102results about How to "Improve inversion accuracy" patented technology

The invention discloses a rock-fill dam material parameter uncertainty inversion method based on Bayesian inference, and the method specifically comprises the steps: building a rock-fill dam finite element model, and determining the prior distribution of to-be-inverted parameters; constructing an sPCE proxy model based on prior distribution of inversion parameters; a rock-fill dam displacement statistical model is established, and actually-measured displacement water pressure components are separated; constructing a rock-fill dam material likelihood function; carrying out uncertainty inversion on the rock-fill dam material parameters; and updating and inverting the uncertainty of the rock-fill dam material parameters. According to the method, a Bayesian inference method is utilized, and uncertainty distribution characteristics of different partition material parameters of a dam body are obtained through actual measurement displacement data, parameter prior distribution and a likelihood function of the dam. According to the method, the defect that parameter uncertainty cannot be considered in a traditional inversion method is overcome, and obtained parameter values are closer to engineering practice; compared with a traditional inversion method, parameter updating inversion can be carried out through monitoring data in different periods, and continuous tracking of parameter timeliness evolution is achieved.

The invention provides a multi-parameter GNSS-R soil humidity inversion method based on a random forest model. The multi-parameter GNSS-R soil humidity inversion method comprises the following steps: satellite-borne GNSS-R data quality control; satellite-borne GNSS-R data observation quantity extraction and surface reflectance calculation are carried out; eliminating satellite-borne GNSS-R observation data influenced by the open water area; carrying out surface reflectance gridding; preprocessing the SMAP data; constructing an inversion model; and carrying out spaceborne GNSS-R soil humidity inversion and precision evaluation. The method has the beneficial effects that by utilizing the performance advantages of processing a non-linear relationship, good generalization performance, strong robustness and the like of the random forest, after observation data influenced by a water body is accurately eliminated, an inversion model is constructed by combining multi-parameter auxiliary data of a soil humidity active and passive remote sensing satellite; the space-time coverage, the inversion precision and the reliability of soil humidity data are remarkably improved, and powerful data support is provided for the fields of climate forecast, agricultural production, hydrological modeling and the like.

The invention discloses a red soil organic matter spectrum inversion method and device for removing influence of free iron, and the method comprises the steps: obtaining soil sample data, storing organic matter data into a set P, storing free iron content data of typical red soil samples into F, storing spectral reflectivity data of red soil survey samples into H, and obtaining a set P; storing spectral reflectivity data of typical red soil samples into D; according to the set H and the set D, utilizing an MODWT algorithm for decomposition to obtain layered discrete detail coefficient sets CH and CD of the red soil survey sample and the typical red soil sample; removing a noise layer based on CD to obtain an effective layer discrete detail coefficient set C; respectively calculating correlation coefficients between each element in the C and the set F and the set P, and screening out a characteristic wave band set CDG; selecting a detail coefficient CHG located on the characteristic wave band from the layered discrete detail coefficient set CH; a prediction model is constructed, and CHG is adopted for training; and predicting the organic matter content by adopting the trained prediction model. The method can eliminate the influence of free iron oxide, and is low in cost and high in efficiency.

The invention relates to the technical field of remote sensing data processing and earth surface parameter inversion, and discloses an earth surface matrix parameter hyperspectral data inversion method and system based on machine learning. Comprising the following steps: constructing a multi-source heterogeneous hyperspectral data set; performing feature screening on the preprocessed hyperspectral data set based on an adaptive band selection algorithm, constructing a dynamic weight matrix by calculating mutual information entropy and inter-class distance measurement between spectral bands to realize intelligent screening of key feature bands, and combining spectral derivative conversion and spectral index calculation to generate an enhanced feature vector; and a multi-task transfer learning neural network model is constructed, and an output layer realizes multi-parameter collaborative inversion based on a multi-task learning architecture. And performing preprocessing and feature enhancement operation which is the same as that of the training data on the hyperspectral image data of the to-be-inverted region, inputting the trained neural network model, and outputting a surface matrix parameter inversion result.

The embodiment of the invention provides an ionization layer electron density inversion method, device, system and equipment and a medium. The method specifically comprises the following steps: determining a target area of electron density inversion; obtaining observation data corresponding to the signal link in the target area; the signal links comprise a first signal link from a GNSS satellite to a ground station, a second signal link from the GNSS satellite to a low earth orbit satellite, and a third signal link from the low earth orbit satellite to the ground station; determining the slant total electron content corresponding to the signal link according to the observation data corresponding to the signal link; solving an ionized layer chromatography function according to the oblique total electron content corresponding to the signal link so as to obtain an electron density value of a grid in the target area; the ionized layer chromatography function is used for representing a mapping relation between the oblique total electron content and the electron density value of the grid. According to the embodiment of the invention, the inversion precision of the ionosphere electron density can be improved.

The invention provides a satellite solar short-wave radiation inversion method based on knowledge distillation, which relates to the field of solar short-wave radiation inversion, and comprises the following steps: collecting polar orbit satellite data, stationary satellite data, geographic information data and ground observation data; preprocessing the data to obtain input features and labels for training the teacher model and the student model; sorting the importance of the input features by using a random forest model, selecting key features, and removing non-key features; constructing a deep learning model by using the key features, and generating a teacher model of solar short-wave radiation inversion; and taking an output result of the teacher model as a soft label, introducing ground observation data as a hard label, and carrying out knowledge distillation to obtain a student model of solar short-wave radiation inversion. According to the technical scheme of the invention, the problems of data scarcity and high data acquisition cost caused by the fact that a model is constructed only depending on ground observation data in the prior art are solved, and the problem that the inversion precision completely based on satellite data is limited is solved.

The invention discloses a large-area forest vegetation carbon reserve inversion method based on GEE platform multi-source data fusion. The method comprises the following steps: data acquisition and analysis: obtaining forest carbon storage literature analysis data and an index factor data set; performing correlation analysis, constructing a forest carbon reserve index system, and establishing a corresponding characteristic variable relationship between the independent variable index factor and the forest carbon reserve sample; a seq2seq deep learning model based on LSTM (Long Short Term Memory) is imported; precision analysis is carried out; and inverting a large-area forest vegetation carbon reserve data set by using the deep learning model, and analyzing main determinants of forest carbon reserves. According to the method, a Google Earth engine is introduced, factors such as waveband information, vegetation indexes and long-term change trend characteristics are extracted from the Google Earth engine through a huge remote sensing data file of the Google Earth engine and a strong parallel computing capability of the Google Earth engine, and the forest carbon reserve in the loess plateau region is inverted and main determinants of the forest carbon reserve are analyzed based on a seq2seq deep learning model of LSTM.

The invention discloses an SAR (synthetic aperture radar) water depth inversion method driven by variable window sliding segmentation, belongs to the technical field of synthetic aperture radars, and is used for water depth inversion including image segmentation and water depth inversion. The image segmentation comprises original image regularization adjustment, information storage, coastline identification and noise processing, pixel extreme value judgment, pixel change function determination and window sliding; after a coastline is extracted and a starting point is determined, pixels are expanded to two sides of the starting point on the coastline and pixels invaded by the coastline are eliminated, so that utilization of a near-shore image is improved; and the more accurate surge wavelength is obtained by dynamically adjusting the size of the pixel. Compared with the prior art, the method has the advantages that wavelength information of different areas is accurately extracted, complex manual judgment is avoided, and segmentation efficiency and inversion precision are improved; for the irregular coastline, the utilization rate of the water depth inversion of the image in the area near the coastline is increased by changing the starting point along the coastline and expanding outwards.

The invention provides a displacement inversion method for a deepwater riser structure. The method comprises the following steps: collecting and measuring sparse discrete curvature data in real time; acquiring continuous curvature data through an interpolation method; drawing a continuous curve based on the continuous curvature data and the position information thereof, and dividing the continuous curve in real time to obtain the length of a continuous curve infinitesimal segment; the method comprises the following steps: acquiring relative position information of the middle part of a riser and a water surface platform, recursively acquiring a lower half section continuous curvilinear equation from the bottom end to the middle part, recursively acquiring an upper half section continuous curvilinear equation from the water surface to the middle part, integrating the upper half section and the lower half section to acquire a whole section continuous curvilinear equation, and calculating # imgabs 1 #, # imgabs 2 #, # imgabs 3 #, # imgabs 4 # and # imgabs 5 # at any position; wherein # imgabs2 is inversion displacement data corresponding to the position of the outer side of the deepwater riser structure. Deepwater riser structure displacement inversion is carried out only through real-time monitoring curvature data, and the method has high adaptability; and the provided end error elimination strategy can solve the problem of the end accumulated error amplification effect in the recursion process of the traditional space curve method.

The invention discloses a full-waveform inversion method for enhancing boundary perception. The full-waveform inversion method comprises the following steps: acquiring seismic data; constructing a seismic waveform inversion network model; the seismic waveform inversion network model comprises an encoder, a decoder and a jump connection; carrying out the size adjustment of the seismic data through an encoder, and then extracting the advanced features of the reserved low-frequency information, and obtaining a multi-scale coding feature map; inputting the multi-scale encoding feature maps extracted by the encoder into different levels of the decoder by utilizing jump connection; a decoder is used for obtaining distinguishing information of the spatial relation contained in the multi-scale coding feature map, and a prediction speed model is generated; multi-scale analysis is executed through a wavelet convolution layer in an encoder, and low-frequency features are enhanced; then, distinguishing information is extracted by utilizing the spatial relationship of the features through a spatial attention module in a decoder so as to improve the resolution; finally, a reflection coefficient is introduced into the loss function to emphasize punishment on the low-contrast boundary; therefore, the full-waveform inversion precision is improved.

The invention relates to the technical field of geophysical prospecting and computers, in particular to a CSSA-RELM resistivity inversion algorithm, a method for monitoring a seawater invasion interface through the CSSA-RELM resistivity inversion algorithm and application of the CSSA-RELM resistivity inversion algorithm. According to the CSSA-RELM resistivity inversion algorithm disclosed by the invention, a chaos sparrow search algorithm is utilized to optimize a regularization extreme learning machine, so that the CSSA-RELM resistivity inversion algorithm is formed; according to the characteristics of an inversion algorithm, a sample modeling mode is improved, sample data constraint conditions are added for specific problems, and the inversion precision of seawater intrusion test data is improved; the inversion algorithm is tested and verified by using the simulation data of the seawater intrusion test model in the inhomogeneous medium, and the application value of the extreme learning correlation inversion algorithm in the monitoring and research of the seawater intrusion interface position and the seawater wedge form is improved.

The invention relates to the technical field of electromagnetic inverse scattering, in particular to a three-dimensional electromagnetic inverse scattering method based on a diffusion model of physical constraints and BCGS-FFT (Binary Common Gateway Standard-Fast Fourier Transform), which combines the BCGS-FFT, physical models (CF-BPA and CC-SOM) and the diffusion model. The BCGS-FFT physical constraint consistent with the BCGS-FFT physical constraint in a data generation stage is introduced in a back diffusion stage, so that the physical consistency of a three-dimensional reconstruction process is ensured, and the problem of physical distortion is effectively reduced; the step-by-step denoising process of the diffusion model significantly improves the noise suppression capability in the three-dimensional reconstruction, and the preliminary inversion result of the physical model is combined, so that the error accumulation in the whole three-dimensional reconstruction process is reduced, the precision and stability are higher, and the adaptability is also greatly improved.

The invention provides a mobile terminal skin parameter inversion method based on spectral information, and the method comprises the following steps: constructing an optical model according to the characteristics of a skin layered structure and a spectral response mechanism of three skin parameters: melanin content, hemoglobin concentration and dermis thickness, and building a skin parameter and reflection spectrum database based on the model; based on the database, establishing a skin multi-parameter inversion MLP neural network structure adaptive to the collection characteristics of a mobile phone end sensor, and screening an optimal four-waveband channel through multi-waveband combination traversal; the mobile phone spectrum sensor is subjected to wave band optimization, data acquisition and skin parameter inversion. According to the method, through efficient decoupling of a nonlinear coupling relation among multiple skin parameters by waveband optimization and an MLP model, and targeted design of four optimal visible light narrow-band channels, synchronous quantitative detection of melanin, hemoglobin and dermis thickness is realized, core physiological parameters from epidermis to dermis superficial layer are covered, information coverage is comprehensive, and specificity is high.

The application discloses a kind of GNSS-R microwave surface emissivity inversion method and system.First, obtain spaceborne GNSS-R data, spaceborne brightness temperature data, soil temperature data;Surface reflectivity is calculated using spaceborne GNSS-R data, the multiple surface reflectivity calculated is aggregated, to generate single surface reflectivity observation value;Surface emissivity reference value is calculated using spaceborne brightness temperature data and soil temperature data;Then single surface reflectivity observation value is input into surface emissivity inversion model, to obtain surface emissivity numerical value;Finally, the difference between surface emissivity numerical value and surface emissivity reference value is calculated, and the surface emissivity numerical value result of surface emissivity inversion model when the difference satisfies least square is used as inversion result.The application introduces aggregation algorithm in the calculation of aggregated GNSS-R surface reflectivity, which can effectively solve the problem of aggregation error caused by the difference of GNSS-R data space-time position distribution in the current GNSS-R inversion microwave surface emissivity method, and improve the inversion accuracy of microwave surface emissivity.

The present application relates to the field of sea ice detection technology, and in particular to a method and device for estimating the thickness of sea ice based on GNSS reflection remote sensing technology, wherein the method comprises: collecting target data of a ship during navigation in a target sea area, wherein the target data comprises real-time GNSS data, hull height, antenna height and ship draft; obtaining the dynamic sea surface height difference of the target sea area based on real-time GNSS data; estimating the sea ice thickness of the target sea area based on the hull height, antenna height, ship draft and dynamic sea surface height difference. Thus, the problems in the related technology that the ice core drilling method has spatial limitations, the real-time performance of satellite remote sensing data in polar regions is poor, and the ship-borne sea ice camera technology is easily affected by weather are solved.

This invention discloses a method for inverting sea surface wind speed from GNSS reflected signals based on mask perception and image-based Mamba-CNN. The method includes: performing spatiotemporal matching and interpolation processing on multi-source spaceborne GNSS-R observation data and ERA5 reanalysis data; gridding the processed observation parameters according to geographical location to obtain a multi-channel grid image and acquiring a binarized occupancy mask; inputting the multi-channel grid image and the binarized occupancy mask into a mask perception Mamba-CNN model; calibrating the grid features through a mask conditional encoder and a feature linear modulation layer, and performing spatial modeling using feature extraction blocks combining convolutional and Mamba state space paths to obtain the wind speed inversion result; aggregating the extracted features using center-perception pooling, and outputting the final sea surface wind speed inversion image through a regression network. This invention can significantly reduce computational complexity, effectively reconstruct the continuous wind field structure, and improve the inversion accuracy and robustness in high-wind-speed areas.

This invention relates to a multi-temporal radiation signal inversion method based on Conditional Transformer, belonging to the field of satellite remote sensing data processing / spatial information science and technology. The method includes: acquiring meteorological time-series data of the target area to establish a dataset; designing a Conditional Transformer deep learning network; combining the target time-series data and meteorological data encoded by time and meteorological code generators with the output of the Encoder layer, and inputting this combination into the Decoder layer to train the Conditional Transformer deep learning network to obtain a surface radiation signal inversion model; inverting surface radiation brightness temperature data and evaluating the inversion accuracy; efficiently capturing long-distance spatiotemporal dependencies of radiation signals; flexibly inverting radiation changes according to the target time and meteorological conditions; removing nonlinear interference from the atmosphere on radiation; and more accurately inverting the true radiation characteristics of the surface.

This disclosure provides a lidar wind field inversion method based on a filtered velocity-volume processing algorithm, applicable to the field of lidar technology. The method includes acquiring basic observation data for each voxel in the lidar wind field inversion. This basic data includes the signal-to-noise ratio (SNR) at each observation point within the voxel, used to calculate the average SNR within the voxel. When the average SNR is greater than or equal to a preset SNR switching threshold, the three-dimensional wind vector within the voxel is inverted based on the velocity-volume processing algorithm; otherwise, the three-dimensional wind vector is inverted based on a preset filtered velocity-volume processing algorithm. In this way, the velocity-volume processing algorithm can be used to invert the three-dimensional wind vector from near-field wind field data with a high SNR to improve computational efficiency. In the far-field, when the SNR drops to the preset SNR switching threshold, the three-dimensional wind vector is inverted based on the preset filtered velocity-volume processing algorithm, thereby improving the accuracy of lidar wind field inversion.

The present application relates to the technical field of ecological protection, and more particularly to a forest water conservation capacity remote sensing inversion method suitable for Nanling cloudy and rainy areas, aiming at the problem that the existing inversion method cannot overcome the cloud cover and terrain influence, and the precision of forest water conservation capacity is low, the present application obtains optical and microwave remote sensing data, pre-processes the optical and microwave remote sensing data, extracts corresponding optical characteristics and microwave characteristics, weights and fuses the optical characteristics and microwave characteristics, calculates the forest water conservation amount by using a water conservation amount physical model, and generates a Nanling forest water conservation capacity spatial distribution map. The method overcomes the errors caused by weather, terrain shadow and distortion, constitutes a full-weather, high-precision, Nanling special environment-adapted forest water conservation capacity remote sensing inversion system, and realizes accurate quantification of the water conservation capacity of the Nanling special forest ecosystem.

The water-sand multi-element acoustic online monitoring system and method relate to the technical fields of hydrological monitoring and acoustic measurement. The application is to solve the problems that the traditional single-frequency acoustic monitoring method cannot distinguish the echo intensity changes caused by concentration changes or particle distribution, and it is difficult to realize high-precision flow rate and concentration synchronous monitoring in the same system. The application amplifies and filters the reverberation signal received by the acoustic transducer and extracts the echo envelope. The echo envelope data is calculated by inversion to obtain the water-sand concentration and particle size information. The application has the ability of synchronous measurement of flow rate and concentration, and uses Doppler shift technology to solve the flow rate profile, which provides key data support for sediment transport flux calculation.

The application relates to the technical field of microwave remote sensing, and proposes an adaptive wet troposphere path delay inversion method and system fusing wind speed overlapping partition modeling, multi-scale collaborative sample balancing and model soft fusion. The method comprises the following steps: acquiring multi-source data and preprocessing the multi-source data to obtain to-be-inverted samples; according to a preset threshold, the full-range wind speed of the to-be-inverted samples is divided into a plurality of wind speed subintervals with overlapping transition zones; the to-be-inverted samples are input into trained inversion submodels, the output results of the inversion submodels are weighted and summed according to final fusion weights, and finally, wet troposphere path delay inversion results are obtained; when abnormal wind speed or invalid submodel prediction output is detected, the output result of a pre-trained global unified model is taken as the final inversion result. While guaranteeing overall inversion precision and stability, the application can significantly suppress systematic deviation under sea conditions of extremely low and extremely high wind speed.

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.

The invention discloses an inversion optimization algorithm based on vegetation leaf ecological correlation. According to the algorithm, a simulation data set containing leaf biochemical parameters and corresponding spectrums is generated through forward operation of a leaf radiation transmission physical model. According to the algorithm, correlation among biochemical parameters of different leaves is analyzed based on a simulation data set, and a probability sample confidence interval is constructed for the biochemical parameters with correlation coefficients larger than a set threshold value. And if a biochemical parameter inversion value obtained through physical model inversion is in the confidence interval, taking the biochemical parameter inversion value as a final inversion value of the parameter. Otherwise, adjusting by adjusting the weight sequence value until entering the confidence interval. The algorithm effectively reduces the probability of occurrence of error parameter combinations, alleviates the ill-conditioned inversion problem, improves the credibility of inversion results, and has high universality.

This invention discloses an integrated inversion method for cloud macro- and micro-parameters in the interdisciplinary field of atmospheric remote sensing and deep learning. The method includes: inputting multimodal input data into a dual-branch encoder to extract visual multi-scale features and meteorological physical background features; progressively injecting meteorological physical background features into the visual multi-scale features to achieve cross-scale fusion and obtain physically guided decoding features; based on the decoding features, generating spatial gating weights by constructing a cloud area auxiliary map, followed by element-wise gating modulation and feature refinement to obtain spatially focused features; inputting the spatially focused features into a multi-task prediction head to obtain the integrated inversion results of four cloud macro- and micro-parameters: cloud mask probability map, cloud top height, cloud optical thickness, and effective radius of cloud particles. This invention can solve the problems of difficulty in effectively fusing high-resolution satellite visual features with low-resolution atmospheric background fields and insufficient physical consistency caused by independent inversion of multiple cloud macro- and micro-parameters.

The application discloses a trace gas supersampling method and system based on TROPOMI satellite data, and relates to the fields of atmospheric environmental science and remote sensing technology.The method comprises the following steps: obtaining effective remote sensing observation data by preprocessing remote sensing observation data of a target research region acquired by a troposphere detector TROPOMI L2, and obtaining corrected effective remote sensing observation data by performing multiplication factor correction processing on the effective remote sensing observation data; constructing a target grid system according to the longitude and latitude of the target research region and a target grid resolution, and establishing spatial indexes for each grid unit in the target grid system by using R-trees; and resampling the corrected effective remote sensing observation data into the target grid system by using a supersampling method to obtain the concentration value of trace gas in each grid unit in the target grid system.

The invention discloses an ear plate local stress high spatial resolution monitoring method and system based on ultra-weak grating spectral shape demodulation. The system comprises an optical fiber sensing array, a light source module, a detection module, a data acquisition and physical information neural network inversion module and a display alarm module. The optical fiber sensing array is composed of N ultra-weak gratings arranged in a stress concentration area of the ear plate and is used for sensing local strain; the light source and detection module adopts a time division and wavelength division multiplexing strategy to realize synchronous monitoring of multiple ear plates; the data acquisition and inversion module acquires stress distribution along the length direction of the ultra-weak grating based on a physical information neural network inversion model; and the display alarm module realizes local stress threshold alarm of the ear plate. According to the invention, high-precision and real-time monitoring of local stress of metal connection nodes such as ear plates can be realized, the method is suitable for long-term online health monitoring of large-scale structure connection areas such as bridge inhaul cables, steel trussed beams and cable bent towers, and technical support is provided for structural fatigue early warning and safety assessment.

The invention discloses a PPI wind shear extraction method, device and storage medium based on a laser wind measuring radar, comprising the following steps: S1, based on PPI scanning, obtaining radial data of N light beams within a cycle, and screening and marking the radial data; S2, inverting the horizontal wind field based on a light beam sliding window to obtain the horizontal wind speed and horizontal wind direction of the light beam; S3, gridding the horizontal wind field, and interpolating based on the detection distance; compared with the prior art, the invention is based on the radial data obtained by PPI scanning, adopts a sliding window method, substitutes the horizontal wind speed and horizontal wind direction calculated by the previous sliding window as the initial values ​​of the model parameters into the gradient descent solution model of the current light beam, thereby inverting the horizontal wind speed and horizontal wind direction of the current light beam, reducing the wind field inversion error when the wind direction is close to perpendicular to the light beam direction, and improving the inversion accuracy of the horizontal wind field.

This invention discloses a method for identifying the deterioration of subgrades of long-service highways based on a shear modulus inversion model. It utilizes various sensors embedded in the subgrade to acquire multi-source monitoring data of the target road segment, including environmental parameters, mechanical response parameters, physical state parameters, and spatial location information, and performs preprocessing. By constructing hysteresis features and removing recursive features, an in-situ subgrade shear modulus inversion model is established, transforming the multi-source monitoring data into a shear modulus distribution field that considers the temporal changes in the subgrade's state and includes spatial information. Based on the inverted spatial distribution field of the shear modulus, spatial variability indices such as the coefficient of variation and Moran's index are calculated, and the subgrade deterioration state is identified based on its evolution trend, outputting the deterioration identification result. This invention provides a scientific basis for maintenance decisions.

The present invention discloses a high-resolution assimilation inversion system and method for methane emission sources, including a data preprocessing module, a regional atmospheric transmission mode module, a methane emission source diagnosis module, an ensemble Kalman smoothing module, a sub-sector accounting module of assimilated sources, and an output module. The ensemble forecast methane concentration field under the regional atmospheric transmission mode is obtained through the regional atmospheric transmission mode module; the methane emission source is constructed through the methane emission source diagnosis module; the methane assimilation source is obtained through the ensemble Kalman smoothing module; the bottom-up methane emission inventory is used as a priori estimation through the sub-sector accounting module of the assimilated source, and the assimilated emissions of each department are obtained on the basis of the methane assimilated source. The present invention has the advantages of high estimation accuracy, improved spatiotemporal resolution, etc., and can solve the uncertainty problem of bottom-up priori emission source accounting.

The present application belongs to the field of laser radar signal processing, and provides a method and system for retrieving vertical visibility based on Mie scattering laser radar, the method comprising the following steps: S1, acquiring signals; S2, signal preprocessing, sequentially comprising background noise correction, overlap factor correction, distance square correction, denoising processing and normalization; S3, vertical visibility retrieval, comprising the construction, training and use of a DBO-BP optimized neural network model. The system comprises a Mie scattering laser radar, a data preprocessing module and a DBO-BP optimized neural network model module. When retrieving vertical visibility, the present application takes into account the physical characteristics and data characteristics of the original echo signal, significantly enhances the anti-noise performance through multiple denoising when preprocessing the signal, and balances the efficiency and accuracy by adopting a hierarchical iteration strategy when training the model.