering matrix eigenvalue from a reconstructed signal, and calculating a coupling characteristic quantity of eigenvalue entropy and micro-Doppler frequency; inputting the coupling characteristic quantity into a deep reinforcement learning model, and dynamically outputting a constant false alarm detection threshold, a moving target display filter order and a resource allocation weight; and detecting a threshold based on a constant false alarm rate.

The invention relates to the technical field of robot navigation, and discloses a multi-sensor fusion navigation method and system of a robot. The method comprises the following steps: collecting point cloud data of a laser radar sensor and motion data of an IMU six-axis sensor; executing generalized likelihood ratio test and sliding window interval search on the point cloud data and the motion data to obtain a zero-speed abnormal interval, and creating a detection threshold value for the current ground environment according to the zero-speed abnormal interval; performing spatial superposition on the position coordinates of the zero-speed abnormal interval and a laser radar scanning blind area to obtain an obstacle distribution map; cleaning control parameters are adjusted based on the detection threshold value, a re-planning cleaning path is generated based on the obstacle distribution map and the cleaning control parameters, the method can actively identify the potential trapped area and re-plan the cleaning path, and the autonomous navigation ability and cleaning efficiency of the intelligent cleaning robot are improved.

The invention discloses an abnormal data monitoring method and device based on artificial intelligence, and the method comprises the steps: 1, dividing an original data stream through a sliding window, extracting statistics, time sequence and change rate features, and dynamically screening features adaptive to data distribution based on an SHAP value; 2, constructing a double-flow model, capturing a global isolated mode by adopting an improved isolated forest in a static flow, capturing time sequence dependence on the basis of LSTM-AE in a dynamic flow, and fusing two-flow scores through performance-driven dynamic weight distribution; 3, combining a density peak value algorithm with historical density attenuation weighting, and dynamically adjusting an abnormal threshold value; 4, realizing low-delay incremental learning through a double-trigger mechanism and experience playback; 5, multi-granularity interpretation is generated, manual annotation feedback is supported, feature engineering and model training are integrated, and a'detection-interpretation-feedback-optimization 'closed loop is formed; high-adaptability anomaly monitoring is realized through dynamic feature screening, double-flow fusion detection, threshold value self-adaption and man-machine collaborative optimization.

The invention discloses a non-uniform clutter and interference cooperative suppression target fusion detection method and system, and relates to the technical field of broadband radar signal processing, and the method comprises the steps: carrying out the unitary transformation of test data, a clutter covariance matrix, a target coordinate matrix and an interference coordinate matrix based on the skew symmetry of the clutter covariance matrix; constructing a distance extension target Gradient detection statistical magnitude under the condition of a known clutter skew symmetry covariance matrix; calculating the maximum likelihood estimation of the clutter skew symmetry covariance matrix; based on the maximum likelihood estimation of the clutter skew-symmetric covariance matrix and the distance expansion target Gradant detection statistic, constructing a target detection statistic of non-uniform clutter and interference cooperative suppression; performing target fusion detection on the target detection unit based on the target detection statistical magnitude and a preset detection threshold; according to the invention, the technical problems of complex construction process and high calculation complexity of the target detector in the prior art are solved.

The invention provides a dynamic threshold anomaly detection method and system based on deep semantic reconstruction and an isolated forest, and belongs to the technical field of crossing of network security and artificial intelligence. A sliding window segmentation and dynamic zero padding mechanism is adopted to unify texts of any length into standardized segmented data; the method comprises the following steps: constructing a nine-layer depth auto-encoder (four encoding layers, a middle layer and four decoding layers), learning multi-dimensional semantic features of normal data, calculating a reconstruction error matrix of each data segment, extracting error distribution statistics (mean value, variance, skewness and kurtosis), performing unsupervised clustering on the extracted error features by adopting an improved isolated forest algorithm, and obtaining a clustering result; a dynamic detection threshold value is automatically generated, and after semantic reconstruction and feature analysis are carried out on real-time data, whether the real-time data are abnormal or not is judged in combination with the dynamic detection threshold value; a self-adaptive anomaly detection model is constructed through conjoint analysis of a dynamic threshold generation mechanism and deep semantic features, and dependence on artificial experience is reduced while high detection precision is kept.

The invention discloses a GNSS (Global Navigation Satellite System) signal spoofing detection method, device and equipment, a storage medium and a program product. The method comprises the following steps: tracking and monitoring a plurality of GNSS satellite signals, calling a tracking loop correlator to carry out autocorrelation operation to obtain the autocorrelation power of each satellite signal, constructing the AP detection quantity of each satellite signal based on the autocorrelation power, verifying the Gaussian property of the AP detection quantity, and carrying out moving average filtering processing on the AP detection quantity conforming to Gaussian distribution to obtain the AP detection quantity of each satellite signal. The method comprises the following steps: acquiring Gaussian distribution characteristic information of a GNSS satellite signal, constructing an AP-MA detection quantity, determining a detection threshold according to the Gaussian distribution characteristic information, detecting the AP-MA detection quantity based on the detection threshold, and judging whether the GNSS satellite signal has deception jamming or not; the AP detection quantity is constructed based on the self-correlation power to carry out signal spoofing detection, so that the detection range and the detection precision are effectively improved, and the timeliness and the stability of spoofing detection are ensured under the condition of not increasing additional equipment and algorithm complexity.

The invention discloses a structure construction deviation automatic identification and safety evaluation method, which comprises the following steps: acquiring multi-modal original signals such as space measurement, environment, equipment operation and images from multiple positions of a structure construction site, and establishing a trend drift feature distribution model through time sequence alignment, normalization, noise reduction and feature extraction; then through combination with generative artificial intelligence, virtual samples of historical and extreme working conditions are expanded, and construction of a mixed working condition data pool is realized; through distribution drift analysis and multi-target parameter optimization, a deviation detection threshold value is dynamically and adaptively adjusted, parameters and a judgment result are pushed to a digital twinning and safety control platform in real time, and meanwhile, whole-process log and performance archiving are carried out, so that the sensitivity, adaptability and system safety of structure construction deviation detection are improved, and the construction safety is improved. And the method has relatively high data fusion efficiency and algorithm generalization ability.

The invention discloses a narrow environment high-precision laser inertial navigation and SLAM (Simultaneous Localization and Mapping) method based on adaptive parameters. According to the method, firstly, radar and IMU data are collected and preprocessed to form a unified input data stream; obtaining a pose prediction result by using the data stream, constructing an observation model, and obtaining a laser observation residual error; calculating a characteristic value proportion in the matching process of the point cloud and the sub-map, and judging a geometric degradation scene; when degradation is detected, adaptively adjusting a voxel filtering radius and a loopback detection threshold value; fusing the pose prediction result and the laser observation residual error by adopting a Kalman filtering model to obtain an optimal pose estimation result, and carrying out loopback detection and back-end map optimization under a dynamic threshold value to correct accumulated drift and update a voxel map; and finally outputting a continuous pose track and a three-dimensional voxel map. According to the method, the robustness and the positioning precision of the laser inertial navigation fusion SLAM in narrow scenes such as pipe galleries, tunnels and chemical plants are effectively improved.

The invention discloses a fault detection method for a low-voltage AC system containing a distributed power supply based on SOGI and wavelet, and the method comprises the steps: carrying out the filtering of a fault signal through a second-order generalized integrator SOGI, generating an orthogonal signal, and calculating the amplitude; performing wavelet packet decomposition on the amplitude signal, and extracting a high-frequency detail component as a fault feature; and dynamically adjusting a detection threshold according to the permeability of the distributed power supply, and judging a short-circuit state based on the high-frequency detail component. According to the method, alternating current side short circuit fault detection under different fault types under different direct current system source load access conditions is realized, and the rapidity of a fault detection algorithm in a high-permeability scene is improved. The method is beneficial to improving the efficiency, reliability and universality of short-circuit fault identification.

The invention discloses a multi-channel NDIR infrared gas sensor detection data analysis system and method, and relates to the technical field of gas detection.The method comprises the steps that infrared light intensity of each gas channel is collected to serve as original data, and a fluctuation value of the original data is calculated to serve as an original fluctuation value; acquiring historical temperature and humidity data, fitting a linear relationship between the historical temperature and humidity data and the historical infrared light intensity data, and calibrating the original data based on a correction coefficient obtained by fitting to obtain correction data and a corresponding calibration fluctuation value; calculating a variable coefficient of the correction data, and setting a detection threshold value for each gas channel in combination with the calibration fluctuation value so as to screen out effective original data; distributing an initial weight for each gas channel, dynamically adjusting the weight based on the variable coefficient of the effective data, and processing the adjusted weight through a smoothing algorithm to obtain a smooth weight; and converting the effective original data into gas concentration data, and carrying out weighted fusion on the gas concentration data and the smoothed weight so as to output final gas concentration data.

The invention relates to the technical field of radars, and provides a frequency diversity array multi-input multi-output radar target detection system. The system comprises a detection statistic construction module, a detection optimization module, a detection threshold determination module and a target judgment module. Wherein the detection statistic construction module is used for constructing a detection statistic; according to the detection statistics, constructing a semi-definite programming optimization model; the detection optimization module is used for constructing a semi-definite programming optimization model according to the detection statistics; the detection optimization module is also used for solving the optimization model to obtain an optimal value of the detection statistics; and the target judgment module is used for comparing the detection statistic optimal value with a detection threshold so as to detect whether a target exists or not, so that the target detection probability and the anti-interference capability of the frequency diversity array multi-input multi-output radar in a complex environment are improved.

The invention relates to the technical field of data processing, in particular to a data analysis method fusing a knowledge graph and deep learning, and the method comprises the steps: obtaining multimode data and historical market abnormal fluctuation information; constructing a dynamic medicine knowledge graph; determining an abnormal node and a causal link; generating a joint feature vector; capturing a market index evolution trend to obtain candidate market anomalies; judging hidden market signals; adjusting a detection threshold value; and generating a market analysis report. According to the invention, the multi-mode data is combined with the knowledge graph reflecting the entity association and fluctuation evolution relationship of the medicine market, so that the dynamic time sequence change characteristics of the market are fully expressed in the deep learning model; the problems of low data processing accuracy and slow response speed in a complex market environment caused by difficulty in adapting to market dynamic evolution due to incomplete feature representation caused by single data modality are effectively solved.

The invention discloses a radar weak target detection and positioning method based on four-polarization time-frequency feature fusion. The method comprises the following steps: constructing an initial four-polarization time-frequency feature detector; the initial four-polarization time-frequency characteristic detector is constructed by introducing four parallel Backbones into a YOLOv11 network; one parallel Backbone correspondingly processes one polarization channel; training the initial four-polarization time-frequency feature detector by using a pre-constructed training label set to obtain a four-polarization time-frequency feature detector; calculating detection statistic distribution based on the confidence corresponding to each pure clutter sample map, and determining a detection threshold according to the detection statistic distribution; based on a to-be-detected time-frequency diagram, a radar weak target detection and positioning method which is sufficient in feature utilization, high in detection capability and stable is provided by using a four-polarization time-frequency feature detector and a detection threshold.

The invention relates to the technical field of GNSS high-precision positioning, and provides a BIE-based real-time dynamic positioning integer ambiguity resolving method and system, and the method comprises the steps: obtaining a preliminary ambiguity subset through the screening of a satellite elevation angle and a carrier-to-noise ratio, and carrying out the correlation reduction processing; a multi-level screening mechanism is adopted and comprises three stages of dynamic optimization of coarse screening, fine screening and candidate group generation; a weight distribution model is improved based on Laplacian distribution to calculate candidate solution weights and probability weighted fusion is carried out; in combination with an ILS and BIE mixed fixing strategy, BIE weighted averaging is adopted when ILS fixing fails; a positioning result is optimized through dynamic satellite screening and multi-system data fusion, and a jump detection threshold value is set to realize abnormal recalculation. The method improves the reliability of real-time dynamic positioning, and guarantees the continuous and stable operation of the system in a signal shielding or multi-path interference scene through a jump detection and recalculation mechanism.

The invention belongs to the technical field of target detection, and particularly discloses a two-dimensional adaptive constant false alarm detection method for a gridding complementary reference sliding window, which comprises the following steps: constructing the gridding complementary reference sliding window under a detection matrix and distributing reference units; key parameters of a detection matrix are initialized, and an original image signal matrix is converted into a signal power matrix; based on the window-type topological structure, coordinate features of the reference unit are discriminated and assigned, and after traversal, a mean value is calculated to extract background clutters; and calculating a second-order statistic VI and a mean ratio MR, and selecting a constant false alarm algorithm to calculate a detection threshold through three threshold factor calculation formulas in combination with a judgment rule. According to the two-dimensional self-adaptive constant false alarm detection method of the gridding complementary reference sliding window, the detection accuracy is improved by 49.4% under the multi-target and clutter interference environment, the threshold stability and the boundary processing capacity are better under different backgrounds, the false alarm and missing report rate is effectively reduced, and the target detection performance is improved.

The invention discloses a target detection method, device and equipment based on millimeter wave radar, a storage medium and a program product. Determining a distance-Doppler map based on the echo signal received by the millimeter wave radar; determining the angle of each target based on the phase echo signal of each target; filtering the targets based on a first set threshold value and the echo intensity of each target to obtain candidate targets; determining second detection thresholds corresponding to different angles and / or different speeds; and filtering the candidate targets based on the second detection threshold, the echo intensity of each candidate target and the angle and / or speed of each candidate target to obtain a final detection target. According to the target detection method based on the millimeter wave radar provided by the embodiment of the invention, weak targets, false noise targets and sidelobe false targets caused by strong targets can be effectively filtered out, so that the accuracy of target detection is improved, and calculation resources for performing angle estimation on the targets subsequently are further saved.

The invention provides a covert communication method based on STAR-RIS and full-duplex cognitive interference. The method comprises the following steps: establishing a covert communication system model based on STAR-RIS and full-duplex cognitive interference; based on a binary hypothesis testing problem, evaluating a false alarm probability and a missed detection probability of hidden user UR spectrum interception; calculating the false alarm probability, the missed detection probability and the detection error probability of malicious monitor UW detection based on the average receiving power of the malicious monitor UW and the judgment criterion; obtaining the optimal detection threshold and the minimum detection error probability of the malicious monitor UW based on the definition of the detection error probability and the monotonicity of the false alarm probability and the missed detection probability under different classifications; the minimum average detection error probability of the malicious monitor UW is calculated based on the statistical magnitude of the channel hypothesis and the channel power gain. According to the method, the interference signal is transmitted by adopting the full-duplex hidden user, hidden communication is ensured, the inhibition effect of the interference of the direct and cascade double links on monitors in the same area is better, and the probability that information is detected can be effectively reduced.

The invention particularly relates to an airborne SAR distance compressed domain sea clutter suppression method based on generalized gamma distribution, and the method comprises the steps: carrying out the range pulse compression processing of an echo signal received by an airborne SAR, and extracting a pure sea clutter sample from compressed data through employing a non-local sampling strategy; then, a generalized gamma distribution model is quickly fitted for the clutter samples by using an analytical method based on a second type logarithmic cumulant, and a self-adaptive detection threshold is solved through an incomplete gamma function based on the model and a set false alarm probability. And finally, according to the threshold value, carrying out judgment and filtering on distance compressed domain data, suppressing clutter regions lower than the threshold value, and meanwhile, retaining potential target signals, thereby realizing efficient and steady clutter suppression under complex sea conditions.