160 results about "High temporal resolution" patented technology

The invention discloses an eye movement tracking method, an eye movement tracking device, eye movement tracking equipment, computer equipment and a storage medium. The method comprises the following steps: calculating calibration gaze information and a calibration pupil position according to first event data collected by an event camera; determining a region of interest based on the calibrated pupil position; processing second event data collected by the event camera according to the region of interest to generate region of interest data, the collection time of the second event data being later than the collection time of the first event data; according to the region-of-interest data, calculating a current fixation position offset; according to the calibration gaze information and the current gaze position offset, the current gaze information is determined, the calibrated pupil position is updated based on the current gaze position offset, the gaze direction, the moving direction and the moving distance of the pupil are accurately captured in real time by utilizing the characteristics of high time resolution and the like of the event camera, and the eye movement tracking effect is effectively improved.

The invention relates to the technical field of territorial space planning, and discloses a monitoring and evaluation method based on multi-source remote sensing images and geographic information vector data. Comprising the following steps: acquiring a multi-source remote sensing image and geographic information vector data of a target area; performing preprocessing and fusion analysis on the multi-source remote sensing image, generating a dynamic earth surface change detection model with high time resolution, and constructing an initial monitoring baseline in combination with geographic information vector data; according to the method, a high-frequency change detection condition and a semantic segmentation condition are loaded on the basis of an initial monitoring baseline to form a comprehensive interpretation model, a real-time early warning evaluation index is output, and a dynamic earth surface change detection model is generated by preprocessing a remote sensing image. And generating an illegal behavior distribution result by using the key identification parameters, establishing a task distribution model to optimize a supervision scheduling scheme, and finally realizing accurate management and control. According to the invention, the efficiency and accuracy of territorial space planning monitoring are improved, and technical support is provided for intelligent supervision.

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

Standard event-based cameras capture changes in luminance with high temporal resolution, but they do not measure static images. An event-based imaging system and methods that can capture static intensity is provided that eliminates the need for a dual-sensing system and significantly reduces the cost, footprint, and data bandwidth of the imaging system. The system is suited for applications where high temporal resolution and dynamic range are required, but where static scene information is also important. The system has a Dynamic Vision Sensor for capturing data and a processing unit for data aggregation, a fast approximate extraction of static information from noise, and scene reconstruction. The methods may use a model for reconstruction or may calibrate during system deployment by making measurements of known static luminance and recording average noise levels over a predetermined period to map between luminance and noise for reconstruction.

The invention discloses a remote sensing data normalization method and system based on geographically weighted nonparametric regression. The method comprises the following steps: establishing a geographically weighted nonparametric regression estimation model; obtaining a target image and a reference remote sensing image set, and forming a remote sensing image pair based on the target image and the corresponding reference remote sensing image; preprocessing a target image in the remote sensing image pair to obtain a resampled target image consistent with the reference remote sensing image in spatial resolution; judging a variable coefficient in each local window in the resampled target image and the reference remote sensing image, and screening out homogeneous pixels meeting conditions based on the variable coefficient and a preset threshold to form a homogeneous pixel set; and based on the geographically weighted nonparametric regression estimation model and the homogeneous pixel set, performing pixel-by-pixel calculation on a to-be-normalized image to obtain a normalization result. Remote sensing data with large breadth, high time resolution, consistent spectrum and consistent radiation can be used as a radiation reference, and normalization of any target image is realized.

In order to solve the problem that the snow depth predicted by a sentinel No.1 data set is difficult to meet high spatial resolution and high temporal resolution at the same time, the invention provides a snow depth prediction method, a storage medium and electronic equipment, and the method comprises the steps: obtaining sentinel No.1 data, auxiliary data and station data about the snow depth, constructing a geographic time weighted snow depth parameter based on the site data; carrying out resampling and space-time matching on the data to obtain a first data set; inputting the first data set into an integrated deep learning model to obtain a preliminary snow depth; obtaining time sequence climate data and geographical time data of the research area, performing resampling, and converting the preliminary snow depth data into time sequence data; the resampled time sequence climate data and geographical time data and the time sequence data of the initial snow depth form a second data set; and inputting the second data set into the ResNet-BiLSTM time sequence model to obtain snow depth data. According to the invention, snow depth prediction data with high temporal-spatial resolution can be generated.

The invention discloses a high-speed imaging method, system and device based on an SPAD array and a time counting circuit. The method comprises the following steps: acquiring original data of a photon event to obtain an original photon counting matrix of each frame; performing statistical modeling based on the original photon counting matrix to obtain a dynamic threshold matrix; performing threshold filtering based on the dynamic threshold matrix in combination with the original photon counting matrix to obtain a sparse matrix; and performing accumulation and image reconstruction based on the multi-frame original photon counting matrix to output a final image. The high time resolution of the SPAD array and the TCSPC is combined with the BNQSA algorithm, the noise problem of high frame rate imaging under extremely low illumination is solved, the system has the characteristics of high sensitivity, real-time performance and low power consumption, and a breakthrough solution is provided for a weak light scene.

The invention discloses an earth surface downlink short-wave radiation space downscaling method based on a super-resolution reconstruction technology, belongs to the technical field of meteorological data analysis, and solves the problems of insufficient solar radiation data space resolution and weak earth surface heterogeneity characterization capability in the prior art. Carrying out super-resolution reconstruction on the radiation data set by adopting a super-resolution combination model, and carrying out weighted fusion on a reconstructed data set output by the super-resolution combination model; according to the method, the output results of the multiple models are subjected to weighted fusion, weight optimization of the super-resolution sub-models is performed in combination with the high-resolution terrain factors, spatial downscaling from 5km to 1km resolution is finally realized, the spatial precision of the solar radiation data is remarkably improved on the basis of keeping the hour-level time resolution of the original data, and the accuracy of the solar radiation data is improved. And more earth surface heterogeneity information can be captured, and meanwhile, the high-time-resolution application requirement is met.

The present invention provides a Transformer-based method for predicting floods over varying time periods. The method comprises obtaining and preprocessing hydrological and meteorological data for equal time periods; constructing and training a Transformer model; redistributing water volume over varying time periods in the watershed; and, for each time step, inputting the redistributed rainfall data into the trained Transformer model to obtain a predicted runoff sequence. The method can automatically identify rainfall events across time periods and generate new, unequal-time rainfall sequences based on the maximum short-duration rainfall. The Transformer model is used to generate unequal-time flood sequences with higher temporal resolution. The method can more accurately reflect the relationship between maximum rainfall and flood peaks, improving the accuracy and reliability of flood simulations and providing stronger technical support for disaster prevention and mitigation.

The invention provides an airborne high-precision imaging system and method for a single-photon unmanned aerial vehicle, and relates to the field of airborne imaging systems, and the system comprises a jitter collection unit for real-time attitude sensing, a signal collection and emission unit for high-speed transceiving control, a two-dimensional scanning unit for spatial scanning, and a motion compensation unit for real-time data processing. And the main control unit is used for overall planning of global time sequence control. The signal acquisition unit adopts a transmitting-receiving integrated coaxial optical structure, and a dynamic light leakage suppression mechanism is introduced. The jitter acquisition unit is integrated with a high-frequency inertial measurement unit IMU, the sampling frequency of the jitter acquisition unit is higher than the laser pulse frequency through a compensation algorithm, and the three-axis attitude and deflection angle data of the airframe in a dynamic environment are acquired in real time with high time resolution. The motion compensation unit carries out the registration of an IMU coordinate system, a laser radar coordinate system and an earth fixed coordinate system, solves the instantaneous attitude of each laser pulse receiving and transmitting moment, carries out the calibration of the position information of a photon event, and solves the point cloud motion distortion.

The invention relates to the technical field of computer vision, in particular to an event image fusion semantic segmentation method based on lightweight pulse driving. The method comprises the following steps: S1, acquiring image data and corresponding event data; s2, preprocessing the event data; s3, inputting the image data and the processed event data into a pulse encoder; and S4, inputting the shallow-deep features extracted by the pulse encoder into a pulse decoder to obtain a semantic segmentation result. According to the method, the advantages of high time resolution and high dynamic range of event data and the advantage of rich static details of image data are fully utilized, and accurate semantic segmentation is realized; meanwhile, due to the adoption of the pulse neural network, the method also has the characteristics of high calculation efficiency and low energy consumption.

The invention provides an InSAR (Interferometric Synthetic Aperture Radar) and GNSS (Global Navigation Satellite System) fusion settlement monitoring method based on a double-branch neural network, which comprises the following steps of: converting low-time-resolution InSAR data into a high-resolution time sequence matched with GNSS through an interpolation algorithm, and constructing an input matrix with aligned dimensions; a heterogeneous dual-channel network is adopted to respectively extract InSAR spatial features and GNSS time sequence features, after key information is extracted through dimension reduction processing, fusion data is reconstructed, a data splitting strategy is utilized to train a model and optimize parameters, and finally accurate modeling of a multi-source data nonlinear relation is realized. According to the method, the limitation of traditional linear fusion is broken through, a heterogeneous neural network is designed, multi-source data fusion is realized by using the high spatial resolution feature of the InSAR image and the high temporal resolution feature of the GNSS, the problem of insufficient modeling of a non-linear relationship of heterogeneous data in a traditional method is solved, and the space-time precision and robustness of settlement monitoring are improved.

The invention relates to the technical field of stereoscopic vision, and discloses a synchronous speed visual matching method and system, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting a stereoscopic video sequence with a predefined frame rate; executing multi-target hybrid tracking and motion induction detection, and outputting target motion information including position and velocity vectors; extracting hierarchical motion features of the target from continuous multiple frames of the stereoscopic video sequence, and performing unified space-time coding; under geometric constraints of stereoscopic vision, scale cosine similarity, direction similarity and trajectory consistency measurement are calculated and serve as observation evidences to be input into the probabilistic reasoning model for fusion, and a posterior probability representing matching reliability is output; the weight distribution of the speed similarity and the direction similarity is adjusted according to the motion characteristics of the targets in the scene, and the stable corresponding matching relation between the left view target and the right view target is established. According to the method, high-time-resolution information can be utilized, motion features and geometric constraints can be effectively fused, and the method has self-adaptive capacity.

The invention provides an event-driven image motion deblurring method. The event-driven image motion deblurring method comprises the following steps: S1, acquiring a deblurring data set comprising a blurred image, a clear image and an event stream within exposure time; s2, performing preprocessing by adopting a cyclic event representation method, and converting an event stream into an event frame similar to a potential real image; and S3, constructing a deblurring network by adopting linear deformable convolution and a multi-branch feature extraction attention mechanism. And S4, inputting the fuzzy data set into the trained deblurring network to obtain a clear image after deblurring. According to the method, the high time resolution characteristic of the event stream data is utilized, and an advanced convolution and attention mechanism is combined, so that a clear image can be effectively recovered from a motion blurred image.

The invention relates to a remote sensing image space-time fusion method combining an optical flow and a spectrum correction factor. The method comprises the following steps: dividing a surface feature vegetation index difference image into a change region and a non-change region; calculating a time weight matrix and a global space weight matrix for the change area and the non-change area according to the motion sum of all the pixels on the optical flow motion vector, and fusing the time weight matrix and the global space weight matrix to obtain a global space-time weight matrix; performing spatial interpolation on the low-space high-temporal-resolution image at the T1 moment by using cubic spline interpolation to obtain a spatial interpolation image; performing Gaussian low-pass filtering on the high-space low-time-resolution image at the T0 moment, and performing correlation calculation on the filtered image and the low-space high-time-resolution image at the T0 moment to obtain a spectral difference correction factor matrix; and fusing the spatial interpolation image and the high-space low-temporal-resolution image at the T0 moment by using the spectral difference correction factor matrix and the global space-time weight matrix to obtain a high-time high-spatial-resolution image at the T1 moment. According to the invention, the fusion precision and efficiency are improved.

A method for determining wear volume of a mechanical seal includes measuring a temperature at a seal face or seat of the mechanical seal with high temporal resolution. An area under a function of a measured temperature profile in a case of short-term temperature changes which are less than 60 s, compared with a normal temperature, which corresponds to a temperature profile of a mechanical seal without wear, is determined by measuring the temperature profile with subsequent integration of a function of the measured temperature profile. The area being multiplied by a mechanical seal-specific factor in order to calculate the wear volume.

The invention relates to the technical field of atmospheric layer height detection, in particular to a coherent Doppler laser radar-based atmospheric boundary layer height identification and detection method, which comprises the following steps of: transmitting a laser beam through a coherent Doppler laser radar to obtain echo original radial wind speed data under different azimuth angles; performing inversion operation on the echo original radial wind speed data to obtain three-dimensional wind field information; horizontal wind speed is extracted from the three-dimensional wind field information, and turbulence intensity is obtained through calculation; and performing cubic root gradient calculation on the turbulence intensity, and selecting the height corresponding to the minimum value of the cubic root gradient calculation result, namely the height of the atmospheric boundary layer. According to the method, the accurate atmospheric boundary layer height with high time resolution and high spatial resolution can be obtained, so that the problems of large detection height limitation, poor continuity, poor time resolution, poor spatial resolution, limited deployment, low accuracy and the like in an existing atmospheric boundary layer observation mode are solved.

The invention discloses a fast response flood disaster monitoring method and system based on super-resolution reconstruction. The method comprises the following steps: continuously acquiring a plurality of geostationary orbit satellite remote sensing images of an affected area and preprocessing the images; inputting the preprocessed geostationary orbit satellite remote sensing image into the trained super-resolution image reconstruction deep learning model, and outputting a reconstructed disaster area remote sensing image; and inputting the reconstructed disaster area remote sensing image into the trained flood disaster monitoring neural network model, and outputting a disaster range extraction result and a disaster situation evaluation result. According to the method, the characteristic of high time resolution of the geostationary orbit satellite is utilized to provide emergency response when a disaster occurs, cloud removal processing, super-resolution reconstruction and flood disaster area extraction are completed based on pure vision and a deep learning model algorithm, application under various extreme weathers can be supported, and the method has a wide application prospect. And a quick-response basic data service is provided for timely rescue and disaster relief and economic loss estimation.

The invention relates to the technical field of data fusion, and particularly provides a multi-scale eco-hydrological parameter joint inversion method and system based on unmanned aerial vehicle and satellite-ground data fusion, and the method comprises the steps: obtaining multispectral, hyperspectral and thermal infrared data of a research region through a high-resolution sensor carried by an unmanned aerial vehicle; the method comprises the following steps: acquiring unmanned aerial vehicle data, acquiring satellite remote sensing data, performing radiation correction on the unmanned aerial vehicle data and the satellite data, and performing feature extraction and space-time fusion on the unmanned aerial vehicle data and the satellite data by using a deep learning algorithm to generate high-resolution fusion data; through a multi-resolution analysis or spatial interpolation technology, coordinating differences of unmanned aerial vehicle data and satellite data in space and time scales, and constructing a multi-scale data set; based on a physical model and a data driving model, combined with fusion data, an inversion result is visually displayed, and the method has the effects of meeting the hydrological monitoring requirement of high time resolution, and particularly providing real-time monitoring data in the hydrological process of rapid change.

The invention relates to the technical field of satellite navigation, and particularly discloses a GNSS elastic code deviation calculation method and system suitable for a full scene, and the method comprises the steps: analyzing the signal power variation amplitude of a monitoring station based on the satellite signal power variation of the GNSS monitoring station, so as to determine the state of the satellite signal power; constructing a high-time-resolution code deviation state equation and an observation equation, and realizing GNSS high-time-resolution code deviation estimation based on Kalman filtering epoch-by-epoch estimation code deviation, an improved phase smoothing pseudo-range method and an ionized layer spherical harmonic function; calculating an elastic code deviation elastic factor of the GNSS based on the GNSS signal power state, the code deviation variation amplitude of the GNSS, the product precision and the time sequence stability; and determining the elastic code deviation value of the GNSS according to the state of the satellite signal power, the code deviation estimation of the high time resolution of the GNSS and the elastic code deviation elastic factor of the GNSS.

The invention discloses a remote sensing image reconstruction method, system and device based on a ground object structure and a medium. Historical satellite multispectral remote sensing data of a to-be-monitored area are acquired; extracting a surface feature structure end member abundance matrix of the high-time-resolution image data based on a pixel purity index algorithm; extracting a global dependency relationship matrix of historical satellite multispectral remote sensing data based on a multi-channel self-attention mechanism; processing the global dependency relationship matrix based on a feedforward neural network model to obtain global features; fusing the global features and the surface feature structure end member abundance matrix based on a convolutional neural network model to obtain high-resolution feature map data; and performing parameter iterative optimization according to a loss function calculated by the high-resolution feature map data and the high-spatial-resolution image data, and inputting the real-time high-temporal-resolution image data into the optimized target image reconstruction model to obtain a reconstructed image. According to the method provided by the invention, the influence of the mixed pixels on image reconstruction is reduced, and the reconstruction precision is improved.

The invention relates to a remote sensing data fusion method based on bidirectional iteration, and the method comprises the steps: carrying out the time sequence matching of a high-time-resolution remote sensing image and a high-spatial-resolution remote sensing image through dynamic time warping, and constructing a reference image pair; based on the variation trend of the high-temporal-resolution remote sensing image, predicting the high-temporal-resolution remote sensing image at the target moment by using linear regression, and correcting the high-temporal-resolution remote sensing images at the reference moment and the target moment through weighted regression; based on the change trend of the corrected high-temporal-resolution remote sensing image, reversely deducing the high-spatial-resolution remote sensing image at the reference moment by using linear regression, and performing difference comparison with the high-spatial-resolution remote sensing image at the original reference moment; and continuously reducing the difference through an iterative correction mode until a convergence condition is met, and obtaining a fusion sequence. According to the method, the precision of the predicted image is continuously optimized through bidirectional iterative correction, and a more accurate high-temporal-spatial-resolution image can be provided in a high dynamic environment.

The application belongs to the technical field of photon imaging detection, and particularly relates to a photon counting imaging detection system based on a mixed anode. The system comprises a vacuum sealed tube detector composed of a window coated with a photocathode, a microchannel plate stack and a mixed anode, and a signal processing circuit. The photocathode is used for converting photons incident through the window into photoelectrons. The microchannel plate stack is used for converting the photoelectrons into electron clouds. The mixed anode comprises a strip anode and a delay line anode arranged orthogonally, and the mixed anode transmits the collected charge short pulses of each channel to the signal processing circuit. The signal processing circuit calculates the two-dimensional coordinates of the photons based on the collected charge short pulses of each channel, and then transmits the two-dimensional coordinates of the photons to a terminal for imaging display. The application provides a photon counting imaging detection system based on a cross strip-delay line mixed anode, and solves the problem that the existing position sensitive anode detector cannot simultaneously have high spatial resolution and high temporal resolution.

The invention discloses an intelligent early warning system for debris flow disasters in a mountainous area based on a remote sensing network, and particularly relates to the technical field of intelligent early warning of debris flow disasters. Calculating a brightness change coefficient and a color difference coefficient of the low-resolution remote sensing image at the same pixel point from the perspective of high time resolution, quantifying a brightness change fluctuation condition and a color difference condition of the low-resolution remote sensing image at the same pixel point, and calculating a first early warning coefficient; obtaining high-resolution remote sensing images at different moments, calculating boundary similarity, verifying debris flow disaster precursor signals captured in the low-resolution remote sensing images in the high-resolution remote sensing images according to the boundary similarity, and calculating a second early warning coefficient to construct a debris flow precursor comprehensive early warning model; and outputting a debris flow precursor comprehensive early warning index, and comprehensively evaluating the precursor risk of the debris flow disaster, thereby realizing efficient and accurate debris flow disaster precursor early warning.

The present invention discloses a method for detecting coal falling on a belt of a resource-constrained device, belonging to the technical field of computer vision object detection. This method collects based on the real-time data stream of an event camera, utilizes the advantage of the high temporal resolution of the event camera to dynamically capture the change information on the belt; through a specific time window, the captured event data is organized into event frames, and then spatial features, temporal features, and edge features are extracted; the spatial features are extracted by an improved YOLOV12 network, the temporal features are calculated by the optical flow method, and the edge features are obtained by the Sobel operator; these multi-source features are fused into a comprehensive feature through a weighted fusion technique and input into an improved object detection model for coal falling detection. By introducing an event camera and multi-feature fusion, the present invention improves the robustness and detection accuracy of the system in complex environments, reduces the computational amount, and this method is very suitable for resource-constrained edge devices to perform the task of detecting coal falling on a belt.

A high-speed excitation spectral imaging method based on cross-spectral decomposition includes sequentially exciting a fluorescent sample according to eight excitation wavelengths to achieve periodic acquisition of spectral images; selecting (from the previous period) spectral images from the acquired spectral images. k +1) ~8 frames and the previous frame of the current cycle k The process involves cross-combining frames; performing spectral decomposition on the cross-combined three-dimensional spectral data; and performing spectral decomposition once for each newly acquired fluorescence image, thereby obtaining high temporal resolution fluorescence images of multiple targets. This invention fully utilizes the periodicity of excitation wavelength scanning, improving the temporal resolution of excitation spectral imaging by 8 times, and achieving effective spectral imaging speed that is completely consistent with the camera's acquisition frame rate.

The invention discloses a spectral resolution parallel fluorescence lifetime scanning imaging system, and aims to solve the problems of lack of spectral resolution and slow imaging speed in the conventional fluorescence lifetime scanning imaging technology. The system comprises an exciting light module, a scanning imaging module and a spectral resolution life detection module, the exciting light module generates exciting light with specific repetition frequency; the scanning imaging module is used for realizing relative position mobile scanning of exciting light and a sample and separating to obtain a fluorescence signal; the spectral resolution lifetime detection module adopts a multi-channel photoelectric detector to detect fluorescence of different wavelengths at the same time so as to realize parallel fluorescence lifetime measurement. According to the invention, rapid measurement of spectral resolution fluorescence lifetime imaging is realized, the system has the advantages of high time resolution, high spatial resolution and multifunctional measurement, the system structure is simple, the cost is low, and an ideal tool is provided for imaging research such as biomedicine, chemical analysis and environmental monitoring.

The invention discloses a method and system for measuring the dielectric constant of a material, and the method comprises the steps: enabling a terahertz pulse signal to flow through a transmission line and generate reflection through growing a coplanar waveguide transmission line on the surfaces of an electro-optical crystal and a measured material; after femtosecond laser is processed, a terahertz signal is sampled, an optical polarization state analysis system reproduces an electric signal and extracts a peak moment, and a relative dielectric constant is calculated by combining transmission line structure parameters and a formula. According to the invention, electro-optical sampling and time domain reflection technologies are combined, high time resolution and structure adaptability are realized, and accurate measurement of the dielectric constant of the material is realized.

The invention relates to a moving target anchoring observation device and a moving target anchoring method based on the joint effect of trajectory infinitesimal elements and target accumulation. The moving target anchoring observation device comprises a precise tracking module, a light splitting module, a dual-mode imaging module and a satellite-borne calculation module, after light in the target direction enters the moving target anchoring observation device through the precise tracking module, the light is divided into two parts with unequal energy through the light splitting module and then is projected into the dual-mode imaging module, and the satellite-borne calculation module controls the precise tracking module through frame data and event flow provided by the dual-mode imaging module to enable the precise tracking module to search or track a target. According to the moving target anchoring observation device and the moving target anchoring method based on the joint effect of the track infinitesimal element and the target accumulation, pixel-level anchoring is achieved by means of high time resolution of the event camera and high pointing precision of the fast reflecting mirror, and effective capturing, stable tracking and observation of the moving target are achieved by means of the imaging capacity of the frame camera.

The application provides a micro-transient temperature field detection system and method. The detection system comprises: a light source module adapted to emit laser light; a sensing module comprising a reference grating and an atomic force probe; a feedback control module connected to the atomic force probe in the sensing module, the feedback control module being adapted to obtain a surface topography scanning result of a sample to be measured after the atomic force probe scans the sample to be measured; and a data processing terminal configured to process temperature sensing information by a spectral inversion algorithm and combine the surface topography scanning result to obtain a temperature field distribution of the surface of the sample to be measured. The micro-transient temperature field detection system and method can improve the accuracy and speed of micro-transient temperature field detection, and the detection result has high spatial and temporal resolution.