123results about How to "Improve time resolution" patented technology

The invention discloses a slope structure safety monitoring method and system based on a distributed sensing technology, and the method comprises the steps: carrying out the distributed monitoring of the surface and deep part of a slope, and obtaining monitoring data; data transmission is carried out on the monitoring data through a wireless network; preprocessing and analyzing the transmitted monitoring data to obtain a monitoring data set; according to the monitoring data set, a safety evaluation model of the slope structure is established, and slope surface displacement, deep stress and pore water pressure are comprehensively considered in the safety evaluation model; based on the safety evaluation model, obtaining a quantitative evaluation result of the slope safety state; and establishing a slope disaster early warning model, and performing trend analysis and threshold judgment on the quantitative evaluation result through the slope disaster early warning model to realize early warning and real-time early warning of the slope disaster. The monitoring method provided by the invention has the characteristics of distribution, multiple parameters, long-term stability and high reliability, can comprehensively sense the change of the slope safety state, and provides a reliable basis for disaster early warning.

The invention discloses an external bias pixel structure of a light receiving stage circuit in a dynamic vision sensor pixel. The external bias pixel structure comprises a photodiode, a logarithmic tube, an amplifier tube, a switch tube and a bias tube, the bias tube, the switch tube and the amplifier tube are sequentially connected in series, a source electrode of the bias tube is connected with a power supply, a drain electrode of the bias tube is connected with a drain electrode of the switch tube, a source electrode of the switch tube is connected with a drain electrode of the amplifier tube, and a source electrode of the amplifier tube is grounded; the grid electrode of the bias tube is in signal connection with Vbias, the grid electrode of the switch tube is in signal connection with Vcpr, the source electrode of the switch tube and the drain electrode of the amplifier tube are connected with the grid electrode of the logarithmic tube, the drain electrode of the logarithmic tube is connected with a power supply, the source electrode of the logarithmic tube is connected with the negative electrode of the photodiode, and the positive electrode of the photodiode is grounded. And a grid electrode of the amplifier tube is also connected between the source electrode of the logarithmic tube and the negative electrode of the photodiode.

The invention provides a distributed photovoltaic access distribution network voltage fluctuation evaluation method, medium and system, and belongs to the technical field of distributed photovoltaic, and the method comprises the steps: firstly collecting the parameters of a photovoltaic system and a distribution network, and building a power grid response equation set considering the parameters; more parameter samples are obtained through data augmentation and expansion, and indexes such as voltage, power and harmonic waves of each node are calculated according to the response equation set. Constructing a voltage fluctuation evaluation index matrix including voltage deviation, fluctuation, harmonic waves, short-circuit capacity change and the like; and decomposing the matrix by adopting singular value decomposition to obtain a voltage stability and fluctuation evaluation matrix, and finally calculating a voltage stability index and a fluctuation index. If the voltage fluctuation evaluation index exceeds a preset threshold value, an alarm is triggered, and decision support is provided for distribution network planning and operation. According to the invention, the technical problems of short evaluation time scale and difficulty in capturing the dynamic characteristics of voltage fluctuation in the evaluation of the distributed photovoltaic access distribution network in the prior art are solved.

The invention discloses a rolling bearing fault diagnosis method based on a WSETCNNLBKALSSVM, and the method comprises the steps: S1, carrying out the modal decomposition of a rolling bearing vibration signal collected by a sensor through employing the high time-frequency resolution characteristic of wavelet synchronous extraction transformation, obtaining a decomposed time-frequency image, and carrying out the lossless compression processing of the time-frequency image; s2, dividing the time-frequency image data after lossless compression processing into a training set and a test set according to a set proportion; s3, inputting the generated time-frequency image data in the training set into a two-dimensional convolutional neural network, and performing adaptive fault feature extraction; and S4, taking an output result of a full connection layer of the two-dimensional convolutional neural network as an input of a least square support vector machine, optimizing hyper-parameters of the LSSVM by adopting an improved black-wing optimization algorithm, and completing classification of fault types through the LSSVM. The rolling bearing fault diagnosis method has higher classification precision and diagnosis accuracy in rolling bearing fault diagnosis.

The invention discloses a high temporal-spatial resolution risk assessment method for agricultural non-point source pollution in a plain river network region. The method comprises the following steps: selecting an assessment region; dividing evaluation units; selecting an evaluation time period; basic data collection and necessary supplementary monitoring of an evaluation area; agricultural non-point source pollution in-river load accounting; evaluating the bearing capacity of the riverway on non-point source pollution; calculating evaluation indexes; carrying out risk grade delimiting; compiling a priority treatment area list; agricultural non-point source pollution control measures are implemented; and evaluating agricultural non-point source pollution prevention and control performance. The polder area and the irrigation and drainage area are used as agricultural non-point source pollution risk last-stage evaluation units, so that the spatial resolution of agricultural non-point source pollution risk evaluation is greatly improved; a monthly risk assessment method is provided, and the time resolution of agricultural non-point source pollution risk assessment is greatly improved; a land and water linkage evaluation index system is adopted, excessive adoption of weights is avoided, the index meaning is clear, and targeted accurate recognition of a key source area is achieved through mutual verification of different indexes.

The invention belongs to the technical field of hydraulic engineering, and provides a river section form measurement method and system, a storage medium and electronic equipment, and the method comprises the steps: collecting and processing data needed for measuring a river section form; extracting a waterline and a center line from data required for measuring the river section form; calculating a relative stability index of the section based on the center line and the waterline, and identifying a stable river reach in the river channel through the relative stability index of the section; dividing the stable river reach into a curved river reach and a straight river reach; giving elevation values to the waterline of the curved river reach and the waterline of the straight river reach by using data required for measuring the shape of the cross section of the river to obtain waterline elevation values; and measuring the river section form based on the waterline elevation value. According to the method, any section form of the river channel can be effectively constructed, the space-time limitation of measurement is broken, basic data support is provided for runoff monitoring, and an important basis is provided for large-scale flood process monitoring and water resource management decision.

The invention discloses a non-contact real-time monitoring method for human respiration in a Wi-Fi (Wireless Fidelity) complex interference environment. And analyzing the CSI interference intensity and the CSI active ratio, constructing a Wi-Fi interference feature mapping matrix, and calculating each channel interference index by using the matrix to realize interference discrimination. Then, through a dynamic subcarrier selection algorithm CSI-DSSA based on an interference index, a subcarrier combination with the weakest cross correlation in interference data is selected for interference processing, and time domain feature information of multiple data streams in data which is not interfered in a multi-link data fusion method aggregation mode is analyzed. And finally, extracting a time domain characteristic value and constructing an SVM multi-activity classification model in a Wi-Fi interference environment to obtain a tumble activity identification result. According to the method, the human respiratory activity recognition accuracy in the Wi-Fi interference environment can be effectively improved.

The invention belongs to the technical field of marine instruments, and particularly relates to an offshore real-time internal wave observation system and method based on three anchors, and the system comprises a three-anchor fixing frame system, a real-time internal wave observation system and a shore station terminal system. Wherein the real-time internal wave observation system is fixed in the three-anchor fixing frame system in a sliding connection manner, and the real-time internal wave observation system moves up and down along the three-anchor fixing frame system and cannot rotate in the horizontal direction under different sea surface heights, so that the stability of an observation environment is ensured; the real-time internal wave observation system is used for collecting real-time observation data and sending the real-time observation data to the shore station terminal system for processing; and the shore station terminal system is used for analyzing according to the received real-time observation data and detecting whether internal waves exist so as to judge whether to provide early warning. According to the invention, the complete measurement of the internal solitary wave in the ocean can be realized for a long time, the time resolution and the space resolution are very high, and good data are provided for the research of the internal wave.

The invention relates to a residual error pulse broadening interpolation quantization type column-level ADC and a CMOS image sensor, and the residual error pulse broadening interpolation quantization type column-level ADC comprises a comparator, a residual error extraction circuit, a pulse broadening circuit, a coarse quantization counter, a fine quantization counter and an error correction circuit. The comparator is used for comparing a ramp signal and a pixel signal, and the output end of the comparator is connected with the input end of the residual error extraction circuit and the input end of the coarse quantization counter through a first AND gate; the residual error extraction circuit is used for extracting a time difference between an output falling edge of the first AND gate and a first clock rising edge behind the residual error extraction circuit; an output end of the residual error extraction circuit is connected with an input end of the pulse broadening circuit and an input end of the error correction circuit; and the pulse broadening circuit is used for broadening the output of the residual error extraction circuit, and the output end of the pulse broadening circuit is connected with the input end of the fine quantization counter. The problems that an existing SSADC is low in quantization speed and not high in time precision are solved.

The invention relates to the technical field of remote sensing image processing, in particular to a residual feature fusion inversion-based accumulated snow coverage estimation method, which comprises the following steps of: acquiring terrestrial satellite data and performing image data screening according to a screening condition; acquiring meteorological satellite data, preprocessing the meteorological satellite data and splicing the meteorological satellite data with the geographic elevation data; generating a binary accumulated snow map, calculating the accumulated snow coverage according to a definition method and a meteorological satellite resolution, and obtaining an accumulated snow coverage label; cutting the meteorological satellite image into a rectangle according to the accumulated snow coverage label; establishing an accumulated snow coverage estimation model based on deep learning; optimal experiment parameters are found through repeated experiment debugging and comparison; after the prediction is completed, obtaining respective accumulated snow coverage estimation results and splicing the accumulated snow coverage estimation results into a complete accumulated snow coverage estimation result; and the cloud shielding interference is effectively filtered by combining the generated snow coverage estimation result with the satellite data of the front and back time phases, so that the method has higher time resolution and reliability.

The invention discloses an ultrafast electron diffraction system based on 300kV direct current acceleration and radio frequency compression and a diffraction imaging method, and solves the problems that the time resolution of the current keV ultrafast electron diffraction system is insufficient due to the space charge effect, and the time-varying electric field generated by a radio frequency compression cavity is used for carrying out time compression on the beam bunch length of an electronic pulse; and the electronic pulse energy is low. According to the invention, femtosecond laser is split, one beam of light generates an ultrafast electronic pulse by using an ultrafast electron generation module based on 300kV direct current acceleration, the energy of the ultrafast electronic pulse is relatively high, and then the pulse width of the ultrafast electronic pulse is compressed by using a radio frequency compression module; the pulse broadening caused by the space charge effect of the ultrafast electronic pulse is effectively inhibited, the time resolution below hundred femtoseconds can be realized, meanwhile, the charge quantity of a single beam of ultrafast electronic pulse reaches 16fC (105e-), and the method can be used for ultrafast kinetic research of irreversible materials.

The embodiment of the invention provides a time detection device and method for a scintillation pulse signal, an electronic device and a medium, a system module determines the number of target threshold modules needing to be started for detection according to the estimated voltage amplitude of the scintillation pulse signal, and starts each target threshold module through a starting instruction; the flicker pulse signals are input into the target threshold modules in parallel, the flicker pulse signals and corresponding voltage threshold signals output by the voltage unit are connected to the input end of the threshold comparator, and comparison signals output by the threshold comparator are sent to the system module; and the system module determines each voltage threshold of the scintillation pulse signal and the corresponding detection time according to the comparison signal. According to the method, time detection can be carried out on the scintillation pulse signal, the real zero-crossing point of the scintillation pulse is obtained in a full-digital mode, the threshold comparator is used, meanwhile, the influence of baseline noise caused by too low threshold setting is avoided, and the obtained pulse detection time is more accurate.

The invention relates to a time-resolved spectral measurement method, device and equipment based on a multi-event time-to-digital converter, and a medium, and the method comprises the steps: carrying out the histogram statistics of photon signals of different time responses in each column of single-photon avalanche diodes in different laser pulse periods through the multi-event time-to-digital converter, the photon signal distribution of the photon signal of the specific wavelength corresponding to each column of single-photon avalanche diodes in different laser pulse periods is determined; the multi-event time-to-digital converter carries out delay combination on photon signal distribution in different laser pulse periods so as to determine a photon lifetime curve corresponding to the photon signal of the specific wavelength of each column of single-photon avalanche diodes. And integrating the photon lifetime curves corresponding to the photon signals of the specific wavelength of each column of single-photon avalanche diodes so as to construct the time-resolved spectrum of the sample to be detected. According to the invention, counting loss or time error caused by dead time can be reduced.

The invention discloses a seismic data time-frequency analysis method and system based on time sparse S transformation, and the method comprises the steps: collecting seismic data, carrying out the time-frequency analysis of the collected seismic data through S transformation, and obtaining a time-frequency spectrum containing the time-frequency characteristics of the seismic data; based on sampling duration and sampling rate characteristics of the seismic data, obtaining a window function of each frequency of the seismic data by using S transformation time-frequency analysis; and constructing a Toeplitz matrix based on a window function, carrying out reverse convolution along the time direction by using the time-frequency spectrum and the Toeplitz matrix, and traversing all frequencies to obtain a seismic data analysis result. The method is applied to time-frequency analysis of seismic data for the first time, a good effect is achieved in multi-scale analysis of two-dimensional actual seismic data, the time resolution of time-frequency analysis of the seismic data is remarkably improved through de-windowing processing, and thin-layer identification of transient signals and seismic data is more accurate especially in a high-frequency part.

The invention discloses a GNSS-R soil humidity inversion method and device and a medium, and relates to the field of data processing.The method comprises the steps that the soil reflectivity and the reflected signal phase of a research area are determined based on a reflected signal of a global navigation satellite system, and a normalized vegetation index is obtained; determining a temperature drought vegetation index based on the normalized vegetation index; performing space-time matching on the soil reflectivity, the reflected signal phase and the temperature drought vegetation index according to the timestamp of the reflected signal of the global navigation satellite system and the longitude and latitude information of the research area to obtain input data; performing normalization processing on the input data to obtain normalized data; constructing an NARX neural network, optimizing parameters of the NARX neural network by adopting a swarm intelligence optimization algorithm, and obtaining an inversion model based on the NARX neural network after parameter optimization; and inputting the normalized data into an inversion model to obtain a soil humidity inversion result. According to the invention, the inversion precision and the time resolution can be improved.

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.

This invention discloses an all-weather autonomous road defect detection method and system, relating to the field of computer vision. It includes a hybrid vision sensor for all-weather autonomous road defect detection mounted on an unmanned vehicle (UAV). The hybrid vision sensor is equipped with a computing unit, which runs a hybrid vision road defect recognition and localization algorithm. The hybrid vision in the UAV sensor includes traditional RGB vision and DVS pulse vision. The traditional RGB vision is used to detect the texture details of the road, while the DVS pulse vision is used to detect road anomalies at high speeds. By utilizing the rich texture details of RGB and combining the sensitive response of DVS vision to low light and dynamic lighting changes, the accuracy of road defect detection is improved. This invention features real-time asynchronous response, high dynamic range, information capture capability in low-light scenarios, and reduced motion blur.

The invention discloses a single femtosecond laser pulse waveform measuring device. A measured vertically-polarized space-uniform measured femtosecond laser pulse is divided into transmission light and reflected light after passing through a spectroscope, the transmission light is divided into two beams, and the three beams are incident to a nonlinear crystal at the same angle and equal optical path along the edge of a regular triangular prism for third-order nonlinear harmonic conversion; the generated third harmonic light beam is a double-delay third-order intensity related signal of the measured pulse after space-time transformation, and pulse waveform information is obtained through image decoding. The measuring device improves time resolution, and is low in cost and convenient to adjust.

The present invention provides a compact gamma-ray detection optical system and detection method, which solves the problems of low time resolution of fusion process detection and interference with adjacent equipment in the prior art. The system includes a sealed shell, a detector, and a converter, a secondary reflector, and a primary reflector arranged along the gamma-ray emission direction; a sealing sleeve is arranged in the sealed shell, one end of the sealing sleeve is sealed by a pressure sealing window, and the other end is connected to the sealed shell; a cavity formed by the sealing sleeve, the pressure sealing window, and the sealed shell is filled with high-pressure gas; the primary reflector is sleeved on the sealing sleeve; the detector is arranged in the sealing sleeve, and the detection surface is located between the primary reflector and the secondary reflector; both the primary and secondary reflectors are concave reflectors, and the distance between the two satisfies: T1<T<T2; T is the time when the gamma ray enters the converter to generate photons and reaches the detection surface through the primary, secondary reflectors, and the pressure sealing window; T1 is the time when the direct gamma ray reaches the detection surface; T2 is the time when the secondary gamma ray reaches the detection surface.

The present invention discloses a data processing method and system for a weather radar, the method comprising the steps of: obtaining an atmospheric temperature profile collected by a microwave radiometer and performing quality control, extracting a 0°C height and a -20°C height from the atmospheric temperature profile after quality control; obtaining weather radar data of multiple radars and performing quality control, fusing the weather radar data after quality control of multiple radars, obtaining three-dimensional grid point data arranged in a unified height, latitude and longitude, identifying storm components and storm cells based on the three-dimensional grid point data; calculating a storm cell hail characteristic height according to the 0°C height and the centroid height of the storm components contained in the storm cell; calculating the height difference between the storm cell hail characteristic height and the 0°C height, and calculating the probability of hail occurring in the hail cell according to the height difference. The present invention can reduce the false alarm rate of hail monitoring of weather radar and improve the accuracy of hail monitoring.

The invention discloses a point spread function and dislocation superposition-based compression imaging method and system, and relates to the technical field of compression imaging, and the method comprises the specific steps: carrying out the PSF imaging of a collected image based on a point spread function, and obtaining a blurred image; encoding the blurred image based on an encoding matrix to obtain an encoded image; carrying out frame-by-frame dislocation superposition on the coded image to obtain an original image; and constructing a measurement matrix based on the original image, and decoding and reconstructing the original image by using a reconstruction algorithm according to the measurement matrix to obtain a reconstructed image. According to the method, a high-speed compression imaging model based on PSF diffusion, frosted glass coding and frame-by-frame staggered superposition is constructed on the basis of a compressed sensing theory, and multiple frames of images are superposed on a photosensitive chip of a camera in a staggered manner by the model between two electronic reading times of the camera. Therefore, the electronic reading speed of the camera is improved in a disguised manner.

The present invention relates to the technical field of neutron scattering, and in particular to a hyperbolic single crystal graphite analyzer used in a secondary spectrometer of a neutron spectrometer, which improves the energy resolution and detection efficiency of the neutron spectrometer by optimizing the geometric shape of the analyzer; the analyzer comprises a substrate, and both the horizontal (x) and vertical (y) directions of the substrate are designed into a hyperbolic trapezoidal structure according to a fourth-order polynomial, which determines the overall geometric shape of the analyzer; the present invention effectively solves the problems existing in the prior art through an innovative design of a hyperbolic single crystal graphite analyzer, significantly improves the performance of the neutron spectrometer, and has important value for neutron scattering research and applications in related fields.

The invention belongs to the technical field of wind tunnel experiments, and discloses a grid-free field-lens-free large-view-field focusing schlieren system. The grid-free field-lens-free large-view-field focusing schlieren system comprises a light source, a small hole, a collimating convex lens, a spectroscope, a model, a focusing convex lens, a knife edge and a scientific camera. Light emitted by the light source passes through the small hole, reaches the collimating convex lens and becomes a quasi-parallel light beam after passing through the collimating convex lens, the light beam passes through the spectroscope, reaches the model, returns to the spectroscope after being reflected by the model and reaches the focusing convex lens after being reflected by the spectroscope, and the focusing convex lens converges the light beam to the knife edge; and finally, focusing the to-be-measured flow field area by the scientific camera and then acquiring a schlieren image. According to the grating-free field-lens-free large-view-field focusing schlieren system, flow field density gradient distribution with small optical path integral effect under the large-view-field condition is obtained in a large wind tunnel, and the grating-free field-lens-free large-view-field focusing schlieren system has engineering practical value.

The invention discloses a weather radar echo frame-by-frame prediction neural network training state judgment method, which comprises the following steps of 1, collecting a weather radar echo data set, preprocessing data, and establishing a weather radar echo data sample; step 2, establishing a weather radar echo frame-by-frame prediction neural network; and step 3, establishing a weather radar echo frame-by-frame prediction neural network training state determination method, carrying out neural network training, and determining the training state until completion. According to the method disclosed by the invention, a training state judgment condition based on a prediction result and input-target time sequence sample similarity comparison method is added, so that the neural network can be ensured to have an effective prediction capability, and the method is particularly suitable for weather radar echo prediction with relatively small time sequence sample difference; comprising application scenes with high time resolution and large detection range, such as a phased array scanning weather radar with high time resolution or an S-band weather radar with large-range detection capability.

The application discloses a kind of surface solar radiation prediction method and equipment based on satellite observation data, belong to solar radiation remote sensing prediction technical field.The method includes obtaining target observation data and prediction model;Difference is obtained by the target observation data of the adjacent observation time of each spectral channel of the top reflectivity of atmosphere layer, and cloud movement sensing feature is obtained;The input sequence that target observation data and cloud movement sensing feature jointly constitute is input into the prediction model, and the surface solar radiation prediction result is obtained.The application can realize more rapid, more continuous surface solar radiation short-term prediction, and reduce the dependence of intermediate inversion product and multi-source auxiliary data while maintaining good prediction accuracy in actual prediction stage, and has practical application value for solar energy resource monitoring, photovoltaic power generation power estimation and short-time power dispatching.

The invention provides a single-time ultrafast multi-mode time sequence phase imaging device, and relates to the technical field of ultrafast optical imaging and computational optical imaging, and the imaging device comprises a light source system which is used for generating a linearly polarized femtosecond laser pulse; the chirp laser pulse generation system generates a chirp laser pulse based on the femtosecond laser pulse; the pulse shaping system is used for generating a time sequence multi-wavelength sub-pulse based on the chirp laser pulse; and the coherent modulation imaging system performs coherent modulation imaging based on the time sequence multi-wavelength sub-pulses, and realizes single-time ultrafast multi-modal time sequence phase imaging by using a wavelength and modulation coding combined constraint inversion algorithm. After femtosecond laser pulses are converted into chirped pulses, coaxial time sequence multi-wavelength sub-pulses with harmonious time resolution and harmonious sub-pulse number are generated by using a pulse shaping technology.

The invention relates to an urban atmospheric ventilation coefficient estimation method based on a dual-wavelength laser radar, and belongs to the field of radar detection, and the method comprises the steps: collecting data, and calculating a first local fluctuation ratio of the data detected by the dual-wavelength laser radar; drawing a change characteristic curve, obtaining label data of a second local volatility, checking the label data to obtain label data of a third local volatility, obtaining data after segmented structure optimization, and inputting the data into a solution model to obtain corrected laser radar water vapor profile data; calculating RI indexes of the collected data at different heights, and constructing an original feature data set to obtain estimation data of the urban atmosphere mixed height; constructing a data set, inputting the data set into an analysis model, and inputting an obtained result into an extreme gradient rising model to obtain a corrected horizontal wind speed; and calculating to obtain an estimated value of the urban atmospheric ventilation coefficient. According to the method, the accuracy of an estimation result can be improved, the advantages of various observation devices and data are exerted, and support is provided for urban environment management and weather forecast.

The present invention provides a method for three-dimensional dynamic imaging of fluid based on multi-channel OPT and neural network, comprising the following steps: using laser to provide illumination, emitting laser to biological samples, the biological samples will scatter light and form sample images, continuously collecting sample images of n angles of the biological samples, and obtaining a number of original data graphs; using neural networks to train the collected sample images with spatiotemporal characteristics to obtain a network model; using the network model to predict the original image; obtaining a two-dimensional sample feature distribution map, and using OPT technology to reconstruct three-dimensional images of these two-dimensional sample feature distribution maps to obtain dynamic three-dimensional images. The present invention also provides an imaging device. The present invention improves the accuracy of image feature recognition, has good imaging quality, and can quickly obtain three-dimensional dynamic images.

The invention discloses an image processing method and device and electronic equipment, and belongs to the technical field of images. The image processing method comprises the steps of inputting an RGB image and corresponding event camera data into a feature extraction module of an image processing model, performing feature extraction on the RGB image and the event camera data, and outputting image feature data and brightness change feature data; inputting the image feature data and the brightness change feature data into a cross-modal channel attention mechanism module, performing feature fusion processing on the image feature data and the brightness change feature data, and outputting fused feature data; inputting the image feature data and the fused feature data into a coding and decoding module, performing feature splicing extraction on the image feature data and the fused feature data, and outputting spliced feature data; and inputting the RGB image and the splicing feature data into a fusion module, and performing fusion processing on the splicing feature data and the RGB image to obtain a deblurred image of the RGB image.

This invention discloses a method and system for synchronous inversion of multiple atmospheric parameters, belonging to the field of atmospheric laser remote sensing technology. The probe light and the reference light are obtained by splitting laser pulses; the probe light is expanded, reflected, and then perpendicularly incident into the atmosphere, where it is scattered by the atmosphere to obtain the echo light. That is, all parameters originate from the same measurement and the same spectrum, resulting in perfect spatiotemporal consistency of the data, efficient measurement methods, and accurate measurement results. The temperature and pressure parameters in the atmosphere are calculated using the normalized scattering spectrum, and then aerosol and wind speed parameters are obtained, achieving true synchronous inversion of the four key parameters of temperature, pressure, wind speed, and aerosols for the first time. Furthermore, in applications such as meteorological forecasting, achieving synchronous measurement of atmospheric temperature, pressure, wind speed, and aerosols can save costs and improve efficiency, which has significant practical implications.