68 results about "Energy matrix" patented technology

The invention discloses a digital twinning and AI analysis sewage treatment control system, and relates to the technical field of sewage equipment control, and the system comprises the steps: deploying a sensor to obtain a sewage treatment acoustic signal to form an acoustic energy matrix, and synchronously obtaining the operation data of a treatment plant to construct an operation data matrix; fusing the acoustic energy matrix and the operation data matrix to form a high-dimensional data matrix; mapping the high-dimensional data matrix into a low-dimensional point cloud, constructing a point cloud adjacent matrix, recognizing topological features according to the adjacent matrix, analyzing abnormal topological features by combining persistent coherence with topological entropy, and calculating an abnormal confidence coefficient; and establishing a hierarchical agent based on the abnormal confidence coefficient, defining a reward function through life cycle evaluation, adjusting a hierarchical agent optimization strategy by adopting DDPG reinforcement learning, predicting the performance of the optimization strategy in combination with a multi-step prediction loss function, and outputting the optimization strategy. According to the invention, the control strategy of the sewage treatment equipment is continuously optimized, and the strategy implementation efficiency is improved.

The present application relates to a method, apparatus, and device for analyzing a lithography process window based on stepwise regression. The method comprises: obtaining photoresist pattern image data and determining the constraints required for the lithography process window; determining a lithography process model that satisfies the relationship between the photoresist pattern features, exposure energy, and focal length, and a stepwise regression direction for the lithography process model; performing stepwise regression on the lithography process model based on the photoresist pattern image data in the direction indicated by the stepwise regression direction to obtain multiple candidate lithography process models and model selection index values ​​for each candidate lithography process model; determining a target lithography process model from the multiple candidate lithography process models based on the model selection index values; and determining the lithography process window based on the constraints and the target lithography process model. This method can improve the accuracy of the analysis results of focal length energy matrix data.

The invention belongs to the technical field of reduced-order model development, and particularly relates to a POD-SVD random forest reduced-order method and system for digital twin engines. Adjusting a plurality of simulation data generated by the digital twin engine under different working condition boundary conditions; extracting a physical quantity of the region of interest; combining and forming an original matrix required by PODSVD decomposition; decomposing an original matrix into three matrixes by using a PODSVD algorithm, and converting the three matrixes into a modal coefficient matrix, a modal matrix and an energy matrix through a calculation formula; training a random forest algorithm; inputting a boundary condition parameter corresponding to a working condition needing to be predicted into the trained random forest model, and generating a prediction modal coefficient; calculating and restoring a physical quantity needing to be predicted; and adding grid information for the obtained reduced physical quantity, and generating a. Dat file for post-processing. The method is used for breaking through the limitation of tradeoff between cost and model precision in traditional researches such as test / CFD, and meanwhile, the model precision and the evaluation speed are improved.

The invention relates to the technical field of electrical sensor measurement, and provides a fault test method, system and equipment suitable for a fan converter, and the method comprises the steps: collecting current data in an IGBT module of the fan converter, and taking the current data as an original current signal; screening a plurality of dominant frequencies of the original current signal; decomposing the original current signal to obtain a plurality of component signals, and corresponding to each dominant frequency to obtain a plurality of dominant frequency signals; screening to obtain a reference signal; according to the variation trend difference between each component signal and the reference signal, the variation abnormity of each component signal is obtained; analyzing the energy difference between each component signal and the reference signal in the time-frequency energy matrix to obtain the energy difference index of each component signal; obtaining an anomaly index of each component signal; and performing fault detection on the fan converter based on the anomaly index of each component signal. The invention aims to solve the problem that high-frequency electromagnetic noise affects signal performance of a current sampling loop and covers real fault features.

The invention discloses an irrigation district water distribution scheduling method and device and a medium, belongs to the field of irrigation district management, and aims to solve the problem that accurate irrigation cannot be uniformly and efficiently carried out, and the method comprises the following steps: determining a digital elevation model and a crop planting distribution diagram of an irrigation district; according to the digital elevation model and the crop planting distribution map, determining different irrigation subareas of the irrigation area, and a water delivery energy consumption coefficient, a crop suitability coefficient and a subarea water demand critical period of each irrigation subarea; constructing a water delivery energy matrix of the irrigation area according to the water delivery energy consumption coefficient of each irrigation subarea and the irrigation water demand in the irrigation period; determining a rotation irrigation group of the irrigation area according to the water delivery energy weight combination and the water delivery energy matrix of each irrigation subarea in the current irrigation period; and determining the irrigation time sequence of the rotation irrigation group according to the crop suitability coefficient of each irrigation subarea and the subarea water demand critical period. And water distribution in the irrigated area can be more efficiently and reasonably carried out.

The invention discloses a power distribution feeder fault detection method considering transient frequency response of a distributed power supply, which relates to the technical field of power distribution network fault detection and comprises the following steps: acquiring a voltage traveling wave signal and performing wavelet transformation to obtain a multi-scale wavelet coefficient; calculating wavelet energy entropy and extracting local time-frequency energy characteristics; constructing a time-frequency energy matrix and calculating a norm of the time-frequency energy matrix to judge whether detection is started or not; and after a threshold condition is met, normalizing the energy matrix, screening out invalid data, calculating a non-zero norm, obtaining a distribution proportionality coefficient according to the non-zero norm, and finally completing fault identification. The method has better effects in the aspects of accuracy and applicability.

The invention relates to a hydro-generator partial discharge pulse extraction method based on Taylor-S transformation. The method comprises the following steps: collecting a partial discharge signal of a stator winding of a hydro-generator; preprocessing the partial discharge signal to obtain a preprocessed signal; performing time-frequency transformation on the preprocessed signal to obtain a time-frequency matrix; estimating an instantaneous frequency based on phase information of the time-frequency matrix; synchronously extracting the time-frequency matrix according to the instantaneous frequency to obtain a time-frequency energy matrix; extracting a time-frequency ridge line based on the time-frequency energy matrix, and performing feature optimization on the time-frequency ridge line to obtain an optimized ridge line; and determining starting and ending positions of the partial discharge pulse according to the time-frequency energy matrix and the optimized ridge line. According to the method, Taylor-S transformation is combined with synchronous extraction operation, so that the problems of energy dispersion and serious cross term interference in a strong noise environment of a traditional time-frequency analysis method are effectively solved, and the definition and focusing property of partial discharge pulse time-frequency representation are remarkably improved.

The invention discloses an intelligent energy management system of a photovoltaic-energy storage integrated prefabricated external wall panel. Efficient energy management is realized through a three-layer closed-loop architecture. A data acquisition layer acquires photovoltaic power Ppv, a battery SOC and a temperature Tbatt in real time; the central control layer constructs an energy matrix Ematrix based on an edge computing chip, and executes a three-level scheduling algorithm, and photovoltaic power supplies load preferentially, energy storage and charging are performed, and a surplus feed network is formed; and the communication layer interacts with a power grid through the LoRa module. The system integrates fuzzy PID (Proportion Integration Differentiation) control, LSTM (Long Short Term Memory) prediction, multi-mode dynamic switching (self-sufficiency / power grid assistance / peak shaving / emergency standby) and a safety protection mechanism (thermal runaway blocking and battery health management), and dynamic impedance matching and line loss compensation are combined, so that the energy utilization rate is improved, and the accident rate is reduced.

A relay for a beam of wireless power and a satellite with the relay are disclosed. The relay includes: an array of coaxial waveguide elements, each element including: an input polarizing section, an output polarizing section, and a phase shifting section located between said input and output polarizing sections, wherein said input polarizing section, said output polarizing section, and said phase shifting section are controllably rotatable around a longitudinal axis of said coaxial waveguide; and a processor to control rotation of said input polarizing section, said output polarizing section, and said phase shifting section.

The invention discloses an emergency charging control system of a mobile energy storage power supply, belongs to the technical field of energy storage power supplies, and realizes intelligent breakthrough of energy capture and storage scheduling through deep cooperation of an environment adaptive energy matrix and a bionic group charging management module. A multi-dimensional energy capture layer of the environment adaptive matrix can autonomously identify and fuse solar energy, vibration energy, radio frequency energy and other multi-element environment energy, and a dynamic impedance network of the multi-dimensional energy capture layer optimizes an energy input path in real time to ensure that stable energy supply can still be maintained in rainstorm, extremely cold or electromagnetic interference and other complex scenes. The honeycomb unit array of the bionic charging module enables the energy storage unit to have the dynamic recombination capability like a biological cluster through the bionic topology reconstruction technology, the high-power pulse requirement can be quickly responded, millisecond-level fault isolation and self-healing repair can be achieved, and the energy supply interruption risk of a traditional system under the extreme working condition is fundamentally solved.

The invention provides a method for monitoring the motion state of a mask plate workpiece table by exposing an active area, which comprises the following steps of: 1, providing a wafer, performing active area exposure on the wafer, and aiming at the central energy / focal length of a focal length energy matrix test of the exposure, taking the optimal energy / focal length for not performing any registration error compensation on the active area under a corresponding process platform; step 2, determining whether to trigger the fault detection and classification system to work according to the standard deviation of the rotation amount of the mask workpiece table around the Z axis in the movement process generated by different exposure energy values of the focal length energy matrix test; and step 3, after the fault detection and classification system is started to work, checking the mask plate workpiece table. According to the application, the motion state of the mask plate workpiece table can be effectively monitored in real time.

The invention discloses a same-frequency multi-target signal detection method for Doppler-code phase two-dimensional joint search, and relates to the field of spaceflight measurement and control. Through Doppler frequency shift generated by satellite motion and code phase deviation caused by signal propagation time delay difference, two-dimensional joint correlation calculation and incoherent accumulation are carried out at a receiving end to generate a global energy matrix, an adaptive threshold is calculated, strong signal side lobe interference is suppressed by defining a protection area, the weak signal detection probability is increased to 90% or above, and the detection accuracy is improved. Tracking channels are distributed according to an energy descending order, and the purpose of simultaneously and efficiently detecting multiple targets is achieved. The method is particularly suitable for same-frequency multi-target signal detection based on Doppler-code phase two-dimensional joint search in a low-orbit satellite constellation or a dense satellite network, and solves the problems of low spectrum utilization rate, complex pseudo code management and the like of the traditional frequency division / code division technology.

This invention relates to the field of power cable testing technology, and discloses a method and system for detecting cable dimensional parameters based on point cloud feature registration. The method includes: acquiring multispectral image signals; obtaining the light trace energy matrix through physical simulation; constructing an error fingerprint database by combining topological mapping; performing semantic segmentation on the image signals to obtain a probability segmentation mask; calling the fingerprint database to obtain a correction matrix and extracting the original sub-pixel point cloud; performing confidence assessment based on environmental stress parameters to obtain a weighted confidence feature set; performing geometric center alignment to output the cable geometric center set, and then extracting the dimensions to obtain the cable cross-section measurement results. This invention achieves digital decoupling of stress-induced optomechanical coupling pseudo-drift, solves the technical problems of numerical jumps in insulation thickness and deviations in eccentricity calculation, and improves the confidence and measurement accuracy of cable cross-section configuration reconstruction under complex mechanical stress conditions.

A kind of soft robot control method of fusing attention mechanism and physics-inspired neural network, the present application relates to soft robot dynamics modeling method.The present application belongs to the field of soft robot dynamics modeling.The purpose of the present application is to solve the problem of low control precision of existing soft robot.Process is: the inherent properties of soft robot, position and energy as the input of model, mass matrix function, dissipation matrix function, energy matrix function as the output of model respectively;The next time position, velocity and acceleration of soft robot are predicted based on Lagrange function and the output of model;Obtain the trained model;The inherent properties of the same soft robot to be measured, position and energy are input into the trained model, and the mass matrix function, dissipation matrix function and energy matrix function are output from the model;The next time position, velocity and acceleration of soft robot are obtained based on Lagrange function and the output of model.

The invention discloses an underwater target radiation noise identification method based on deep learning. The method comprises the following steps: 1, acquiring a logarithmic Mel frequency band energy matrix of a target radiation noise signal; 2, obtaining a processed logarithm Mel band energy matrix through distortion mask of the logarithm Mel band energy matrix; 3, constructing an underwater target radiation noise identification model; 4, feature extraction of the processed logarithmic Mel frequency band energy matrix; 5, training an underwater target radiation noise identification model based on the target category probability vector; and 6, recognizing subsequent target radiation noise signals by using the trained underwater target radiation noise recognition model. The method is reasonable in design, the logarithmic Mel frequency band energy matrix for obtaining the target radiation noise signal is processed through the random mask matrix, the processing capacity of the model for the distortion condition is improved, the robustness of the model is improved, richer features of the full text are captured through multi-scale feature extraction in the model, and the robustness of the model is improved. And a sliding window is used to pay attention to extraction of local detail features, so that the recognition accuracy is improved.

The invention provides an energy balancing system and method based on GaN high-frequency multiplexing, and the system comprises a cell group module which is used for storing and supplying energy; the matrix type energy routing module is used for receiving the energy flow decision of the central control module, and a switch matrix adjusts the channel state of an energy transmission channel between the energy storage cell and the GaN-based DC-DC converter according to the energy flow decision; the sensing module is used for collecting state parameters of each energy storage battery cell in the battery cell group module, compensating the state parameters according to a preset calibration database, and transmitting the compensated state parameters to the central control module; and the central control module is used for determining the charge state and the cell health degree of the energy storage cell according to the compensated state parameters, generating an energy flow decision according to the charge state and the cell health degree, and transmitting the energy flow decision to the matrix type energy routing module. And through a charge state and cell health degree two-parameter decision and a matrix type dynamic multiplexing architecture, the energy balance effect is improved while miniaturized integration is realized.

The invention discloses a synthetic aperture radar broadband radio frequency interference detection method and system based on out-of-band signal energy, a storage device and electronic equipment, and the method comprises the following steps: carrying out the range Fourier transform of broadband interference echo data, and converting the data into an azimuth time domain-range frequency domain; extracting signals outside a frequency band to obtain an out-of-band energy matrix only containing noise and radio frequency interference; summing pulse by pulse along the distance direction according to the out-of-band energy matrix to obtain a final energy sequence of which the length is the azimuth sampling number; gaussian weighted moving average filtering is carried out on the energy sequence; calculating the filtered data by using an Otsu algorithm to obtain a self-adaptive threshold value; and segmenting the energy sequence based on a filtering result and an adaptive threshold to obtain an interference detection result. According to the invention, whether the pulse of the synthetic aperture radar echo contains the broadband radio frequency interference can be detected with high precision, so that the interference suppression efficiency is greatly improved, and the interference can be suppressed accurately and effectively subsequently.

The invention discloses a lung ultrasonic signal feature identification method based on an RF signal, and relates to the technical field of feature identification, and the method comprises the steps: collecting an original lung ultrasonic radio frequency signal, extracting the local frequency spectrum energy distribution of the original lung ultrasonic radio frequency signal in a frequency sub-band, and forming a frequency-time energy matrix; calculating a resonance period vector of a local energy peak value according to the frequency-time energy matrix, and constructing a lung texture dynamic period sensing map through multi-scale fusion mapping; the method comprises the following steps: performing cepstrum transformation on an original lung ultrasonic radio-frequency signal to generate an original radio-frequency cepstrum spectrum, constructing a multi-path reflection structure model based on the original radio-frequency cepstrum spectrum, and then aligning periodic structure candidate features in a lung texture dynamic periodic sensing spectrum with the original radio-frequency cepstrum spectrum to obtain a time domain alignment error; fusion modeling of a periodic change mode of an original lung ultrasonic radio-frequency signal under a multi-frequency sub-band is realized through a step of constructing a lung texture dynamic periodic sensing map.

The application discloses a kind of ball screw pair fault diagnosis methods based on short-time window energy kurtosis spectrum, current, position and speed and other servo control signals in the running process of ball screw pair are collected, according to position signal and speed signal extract stable running section;Zero-mean processing is carried out to stable running section current signal and short-time Fourier transform is used to construct time-frequency spectrum matrix;According to preset bandwidth, sub-band division is carried out to time-frequency spectrum matrix and the energy of each sub-band in each short-time window is calculated;Further combined with position signal, the energy of sub-band is mapped to lead space, and lead-energy matrix is formed;The kurtosis and / or negative entropy of the energy distribution sequence of each sub-band in the lead direction is calculated, to determine the fault sensitive band;According to the lead energy anomaly corresponding to fault sensitive band, fault diagnosis, fault location and fault degree evaluation are completed;The application has the advantages of strong working condition adaptability, small amount of calculation, good interpretability and easy engineering deployment.

The invention provides a tensegrity module dynamic behavior analysis method based on an energy-preserving matrix perturbation method, which is different from an existing modeling method aiming at a tensegrity structure that a rope and a rod piece are simulated into a rod without considering bending rigidity or both the rope and the rod piece are regarded as beams. The string units are adopted to model the rope, and the actual vibration condition of the structure can be reflected. Taking a three-rod tensegrity structure module unit as an example, firstly, dynamic stiffness matrixes with prestress are established for ropes and rod pieces in the structure respectively; solving the established dynamic stiffness matrix by adopting a W-W algorithm to obtain a structure frequency; and finally, structural response is obtained and verified under the condition of existing frequency, and verification results show that the method has good calculation efficiency on the premise of ensuring accuracy.

The invention relates to the technical field of engine testing, in particular to an aero-engine performance detection method, which comprises the following steps of: acquiring vibration signals mounted on an engine shell and blade tip timing data at the periphery of a gas compressor blade, and performing segmented processing on the continuous vibration signals according to an engine rotating speed period; and calculating time-frequency distribution of each signal segment to obtain a time-frequency energy matrix. According to the method, the vibration signals and the blade tip timing data are synchronously collected and segmented processing is carried out in combination with the rotating speed period, so that the frequency characteristics in different time periods can be completely extracted, and information missing caused by single time domain or frequency domain processing in a traditional method is avoided; furthermore, spectral density is calculated on a frequency domain and a cyclic frequency domain through a time frequency energy matrix, a two-dimensional spectrogram capable of reflecting energy distribution and cyclic characteristics at the same time is constructed, and full expression of characteristic information under multiple dimensions is guaranteed.

The present disclosure is directed to a novel redesigned or retrofitted cabin air filtration system utilizing an ultraviolet light, radiation, or an energy matrix membrane and related technology to eliminate toxins such as viruses, bacteria, mold, fungi, rickettsia, protozoa, etc. from the air system in a vehicle, such as an automobile, marine vehicle airplane, etc., thereby improving the current cabin air filter functionality and providing a sterile air distribution environment throughout the cabin of the vehicle or small area.

The invention discloses a frequency domain-time frequency domain adaptive time-varying filtering method with human-computer interaction characteristics, which comprises the following steps: manually selecting reference data points according to the energy distribution of signals in a time frequency domain, and obtaining an instantaneous frequency curve of a component to be extracted through cubic spline interpolation fitting; based on the short-time Fourier transform energy matrix and the cumulative distribution function, the instantaneous transmission band width at each moment is calculated in a self-adaptive mode; taking the instantaneous frequency as a center frequency and the instantaneous bandwidth as a transmission band boundary, constructing an elliptical digital band-pass filter bank, and forming a self-adaptive time-varying filter; performing time-frequency domain filtering decomposition on the original signal, and performing inverse short-time Fourier transform reconstruction to obtain a pure component signal; order spectrum / envelope order spectrum analysis is carried out on the components, and gear fault features are extracted. The method gives consideration to time domain and frequency domain information, achieves one-time filtering decomposition, is high in calculation efficiency, is high in anti-noise performance, and is suitable for the fault diagnosis of equipment with complex non-stationary signal features.

The application discloses a kind of PCCP pipeline abnormal vibration high frequency automatic identification method, system and equipment, belong to concrete pipeline vibration automatic monitoring field. Including: the modal decomposition is carried out to the broken wire vibration data monitored, and Hilbert spectrum calculation is carried out, obtain time-frequency spectrum;Calculate the vibration energy curve after each filtering with time variation;Curve is normalized, and the one-dimensional vibration energy data after normalization is dimension, form filter threshold-time-filtered energy matrix;Based on energy-time curve before filtering selects key analysis time period, and obtains the corresponding filter threshold-time-filtered energy matrix sub-matrix;To the row (or column) of each filter frequency threshold value corresponding to sub-matrix selects upper quartile and handles, form filter frequency threshold-normalized filtered energy mean curve;According to the maximum on the curve, obtain the highest frequency of PCCP pipeline abnormal vibration.The application can realize automatic identification.

The application relates to the field of electric digital data processing and discloses a metal surface processing quality detection method and system, which comprises the following steps: acquiring a two-dimensional gray data matrix of a surface object to be detected; mapping the matrix into a frequency energy matrix by using a frequency transformation operator and determining an energy decision threshold; extracting a local energy peak value coordinate set and locking a retardation radius according to the radial energy distribution of the local energy peak value coordinate set, so as to generate a dynamic retardation mask matrix; logically point-multiplying the dynamic retardation mask matrix and the frequency energy matrix and rearranging a basic diffuse energy integral value, so as to generate a residual frequency domain matrix; and reconstructing a spatial domain data matrix by using an inverse transformation operator. The application realizes orthogonal decoupling of interference characteristics and target characteristics in the data dimension, relies on a mask generation logic driven by a data stream to cope with working condition fluctuations, and enhances the high-fidelity reconstruction strength for weak abnormal signals.

The application relates to the technical field of electrical sensor measurement, and discloses a fault test method, system and equipment suitable for a fan converter, which comprises the following steps: collecting current data in an IGBT module of the fan converter and taking the current data as original current signals; screening a plurality of main frequencies of the original current signals; decomposing the original current signals to obtain a plurality of component signals and corresponding main frequency signals; screening a reference signal; obtaining variation abnormality of the component signals according to variation trend differences between the component signals and the reference signal; analyzing energy differences between the component signals and the reference signal in a time-frequency energy matrix to obtain energy difference indexes of the component signals; obtaining abnormality indexes of the component signals; and performing fault detection on the fan converter based on the abnormality indexes of the component signals. The application aims to solve the problem that high-frequency electromagnetic noise can affect signal performance of a current sampling loop and cover real fault characteristics.

The invention discloses a broadband stability analysis method of a doubly-fed fan-energy storage combined system, and relates to the field of broadband stability analysis of power systems. The method comprises the following steps: firstly, carrying out preparation work before data acquisition, further carrying out multi-channel synchronous sampling and obtaining four types of features, fusing the four types of features to form a continuous time sequence, and preprocessing the continuous time sequence to obtain a standardized feature set; performing broadband domain wavelet transform on the standardized feature set, and outputting a time-frequency energy matrix and a frequency table; and extracting a core feature set based on the time-frequency energy matrix, and outputting a single-frequency-band stability evaluation value, a double-fed fan-energy storage combined system stability level and a weak-frequency-band positioning result through the optimized broadband stability comprehensive evaluation model. The problems that in stability analysis of a traditional doubly-fed fan-energy storage combined system, broadband disturbance feature coverage is incomplete, a parameter calculation link is fragmented, weight setting is subjective and cannot adapt to dynamic changes of working conditions, and engineering landing performance is poor are solved.

This invention belongs to the field of positioning technology, specifically relating to a transient traveling wave positioning method for broken strand faults in overhead insulated conductors. The method includes: acquiring a single-end transient current signal of the line, obtaining a reconstructed signal through bandpass filtering, and performing an initial time-frequency domain transformation to obtain an initial time-frequency distribution; calculating the power spectrum and constructing a parallel time-varying parameter oscillator array to obtain multiple narrowband enhanced signals; performing a second time-frequency domain transformation on the enhanced signals to construct a fused time-frequency energy matrix; extracting the modulus maxima propagation path from the matrix; determining the arrival times of the initial traveling wave and the reverse polarity fault reflection wave based on the moment when the singularity characteristic index first enters the abrupt change threshold interval; and calculating the fault distance based on the calibrated traveling wave velocity and the single-end traveling wave ranging principle. This method effectively suppresses noise and enhances weak features, significantly improving the accuracy of wavefront identification and the reliability of single-end positioning.

The present application relates to the field of construction engineering structure health monitoring, and particularly relates to a distributed optical fiber intelligent monitoring and early warning system and method for deep foundation pit construction, which comprises the following steps: obtaining an optical fiber strain sequence and differentially extracting a gradient sequence; performing multi-scale space-frequency decomposition to obtain a space-frequency energy matrix; extracting a slowly varying component to construct a background model, calculating a logarithmic domain difference to generate a residual saliency map; statistically quantifying multi-scale gradient direction consistency to quantify edge confidence, and adaptively adjusting a smoothing scale to output refined features; combining feature purity and spatial dispersion to generate a comprehensive atlas through weighted fusion; and constructing an energy function considering spatial adjacent consistency and performing global optimization to solve the problem. The present application effectively improves the weak anomaly detection and early warning accuracy by segmenting physically coherent abnormal areas to trigger early warning.

The invention discloses a radar target detection method based on morphology, and the method comprises the steps: 1, carrying out the two-dimensional FFT processing of a two-dimensional echo data matrix collected by a radar, and obtaining an energy matrix after the two-dimensional FFT processing; step 2, performing two-dimensional CA-CFAR preliminary screening on the energy matrix after the two-dimensional FFT processing to obtain data after preliminary screening; 3, performing morphological false alarm suppression on the data subjected to the two-dimensional CA-CFAR preliminary screening to obtain a region subjected to false alarm suppression; and step 4, parameterized GLRT fine detection is carried out on the area after false alarm suppression, and whether a target exists is finally determined. The method can further improve the detection probability, improves the radar detection precision, reduces the false alarm number, provides a high-robustness solution for the real-time detection of a low observable target, and effectively solves a problem that the FFT speed measurement precision is insufficient in the prior art.