399 results about "Detection threshold" patented technology

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 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 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 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 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 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.

The invention belongs to the field of global navigation satellite system (GNSS) data processing, and particularly relates to a real-time cycle slip detection method based on ionosphere variation trend constraint, which comprises the following steps of: firstly, fitting an ionosphere variation trend by using all observation satellite data at the current moment, and selecting a non-geometric combination observation value according to a sliding window; then ionosphere variation trend constraint is applied on the basis of a polynomial function, geomagnetic latitude is used as an independent variable to fit a polynomial coefficient, the ionosphere variation of each satellite observed value at the current moment is forecasted, and then the ionosphere variation and the non-geometric combination are differentiated to obtain ambiguity variation; and finally, a detection threshold is determined through the adaptive expansion factor changing along with latitude, and the ambiguity variation is detected to judge whether cycle slip occurs or not. The method is convenient to calculate, fully considers the change trend of the ionosphere, is suitable for real-time cycle slip detection application, and is more suitable for cycle slip detection application in the ionosphere disturbance period.

The invention discloses a data management system and method based on artificial intelligence, and relates to the technical field of data intelligent processing, and the method comprises the steps: carrying out the frequency spectrum transformation of an encrypted feature vector, calculating the energy density of each frequency spectrum coefficient, constructing a cumulative distribution function, determining an effective frequency spectrum interval based on the energy distribution function, and constructing a projection matrix. Compressing the spectrum vector to a low-dimensional space by using a projection matrix to generate a compressed feature vector; and constructing a neural network model to calculate an abnormal score, setting a detection threshold, performing label detection on the abnormal score, forming a label vector, performing homomorphic decryption on the label vector, and generating a plaintext feature vector. According to the method, a frequency spectrum energy distribution function is introduced on the basis of homomorphic encryption, safe dimensionality reduction and rearrangement of feature vectors are realized by combining periodic mapping and an integer mechanism, the structural identifiability of the feature vectors in a finite field is enhanced, an offset period is optimized through a fluctuation potential function, and the sensitivity to abnormal changes is improved.

The invention discloses a fault monitoring and self-adaptive regulation and control method and system for a ship desulfurization system, and the method comprises the steps: collecting multi-source heterogeneous data in real time through a distributed sensor network, and carrying out the preprocessing; extracting multi-dimensional statistical features, performing weak fault feature enhancement by using an improved singular spectrum analysis algorithm, training a multi-channel attention mechanism LSTM network by using a high-discrimination feature sequence, learning a dynamic time sequence rule of a normal mode and a plurality of early fault modes, and performing early probability prediction of faults; identifying a complex fault source caused by coupling of a plurality of potential factors; a detection threshold value is dynamically adjusted by using an EWMA model driven by reinforcement learning, and the sensitivity and specificity of fault monitoring are dynamically optimized; and a fuzzy logic model is used for calculating a severity index, evaluating the severity of a fault, generating hierarchical early warning and iteratively optimizing a self-adaptive regulation and control strategy, so that the robustness and reliability of the system under strong noise and multivariable coupling are improved.

The invention discloses an aircraft control surface sharp deviation and oscillation fault monitoring method with a self-adaptive threshold value. Aiming at sharp deviation and oscillation faults generated by a redundancy sensor and an actuator in an aircraft control surface control system, the monitor structure design is realized in a related simulation platform, a dual-monitoring technology integrating residual comparison and redundancy channel voting is adopted, and the difference between a monitoring instruction and a feedback signal is compared and monitored; and a fault counter is constructed to realize fault abnormal counting and channel preliminary elimination. According to redundancy voting monitoring, redundancy difference signals are repeatedly compared to identify signals with too large differences, an MODWT method is adopted to decompose redundancy channel signals for time-frequency domain analysis, signal oscillation and stable states are judged according to analysis results, and a detection threshold value is adjusted in a self-adaptive mode. And then fault redundancy channels after comparison and voting monitoring are eliminated, redundancy voting calculation is realized based on an effective number, and finally, the design of the monitoring method for the control surface sharp deviation and oscillation faults is realized.

The invention discloses a power distribution network reliability intelligent analysis method and analysis system, particularly relates to the technical field of power system fault detection, and is used for solving the problems of misjudgment and missed judgment caused by fault feature weakening and harmonic interference in a bidirectional power flow scene in an existing method. The method comprises the following steps: firstly, synchronously collecting fundamental wave and harmonic components of nodes of a power distribution network, extracting a current amplitude change rate and a phase offset by adopting time-frequency domain conjoint analysis, and generating dynamic distribution characteristics by combining harmonic energy clustering; identifying high-frequency interference components in a bidirectional power flow mode by quantizing a phase deviation direction and harmonic energy aggregation characteristics; based on a harmonic frequency band coupling effect and a current dynamic association relationship, an adaptive detection threshold function is constructed, and accurate separation of fault features is realized; and finally, combining a real-time fault judgment result with historical repair data to generate a reliability evaluation parameter. And the sensitivity of fault detection and the rationality of reliability evaluation in a complex operation environment are remarkably improved.

The invention discloses a PLC-IoT street lamp electric leakage detection method and system based on edge calculation, and relates to the technical field of smart power grids and Internet of Things. According to the invention, the self-adaptive threshold adjustment algorithm is adopted to dynamically calibrate the electric leakage detection threshold, so that the problem that the traditional fixed threshold method is easy to generate false alarm or missing alarm when the environment changes is effectively solved, and the accuracy and reliability of detection are remarkably improved; through a multi-node collaborative decision algorithm and a data fusion technology, the regional electric leakage risk can be accurately judged, misjudgment caused by local interference in single-node detection is avoided, the robustness of the system is enhanced, through a parameter optimization mechanism and a machine learning regression algorithm, an environment correction coefficient and detection algorithm parameters can be dynamically adjusted, and the reliability of the system is improved. The electric leakage risk trend is predicted, dynamic and accurate evaluation of the electric leakage risk is realized, and the intelligent level and the management efficiency of the system are improved.

The invention provides an electric power meter data real-time analysis and abnormity early warning method based on edge calculation, and belongs to the technical field of data analysis and early warning, and the method comprises the steps: collecting the multi-modal data of an electric power meter based on a multi-channel analog-to-digital converter, employing a hardware filter circuit to eliminate high-frequency noise, and obtaining the data of the electric power meter; signal gain self-adaptive adjustment is carried out based on a reconfigurable amplifier; a digital signal processing accelerator is used to carry out Fourier transform on a matrix constructed by the standardized data stream, and a multi-core processor is combined to carry out analysis to generate a feature vector; the method comprises the following steps: acquiring environmental parameters in real time based on an environmental sensor, dynamically adjusting an anomaly detection threshold in combination with a machine learning model trained by historical data, and comparing a feature vector with the dynamic threshold to generate an anomaly signal; the relay control circuit triggers sound-light alarm, the physically isolated communication module sends an early warning instruction to the cloud platform, and meanwhile, the redundant power supply module is started to control the electric power meter to operate continuously. And the efficiency and accuracy of subsequent abnormity early warning are improved.

The invention discloses a coherent accumulation measurement method of an S-mode low-power signal and related equipment, and relates to the field of aviation communication signal measurement. Comprising the following steps: performing noise statistics and baseline calibration on digital I / Q sampling data filtered and amplified by an analog front end to obtain a noise mean value and a noise standard deviation; setting a detection threshold value based on the noise standard deviation, and performing sliding correlation on the net signal and the S-mode lead pulse reference template to capture the lead pulse; taking the capturing moment as a starting point, intercepting a preset-frame-length signal sequence covering the S-mode leading pulse and the data segment from the net signal, and distinguishing an S-mode complete frame and an A / C-mode short pulse string through dual-threshold verification; and performing coherent accumulation on the I-path and Q-path sampling points in the complete frame according to a complex number form to obtain a coherent accumulation result, and calculating the average power of the S-mode low-power signal according to the coherent accumulation result. Accurate measurement of an extremely weak S-mode response signal can be realized under a noise background.

In various embodiments, a system for detecting an obscurant on an environment surface includes a light source; a scanner; a receiver that detects scattered reflection returns, some of which may be below a detection threshold; and a processor. The processor determines whether the portion below the threshold corresponds to an obscurant on an environment surface, including by: receiving a new point cloud including a group of points corresponding to the environment surface, clustering at least a portion of the group of points to form a projected shape, and clustering into a candidate cluster at least a portion of the portion below the threshold that belong to projected locations within the shape. The obscurant candidate cluster is compared with a previously determined cluster to determine whether a detected change conforms to a detected physical movement of the system. If so, the obscurant candidate cluster is an obscurant on the environment surface.

The invention discloses an LSTM-SVDD anomaly detection method and system based on rule guidance, belongs to the technical field of industrial nondestructive quality inspection, and aims to solve the technical problems that noise samples are sensitive and excessively depend on annotation of abnormal data, and expert experience and knowledge cannot be effectively introduced. Constructing a feature extraction model based on a bidirectional LSTM network, a rule template engine and a feature projection fusion layer; calculating a hypersphere radius through a quantile method, constructing an SVDD loss function as a basic loss function, introducing a rule penalty term to construct a rule loss function, carrying out weighted summation on the basic loss function and the rule loss function to construct a total loss function, and obtaining a trained feature extraction model and an optimized hypersphere radius through minimizing the total loss function; and inputting a to-be-detected sample into the trained feature extraction model, calculating an Euclidean distance between a fusion feature vector and the center of the hypersphere, and comparing the Euclidean distance with a dynamic detection threshold value.

The invention provides an unmanned aerial vehicle image ship detection method and system based on FAST features and an HSO algorithm, and relates to the technical field of infrared image processing. The problems that a traditional method cannot efficiently position the ROI and is poor in threshold segmentation effect are solved. According to the technical key points, the method comprises the following steps: carrying out FAST feature detection on a target unmanned aerial vehicle image, and determining an interest region image according to a feature detection result; determining a candidate detection threshold value set for the interest area image by adopting an HSO algorithm, performing iterative optimization processing on the candidate detection threshold value set until a termination condition is met, and outputting a detection threshold value when the termination condition is met; and carrying out image segmentation on the region of interest image by adopting the detection threshold, and carrying out ship detection on the segmented image to obtain a ship detection result in the target unmanned aerial vehicle image. According to the method, the processing range can be greatly reduced, the target detection segmentation threshold search efficiency is improved, the noise immunity is enhanced, and an image target extraction technical system adaptive to multiple complex scenes is formed.

The invention discloses an under-water flow tumble detection system based on multi-scale fusion and decision, which comprises the following steps of: acquiring radar signals in a detection area by utilizing a millimeter wave radar, constructing a distance Doppler spectrum, calculating a water flow dynamic clutter index based on energy distribution statistical characteristics, identifying a background noise type and dynamically generating a detection threshold; target point cloud extraction under a complex dynamic water flow background is realized; the method comprises the following steps: performing track initialization and state updating by constructing a two-stage gating and evidence judgment framework, executing online parameter self-adaption and target physical size estimation, extracting time sequence characteristics such as a centroid position and Doppler velocity, and constructing a characteristic vector; and establishing a finite state machine model based on the feature vectors, identifying state transition, fall confirmation and state rollback conditions, and outputting a fall judgment result or a rollback result. According to the invention, accurate identification and dynamic determination of a fall event in a wet, slippery and water flow complex environment are realized.

The invention discloses an environment self-adaptive ultrasonic gas leakage detection method based on a self-encoder. The method comprises the following steps of: 1, acquiring a sound signal, and collecting environmental background noise when no gas leaks under a normal working condition; 2, carrying out fixed-length segmentation processing on the sound signals, and extracting logarithmic Mel spectrum features; step 3, establishing a convolutional auto-encoder neural network model; step 4, extracting embedded representation vectors of all normal samples in the training set by using the auto-encoder model trained in the step 3 to form a normal sample feature library, then training an anomaly detector, and determining an optimal detection threshold according to anomaly score values of all the normal samples in the verification set; and 5, when the environment changes, updating the normal sample feature library and the abnormal sample feature library by using newly accumulated sound data, retraining the abnormal detector and updating the detection threshold. According to the method, the problems of scarcity of leaked data, high acquisition cost and poor environmental adaptability in a real scene are effectively solved.

The invention relates to the technical field of fuel nozzle performance testing, in particular to an integrated high-precision fuel flow testing method and detection method.The testing method comprises the steps that a spraying test bed or a nozzle test bed is correspondingly selected based on whether a to-be-tested workpiece tests the spraying angle or not; after the integrated high-precision fuel oil flow testing device is in a reset state, a corresponding motor rotating speed is input to a digital control system based on the calibrated flow of the workpiece to be tested, a plunger pump of a high-pressure oil supply system is started, and oil liquid in an oil tank system is pumped to a corresponding spray test bed or nozzle test bed to run; the actual pressure difference of the workpiece to be measured is collected and transmitted to the digital control system, the actual pressure difference and the target pressure difference are compared, and the actual pressure difference is adjusted; when the preliminarily obtained actual oil flow is larger than a detection threshold value, the actual oil flow corresponding to the current flowmeter is defined as corresponding detection data; and when the preliminarily obtained actual oil flow is smaller than or equal to the detection threshold value, a weighing method is adopted for flow detection.

The invention discloses a portable gas chromatographic peak automatic identification and integration method. According to the method, filtering processing is carried out on the obtained hydrogen flame ionization detector signal based on wavelet transform, a smooth curve is reconstructed, and a peak searching threshold value is dynamically adjusted by using a dynamic threshold value method based on the hydrogen flame ionization detector signal. And carrying out peak searching processing on the smooth curve based on the determined peak searching threshold. And carrying out secondary screening adjustment on the preliminary peak searching result, and calculating the chromatographic peak area. And finally, performing chromatographic peak matching according to the target compound retention time set by a user, and converting the peak area of each matched chromatographic peak into concentration according to the calibration curve information. According to the invention, the dynamic self-adaption of the detection threshold is realized, so that the detection threshold can adapt to chromatographic signals with different signal-to-noise ratio levels.

The invention discloses a wind shear dynamic risk assessment and grading alarm method, and aims to solve the problems that a wind shear detection method is easily influenced by turbulent flow and topographic factors, multi-dimensional information and environmental factors are not fully utilized, so that the false alarm rate is high, the missing report risk is high, the environmental adaptability is poor, and an effective dynamic assessment mechanism is lacked. And real-time reliability evaluation and optimization cannot be carried out on a detection result, so that the flight safety of the airplane is low. According to the method, the comprehensive risk index and multiple performances of the multi-dimensional features are evaluated according to the multi-dimensional features of the aircraft, the comprehensive reliability index is calculated according to the evaluation result, the comprehensive risk index and the comprehensive reliability index are updated according to the historical detection rate and the false alarm rate, the detection threshold value is updated according to the reference threshold value and the updated comprehensive reliability index, and the reliability of the aircraft is improved. And performing risk degree grading according to the updated comprehensive risk degree index and the detection threshold value, performing reliability grading according to the updated comprehensive reliability index, and generating grading alarm information and comprehensive decision suggestions according to the two results.